null  null
Praise for the First Edition
“2005 Best Java Book!”
—Java Developer’s Journal
Hibernate In Action has to be considered the definitive tome on Hibernate. As
the authors are intimately involved with the project, the insight on Hibernate
that they provide can’t be easily duplicated.
“Not only gets you up to speed with Hibernate and its features…It also introduces you to the right way of developing and tuning an industrial-quality Hibernate application. …albeit very technical, it reads astonishingly
easy…unfortunately very rare nowadays…[an] excellent piece of work…”
“The first and only full tutorial, reference, and authoritative guide, and one of
the most anticipated books of the year for Hibernate users.”
—Dr. Dobb’s Journal
“…the book was beyond my expectations…this book is the ultimate
—, (second review, fall 2005)
“…from none others than the lead developer and the lead documenter, this
book is a great introduction and reference documentation to using Hibernate.
It is organized in such a way that the concepts are explained in progressive
order from very simple to more complex, and the authors take good care of
explaining every detail with good examples. …The book not only gets you up
to speed with Hibernate and its features (which the documentation does quite
well). It also introduces you to the right way of developing and tuning an industrial-quality Hibernate application.”
“Strongly recommended, because a contemporary and state-of-the-art topic is
very well explained, and especially, because the voices come literally from the
horses’ mouths.”
—C Vu, the Journal of the ACCU
“The ultimate guide to the Hibernate open source project. It provides in-depth
information on architecture of Hibernate, configuring Hibernate and development using Hibernate…It also explains essential concepts like, object/relational mapping (ORM), persistence, caching, queries and describes how they
are taken care with respect to Hibernate…written by the creators of Hibernate
and they have made best effort to introduce and leverage Hibernate. I recommend this book to everyone who is interested in getting familiar with
“Well worth the cost…While the on-line documentation is good, (Mr. Bauer,
one of the authors is in charge of the on-line documentation) the book is better. It begins with a description of what you are trying to do (often left out in
computer books) and leads you on in a consistent manner through the entire
Hibernate system. Excellent Book!”
“A compact (408 pages), focused, no nonsense read and an essential resource
for anyone venturing into the ORM landscape. The first three chapters of this
book alone are indispensable for developers that want to quickly build an
application leveraging Hibernate, but more importantly really want to understand Hibernate concepts, framework, methodology and the reasons that
shaped the framework design. The remaining chapters continue the comprehensive overview of Hibernate that include how to map to and persist objects,
inheritance, transactions, concurrency, caching, retrieving objects efficiently
using HQL, configuring Hibernate for managed and unmanaged environments, and the Hibernate Toolset that can be leveraged for several different
development scenarios.”
—Columbia Java Users Group
“The authors show their knowledge of relational databases and the paradigm
of mapping this world with the object-oriented world of Java. This is why the
book is so good at explaining Hibernate in the context of solving or providing
a solution to the very complex problem of object/relational mapping.”
—Denver JUG
Java Persistence
with Hibernate
(74° w. long.)
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brief contents
Understanding object/relational persistence
Starting a project 37
Domain models and metadata
MAPPING CONCEPTS AND STRATEGIES ........................ 155
Mapping persistent classes
Inheritance and custom types
Mapping collections and entity associations
Advanced entity association mappings
Legacy databases and custom SQL
Working with objects
Transactions and concurrency
Implementing conversations
Modifying objects efficiently
Optimizing fetching and caching
Querying with HQL and JPA QL
Advanced query options
Creating and testing layered applications
Introducing JBoss Seam
appendix A
SQL fundamentals
appendix B
Mapping quick reference
foreword to the revised edition xix
foreword to the first edition xxi
preface to the revised edition xxiii
preface to the first edition xxv
acknowledgments xxviii
about this book xxix
about the cover illustration xxxiii
AND EJB 3.0 .......................................................1
Understanding object/relational persistence 3
What is persistence?
Relational databases 5 Understanding SQL 6 Using SQL
in Java 7 Persistence in object-oriented applications 8
The paradigm mismatch
The problem of granularity 12 The problem of subtypes
The problem of identity 14 Problems relating to
associations 16 The problem of data navigation 18
The cost of the mismatch 19
Persistence layers and alternatives
Layered architecture 20 Hand-coding a persistence
layer with SQL/JDBC 22 Using serialization 23
Object-oriented database systems 23 Other options 24
Object/relational mapping 24
What is ORM? 25 Generic ORM problems 27
Why ORM? 28 Introducing Hibernate, EJB3,
and JPA 31
Starting a project
Starting a Hibernate project
Selecting a development process 39 Setting up
the project 41 Hibernate configuration and
startup 49 Running and testing the application
Starting a Java Persistence project 68
Using Hibernate Annotations 68 Using Hibernate
EntityManager 72 Introducing EJB components 79
Switching to Hibernate interfaces 86
Reverse engineering a legacy database
Creating a database configuration 89 Customizing
reverse engineering 90 Generating Java source code 92
Integration with Java EE services
Integration with JTA 97 JNDI-bound SessionFactory
JMX service deployment 103
Domain models and metadata 105
The CaveatEmptor application
Analyzing the business domain 107
domain model 108
The CaveatEmptor
Implementing the domain model
Addressing leakage of concerns 111 Transparent and
automated persistence 112 Writing POJOs and persistent
entity classes 113 Implementing POJO associations 116
Adding logic to accessor methods 120
Object/relational mapping metadata
Metadata in XML 123 Annotation-based metadata 125
Using XDoclet 131 Handling global metadata 133
Manipulating metadata at runtime 138
Alternative entity representation
Creating dynamic applications 141
in XML 148
Representing data
Mapping persistent classes 157
Understanding entities and value types
Fine-grained domain models 158
Identifying entities and value types
Mapping entities with identity
Defining the concept 159
Understanding Java identity and equality 162
database identity 162 Database primary keys
Class mapping options
Dynamic SQL generation 172 Making an entity
immutable 173 Naming entities for querying 173
Declaring a package name 174 Quoting SQL identifiers
Implementing naming conventions 175
Fine-grained models and mappings
Mapping basic properties 177
Mapping components
Inheritance and custom types 191
Mapping class inheritance
Table per concrete class with implicit polymorphism 192
Table per concrete class with unions 195 Table per
class hierarchy 199 Table per subclass 203
Mixing inheritance strategies 207 Choosing a
strategy 210
The Hibernate type system
Recapitulating entity and value types 212
Built-in mapping types 214 Using mapping
types 219
Creating custom mapping types
Considering custom mapping types 221 The
extension points 222 The case for custom
mapping types 223 Creating a UserType 224
Creating a CompositeUserType 228 Parameterizing
custom types 230 Mapping enumerations 233
Mapping collections and entity associations 240
Sets, bags, lists, and maps of value types
Selecting a collection interface 241 Mapping a
set 243 Mapping an identifier bag 244
Mapping a list 246 Mapping a map 247
Sorted and ordered collections 248
Collections of components
Writing the component class 252 Mapping the
collection 252 Enabling bidirectional navigation 253
Avoiding not-null columns 254
Mapping collections with annotations
Basic collection mapping 256 Sorted and ordered
collections 257 Mapping a collection of embedded objects
Mapping a parent/children relationship 260
Multiplicity 261 The simplest possible association 261
Making the association bidirectional 264 Cascading object
state 267
Advanced entity association mappings 277
Single-valued entity associations
Shared primary key associations 279 One-to-one foreign
key associations 282 Mapping with a join table 285
Many-valued entity associations
One-to-many associations 290 Many-to-many
associations 297 Adding columns to join tables
Mapping maps 310
Polymorphic associations
Polymorphic many-to-one associations 313 Polymorphic
collections 315 Polymorphic associations to unions 316
Polymorphic table per concrete class 319
Legacy databases and custom SQL 322
Integrating legacy databases
Handling primary keys 324 Arbitrary join conditions
with formulas 337 Joining arbitrary tables 342 Working
with triggers 346
Customizing SQL
Writing custom CRUD statements 351
Integrating stored procedures and functions
Improving schema DDL
Custom SQL names and datatypes 365 Ensuring data
consistency 367 Adding domains and column
constraints 369 Table-level constraints 370
Database constraints 373 Creating indexes 375
Adding auxiliary DDL 376
Working with objects 383
The persistence lifecycle
Object states 385
The persistence context 388
Object identity and equality
Introducing conversations 391 The scope of object
identity 393 The identity of detached objects 394
Extending a persistence context 400
The Hibernate interfaces
Storing and loading objects 402 Working with detached
objects 408 Managing the persistence context 414
The Java Persistence API
Storing and loading objects 417
entity instances 423
Working with detached
Using Java Persistence in EJB components
Injecting an EntityManager 426 Looking up an
EntityManager 429 Accessing an
EntityManagerFactory 429
Transactions and concurrency 433
Transaction essentials
Database and system transactions 435 Transactions in
a Hibernate application 437 Transactions with Java
Persistence 449
Controlling concurrent access
Understanding database-level concurrency 453 Optimistic
concurrency control 458 Obtaining additional isolation
guarantees 465
Nontransactional data access
Debunking autocommit myths 470 Working
nontransactionally with Hibernate 471 Optional
transactions with JTA 473
Implementing conversations 476
Propagating the Hibernate Session
The use case for Session propagation 478 Propagation
through thread-local 480 Propagation with
JTA 482 Propagation with EJBs 483
Conversations with Hibernate
Providing conversational guarantees 485 Conversations
with detached objects 486 Extending a Session for a
conversation 489
Conversations with JPA 497
Persistence context propagation in Java SE 498
Merging detached objects in conversations 499
Extending the persistence context in Java SE 501
Conversations with EJB 3.0
Context propagation with EJBs 506
Extended persistence contexts with EJBs
Modifying objects efficiently 517
Transitive persistence 518
Persistence by reachability 519 Applying cascading to
associations 520 Working with transitive state 524
Transitive associations with JPA 531
Bulk and batch operations
Bulk statements with HQL and JPA QL 533 Processing
with batches 537 Using a stateless Session 539
Data filtering and interception
Dynamic data filters 541
The core event system 553
Intercepting Hibernate events 546
Entity listeners and callbacks 556
Optimizing fetching and caching 559
Defining the global fetch plan
The object-retrieval options 560 The lazy default fetch
plan 564 Understanding proxies 564 Disabling proxy
generation 567 Eager loading of associations and
collections 568 Lazy loading with interception 571
Selecting a fetch strategy
Prefetching data in batches 574 Prefetching collections with
subselects 577 Eager fetching with joins 578 Optimizing
fetching for secondary tables 581 Optimization
guidelines 584
Caching fundamentals
Caching strategies and scopes
architecture 597
The Hibernate cache
Caching in practice 602
Selecting a concurrency control strategy 602 Understanding
cache regions 604 Setting up a local cache provider 605
Setting up a replicated cache 606 Controlling the second-level
cache 611
Querying with HQL and JPA QL 614
Creating and running queries
Preparing a query 616 Executing a query
Using named queries 629
Basic HQL and JPA QL queries
Selection 633
Restriction 635
Joins, reporting queries, and subselects
Joining relations and associations 643
queries 655 Using subselects 659
Advanced query options 663
Querying with criteria and example
Basic criteria queries 665 Joins and dynamic
fetching 670 Projection and report queries 676
Query by example 680
Using native SQL queries
Automatic resultset handling 683 Retrieving scalar
values 684 Native SQL in Java Persistence 686
Filtering collections
Caching query results
Enabling the query result cache 691 Understanding
the query cache 692 When to use the query cache 693
Natural identifier cache lookups 693
Creating and testing layered applications 697
Hibernate in a web application
Introducing the use case 698 Writing a controller 699
The Open Session in View pattern 701 Designing smart
domain models 705
Creating a persistence layer
A generic data-access object pattern 709 Implementing the
generic CRUD interface 711 Implementing entity DAOs 713
Using data-access objects 715
Introducing the Command pattern
The basic interfaces 719 Executing command objects 721
Variations of the Command pattern 723
Designing applications with EJB 3.0
Implementing a conversation with stateful beans 725 Writing
DAOs with EJBs 727 Utilizing dependency injection 728
Understanding different kinds of tests 731 Introducing
TestNG 732 Testing the persistence layer 736
Considering performance benchmarks 744
Introducing JBoss Seam 747
The Java EE 5.0 programming model 748
Considering JavaServer Faces 749 Considering EJB 3.0
Writing a web application with JSF and EJB 3.0 752
Analyzing the application 762
Improving the application with Seam
Configuring Seam
components 767
766 Binding pages to stateful Seam
Analyzing the Seam application 773
Understanding contextual components
Writing the login page 779 Creating the components 781
Aliasing contextual variables 784 Completing the login/logout
feature 786
Validating user input 789
Introducing Hibernate Validator 790 Creating the registration
page 791 Internationalization with Seam 799
Simplifying persistence with Seam
Implementing a conversation 804
persistence context 811
appendix A SQL fundamentals 818
appendix B Mapping quick reference 822
references 824
index 825
Letting Seam manage the
foreword to the revised edition
When Hibernate in Action was published two years ago, it was immediately recognized not only as the definitive book on Hibernate, but also as the definitive work
on object/relational mapping.
In the intervening time, the persistence landscape has changed with the
release of the Java Persistence API, the new standard for object/relational mapping for Java EE and Java SE which was developed under the Java Community Process as part of the Enterprise JavaBeans 3.0 Specification.
In developing the Java Persistence API, the EJB 3.0 Expert Group benefitted
heavily from the experience of the O/R mapping frameworks already in use in
the Java community. As one of the leaders among these, Hibernate has had a very
significant influence on the technical direction of Java Persistence. This was due
not only to the participation of Gavin King and other members of the Hibernate
team in the EJB 3.0 standardization effort, but was also due in large part to the
direct and pragmatic approach that Hibernate has taken towards O/R mapping
and to the simplicity, clarity, and power of its APIs--and their resulting appeal to
the Java community.
In addition to their contributions to Java Persistence, the Hibernate developers also have taken major steps forward for Hibernate with the Hibernate 3
release described in this book. Among these are support for operations over large
datasets; additional and more sophisticated mapping options, especially for handling legacy databases; data filters; strategies for managing conversations; and
integration with Seam, the new framework for web application development with
JSF and EJB 3.0.
Java Persistence with Hibernate is therefore considerably more than simply a second edition to Hibernate in Action. It provides a comprehensive overview of all the
capabilities of the Java Persistence API in addition to those of Hibernate 3, as well
as a detailed comparative analysis of the two. It describes how Hibernate has been
used to implement the Java Persistence standard, and how to leverage the Hibernate extensions to Java Persistence.
More important, throughout the presentation of Hibernate and Java Persistence, Christian Bauer and Gavin King illustrate and explain the fundamental
principles and decisions that need to be taken into account in both the design
and use of an object/relational mapping framework. The insights they provide
into the underlying issues of ORM give the reader a deep understanding into the
effective application of ORM as an enterprise technology.
Java Persistence with Hibernate thus reaches out to a wide range of developers—
from newcomers to object/relational mapping to experienced developers—seeking to learn more about cutting-edge technological innovations in the Java community that have occurred and are continuing to emerge as a result of this work.
Specification Lead
Enterprise JavaBeans 3.0 and Java Persistence
Sun Microsystems
foreword to the first edition
Relational databases are indisputably at the core of the modern enterprise.
While modern programming languages, including JavaTM, provide an intuitive,
object-oriented view of application-level business entities, the enterprise data
underlying these entities is heavily relational in nature. Further, the main strength
of the relational model—over earlier navigational models as well as over later
OODB models—is that by design it is intrinsically agnostic to the programmatic
manipulation and application-level view of the data that it serves up.
Many attempts have been made to bridge relational and object-oriented technologies, or to replace one with the other, but the gap between the two is one of
the hard facts of enterprise computing today. It is this challenge—to provide a
bridge between relational data and JavaTM objects—that Hibernate takes on
through its object/relational mapping (ORM) approach. Hibernate meets this
challenge in a very pragmatic, direct, and realistic way.
As Christian Bauer and Gavin King demonstrate in this book, the effective use
of ORM technology in all but the simplest of enterprise environments requires
understanding and configuring how the mediation between relational data and
objects is performed. This demands that the developer be aware and knowledgeable both of the application and its data requirements, and of the SQL query language, relational storage structures, and the potential for optimization that
relational technology offers.
Not only does Hibernate provide a full-function solution that meets these
requirements head on, it is also a flexible and configurable architecture. Hibernate’s developers designed it with modularity, pluggability, extensibility, and user
customization in mind. As a result, in the few years since its initial release,
Hibernate has rapidly become one of the leading ORM technologies for enterprise developers—and deservedly so.
This book provides a comprehensive overview of Hibernate. It covers how to
use its type mapping capabilities and facilities for modeling associations and
inheritance; how to retrieve objects efficiently using the Hibernate query language; how to configure Hibernate for use in both managed and unmanaged
environments; and how to use its tools. In addition, throughout the book the
authors provide insight into the underlying issues of ORM and into the design
choices behind Hibernate. These insights give the reader a deep understanding
of the effective use of ORM as an enterprise technology.
Hibernate in Action is the definitive guide to using Hibernate and to object/relational mapping in enterprise computing today.
Lead Architect, Enterprise JavaBeans
Sun Microsystems
preface to the revised edition
The predecessor of this book, Hibernate in Action, started with a quote from
Anthony Berglas: “Just because it is possible to push twigs along the ground with
one’s nose does not necessarily mean that that is the best way to collect firewood.”
Since then, the Hibernate project and the strategies and concepts software developers rely on to manage information have evolved. However, the fundamental
issues are still the same—every company we work with every day still uses SQL databases, and Java is entrenched in the industry as the first choice for enterprise
application development.
The tabular representation of data in a relational system is still fundamentally
different than the networks of objects used in object-oriented Java applications.
We still see the object/relational impedance mismatch, and we frequently see that
the importance and cost of this mismatch is underestimated.
On the other hand, we now have a range of tools and solutions available to
deal with this problem. We’re done collecting firewood, and the pocket lighter
has been replaced with a flame thrower.
Hibernate is now available in its third major release; Hibernate 3.2 is the version we describe in this book. Compared to older Hibernate versions, this new
major release has twice as many features—and this book is almost double the size
of Hibernate in Action. Most of these features are ones that you, the developers
working with Hibernate every day, have asked for. We’ve sometimes said that
Hibernate is a 90 percent solution for all the problems a Java application devel-
oper has to deal with when creating a database application. With the latest Hibernate version, this number is more likely 99 percent.
As Hibernate matured and its user base and community kept growing, the Java
standards for data management and database application development were
found lacking by many developers. We even told you not to use EJB 2.x entity
beans in Hibernate in Action.
Enter EJB 3.0 and the new Java Persistence standard. This new industry standard is a major step forward for the Java developer community. It defines a lightweight and simplified programming model and powerful object/relational
persistence. Many of the key concepts of the new standard were modeled after
Hibernate and other successful object/relational persistence solutions. The latest
Hibernate version implements the Java Persistence standard.
So, in addition to the new all-in-one Hibernate for every purpose, you can now
use Hibernate like any Java Persistence provider, with or without other EJB 3.0
components and Java EE 5.0 services. This deep integration of Hibernate with
such a rich programming model enables you to design and implement application functionality that was difficult to create by hand before.
We wrote this book to give you a complete and accurate guide to both Hibernate and Java Persistence (and also all relevant EJB 3.0 concepts). We hope that
you’ll enjoy learning Hibernate and that you'll keep this reference bible on your
desk for your daily work.
preface to the first edition
Just because it is possible to push twigs along the ground with one’s nose does
not necessarily mean that that is the best way to collect firewood.
—Anthony Berglas
Today, many software developers work with Enterprise Information Systems (EIS).
This kind of application creates, manages, and stores structured information and
shares this information between many users in multiple physical locations.
The storage of EIS data involves massive usage of SQL-based database management systems. Every company we’ve met during our careers uses at least one SQL
database; most are completely dependent on relational database technology at
the core of their business.
In the past five years, broad adoption of the Java programming language has
brought about the ascendancy of the object-oriented paradigm for software development. Developers are now sold on the benefits of object orientation. However,
the vast majority of businesses are also tied to long-term investments in expensive
relational database systems. Not only are particular vendor products entrenched,
but existing legacy data must be made available to (and via) the shiny new objectoriented web applications.
However, the tabular representation of data in a relational system is fundamentally different than the networks of objects used in object-oriented Java applications. This difference has led to the so-called object/relational paradigm mismatch.
Traditionally, the importance and cost of this mismatch have been underestimated, and tools for solving the mismatch have been insufficient. Meanwhile, Java
developers blame relational technology for the mismatch; data professionals
blame object technology.
Object/relational mapping (ORM) is the name given to automated solutions to the
mismatch problem. For developers weary of tedious data access code, the good
news is that ORM has come of age. Applications built with ORM middleware can be
expected to be cheaper, more performant, less vendor-specific, and more able to
cope with changes to the internal object or underlying SQL schema. The astonishing thing is that these benefits are now available to Java developers for free.
Gavin King began developing Hibernate in late 2001 when he found that the
popular persistence solution at the time—CMP Entity Beans—didn’t scale to nontrivial applications with complex data models. Hibernate began life as an independent, noncommercial open source project.
The Hibernate team (including the authors) has learned ORM the hard way—
that is, by listening to user requests and implementing what was needed to satisfy
those requests. The result, Hibernate, is a practical solution, emphasizing developer productivity and technical leadership. Hibernate has been used by tens of
thousands of users and in many thousands of production applications.
When the demands on their time became overwhelming, the Hibernate team
concluded that the future success of the project (and Gavin’s continued sanity)
demanded professional developers dedicated full-time to Hibernate. Hibernate
joined in late 2003 and now has a commercial aspect; you can purchase
commercial support and training from JBoss Inc. But commercial training
shouldn’t be the only way to learn about Hibernate.
It’s obvious that many, perhaps even most, Java projects benefit from the use of
an ORM solution like Hibernate—although this wasn’t obvious a couple of years
ago! As ORM technology becomes increasingly mainstream, product documentation such as Hibernate’s free user manual is no longer sufficient. We realized that
the Hibernate community and new Hibernate users needed a full-length book,
not only to learn about developing software with Hibernate, but also to understand and appreciate the object/relational mismatch and the motivations behind
Hibernate’s design.
The book you’re holding was an enormous effort that occupied most of our
spare time for more than a year. It was also the source of many heated disputes
and learning experiences. We hope this book is an excellent guide to Hibernate
(or, “the Hibernate bible,” as one of our reviewers put it) and also the first comprehensive documentation of the object/relational mismatch and ORM in general. We hope you find it helpful and enjoy working with Hibernate.
This book grew from a small second edition of Hibernate in Action into a volume of
considerable size. We couldn’t have created it without the help of many people.
Emmanuel Bernard did an excellent job as the technical reviewer of this book;
thank you for the many hours you spent editing our broken code examples. We’d
also like to thank our other reviewers: Patrick Dennis, Jon Skeet, Awais Bajwa,
Dan Dobrin, Deiveehan Nallazhagappan, Ryan Daigle, Stuart Caborn, Patrick
Peak, TVS Murthy, Bill Fly, David Walend, Dave Dribin, Anjan Bacchu, Gary
Udstrand, and Srinivas Nallapati. Special thanks to Linda DiMichiel for agreeing
to write the foreword to our book, as she did to the first edition
Marjan Bace again assembled a great production team at Manning: Sydney
Jones edited our crude manuscript and turned it into a real book. Tiffany Taylor,
Elizabeth Martin, and Andy Carroll found all our typos and made the book readable. Dottie Marsico was responsible for typesetting and gave this book its great
look. Mary Piergies coordinated and organized the production process. We’d like
to thank you all for working with us.
about this book
We had three goals when writing this book, so you can read it as
A tutorial for Hibernate, Java Persistence, and EJB 3.0 that guides you
through your first steps with these solutions
A guide for learning all basic and advanced Hibernate features for object/
relational mapping, object processing, querying, performance optimization, and application design
A reference for whenever you need a complete and technically accurate definition of Hibernate and Java Persistence functionality
Usually, books are either tutorials or reference guides, so this stretch comes at a
price. If you’re new to Hibernate, we suggest that you start reading the book from
the start, with the tutorials in chapters 1 and 2. If you have used an older version
of Hibernate, you should read the first two chapters quickly to get an overview
and then jump into the middle with chapter 3.
We will, whenever appropriate, tell you if a particular section or subject is
optional or reference material that you can safely skip during your first read.
This book is divided into three major parts.
In part 1, we introduce the object/relational paradigm mismatch and explain
the fundamentals behind object/relational mapping. We walk through a hands-
on tutorial to get you started with your first Hibernate, Java Persistence, or EJB 3.0
project. We look at Java application design for domain models and at the options
for creating object/relational mapping metadata.
Mapping Java classes and properties to SQL tables and columns is the focus of
part 2. We explore all basic and advanced mapping options in Hibernate and Java
Persistence, with XML mapping files and Java annotations. We show you how to
deal with inheritance, collections, and complex class associations. Finally, we discuss integration with legacy database schemas and some mapping strategies that
are especially tricky.
Part 3 is all about the processing of objects and how you can load and store
data with Hibernate and Java Persistence. We introduce the programming interfaces, how to write transactional and conversation-aware applications, and how to
write queries. Later, we focus on the correct design and implementation of layered Java applications. We discuss the most common design patterns that are used
with Hibernate, such as the Data Access Object (DAO) and EJB Command patterns. You’ll see how you can test your Hibernate application easily and what other
best practices are relevant if you work an object/relational mapping software.
Finally, we introduce the JBoss Seam framework, which takes many Hibernate
concepts to the next level and enables you to create conversational web applications with ease. We promise you’ll find this chapter interesting, even if you don’t
plan to use Seam.
Who should read this book?
Readers of this book should have basic knowledge of object-oriented software
development and should have used this knowledge in practice. To understand the
application examples, you should be familiar with the Java programming language and the Unified Modeling Language.
Our primary target audience consists of Java developers who work with SQLbased database systems. We’ll show you how to substantially increase your productivity by leveraging ORM.
If you’re a database developer, the book can be part of your introduction to
object-oriented software development.
If you’re a database administrator, you’ll be interested in how ORM affects performance and how you can tune the performance of the SQL database-management system and persistence layer to achieve performance targets. Because data
access is the bottleneck in most Java applications, this book pays close attention to
performance issues. Many DBAs are understandably nervous about entrusting performance to tool-generated SQL code; we seek to allay those fears and also to
highlight cases where applications shouldn’t use tool-managed data access. You
may be relieved to discover that we don’t claim that ORM is the best solution to
every problem.
Code conventions
This book provides copious examples, which include all the Hibernate application artifacts: Java code, Hibernate configuration files, and XML mapping metadata files. Source code in listings or in text is in a fixed-width font like this to
separate it from ordinary text. Additionally, Java method names, component
parameters, object properties, and XML elements and attributes in text are also
presented using fixed-width font.
Java, HTML, and XML can all be verbose. In many cases, the original source
code (available online) has been reformatted; we’ve added line breaks and
reworked indentation to accommodate the available page space in the book. In
rare cases, even this was not enough, and listings include line-continuation markers. Additionally, comments in the source code have often been removed from
the listings when the code is described in the text.
Code annotations accompany some of the source code listings, highlighting
important concepts. In some cases, numbered bullets link to explanations that follow the listing.
Source code downloads
Hibernate is an open source project released under the Lesser GNU Public
License. Directions for downloading Hibernate packages, in source or binary
form, are available from the Hibernate web site:
The source code for all Hello World and CaveatEmptor examples in this book
is available from under a free (BSD-like)
license. The CaveatEmptor example application code is available on this web site
in different flavors—for example, with a focus on native Hibernate, on Java Persistence, and on JBoss Seam. You can also download the code for the examples in
this book from the publisher’s website,
About the authors
Christian Bauer is a member of the Hibernate developer team. He works as a
trainer, consultant, and product manager for Hibernate, EJB 3.0, and JBoss Seam
at JBoss, a division of Red Hat. With Gavin King, Christian wrote Hibernate in
Gavin King is the founder of the Hibernate and JBoss Seam projects, and a
member of the EJB 3.0 (JSR 220) expert group. He also leads the Web Beans JSR
299, a standardization effort involving Hibernate concepts, JBoss Seam, JSF, and
EJB 3.0. Gavin works as a lead developer at JBoss, a division of Red Hat.
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about the cover illustration
The illustration on the cover of Java Persistence with Hibernate is taken from a collection of costumes of the Ottoman Empire published on January 1, 1802, by William Miller of Old Bond Street, London. The title page is missing from the
collection and we have been unable to track it down to date. The book’s table of
contents identifies the figures in both English and French, and each illustration
bears the names of two artists who worked on it, both of whom would no doubt be
surprised to find their art gracing the front cover of a computer programming
book…two hundred years later.
The collection was purchased by a Manning editor at an antiquarian flea market in the “Garage” on West 26th Street in Manhattan. The seller was an American
based in Ankara, Turkey, and the transaction took place just as he was packing up
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check were both politely turned down. With the seller flying back to Ankara that
evening the situation was getting hopeless. What was the solution? It turned out to
be nothing more than an old-fashioned verbal agreement sealed with a handshake. The seller simply proposed that the money be transferred to him by wire
and the editor walked out with the bank information on a piece of paper and the
portfolio of images under his arm. Needless to say, we transferred the funds the
next day, and we remain grateful and impressed by this unknown person’s trust in
one of us. It recalls something that might have happened a long time ago.
The pictures from the Ottoman collection, like the other illustrations that
appear on our covers, bring to life the richness and variety of dress customs of two
centuries ago. They recall the sense of isolation and distance of that period—and
of every other historic period except our own hyperkinetic present.
Dress codes have changed since then and the diversity by region, so rich at the
time, has faded away. It is now often hard to tell the inhabitant of one continent
from another. Perhaps, trying to view it optimistically, we have traded a cultural
and visual diversity for a more varied personal life. Or a more varied and interesting intellectual and technical life.
We at Manning celebrate the inventiveness, the initiative, and, yes, the fun of
the computer business with book covers based on the rich diversity of regional life
of two centuries ago‚ brought back to life by the pictures from this collection.
Part 1
Getting started with
Hibernate and EJB 3.0
n part 1, we show you why object persistence is such a complex topic and what
solutions you can apply in practice. Chapter 1 introduces the object/relational
paradigm mismatch and several strategies to deal with it, foremost object/relational mapping (ORM). In chapter 2, we guide you step by step through a tutorial
with Hibernate, Java Persistence, and EJB 3.0—you’ll implement and test a “Hello
World” example in all variations. Thus prepared, in chapter 3 you’re ready to
learn how to design and implement complex business domain models in Java, and
which mapping metadata options you have available.
After reading this part of the book, you’ll understand why you need object/
relational mapping, and how Hibernate, Java Persistence, and EJB 3.0 work in
practice. You’ll have written your first small project, and you’ll be ready to take on
more complex problems. You’ll also understand how real-world business entities
can be implemented as a Java domain model, and in what format you prefer to
work with object/relational mapping metadata.
This chapter covers
Object persistence with SQL databases
The object/relational paradigm mismatch
Persistence layers in object-oriented
Object/relational mapping background
Understanding object/relational persistence
The approach to managing persistent data has been a key design decision in
every software project we’ve worked on. Given that persistent data isn’t a new or
unusual requirement for Java applications, you’d expect to be able to make a
simple choice among similar, well-established persistence solutions. Think of
web application frameworks (Struts versus WebWork), GUI component frameworks (Swing versus SWT), or template engines (JSP versus Velocity). Each of the
competing solutions has various advantages and disadvantages, but they all share
the same scope and overall approach. Unfortunately, this isn’t yet the case with
persistence technologies, where we see some wildly differing solutions to the
same problem.
For several years, persistence has been a hot topic of debate in the Java community. Many developers don’t even agree on the scope of the problem. Is persistence
a problem that is already solved by relational technology and extensions such as
stored procedures, or is it a more pervasive problem that must be addressed by special Java component models, such as EJB entity beans? Should we hand-code even
the most primitive CRUD (create, read, update, delete) operations in SQL and
JDBC, or should this work be automated? How do we achieve portability if every
database management system has its own SQL dialect? Should we abandon SQL
completely and adopt a different database technology, such as object database systems? Debate continues, but a solution called object/relational mapping (ORM) now
has wide acceptance. Hibernate is an open source ORM service implementation.
Hibernate is an ambitious project that aims to be a complete solution to the
problem of managing persistent data in Java. It mediates the application’s interaction with a relational database, leaving the developer free to concentrate on the
business problem at hand. Hibernate is a nonintrusive solution. You aren’t
required to follow many Hibernate-specific rules and design patterns when writing
your business logic and persistent classes; thus, Hibernate integrates smoothly with
most new and existing applications and doesn’t require disruptive changes to the
rest of the application.
This book is about Hibernate. We’ll cover basic and advanced features and
describe some ways to develop new applications using Hibernate. Often, these
recommendations won’t even be specific to Hibernate. Sometimes they will be
our ideas about the best ways to do things when working with persistent data,
explained in the context of Hibernate. This book is also about Java Persistence, a
new standard for persistence that is part of the also updated EJB 3.0 specification.
Hibernate implements Java Persistence and supports all the standardized mappings, queries, and APIs. Before we can get started with Hibernate, however, you
need to understand the core problems of object persistence and object/relational
What is persistence?
mapping. This chapter explains why tools like Hibernate and specifications such
as Java Persistence and EJB 3.0 are needed.
First, we define persistent data management in the context of object-oriented
applications and discuss the relationship of SQL, JDBC, and Java, the underlying
technologies and standards that Hibernate is built on. We then discuss the socalled object/relational paradigm mismatch and the generic problems we encounter
in object-oriented software development with relational databases. These problems make it clear that we need tools and patterns to minimize the time we have
to spend on the persistence-related code of our applications. After we look at
alternative tools and persistence mechanisms, you’ll see that ORM is the best available solution for many scenarios. Our discussion of the advantages and drawbacks
of ORM will give you the full background to make the best decision when picking
a persistence solution for your own project.
We also take a look at the various Hibernate software modules, and how you
can combine them to either work with Hibernate only, or with Java Persistence
and EJB 3.0-compliant features.
The best way to learn Hibernate isn’t necessarily linear. We understand that
you may want to try Hibernate right away. If this is how you’d like to proceed, skip
to the second chapter of this book and have a look at the “Hello World” example
and set up a project. We recommend that you return here at some point as you
circle through the book. That way, you’ll be prepared and have all the background concepts you need for the rest of the material.
What is persistence?
Almost all applications require persistent data. Persistence is one of the fundamental concepts in application development. If an information system didn’t
preserve data when it was powered off, the system would be of little practical use.
When we talk about persistence in Java, we’re normally talking about storing
data in a relational database using SQL. We’ll start by taking a brief look at the
technology and how we use it with Java. Armed with that information, we’ll then
continue our discussion of persistence and how it’s implemented in object-oriented applications.
Relational databases
You, like most other developers, have probably worked with a relational database.
Most of us use a relational database every day. Relational technology is a known
quantity, and this alone is sufficient reason for many organizations to choose it.
Understanding object/relational persistence
But to say only this is to pay less respect than is due. Relational databases are
entrenched because they’re an incredibly flexible and robust approach to data
management. Due to the complete and consistent theoretical foundation of the
relational data model, relational databases can effectively guarantee and protect
the integrity of the data, among other desirable characteristics. Some people
would even say that the last big invention in computing has been the relational
concept for data management as first introduced by E.F. Codd (Codd, 1970)
more than three decades ago.
Relational database management systems aren’t specific to Java, nor is a relational database specific to a particular application. This important principle is
known as data independence. In other words, and we can’t stress this important fact
enough, data lives longer than any application does. Relational technology provides a
way of sharing data among different applications, or among different technologies that form parts of the same application (the transactional engine and the
reporting engine, for example). Relational technology is a common denominator
of many disparate systems and technology platforms. Hence, the relational data
model is often the common enterprise-wide representation of business entities.
Relational database management systems have SQL-based application programming interfaces; hence, we call today’s relational database products SQL database
management systems or, when we’re talking about particular systems, SQL databases.
Before we go into more detail about the practical aspects of SQL databases, we
have to mention an important issue: Although marketed as relational, a database
system providing only an SQL data language interface isn’t really relational and in
many ways isn’t even close to the original concept. Naturally, this has led to confusion. SQL practitioners blame the relational data model for shortcomings in the
SQL language, and relational data management experts blame the SQL standard
for being a weak implementation of the relational model and ideals. Application
developers are stuck somewhere in the middle, with the burden to deliver something that works. We’ll highlight some important and significant aspects of this
issue throughout the book, but generally we’ll focus on the practical aspects. If
you’re interested in more background material, we highly recommend Practical
Issues in Database Management: A Reference for the Thinking Practitioner by Fabian Pascal (Pascal, 2000).
Understanding SQL
To use Hibernate effectively, a solid understanding of the relational model and
SQL is a prerequisite. You need to understand the relational model and topics
such as normalization to guarantee the integrity of your data, and you’ll need to
What is persistence?
use your knowledge of SQL to tune the performance of your Hibernate application. Hibernate automates many repetitive coding tasks, but your knowledge of
persistence technology must extend beyond Hibernate itself if you want to take
advantage of the full power of modern SQL databases. Remember that the underlying goal is robust, efficient management of persistent data.
Let’s review some of the SQL terms used in this book. You use SQL as a data definition language (DDL) to create a database schema with CREATE and ALTER statements. After creating tables (and indexes, sequences, and so on), you use SQL as a
data manipulation language (DML) to manipulate and retrieve data. The manipulation operations include insertions, updates, and deletions. You retrieve data by executing queries with restrictions, projections, and join operations (including the
Cartesian product). For efficient reporting, you use SQL to group, order, and aggregate
data as necessary. You can even nest SQL statements inside each other; this technique uses subselects.
You’ve probably used SQL for many years and are familiar with the basic operations and statements written in this language. Still, we know from our own experience that SQL is sometimes hard to remember, and some terms vary in usage. To
understand this book, we must use the same terms and concepts, so we advise you
to read appendix A if any of the terms we’ve mentioned are new or unclear.
If you need more details, especially about any performance aspects and how
SQL is executed, get a copy of the excellent book SQL Tuning by Dan Tow (Tow,
2003). Also read An Introduction to Database Systems by Chris Date (Date, 2003) for
the theory, concepts, and ideals of (relational) database systems. The latter book
is an excellent reference (it’s big) for all questions you may possibly have about
databases and data management.
Although the relational database is one part of ORM, the other part, of course,
consists of the objects in your Java application that need to be persisted to and
loaded from the database using SQL.
Using SQL in Java
When you work with an SQL database in a Java application, the Java code issues
SQL statements to the database via the Java Database Connectivity (JDBC) API.
Whether the SQL was written by hand and embedded in the Java code, or generated on the fly by Java code, you use the JDBC API to bind arguments to prepare
query parameters, execute the query, scroll through the query result table,
retrieve values from the result set, and so on. These are low-level data access tasks;
as application developers, we’re more interested in the business problem that
requires this data access. What we’d really like to write is code that saves and
Understanding object/relational persistence
retrieves objects—the instances of our classes—to and from the database, relieving us of this low-level drudgery.
Because the data access tasks are often so tedious, we have to ask: Are the relational data model and (especially) SQL the right choices for persistence in objectoriented applications? We answer this question immediately: Yes! There are many
reasons why SQL databases dominate the computing industry—relational database management systems are the only proven data management technology, and
they’re almost always a requirement in any Java project.
However, for the last 15 years, developers have spoken of a paradigm mismatch.
This mismatch explains why so much effort is expended on persistence-related
concerns in every enterprise project. The paradigms referred to are object modeling and relational modeling, or perhaps object-oriented programming and SQL.
Let’s begin our exploration of the mismatch problem by asking what persistence
means in the context of object-oriented application development. First we’ll
widen the simplistic definition of persistence stated at the beginning of this section to a broader, more mature understanding of what is involved in maintaining
and using persistent data.
Persistence in object-oriented applications
In an object-oriented application, persistence allows an object to outlive the process that created it. The state of the object can be stored to disk, and an object
with the same state can be re-created at some point in the future.
This isn’t limited to single objects—entire networks of interconnected objects
can be made persistent and later re-created in a new process. Most objects aren’t
persistent; a transient object has a limited lifetime that is bounded by the life of
the process that instantiated it. Almost all Java applications contain a mix of persistent and transient objects; hence, we need a subsystem that manages our persistent data.
Modern relational databases provide a structured representation of persistent
data, enabling the manipulating, sorting, searching, and aggregating of data.
Database management systems are responsible for managing concurrency and
data integrity; they’re responsible for sharing data between multiple users and
multiple applications. They guarantee the integrity of the data through integrity
rules that have been implemented with constraints. A database management system provides data-level security. When we discuss persistence in this book, we’re
thinking of all these things:
What is persistence?
Storage, organization, and retrieval of structured data
Concurrency and data integrity
Data sharing
And, in particular, we’re thinking of these problems in the context of an objectoriented application that uses a domain model.
An application with a domain model doesn’t work directly with the tabular representation of the business entities; the application has its own object-oriented
model of the business entities. If the database of an online auction system has ITEM
and BID tables, for example, the Java application defines Item and Bid classes.
Then, instead of directly working with the rows and columns of an SQL result
set, the business logic interacts with this object-oriented domain model and its
runtime realization as a network of interconnected objects. Each instance of a Bid
has a reference to an auction Item, and each Item may have a collection of references to Bid instances. The business logic isn’t executed in the database (as an
SQL stored procedure); it’s implemented in Java in the application tier. This
allows business logic to make use of sophisticated object-oriented concepts such as
inheritance and polymorphism. For example, we could use well-known design
patterns such as Strategy, Mediator, and Composite (Gamma and others, 1995), all of
which depend on polymorphic method calls.
Now a caveat: Not all Java applications are designed this way, nor should they
be. Simple applications may be much better off without a domain model. Complex applications may have to reuse existing stored procedures. SQL and the JDBC
API are perfectly serviceable for dealing with pure tabular data, and the JDBC
RowSet makes CRUD operations even easier. Working with a tabular representation
of persistent data is straightforward and well understood.
However, in the case of applications with nontrivial business logic, the domain
model approach helps to improve code reuse and maintainability significantly. In
practice, both strategies are common and needed. Many applications need to execute procedures that modify large sets of data, close to the data. At the same time,
other application modules could benefit from an object-oriented domain model
that executes regular online transaction processing logic in the application tier.
An efficient way to bring persistent data closer to the application code is required.
If we consider SQL and relational databases again, we finally observe the mismatch between the two paradigms. SQL operations such as projection and join
always result in a tabular representation of the resulting data. (This is known as
Understanding object/relational persistence
transitive closure; the result of an operation on relations is always a relation.) This is
quite different from the network of interconnected objects used to execute the
business logic in a Java application. These are fundamentally different models,
not just different ways of visualizing the same model.
With this realization, you can begin to see the problems—some well understood
and some less well understood—that must be solved by an application that combines both data representations: an object-oriented domain model and a persistent
relational model. Let’s take a closer look at this so-called paradigm mismatch.
The paradigm mismatch
The object/relational paradigm mismatch can be broken into several parts, which
we’ll examine one at a time. Let’s start our exploration with a simple example that
is problem free. As we build on it, you’ll begin to see the mismatch appear.
Suppose you have to design and implement an online e-commerce application. In this application, you need a class to represent information about a user of
the system, and another class to represent information about the user’s billing
details, as shown in figure 1.1.
In this diagram, you can see that a User has many BillingDetails. You can
navigate the relationship between the classes in both directions. The classes representing these entities may be extremely simple:
public class User {
private String username;
private String name;
private String address;
private Set billingDetails;
// Accessor methods (getter/setter), business methods, etc.
public class BillingDetails {
private String accountNumber;
private String accountName;
private String accountType;
private User user;
// Accessor methods (getter/setter), business methods, etc.
Figure 1.1
A simple UML class diagram of the
User and BillingDetails entities
The paradigm mismatch
Note that we’re only interested in the state of the entities with regard to persistence, so we’ve omitted the implementation of property accessors and business
methods (such as getUsername() or billAuction()).
It’s easy to come up with a good SQL schema design for this case:
create table USERS (
USERNAME varchar(15) not null primary key,
NAME varchar(50) not null,
ADDRESS varchar(100)
create table BILLING_DETAILS (
ACCOUNT_NUMBER varchar(10) not null primary key,
ACCOUNT_NAME varchar(50) not null,
ACCOUNT_TYPE varchar(2) not null,
USERNAME varchar(15) foreign key references user
The relationship between the two entities is represented as the foreign key,
USERNAME, in BILLING_DETAILS. For this simple domain model, the object/relational mismatch is barely in evidence; it’s straightforward to write JDBC code to
insert, update, and delete information about users and billing details.
Now, let’s see what happens when we consider something a little more realistic.
The paradigm mismatch will be visible when we add more entities and entity relationships to our application.
The most glaringly obvious problem with our current implementation is that
we’ve designed an address as a simple String value. In most systems, it’s necessary to store street, city, state, country, and ZIP code information separately. Of
course, we could add these properties directly to the User class, but because it’s
highly likely that other classes in the system will also carry address information, it
makes more sense to create a separate Address class. The updated model is
shown in figure 1.2.
Should we also add an ADDRESS table? Not necessarily. It’s common to keep
address information in the USERS table, in individual columns. This design is
likely to perform better, because a table join isn’t needed if you want to retrieve
the user and address in a single query. The nicest solution may even be to create a
user-defined SQL datatype to represent addresses, and to use a single column of
that new type in the USERS table instead of several new columns.
Basically, we have the choice of adding either several columns or a single column (of a new SQL datatype). This is clearly a problem of granularity.
Figure 1.2
The User has an Address
Understanding object/relational persistence
The problem of granularity
Granularity refers to the relative size of the types you’re working with.
Let’s return to our example. Adding a new datatype to our database catalog,
to store Address Java instances in a single column, sounds like the best
approach. A new Address type (class) in Java and a new ADDRESS SQL datatype
should guarantee interoperability. However, you’ll find various problems if you
check the support for user-defined datatypes (UDT) in today’s SQL database
management systems.
UDT support is one of a number of so-called object-relational extensions to traditional SQL. This term alone is confusing, because it means that the database management system has (or is supposed to support) a sophisticated datatype system—
something you take for granted if somebody sells you a system that can handle
data in a relational fashion. Unfortunately, UDT support is a somewhat obscure
feature of most SQL database management systems and certainly isn’t portable
between different systems. Furthermore, the SQL standard supports user-defined
datatypes, but poorly.
This limitation isn’t the fault of the relational data model. You can consider
the failure to standardize such an important piece of functionality as fallout from
the object-relational database wars between vendors in the mid-1990s. Today, most
developers accept that SQL products have limited type systems—no questions
asked. However, even with a sophisticated UDT system in our SQL database management system, we would likely still duplicate the type declarations, writing the
new type in Java and again in SQL. Attempts to find a solution for the Java space,
such as SQLJ, unfortunately, have not had much success.
For these and whatever other reasons, use of UDTs or Java types inside an SQL
database isn’t common practice in the industry at this time, and it’s unlikely that
you’ll encounter a legacy schema that makes extensive use of UDTs. We therefore
can’t and won’t store instances of our new Address class in a single new column
that has the same datatype as the Java layer.
Our pragmatic solution for this problem has several columns of built-in vendor-defined SQL types (such as boolean, numeric, and string datatypes). The
USERS table is usually defined as follows:
create table USERS (
USERNAME varchar(15) not null primary key,
NAME varchar(50) not null,
ADDRESS_STREET varchar(50),
ADDRESS_CITY varchar(15),
ADDRESS_STATE varchar(15),
The paradigm mismatch
Classes in our domain model come in a range of different levels of granularity—
from coarse-grained entity classes like User, to finer-grained classes like Address,
down to simple String-valued properties such as zipcode. In contrast, just two
levels of granularity are visible at the level of the SQL database: tables such as
USERS, and columns such as ADDRESS_ZIPCODE.
Many simple persistence mechanisms fail to recognize this mismatch and so
end up forcing the less flexible SQL representation upon the object model. We’ve
seen countless User classes with properties named zipcode!
It turns out that the granularity problem isn’t especially difficult to solve. We
probably wouldn’t even discuss it, were it not for the fact that it’s visible in so
many existing systems. We describe the solution to this problem in chapter 4, section 4.4, “Fine-grained models and mappings.”
A much more difficult and interesting problem arises when we consider
domain models that rely on inheritance, a feature of object-oriented design we may
use to bill the users of our e-commerce application in new and interesting ways.
The problem of subtypes
In Java, you implement type inheritance using superclasses and subclasses. To
illustrate why this can present a mismatch problem, let’s add to our e-commerce
application so that we now can accept not only bank account billing, but also
credit and debit cards. The most natural way to reflect this change in the model is
to use inheritance for the BillingDetails class.
We may have an abstract BillingDetails superclass, along with several concrete subclasses: CreditCard, BankAccount, and so on. Each of these subclasses
defines slightly different data (and completely different functionality that acts on
that data). The UML class diagram in figure 1.3 illustrates this model.
SQL should probably include standard support for supertables and subtables.
This would effectively allow us to create a table that inherits certain columns from
Figure 1.3
Using inheritance for different billing strategies
Understanding object/relational persistence
its parent. However, such a feature would be questionable, because it would introduce a new notion: virtual columns in base tables. Traditionally, we expect virtual
columns only in virtual tables, which are called views. Furthermore, on a theoretical level, the inheritance we applied in Java is type inheritance. A table isn’t a type,
so the notion of supertables and subtables is questionable. In any case, we can
take the short route here and observe that SQL database products don’t generally
implement type or table inheritance, and if they do implement it, they don’t follow a standard syntax and usually expose you to data integrity problems (limited
integrity rules for updatable views).
In chapter 5, section 5.1, “Mapping class inheritance,” we discuss how ORM
solutions such as Hibernate solve the problem of persisting a class hierarchy to a
database table or tables. This problem is now well understood in the community,
and most solutions support approximately the same functionality.
But we aren’t finished with inheritance. As soon as we introduce inheritance
into the model, we have the possibility of polymorphism.
The User class has an association to the BillingDetails superclass. This is a
polymorphic association. At runtime, a User object may reference an instance of any
of the subclasses of BillingDetails. Similarly, we want to be able to write polymorphic queries that refer to the BillingDetails class, and have the query return
instances of its subclasses.
SQL databases also lack an obvious way (or at least a standardized way) to represent a polymorphic association. A foreign key constraint refers to exactly one target table; it isn’t straightforward to define a foreign key that refers to multiple tables.
We’d have to write a procedural constraint to enforce this kind of integrity rule.
The result of this mismatch of subtypes is that the inheritance structure in your
model must be persisted in an SQL database that doesn’t offer an inheritance
strategy. Fortunately, three of the inheritance mapping solutions we show in chapter 5 are designed to accommodate the representation of polymorphic associations and the efficient execution of polymorphic queries.
The next aspect of the object/relational mismatch problem is the issue of object
identity. You probably noticed that we defined USERNAME as the primary key of our
USERS table. Was that a good choice? How do we handle identical objects in Java?
The problem of identity
Although the problem of object identity may not be obvious at first, we’ll encounter it often in our growing and expanding e-commerce system, such as when we
need to check whether two objects are identical. There are three ways to tackle
The paradigm mismatch
this problem: two in the Java world and one in our SQL database. As expected,
they work together only with some help.
Java objects define two different notions of sameness:
Object identity (roughly equivalent to memory location, checked with
Equality as determined by the implementation of the equals() method
(also called equality by value)
On the other hand, the identity of a database row is expressed as the primary key
value. As you’ll see in chapter 9, section 9.2, “Object identity and equality,” neither equals() nor == is naturally equivalent to the primary key value. It’s common for several nonidentical objects to simultaneously represent the same row of
the database, for example, in concurrently running application threads. Furthermore, some subtle difficulties are involved in implementing equals() correctly
for a persistent class.
Let’s discuss another problem related to database identity with an example. In
our table definition for USERS, we used USERNAME as a primary key. Unfortunately,
this decision makes it difficult to change a username; we need to update not only
the USERNAME column in USERS, but also the foreign key column in BILLING_
DETAILS. To solve this problem, later in the book we’ll recommend that you use
surrogate keys whenever you can’t find a good natural key (we’ll also discuss what
makes a key good). A surrogate key column is a primary key column with no
meaning to the user; in other words, a key that isn’t presented to the user and is
only used for identification of data inside the software system. For example, we
may change our table definitions to look like this:
create table USERS (
USER_ID bigint not null primary key,
USERNAME varchar(15) not null unique,
NAME varchar(50) not null,
create table BILLING_DETAILS (
BILLING_DETAILS_ID bigint not null primary key,
ACCOUNT_NUMBER VARCHAR(10) not null unique,
USER_ID bigint foreign key references USER
The USER_ID and BILLING_DETAILS_ID columns contain system-generated values.
These columns were introduced purely for the benefit of the data model, so how
Understanding object/relational persistence
(if at all) should they be represented in the domain model? We discuss this question in chapter 4, section 4.2, “Mapping entities with identity,” and we find a solution with ORM.
In the context of persistence, identity is closely related to how the system handles caching and transactions. Different persistence solutions have chosen different strategies, and this has been an area of confusion. We cover all these
interesting topics—and show how they’re related—in chapters 10 and 13.
So far, the skeleton e-commerce application we’ve designed has identified the
mismatch problems with mapping granularity, subtypes, and object identity. We’re
almost ready to move on to other parts of the application, but first we need to discuss the important concept of associations: how the relationships between our
classes are mapped and handled. Is the foreign key in the database all you need?
Problems relating to associations
In our domain model, associations represent the relationships between entities.
The User, Address, and BillingDetails classes are all associated; but unlike
Address, BillingDetails stands on its own. BillingDetails instances are stored
in their own table. Association mapping and the management of entity associations are central concepts in any object persistence solution.
Object-oriented languages represent associations using object references; but in
the relational world, an association is represented as a foreign key column, with
copies of key values (and a constraint to guarantee integrity). There are substantial differences between the two representations.
Object references are inherently directional; the association is from one object
to the other. They’re pointers. If an association between objects should be navigable in both directions, you must define the association twice, once in each of the
associated classes. You’ve already seen this in the domain model classes:
public class User {
private Set billingDetails;
public class BillingDetails {
private User user;
On the other hand, foreign key associations aren’t by nature directional. Navigation has no meaning for a relational data model because you can create arbitrary
data associations with table joins and projection. The challenge is to bridge a completely open data model, which is independent of the application that works with
The paradigm mismatch
the data, to an application-dependent navigational model, a constrained view of
the associations needed by this particular application.
It isn’t possible to determine the multiplicity of a unidirectional association by
looking only at the Java classes. Java associations can have many-to-many multiplicity. For example, the classes could look like this:
public class User {
private Set billingDetails;
public class BillingDetails {
private Set users;
Table associations, on the other hand, are always one-to-many or one-to-one. You can
see the multiplicity immediately by looking at the foreign key definition. The following is a foreign key declaration on the BILLING_DETAILS table for a one-tomany association (or, if read in the other direction, a many-to-one association):
USER_ID bigint foreign key references USERS
These are one-to-one associations:
USER_ID bigint unique foreign key references USERS
BILLING_DETAILS_ID bigint primary key foreign key references USERS
If you wish to represent a many-to-many association in a relational database, you
must introduce a new table, called a link table. This table doesn’t appear anywhere
in the domain model. For our example, if we consider the relationship between
the user and the billing information to be many-to-many, the link table is defined
as follows:
USER_ID bigint foreign key references USERS,
BILLING_DETAILS_ID bigint foreign key references BILLING_DETAILS,
We discuss association and collection mappings in great detail in chapters 6 and 7.
So far, the issues we’ve considered are mainly structural. We can see them by
considering a purely static view of the system. Perhaps the most difficult problem
in object persistence is a dynamic problem. It concerns associations, and we’ve
already hinted at it when we drew a distinction between object network navigation
and table joins in section 1.1.4, “Persistence in object-oriented applications.” Let’s
explore this significant mismatch problem in more depth.
Understanding object/relational persistence
The problem of data navigation
There is a fundamental difference in the way you access data in Java and in a relational database. In Java, when you access a user’s billing information, you call
aUser.getBillingDetails().getAccountNumber() or something similar. This is
the most natural way to access object-oriented data, and it’s often described as
walking the object network. You navigate from one object to another, following
pointers between instances. Unfortunately, this isn’t an efficient way to retrieve
data from an SQL database.
The single most important thing you can do to improve the performance of
data access code is to minimize the number of requests to the database. The most obvious way to do this is to minimize the number of SQL queries. (Of course, there are
other more sophisticated ways that follow as a second step.)
Therefore, efficient access to relational data with SQL usually requires joins
between the tables of interest. The number of tables included in the join when
retrieving data determines the depth of the object network you can navigate in
memory. For example, if you need to retrieve a User and aren’t interested in the
user’s billing information, you can write this simple query:
select * from USERS u where u.USER_ID = 123
On the other hand, if you need to retrieve a User and then subsequently visit each
of the associated BillingDetails instances (let’s say, to list all the user’s credit
cards), you write a different query:
select *
from USERS u
left outer join BILLING_DETAILS bd on bd.USER_ID = u.USER_ID
where u.USER_ID = 123
As you can see, to efficiently use joins you need to know what portion of the object
network you plan to access when you retrieve the initial User—this is before you
start navigating the object network!
On the other hand, any object persistence solution provides functionality for
fetching the data of associated objects only when the object is first accessed. However, this piecemeal style of data access is fundamentally inefficient in the context
of a relational database, because it requires executing one statement for each
node or collection of the object network that is accessed. This is the dreaded n+1
selects problem.
This mismatch in the way you access objects in Java and in a relational database
is perhaps the single most common source of performance problems in Java
applications. There is a natural tension between too many selects and too big
The paradigm mismatch
selects, which retrieve unnecessary information into memory. Yet, although we’ve
been blessed with innumerable books and magazine articles advising us to use
StringBuffer for string concatenation, it seems impossible to find any advice
about strategies for avoiding the n+1 selects problem. Fortunately, Hibernate provides sophisticated features for efficiently and transparently fetching networks of
objects from the database to the application accessing them. We discuss these features in chapters 13, 14, and 15.
The cost of the mismatch
We now have quite a list of object/relational mismatch problems, and it will be
costly (in time and effort) to find solutions, as you may know from experience.
This cost is often underestimated, and we think this is a major reason for many
failed software projects. In our experience (regularly confirmed by developers we
talk to), the main purpose of up to 30 percent of the Java application code written
is to handle the tedious SQL/JDBC and manual bridging of the object/relational
paradigm mismatch. Despite all this effort, the end result still doesn’t feel quite
right. We’ve seen projects nearly sink due to the complexity and inflexibility of
their database abstraction layers. We also see Java developers (and DBAs) quickly
lose their confidence when design decisions about the persistence strategy for a
project have to be made.
One of the major costs is in the area of modeling. The relational and domain
models must both encompass the same business entities, but an object-oriented
purist will model these entities in a different way than an experienced relational
data modeler would. The usual solution to this problem is to bend and twist the
domain model and the implemented classes until they match the SQL database
schema. (Which, following the principle of data independence, is certainly a safe
long-term choice.)
This can be done successfully, but only at the cost of losing some of the advantages of object orientation. Keep in mind that relational modeling is underpinned
by relational theory. Object orientation has no such rigorous mathematical definition or body of theoretical work, so we can’t look to mathematics to explain how
we should bridge the gap between the two paradigms—there is no elegant transformation waiting to be discovered. (Doing away with Java and SQL, and starting
from scratch isn’t considered elegant.)
The domain modeling mismatch isn’t the only source of the inflexibility and
the lost productivity that lead to higher costs. A further cause is the JDBC API
itself. JDBC and SQL provide a statement-oriented (that is, command-oriented)
approach to moving data to and from an SQL database. If you want to query or
Understanding object/relational persistence
manipulate data, the tables and columns involved must be specified at least three
times (insert, update, select), adding to the time required for design and
implementation. The distinct dialects for every SQL database management system
don’t improve the situation.
To round out your understanding of object persistence, and before we
approach possible solutions, we need to discuss application architecture and the role
of a persistence layer in typical application design.
Persistence layers and alternatives
In a medium- or large-sized application, it usually makes sense to organize classes
by concern. Persistence is one concern; others include presentation, workflow,
and business logic.1 A typical object-oriented architecture includes layers of code
that represent the concerns. It’s normal and certainly best practice to group all
classes and components responsible for persistence into a separate persistence
layer in a layered system architecture.
In this section, we first look at the layers of this type of architecture and why we
use them. After that, we focus on the layer we’re most interested in—the persistence layer—and some of the ways it can be implemented.
Layered architecture
A layered architecture defines interfaces between code that implements the various concerns, allowing changes to be made to the way one concern is implemented
without significant disruption to code in the other layers. Layering also determines
the kinds of interlayer dependencies that occur. The rules are as follows:
Layers communicate from top to bottom. A layer is dependent only on the
layer directly below it.
Each layer is unaware of any other layers except for the layer just below it.
Different systems group concerns differently, so they define different layers. A typical, proven, high-level application architecture uses three layers: one each for
presentation, business logic, and persistence, as shown in figure 1.4.
Let’s take a closer look at the layers and elements in the diagram:
There are also the so-called cross-cutting concerns, which may be implemented generically—by framework code, for example. Typical cross-cutting concerns include logging, authorization, and transaction
Persistence layers and alternatives
Figure 1.4
A persistence layer is the basis in a layered architecture
Presentation layer—The user interface logic is topmost. Code responsible for
the presentation and control of page and screen navigation is in the presentation layer.
Business layer—The exact form of the next layer varies widely between applications. It’s generally agreed, however, that the business layer is responsible
for implementing any business rules or system requirements that would be
understood by users as part of the problem domain. This layer usually
includes some kind of controlling component—code that knows when to
invoke which business rule. In some systems, this layer has its own internal
representation of the business domain entities, and in others it reuses the
model defined by the persistence layer. We revisit this issue in chapter 3.
Persistence layer—The persistence layer is a group of classes and components
responsible for storing data to, and retrieving it from, one or more data
stores. This layer necessarily includes a model of the business domain entities (even if it’s only a metadata model).
Database—The database exists outside the Java application itself. It’s the
actual, persistent representation of the system state. If an SQL database is
used, the database includes the relational schema and possibly stored procedures.
Helper and utility classes—Every application has a set of infrastructural helper
or utility classes that are used in every layer of the application (such as
Exception classes for error handling). These infrastructural elements don’t
form a layer, because they don’t obey the rules for interlayer dependency in
a layered architecture.
Understanding object/relational persistence
Let’s now take a brief look at the various ways the persistence layer can be implemented by Java applications. Don’t worry—we’ll get to ORM and Hibernate soon.
There is much to be learned by looking at other approaches.
Hand-coding a persistence layer with SQL/JDBC
The most common approach to Java persistence is for application programmers
to work directly with SQL and JDBC. After all, developers are familiar with relational database management systems, they understand SQL, and they know how to
work with tables and foreign keys. Moreover, they can always use the well-known
and widely used data access object (DAO) pattern to hide complex JDBC code and
nonportable SQL from the business logic.
The DAO pattern is a good one—so good that we often recommend its use
even with ORM. However, the work involved in manually coding persistence for
each domain class is considerable, particularly when multiple SQL dialects are
supported. This work usually ends up consuming a large portion of the development effort. Furthermore, when requirements change, a hand-coded solution
always requires more attention and maintenance effort.
Why not implement a simple mapping framework to fit the specific requirements of your project? The result of such an effort could even be reused in future
projects. Many developers have taken this approach; numerous homegrown
object/relational persistence layers are in production systems today. However, we
don’t recommend this approach. Excellent solutions already exist: not only the
(mostly expensive) tools sold by commercial vendors, but also open source
projects with free licenses. We’re certain you’ll be able to find a solution that
meets your requirements, both business and technical. It’s likely that such a solution will do a great deal more, and do it better, than a solution you could build in
a limited time.
Developing a reasonably full-featured ORM may take many developers months.
For example, Hibernate is about 80,000 lines of code, some of which is much
more difficult than typical application code, along with 25,000 lines of unit test
code. This may be more code than is in your application. A great many details can
easily be overlooked in such a large project—as both the authors know from experience! Even if an existing tool doesn’t fully implement two or three of your more
exotic requirements, it’s still probably not worth creating your own tool. Any ORM
software will handle the tedious common cases—the ones that kill productivity.
It’s OK if you need to hand-code certain special cases; few applications are composed primarily of special cases.
Persistence layers and alternatives
Using serialization
Java has a built-in persistence mechanism: Serialization provides the ability to
write a snapshot of a network of objects (the state of the application) to a byte
stream, which may then be persisted to a file or database. Serialization is also used
by Java’s Remote Method Invocation (RMI) to achieve pass-by value semantics for
complex objects. Another use of serialization is to replicate application state
across nodes in a cluster of machines.
Why not use serialization for the persistence layer? Unfortunately, a serialized
network of interconnected objects can only be accessed as a whole; it’s impossible
to retrieve any data from the stream without deserializing the entire stream. Thus,
the resulting byte stream must be considered unsuitable for arbitrary search or
aggregation of large datasets. It isn’t even possible to access or update a single
object or subset of objects independently. Loading and overwriting an entire
object network in each transaction is no option for systems designed to support
high concurrency.
Given current technology, serialization is inadequate as a persistence mechanism for high concurrency web and enterprise applications. It has a particular
niche as a suitable persistence mechanism for desktop applications.
Object-oriented database systems
Because we work with objects in Java, it would be ideal if there were a way to store
those objects in a database without having to bend and twist the object model at
all. In the mid-1990s, object-oriented database systems gained attention. They’re
based on a network data model, which was common before the advent of the relational data model decades ago. The basic idea is to store a network of objects, with
all its pointers and nodes, and to re-create the same in-memory graph later on.
This can be optimized with various metadata and configuration settings.
An object-oriented database management system (OODBMS) is more like an
extension to the application environment than an external data store. An
OODBMS usually features a multitiered implementation, with the backend data
store, object cache, and client application coupled tightly together and interacting via a proprietary network protocol. Object nodes are kept on pages of memory, which are transported from and to the data store.
Object-oriented database development begins with the top-down definition of
host language bindings that add persistence capabilities to the programming language. Hence, object databases offer seamless integration into the object-oriented application environment. This is different from the model used by today’s
Understanding object/relational persistence
relational databases, where interaction with the database occurs via an intermediate language (SQL) and data independence from a particular application is the
major concern.
For background information on object-oriented databases, we recommend the
respective chapter in An Introduction to Database Systems (Date, 2003).
We won’t bother looking too closely into why object-oriented database technology hasn’t been more popular; we’ll observe that object databases haven’t been
widely adopted and that it doesn’t appear likely that they will be in the near
future. We’re confident that the overwhelming majority of developers will have
far more opportunity to work with relational technology, given the current political realities (predefined deployment environments) and the common requirement for data independence.
Other options
Of course, there are other kinds of persistence layers. XML persistence is a variation on the serialization theme; this approach addresses some of the limitations
of byte-stream serialization by allowing easy access to the data through a standardized tool interface. However, managing data in XML would expose you to an
object/hierarchical mismatch. Furthermore, there is no additional benefit from
the XML itself, because it’s just another text file format and has no inherent
capabilities for data management. You can use stored procedures (even writing
them in Java, sometimes) and move the problem into the database tier. So-called
object-relational databases have been marketed as a solution, but they offer only
a more sophisticated datatype system providing only half the solution to our
problems (and further muddling terminology). We’re sure there are plenty of
other examples, but none of them are likely to become popular in the immediate future.
Political and economic constraints (long-term investments in SQL databases),
data independence, and the requirement for access to valuable legacy data call for
a different approach. ORM may be the most practical solution to our problems.
Object/relational mapping
Now that we’ve looked at the alternative techniques for object persistence, it’s
time to introduce the solution we feel is the best, and the one we use with Hibernate: ORM. Despite its long history (the first research papers were published in
the late 1980s), the terms for ORM used by developers vary. Some call it object
relational mapping, others prefer the simple object mapping; we exclusively use
Object/relational mapping
the term object/relational mapping and its acronym, ORM. The slash stresses the
mismatch problem that occurs when the two worlds collide.
In this section, we first look at what ORM is. Then we enumerate the problems
that a good ORM solution needs to solve. Finally, we discuss the general benefits
that ORM provides and why we recommend this solution.
What is ORM?
In a nutshell, object/relational mapping is the automated (and transparent) persistence of objects in a Java application to the tables in a relational database, using
metadata that describes the mapping between the objects and the database.
ORM, in essence, works by (reversibly) transforming data from one representation to another. This implies certain performance penalties. However, if ORM is
implemented as middleware, there are many opportunities for optimization that
wouldn’t exist for a hand-coded persistence layer. The provision and management of metadata that governs the transformation adds to the overhead at development time, but the cost is less than equivalent costs involved in maintaining a
hand-coded solution. (And even object databases require significant amounts of
Isn’t ORM a Visio plug-in? The acronym ORM can also mean object role
modeling, and this term was invented before object/relational mapping
became relevant. It describes a method for information analysis, used in
database modeling, and is primarily supported by Microsoft Visio, a
graphical modeling tool. Database specialists use it as a replacement or as
an addition to the more popular entity-relationship modeling. However, if
you talk to Java developers about ORM, it’s usually in the context of
object/relational mapping.
An ORM solution consists of the following four pieces:
An API for performing basic CRUD operations on objects of persistent
A language or API for specifying queries that refer to classes and properties
of classes
A facility for specifying mapping metadata
A technique for the ORM implementation to interact with transactional
objects to perform dirty checking, lazy association fetching, and other optimization functions
Understanding object/relational persistence
We’re using the term full ORM to include any persistence layer where SQL is automatically generated from a metadata-based description. We aren’t including persistence layers where the object/relational mapping problem is solved manually
by developers hand-coding SQL with JDBC. With ORM, the application interacts
with the ORM APIs and the domain model classes and is abstracted from the
underlying SQL/JDBC. Depending on the features or the particular implementation, the ORM engine may also take on responsibility for issues such as optimistic
locking and caching, relieving the application of these concerns entirely.
Let’s look at the various ways ORM can be implemented. Mark Fussel (Fussel,
1997), a developer in the field of ORM, defined the following four levels of ORM
quality. We have slightly rewritten his descriptions and put them in the context of
today’s Java application development.
Pure relational
The whole application, including the user interface, is designed around the relational model and SQL-based relational operations. This approach, despite its deficiencies for large systems, can be an excellent solution for simple applications
where a low level of code reuse is tolerable. Direct SQL can be fine-tuned in every
aspect, but the drawbacks, such as lack of portability and maintainability, are significant, especially in the long run. Applications in this category often make heavy
use of stored procedures, shifting some of the work out of the business layer and
into the database.
Light object mapping
Entities are represented as classes that are mapped manually to the relational
tables. Hand-coded SQL/JDBC is hidden from the business logic using wellknown design patterns. This approach is extremely widespread and is successful
for applications with a small number of entities, or applications with generic,
metadata-driven data models. Stored procedures may have a place in this kind of
Medium object mapping
The application is designed around an object model. SQL is generated at build
time using a code-generation tool, or at runtime by framework code. Associations
between objects are supported by the persistence mechanism, and queries may be
specified using an object-oriented expression language. Objects are cached by the
persistence layer. A great many ORM products and homegrown persistence layers
support at least this level of functionality. It’s well suited to medium-sized
Object/relational mapping
applications with some complex transactions, particularly when portability
between different database products is important. These applications usually
don’t use stored procedures.
Full object mapping
Full object mapping supports sophisticated object modeling: composition, inheritance, polymorphism, and persistence by reachability. The persistence layer
implements transparent persistence; persistent classes do not inherit from any
special base class or have to implement a special interface. Efficient fetching strategies (lazy, eager, and prefetching) and caching strategies are implemented transparently to the application. This level of functionality can hardly be achieved by a
homegrown persistence layer—it’s equivalent to years of development time. A
number of commercial and open source Java ORM tools have achieved this level
of quality.
This level meets the definition of ORM we’re using in this book. Let’s look at
the problems we expect to be solved by a tool that achieves full object mapping.
Generic ORM problems
The following list of issues, which we’ll call the ORM problems, identifies the fundamental questions resolved by a full object/relational mapping tool in a Java
environment. Particular ORM tools may provide extra functionality (for example,
aggressive caching), but this is a reasonably exhaustive list of the conceptual issues
and questions that are specific to object/relational mapping.
What do persistent classes look like? How transparent is the persistence tool?
Do we have to adopt a programming model and conventions for classes of
the business domain?
How is mapping metadata defined? Because the object/relational transformation is governed entirely by metadata, the format and definition of this
metadata is important. Should an ORM tool provide a GUI interface to
manipulate the metadata graphically? Or are there better approaches to
metadata definition?
How do object identity and equality relate to database (primary key) identity? How
do we map instances of particular classes to particular table rows?
How should we map class inheritance hierarchies? There are several standard strategies. What about polymorphic associations, abstract classes, and
Understanding object/relational persistence
How does the persistence logic interact at runtime with the objects of the business
domain? This is a problem of generic programming, and there are a number of solutions including source generation, runtime reflection, runtime
bytecode generation, and build-time bytecode enhancement. The solution
to this problem may affect your build process (but, preferably, shouldn’t
otherwise affect you as a user).
What is the lifecycle of a persistent object? Does the lifecycle of some objects
depend upon the lifecycle of other associated objects? How do we translate
the lifecycle of an object to the lifecycle of a database row?
What facilities are provided for sorting, searching, and aggregating? The application could do some of these things in memory, but efficient use of relational
technology requires that this work often be performed by the database.
How do we efficiently retrieve data with associations? Efficient access to relational data is usually accomplished via table joins. Object-oriented applications usually access data by navigating an object network. Two data access
patterns should be avoided when possible: the n+1 selects problem, and its
complement, the Cartesian product problem (fetching too much data in a
single select).
Two additional issues that impose fundamental constraints on the design and
architecture of an ORM tool are common to any data access technology:
Transactions and concurrency
Cache management (and concurrency)
As you can see, a full object/relational mapping tool needs to address quite a
long list of issues. By now, you should be starting to see the value of ORM. In the
next section, we look at some of the other benefits you gain when you use an
ORM solution.
Why ORM?
An ORM implementation is a complex beast—less complex than an application
server, but more complex than a web application framework like Struts or Tapestry. Why should we introduce another complex infrastructural element into our
system? Will it be worth it?
It will take us most of this book to provide a complete answer to those questions, but this section provides a quick summary of the most compelling benefits.
First, though, let’s quickly dispose of a nonbenefit.
Object/relational mapping
A supposed advantage of ORM is that it shields developers from messy SQL.
This view holds that object-oriented developers can’t be expected to understand
SQL or relational databases well, and that they find SQL somehow offensive. On
the contrary, we believe that Java developers must have a sufficient level of familiarity with—and appreciation of—relational modeling and SQL in order to work
with ORM. ORM is an advanced technique to be used by developers who have
already done it the hard way. To use Hibernate effectively, you must be able to
view and interpret the SQL statements it issues and understand the implications
for performance.
Now, let’s look at some of the benefits of ORM and Hibernate.
Persistence-related code can be perhaps the most tedious code in a Java application. Hibernate eliminates much of the grunt work (more than you’d expect) and
lets you concentrate on the business problem.
No matter which application-development strategy you prefer—top-down,
starting with a domain model, or bottom-up, starting with an existing database
schema—Hibernate, used together with the appropriate tools, will significantly
reduce development time.
Fewer lines of code (LOC) make the system more understandable, because it
emphasizes business logic rather than plumbing. Most important, a system with
less code is easier to refactor. Automated object/relational persistence substantially reduces LOC. Of course, counting lines of code is a debatable way of measuring application complexity.
However, there are other reasons that a Hibernate application is more maintainable. In systems with hand-coded persistence, an inevitable tension exists
between the relational representation and the object model implementing the
domain. Changes to one almost always involve changes to the other, and often the
design of one representation is compromised to accommodate the existence of
the other. (What almost always happens in practice is that the object model of the
domain is compromised.) ORM provides a buffer between the two models, allowing more elegant use of object orientation on the Java side, and insulating each
model from minor changes to the other.
A common claim is that hand-coded persistence can always be at least as fast, and
can often be faster, than automated persistence. This is true in the same sense that
Understanding object/relational persistence
it’s true that assembly code can always be at least as fast as Java code, or a handwritten parser can always be at least as fast as a parser generated by YACC or
ANTLR—in other words, it’s beside the point. The unspoken implication of the
claim is that hand-coded persistence will perform at least as well in an actual
application. But this implication will be true only if the effort required to implement at-least-as-fast hand-coded persistence is similar to the amount of effort
involved in utilizing an automated solution. The really interesting question is
what happens when we consider time and budget constraints?
Given a persistence task, many optimizations are possible. Some (such as query
hints) are much easier to achieve with hand-coded SQL/JDBC. Most optimizations, however, are much easier to achieve with automated ORM. In a project with
time constraints, hand-coded persistence usually allows you to make some optimizations. Hibernate allows many more optimizations to be used all the time. Furthermore, automated persistence improves developer productivity so much that
you can spend more time hand-optimizing the few remaining bottlenecks.
Finally, the people who implemented your ORM software probably had much
more time to investigate performance optimizations than you have. Did you
know, for instance, that pooling PreparedStatement instances results in a significant performance increase for the DB2 JDBC driver but breaks the InterBase
JDBC driver? Did you realize that updating only the changed columns of a table
can be significantly faster for some databases but potentially slower for others? In
your handcrafted solution, how easy is it to experiment with the impact of these
various strategies?
Vendor independence
An ORM abstracts your application away from the underlying SQL database and
SQL dialect. If the tool supports a number of different databases (and most do),
this confers a certain level of portability on your application. You shouldn’t necessarily expect write-once/run-anywhere, because the capabilities of databases differ, and achieving full portability would require sacrificing some of the strength
of the more powerful platforms. Nevertheless, it’s usually much easier to develop
a cross-platform application using ORM. Even if you don’t require cross-platform
operation, an ORM can still help mitigate some of the risks associated with vendor lock-in.
In addition, database independence helps in development scenarios where
developers use a lightweight local database but deploy for production on a different database.
Object/relational mapping
You need to select an ORM product at some point. To make an educated decision, you need a list of the software modules and standards that are available.
Introducing Hibernate, EJB3, and JPA
Hibernate is a full object/relational mapping tool that provides all the previously
listed ORM benefits. The API you’re working with in Hibernate is native and
designed by the Hibernate developers. The same is true for the query interfaces
and query languages, and for how object/relational mapping metadata is defined.
Before you start your first project with Hibernate, you should consider the EJB
3.0 standard and its subspecification, Java Persistence. Let’s go back in history and
see how this new standard came into existence.
Many Java developers considered EJB 2.1 entity beans as one of the technologies for the implementation of a persistence layer. The whole EJB programming
and persistence model has been widely adopted in the industry, and it has been
an important factor in the success of J2EE (or, Java EE as it’s now called).
However, over the last years, critics of EJB in the developer community became
more vocal (especially with regard to entity beans and persistence), and companies realized that the EJB standard should be improved. Sun, as the steering party
of J2EE, knew that an overhaul was in order and started a new Java specification
request (JSR) with the goal of simplifying EJB in early 2003. This new JSR, Enterprise JavaBeans 3.0 (JSR 220), attracted significant interest. Developers from the
Hibernate team joined the expert group early on and helped shape the new specification. Other vendors, including all major and many smaller companies in the
Java industry, also contributed to the effort. An important decision made for the
new standard was to specify and standardize things that work in practice, taking
ideas and concepts from existing successful products and projects. Hibernate,
therefore, being a successful data persistence solution, played an important role
for the persistence part of the new standard. But what exactly is the relationship
between Hibernate and EJB3, and what is Java Persistence?
Understanding the standards
First, it’s difficult (if not impossible) to compare a specification and a product. The
questions that should be asked are, “Does Hibernate implement the EJB 3.0 specification, and what is the impact on my project? Do I have to use one or the other?”
The new EJB 3.0 specification comes in several parts: The first part defines
the new EJB programming model for session beans and message-driven beans,
the deployment rules, and so on. The second part of the specification deals with
persistence exclusively: entities, object/relational mapping metadata, persistence
Understanding object/relational persistence
manager interfaces, and the query language. This second part is called Java Persistence API (JPA), probably because its interfaces are in the package
javax.persistence. We’ll use this acronym throughout the book.
This separation also exists in EJB 3.0 products; some implement a full EJB 3.0
container that supports all parts of the specification, and other products may
implement only the Java Persistence part. Two important principles were
designed into the new standard:
JPA engines should be pluggable, which means you should be able to take
out one product and replace it with another if you aren’t satisfied—even if
you want to stay with the same EJB 3.0 container or Java EE 5.0 application server.
JPA engines should be able to run outside of an EJB 3.0 (or any other) runtime environment, without a container in plain standard Java.
The consequences of this design are that there are more options for developers
and architects, which drives competition and therefore improves overall quality of
products. Of course, actual products also offer features that go beyond the specification as vendor-specific extensions (such as for performance tuning, or because
the vendor has a focus on a particular vertical problem space).
Hibernate implements Java Persistence, and because a JPA engine must be
pluggable, new and interesting combinations of software are possible. You can
select from various Hibernate software modules and combine them depending on
your project’s technical and business requirements.
Hibernate Core
The Hibernate Core is also known as Hibernate 3.2.x, or Hibernate. It’s the base
service for persistence, with its native API and its mapping metadata stored in XML
files. It has a query language called HQL (almost the same as SQL), as well as programmatic query interfaces for Criteria and Example queries. There are hundreds of options and features available for everything, as Hibernate Core is really
the foundation and the platform all other modules are built on.
You can use Hibernate Core on its own, independent from any framework or
any particular runtime environment with all JDKs. It works in every Java EE/J2EE
application server, in Swing applications, in a simple servlet container, and so on.
As long as you can configure a data source for Hibernate, it works. Your application code (in your persistence layer) will use Hibernate APIs and queries, and
your mapping metadata is written in native Hibernate XML files.
Object/relational mapping
Native Hibernate APIs, queries, and XML mapping files are the primary focus
of this book, and they’re explained first in all code examples. The reason for that
is that Hibernate functionality is a superset of all other available options.
Hibernate Annotations
A new way to define application metadata became available with JDK 5.0: type-safe
annotations embedded directly in the Java source code. Many Hibernate users are
already familiar with this concept, as the XDoclet software supports Javadoc metadata attributes and a preprocessor at compile time (which, for Hibernate, generates XML mapping files).
With the Hibernate Annotations package on top of Hibernate Core, you can now
use type-safe JDK 5.0 metadata as a replacement or in addition to native Hibernate
XML mapping files. You’ll find the syntax and semantics of the mapping annotations familiar once you’ve seen them side-by-side with Hibernate XML mapping
files. However, the basic annotations aren’t proprietary.
The JPA specification defines object/relational mapping metadata syntax and
semantics, with the primary mechanism being JDK 5.0 annotations. (Yes, JDK 5.0
is required for Java EE 5.0 and EJB 3.0.) Naturally, the Hibernate Annotations are
a set of basic annotations that implement the JPA standard, and they’re also a set
of extension annotations you need for more advanced and exotic Hibernate
mappings and tuning.
You can use Hibernate Core and Hibernate Annotations to reduce your lines
of code for mapping metadata, compared to the native XML files, and you may
like the better refactoring capabilities of annotations. You can use only JPA annotations, or you can add a Hibernate extension annotation if complete portability
isn’t your primary concern. (In practice, you should embrace the product you’ve
chosen instead of denying its existence at all times.)
We’ll discuss the impact of annotations on your development process, and how
to use them in mappings, throughout this book, along with native Hibernate XML
mapping examples.
Hibernate EntityManager
The JPA specification also defines programming interfaces, lifecycle rules for persistent objects, and query features. The Hibernate implementation for this part of
JPA is available as Hibernate EntityManager, another optional module you can
stack on top of Hibernate Core. You can fall back when a plain Hibernate
interface, or even a JDBC Connection is needed. Hibernate’s native features are a
superset of the JPA persistence features in every respect. (The simple fact is that
Understanding object/relational persistence
Hibernate EntityManager is a small wrapper around Hibernate Core that provides
JPA compatibility.)
Working with standardized interfaces and using a standardized query language
has the benefit that you can execute your JPA-compatible persistence layer with
any EJB 3.0 compliant application server. Or, you can use JPA outside of any particular standardized runtime environment in plain Java (which really means everywhere Hibernate Core can be used).
Hibernate Annotations should be considered in combination with Hibernate
EntityManager. It’s unusual that you’d write your application code against JPA
interfaces and with JPA queries, and not create most of your mappings with JPA
Java EE 5.0 application servers
We don’t cover all of EJB 3.0 in this book; our focus is naturally on persistence,
and therefore on the JPA part of the specification. (We will, of course, show you
many techniques with managed EJB components when we talk about application
architecture and design.)
Hibernate is also part of the JBoss Application Server (JBoss AS), an implementation of J2EE 1.4 and (soon) Java EE 5.0. A combination of Hibernate Core, Hibernate Annotations, and Hibernate EntityManager forms the persistence engine of
this application server. Hence, everything you can use stand-alone, you can also
use inside the application server with all the EJB 3.0 benefits, such as session
beans, message-driven beans, and other Java EE services.
To complete the picture, you also have to understand that Java EE 5.0 application servers are no longer the monolithic beasts of the J2EE 1.4 era. In fact, the
JBoss EJB 3.0 container also comes in an embeddable version, which runs inside
other application servers, and even in Tomcat, or in a unit test, or a Swing application. In the next chapter, you’ll prepare a project that utilizes EJB 3.0 components, and you’ll install the JBoss server for easy integration testing.
As you can see, native Hibernate features implement significant parts of the
specification or are natural vendor extensions, offering additional functionality if
Here is a simple trick to see immediately what code you’re looking at, whether
JPA or native Hibernate. If only the javax.persistence.* import is visible, you’re
working inside the specification; if you also import org.hibernate.*, you’re
using native Hibernate functionality. We’ll later show you a few more tricks that
will help you cleanly separate portable from vendor-specific code.
What is the future of Hibernate? Hibernate Core will be developed independently from and faster than the EJB 3.0 or Java Persistence specifications. It will be the testing ground for new ideas, as it has always been.
Any new feature developed for Hibernate Core is immediately and automatically available as an extension for all users of Java Persistence with
Hibernate Annotations and Hibernate EntityManager. Over time, if a
particular concept has proven its usefulness, Hibernate developers will
work with other expert group members on future standardization in an
updated EJB or Java Persistence specification. Hence, if you’re interested
in a quickly evolving standard, we encourage you to use native Hibernate
functionality, and to send feedback to the respective expert group. The
desire for total portability and the rejection of vendor extensions were
major reasons for the stagnation we saw in EJB 1.x and 2.x.
After so much praise of ORM and Hibernate, it’s time to look at some actual code.
It’s time to wrap up the theory and to set up a first project.
In this chapter, we’ve discussed the concept of object persistence and the importance of ORM as an implementation technique.
Object persistence means that individual objects can outlive the application process; they can be saved to a data store and be re-created at a later point in time. The
object/relational mismatch comes into play when the data store is an SQL-based
relational database management system. For instance, a network of objects can’t be
saved to a database table; it must be disassembled and persisted to columns of portable SQL datatypes. A good solution for this problem is object/relational mapping
(ORM), which is especially helpful if we consider richly typed Java domain models.
A domain model represents the business entities used in a Java application. In
a layered system architecture, the domain model is used to execute business logic
in the business layer (in Java, not in the database). This business layer communicates with the persistence layer beneath in order to load and store the persistent
objects of the domain model. ORM is the middleware in the persistence layer that
manages the persistence.
ORM isn’t a silver bullet for all persistence tasks; its job is to relieve the developer of 95 percent of object persistence work, such as writing complex SQL statements with many table joins, and copying values from JDBC result sets to objects or
graphs of objects. A full-featured ORM middleware solution may provide database
portability, certain optimization techniques like caching, and other viable functions that aren’t easy to hand-code in a limited time with SQL and JDBC.
Understanding object/relational persistence
It’s likely that a better solution than ORM will exist some day. We (and many
others) may have to rethink everything we know about SQL, persistence API standards, and application integration. The evolution of today’s systems into true relational database systems with seamless object-oriented integration remains pure
speculation. But we can’t wait, and there is no sign that any of these issues will
improve soon (a multibillion dollar industry isn’t very agile). ORM is the best solution currently available, and it’s a timesaver for developers facing the object/relational mismatch every day. With EJB 3.0, a specification for full object/relational
mapping software that is accepted in the Java industry is finally available.
Starting a project
This chapter covers
“Hello World” with Hibernate and Java
The toolset for forward and reverse engineering
Hibernate configuration and integration
Starting a project
You want to start using Hibernate and Java Persistence, and you want to learn it
with a step-by-step example. You want to see both persistence APIs and how you
can benefit from native Hibernate or standardized JPA. This is what you’ll find in
this chapter: a tour through a straightforward “Hello World” application.
However, a good and complete tutorial is already publicly available in the
Hibernate reference documentation, so instead of repeating it here, we show you
more detailed instructions about Hibernate integration and configuration along
the way. If you want to start with a less elaborate tutorial that you can complete in
one hour, our advice is to consider the Hibernate reference documentation. It
takes you from a simple stand-alone Java application with Hibernate through the
most essential mapping concepts and finally demonstrates a Hibernate web application deployed on Tomcat.
In this chapter, you’ll learn how to set up a project infrastructure for a plain
Java application that integrates Hibernate, and you’ll see many more details about
how Hibernate can be configured in such an environment. We also discuss configuration and integration of Hibernate in a managed environment—that is, an environment that provides Java EE services.
As a build tool for the “Hello World” project, we introduce Ant and create
build scripts that can not only compile and run the project, but also utilize the
Hibernate Tools. Depending on your development process, you’ll use the Hibernate toolset to export database schemas automatically or even to reverse-engineer
a complete application from an existing (legacy) database schema.
Like every good engineer, before you start your first real Hibernate project you
should prepare your tools and decide what your development process is going to
look like. And, depending on the process you choose, you may naturally prefer
different tools. Let’s look at this preparation phase and what your options are,
and then start a Hibernate project.
Starting a Hibernate project
In some projects, the development of an application is driven by developers analyzing the business domain in object-oriented terms. In others, it’s heavily influenced by an existing relational data model: either a legacy database or a brandnew schema designed by a professional data modeler. There are many choices to
be made, and the following questions need to be answered before you can start:
Can you start from scratch with a clean design of a new business requirement, or is legacy data and/or legacy application code present?
Starting a Hibernate project
Can some of the necessary pieces be automatically generated from an existing artifact (for example, Java source from an existing database schema)?
Can the database schema be generated from Java code and Hibernate mapping metadata?
What kind of tool is available to support this work? What about other tools
to support the full development cycle?
We’ll discuss these questions in the following sections as we set up a basic Hibernate project. This is your road map:
Select a development process
Set up the project infrastructure
Write application code and mappings
Configure and start Hibernate
Run the application.
After reading the next sections, you’ll be prepared for the correct approach in
your own project, and you’ll also have the background information for more complex scenarios we’ll touch on later in this chapter.
Selecting a development process
Let’s first get an overview of the available tools, the artifacts they use as source
input, and the output that is produced. Figure 2.1 shows various import and
UML Model
Persistent Class
Java Source
Mapping Metadata
Data Access Object
Java Source
Configuration XML
Freemarker Template
Database Schema
Figure 2.1
Mapping Metadata XML
Input and output of the tools used for Hibernate development
Starting a project
export tasks for Ant; all the functionality is also available with the Hibernate Tools
plug-ins for Eclipse. Refer to this diagram while reading this chapter.1
Hibernate Tools for Eclipse IDE —The Hibernate Tools are plug-ins for the
Eclipse IDE (part of the JBoss IDE for Eclipse—a set of wizards, editors, and
extra views in Eclipse that help you develop EJB3, Hibernate, JBoss Seam,
and other Java applications based on JBoss middleware). The features for
forward and reverse engineering are equivalent to the Ant-based tools.
The additional Hibernate Console view allows you to execute ad hoc Hibernate queries (HQL and Criteria) against your database and to browse
the result graphically. The Hibernate Tools XML editor supports automatic completion of mapping files, including class, property, and even
table and column names. The graphical tools were still in development
and available as a beta release during the writing of this book, however,
so any screenshots would be obsolete with future releases of the software.
The documentation of the Hibernate Tools contains many screenshots
and detailed project setup instructions that you can easily adapt to create
your first “Hello World” program with the Eclipse IDE.
The following development scenarios are common:
Top down—In top-down development, you start with an existing domain
model, its implementation in Java, and (ideally) complete freedom with
respect to the database schema. You must create mapping metadata—
either with XML files or by annotating the Java source—and then optionally
let Hibernate’s hbm2ddl tool generate the database schema. In the absence
of an existing database schema, this is the most comfortable development
style for most Java developers. You may even use the Hibernate Tools to
automatically refresh the database schema on every application restart in
Bottom up—Conversely, bottom-up development begins with an existing database schema and data model. In this case, the easiest way to proceed is to
use the reverse-engineering tools to extract metadata from the database.
This metadata can be used to generate XML mapping files, with hbm2hbmxml
for example. With hbm2java, the Hibernate mapping metadata is used to
generate Java persistent classes, and even data access objects—in other
words, a skeleton for a Java persistence layer. Or, instead of writing to XML
Note that AndroMDA, a tool that generates POJO source code from UML diagram files, isn’t strictly
considered part of the common Hibernate toolset, so it isn’t discussed in this chapter. See the community area on the Hibernate website for more information about the Hibernate module for AndroMDA.
Starting a Hibernate project
mapping files, annotated Java source code (EJB 3.0 entity classes) can be
produced directly by the tools. However, not all class association details and
Java-specific metainformation can be automatically generated from an SQL
database schema with this strategy, so expect some manual work.
Middle out—The Hibernate XML mapping metadata provides sufficient
information to completely deduce the database schema and to generate the
Java source code for the persistence layer of the application. Furthermore,
the XML mapping document isn’t too verbose. Hence, some architects and
developers prefer middle-out development, where they begin with handwritten Hibernate XML mapping files, and then generate the database schema
using hbm2ddl and Java classes using hbm2java. The Hibernate XML mapping files are constantly updated during development, and other artifacts
are generated from this master definition. Additional business logic or database objects are added through subclassing and auxiliary DDL. This development style can be recommended only for the seasoned Hibernate expert.
Meet in the middle—The most difficult scenario is combining existing Java
classes and an existing database schema. In this case, there is little that the
Hibernate toolset can do to help. It is, of course, not possible to map arbitrary Java domain models to a given schema, so this scenario usually
requires at least some refactoring of the Java classes, database schema, or
both. The mapping metadata will almost certainly need to be written by
hand and in XML files (though it might be possible to use annotations if
there is a close match). This can be an incredibly painful scenario, and it is,
fortunately, exceedingly rare.
We now explore the tools and their configuration options in more detail and set
up a work environment for typical Hibernate application development. You can
follow our instructions step by step and create the same environment, or you can
take only the bits and pieces you need, such as the Ant build scripts.
The development process we assume first is top down, and we’ll walk through
a Hibernate project that doesn’t involve any legacy data schemas or Java code.
After that, you’ll migrate the code to JPA and EJB 3.0, and then you’ll start a
project bottom up by reverse-engineering from an existing database schema.
Setting up the project
We assume that you’ve downloaded the latest production release of Hibernate
from the Hibernate website at and that you unpacked
the archive. You also need Apache Ant installed on your development machine.
Starting a project
You should also download a current version of HSQLDB from
and extract the package; you’ll use this database management system for your
tests. If you have another database management system already installed, you only
need to obtain a JDBC driver for it.
Instead of the sophisticated application you’ll develop later in the book, you’ll
get started with a “Hello World” example. That way, you can focus on the development process without getting distracted by Hibernate details. Let’s set up the
project directory first.
Creating the work directory
Create a new directory on your system, in any location you like; C:\helloworld is a
good choice if you work on Microsoft Windows. We’ll refer to this directory as
WORKDIR in future examples. Create lib and src subdirectories, and copy all
required libraries:
The libraries you see in the library directory are from the Hibernate distribution,
most of them required for a typical Hibernate project. The hsqldb.jar file is from
the HSQLDB distribution; replace it with a different driver JAR if you want to use a
different database management system. Keep in mind that some of the libraries
you’re seeing here may not be required for the particular version of Hibernate
you’re working with, which is likely a newer release than we used when writing this
book. To make sure you have the right set of libraries, always check the lib/
README.txt file in the Hibernate distribution package. This file contains an up-todate list of all required and optional third-party libraries for Hibernate—you only
need the libraries listed as required for runtime.
In the “Hello World” application, you want to store messages in the database
and load them from the database. You need to create the domain model for this
business case.
Starting a Hibernate project
Creating the domain model
Hibernate applications define persistent classes that are mapped to database tables.
You define these classes based on your analysis of the business domain; hence,
they’re a model of the domain. The “Hello World” example consists of one class
and its mapping. Let’s see what a simple persistent class looks like, how the mapping is created, and some of the things you can do with instances of the persistent
class in Hibernate.
The objective of this example is to store messages in a database and retrieve
them for display. Your application has a simple persistent class, Message, which
represents these printable messages. The Message class is shown in listing 2.1.
Listing 2.1 a simple persistent class
package hello;
public class Message {
private Long id;
private String text;
private Message nextMessage;
Message() {}
Message text
Reference to another
Message instance
public Message(String text) {
this.text = text;
public Long getId() {
return id;
private void setId(Long id) { = id;
public String getText() {
return text;
public void setText(String text) {
this.text = text;
public Message getNextMessage() {
return nextMessage;
public void setNextMessage(Message nextMessage) {
this.nextMessage = nextMessage;
Starting a project
The Message class has three attributes: the identifier attribute, the text of the message, and a reference to another Message object. The identifier attribute allows
the application to access the database identity—the primary key value—of a persistent object. If two instances of Message have the same identifier value, they represent the same row in the database.
This example uses Long for the type of the identifier attribute, but this isn’t a
requirement. Hibernate allows virtually anything for the identifier type, as you’ll
see later.
You may have noticed that all attributes of the Message class have JavaBeansstyle property accessor methods. The class also has a constructor with no parameters. The persistent classes we show in the examples will almost always look something like this. The no-argument constructor is a requirement (tools like
Hibernate use reflection on this constructor to instantiate objects).
Instances of the Message class can be managed (made persistent) by Hibernate, but they don’t have to be. Because the Message object doesn’t implement any
Hibernate-specific classes or interfaces, you can use it just like any other Java class:
Message message = new Message("Hello World");
System.out.println( message.getText() );
This code fragment does exactly what you’ve come to expect from “Hello World”
applications: It prints Hello World to the console. It may look like we’re trying to be
cute here; in fact, we’re demonstrating an important feature that distinguishes
Hibernate from some other persistence solutions. The persistent class can be used
in any execution context at all—no special container is needed. Note that this is
also one of the benefits of the new JPA entities, which are also plain Java objects.
Save the code for the Message class into your source folder, in a directory and
package named hello.
Mapping the class to a database schema
To allow the object/relational mapping magic to occur, Hibernate needs some
more information about exactly how the Message class should be made persistent.
In other words, Hibernate needs to know how instances of that class are supposed
to be stored and loaded. This metadata can be written into an XML mapping document, which defines, among other things, how properties of the Message class map
to columns of a MESSAGES table. Let’s look at the mapping document in listing 2.2.
Starting a Hibernate project
Listing 2.2
A simple Hibernate XML mapping
<?xml version="1.0"?>
<!DOCTYPE hibernate-mapping PUBLIC
"-//Hibernate/Hibernate Mapping DTD//EN"
<generator class="increment"/>
The mapping document tells Hibernate that the Message class is to be persisted to
the MESSAGES table, that the identifier property maps to a column named
MESSAGE_ID, that the text property maps to a column named MESSAGE_TEXT, and
that the property named nextMessage is an association with many-to-one multiplicity
that maps to a foreign key column named NEXT_MESSAGE_ID. Hibernate also generates the database schema for you and adds a foreign key constraint with the
name FK_NEXT_MESSAGE to the database catalog. (Don’t worry about the other
details for now.)
The XML document isn’t difficult to understand. You can easily write and
maintain it by hand. Later, we discuss a way of using annotations directly in the
source code to define mapping information; but whichever method you choose,
Starting a project
Hibernate has enough information to generate all the SQL statements needed to
insert, update, delete, and retrieve instances of the Message class. You no longer
need to write these SQL statements by hand.
Create a file named Message.hbm.xml with the content shown in listing 2.2,
and place it next to your file in the source package hello. The
hbm suffix is a naming convention accepted by the Hibernate community, and
most developers prefer to place mapping files next to the source code of their
domain classes.
Let’s load and store some objects in the main code of the “Hello World”
Storing and loading objects
What you really came here to see is Hibernate, so let’s save a new Message to the
database (see listing 2.3).
Listing 2.3
The “Hello World” main application code
package hello;
import java.util.*;
import org.hibernate.*;
import persistence.*;
public class HelloWorld {
public static void main(String[] args) {
// First unit of work
Session session =
Transaction tx = session.beginTransaction();
Message message = new Message("Hello World");
Long msgId = (Long);
// Second unit of work
Session newSession =
Transaction newTransaction = newSession.beginTransaction();
List messages =
newSession.createQuery("from Message m order by
➥ m.text asc").list();
System.out.println( messages.size() +
" message(s) found:" );
Starting a Hibernate project
for ( Iterator iter = messages.iterator();
iter.hasNext(); ) {
Message loadedMsg = (Message);
System.out.println( loadedMsg.getText() );
// Shutting down the application
Place this code in the file in the source folder of your project, in
the hello package. Let’s walk through the code.
The class has a standard Java main() method, and you can call it from the command line directly. Inside the main application code, you execute two separate
units of work with Hibernate. The first unit stores a new Message object, and the
second unit loads all objects and prints their text to the console.
You call the Hibernate Session, Transaction, and Query interfaces to access
the database:
Session—A Hibernate Session is many things in one. It’s a single-threaded
nonshared object that represents a particular unit of work with the database. It has the persistence manager API you call to load and store objects.
(The Session internals consist of a queue of SQL statements that need to be
synchronized with the database at some point and a map of managed persistence instances that are monitored by the Session.)
Transaction—This Hibernate API can be used to set transaction bound-
aries programmatically, but it’s optional (transaction boundaries aren’t).
Other choices are JDBC transaction demarcation, the JTA interface, or container-managed transactions with EJBs.
Query—A database query can be written in Hibernate’s own object-oriented
query language (HQL) or plain SQL. This interface allows you to create queries, bind arguments to placeholders in the query, and execute the query in
various ways.
Ignore the line of code that calls HibernateUtil.getSessionFactory()—we’ll
get to it soon.
Starting a project
The first unit of work, if run, results in the execution of something similar to
the following SQL:
values (1, 'Hello World', null)
Hold on—the MESSAGE_ID column is being initialized to a strange value. You
didn’t set the id property of message anywhere, so you expect it to be NULL, right?
Actually, the id property is special. It’s an identifier property: It holds a generated
unique value. The value is assigned to the Message instance by Hibernate when
save() is called. (We’ll discuss how the value is generated later.)
Look at the second unit of work. The literal string "from Message m order by
m.text asc" is a Hibernate query, expressed in HQL. This query is internally
translated into the following SQL when list() is called:
order by m.MESSAGE_TEXT asc
If you run this main() method (don’t try this now—you still need to configure
Hibernate), the output on your console is as follows:
1 message(s) found:
Hello World
If you’ve never used an ORM tool like Hibernate before, you probably expected to
see the SQL statements somewhere in the code or mapping metadata, but they
aren’t there. All SQL is generated at runtime (actually, at startup for all reusable
SQL statements).
Your next step would normally be configuring Hibernate. However, if you feel
confident, you can add two other Hibernate features—automatic dirty checking
and cascading—in a third unit of work by adding the following code to your main
// Third unit of work
Session thirdSession =
Transaction thirdTransaction = thirdSession.beginTransaction();
// msgId holds the identifier value of the first message
message = (Message) thirdSession.get( Message.class, msgId );
message.setText( "Greetings Earthling" );
new Message( "Take me to your leader (please)" )
Starting a Hibernate project
This code calls three SQL statements inside the same database transaction:
where m.MESSAGE_ID = 1
values (2, 'Take me to your leader (please)', null)
set MESSAGE_TEXT = 'Greetings Earthling', NEXT_MESSAGE_ID = 2
where MESSAGE_ID = 1
Notice how Hibernate detected the modification to the text and nextMessage
properties of the first message and automatically updated the database—Hibernate did automatic dirty checking. This feature saves you the effort of explicitly asking Hibernate to update the database when you modify the state of an object
inside a unit of work. Similarly, the new message was made persistent when a reference was created from the first message. This feature is called cascading save. It
saves you the effort of explicitly making the new object persistent by calling
save(), as long as it’s reachable by an already persistent instance.
Also notice that the ordering of the SQL statements isn’t the same as the order
in which you set property values. Hibernate uses a sophisticated algorithm to
determine an efficient ordering that avoids database foreign key constraint violations but is still sufficiently predictable to the user. This feature is called transactional write-behind.
If you ran the application now, you’d get the following output (you’d have
to copy the second unit of work after the third to execute the query-display
step again):
2 message(s) found:
Greetings Earthling
Take me to your leader (please)
You now have domain classes, an XML mapping file, and the “Hello World” application code that loads and stores objects. Before you can compile and run this
code, you need to create Hibernate’s configuration (and resolve the mystery of
the HibernateUtil class).
Hibernate configuration and startup
The regular way of initializing Hibernate is to build a SessionFactory object from
a Configuration object. If you like, you can think of the Configuration as an
object representation of a configuration file (or a properties file) for Hibernate.
Let’s look at some variations before we wrap it up in the HibernateUtil class.
Starting a project
Building a SessionFactory
This is an example of a typical Hibernate startup procedure, in one line of code,
using automatic configuration file detection:
SessionFactory sessionFactory =
new Configuration().configure().buildSessionFactory();
Wait—how did Hibernate know where the configuration file was located and
which one to load?
When new Configuration() is called, Hibernate searches for a file named in the root of the classpath. If it’s found, all hibernate.*
properties are loaded and added to the Configuration object.
When configure() is called, Hibernate searches for a file named hibernate.cfg.xml in the root of the classpath, and an exception is thrown if it can’t
be found. You don’t have to call this method if you don’t have this configuration
file, of course. If settings in the XML configuration file are duplicates of properties set earlier, the XML settings override the previous ones.
The location of the configuration file is always the
root of the classpath, outside of any package. If you wish to use a different file or
to have Hibernate look in a subdirectory of your classpath for the XML configuration file, you must pass a path as an argument of the configure() method:
SessionFactory sessionFactory = new Configuration()
Finally, you can always set additional configuration options or mapping file locations on the Configuration object programmatically, before building the SessionFactory:
SessionFactory sessionFactory = new Configuration()
.setProperty(Environment.DEFAULT_SCHEMA, "CAVEATEMPTOR")
Many sources for the configuration are applied here: First the file in your classpath is read (if present). Next, all settings from /persistence/
auction.cfg.xml are added and override any previously applied settings. Finally, an
additional configuration property (a default database schema name) is set programmatically, and an additional Hibernate XML mapping metadata file is added
to the configuration.
You can, of course, set all options programmatically, or switch between different
XML configuration files for different deployment databases. There is effectively no
Starting a Hibernate project
limitation on how you can configure and deploy Hibernate; in the end, you only
need to build a SessionFactory from a prepared configuration.
Method chaining—Method chaining is a programming style supported by
many Hibernate interfaces. This style is more popular in Smalltalk than
in Java and is considered by some people to be less readable and more
difficult to debug than the more accepted Java style. However, it’s convenient in many cases, such as for the configuration snippets you’ve seen in
this section. Here is how it works: Most Java developers declare setter or
adder methods to be of type void, meaning they return no value; but in
Smalltalk, which has no void type, setter or adder methods usually return
the receiving object. We use this Smalltalk style in some code examples,
but if you don’t like it, you don’t need to use it. If you do use this coding
style, it’s better to write each method invocation on a different line. Otherwise, it may be difficult to step through the code in your debugger.
Now that you know how Hibernate is started and how to build a SessionFactory,
what to do next? You have to create a configuration file for Hibernate.
Creating an XML configuration file
Let’s assume you want to keep things simple, and, like most users, you decide to
use a single XML configuration file for Hibernate that contains all the configuration details.
We recommend that you give your new configuration file the default name
hibernate.cfg.xml and place it directly in the source directory of your project, outside of any package. That way, it will end up in the root of your classpath after
compilation, and Hibernate will find it automatically. Look at the file in
listing 2.4.
Listing 2.4
A simple Hibernate XML configuration file
<!DOCTYPE hibernate-configuration SYSTEM
<property name="hibernate.connection.driver_class">
<property name="hibernate.connection.url">
<property name="hibernate.connection.username">
Starting a project
<property name="hibernate.dialect">
<!-- Use the C3P0 connection pool provider -->
<property name="hibernate.c3p0.min_size">5</property>
<property name="hibernate.c3p0.max_size">20</property>
<property name="hibernate.c3p0.timeout">300</property>
<property name="hibernate.c3p0.max_statements">50</property>
<property name="hibernate.c3p0.idle_test_period">3000</property>
<!-- Show and print nice SQL on stdout -->
<property name="show_sql">true</property>
<property name="format_sql">true</property>
<!-- List of XML mapping files -->
<mapping resource="hello/Message.hbm.xml"/>
The document type declaration is used by the XML parser to validate this document
against the Hibernate configuration DTD. Note that this isn’t the same DTD as the
one for Hibernate XML mapping files. Also note that we added some line breaks
in the property values to make this more readable—you shouldn’t do this in your
real configuration file (unless your database username contains a line break).
First in the configuration file are the database connection settings. You need to
tell Hibernate which database JDBC driver you’re using and how to connect to the
database with a URL, a username, and a password (the password here is omitted,
because HSQLDB by default doesn’t require one). You set a Dialect, so that
Hibernate knows which SQL variation it has to generate to talk to your database;
dozens of dialects are packaged with Hibernate—look at the Hibernate API documentation to get a list.
In the XML configuration file, Hibernate properties may be specified without
the hibernate prefix, so you can write either hibernate.show_sql or just
show_sql. Property names and values are otherwise identical to programmatic
configuration properties—that is, to the constants as defined in org.hibernate.cfg.Environment. The hibernate.connection.driver_class property, for
example, has the constant Environment.DRIVER.
Before we look at some important configuration options, consider the last line
in the configuration that names a Hibernate XML mapping file. The Configuration object needs to know about all your XML mapping files before you build
the SessionFactory. A SessionFactory is an object that represents a particular
Starting a Hibernate project
Hibernate configuration for a particular set of mapping metadata. You can either
list all your XML mapping files in the Hibernate XML configuration file, or you
can set their names and paths programmatically on the Configuration object. In
any case, if you list them as a resource, the path to the mapping files is the relative
location on the classpath, with, in this example, hello being a package in the root
of the classpath.
You also enabled printing of all SQL executed by Hibernate to the console, and
you told Hibernate to format it nicely so that you can check what is going on
behind the scenes. We’ll come back to logging later in this chapter.
Another, sometimes useful, trick is to make configuration options more
dynamic with system properties:
<property name="show_sql">${displaysql}</property>
You can now specify a system property, such as with java -displaysql=true, on
the command line when you start your application, and this will automatically be
applied to the Hibernate configuration property.
The database connection pool settings deserve extra attention.
The database connection pool
Generally, it isn’t advisable to create a connection each time you want to interact
with the database. Instead, Java applications should use a pool of connections.
Each application thread that needs to do work on the database requests a connection from the pool and then returns it to the pool when all SQL operations have
been executed. The pool maintains the connections and minimizes the cost of
opening and closing connections.
There are three reasons for using a pool:
Acquiring a new connection is expensive. Some database management systems even start a completely new server process for each connection.
Maintaining many idle connections is expensive for a database management system, and the pool can optimize the usage of idle connections (or
disconnect if there are no requests).
Creating prepared statements is also expensive for some drivers, and the
connection pool can cache statements for a connection across requests.
Figure 2.2 shows the role of a connection pool in an unmanaged application runtime environment (that is, one without any application server).
Starting a project
Nonmanaged JSE environment
Figure 2.2
JDBC connection pooling in a nonmanaged environment
With no application server to provide a connection pool, an application either
implements its own pooling algorithm or relies on a third-party library such as the
open source C3P0 connection pooling software. Without Hibernate, the application code calls the connection pool to obtain a JDBC connection and then executes SQL statements with the JDBC programming interface. When the
application closes the SQL statements and finally closes the connection, the prepared statements and connection aren’t destroyed, but are returned to the pool.
With Hibernate, the picture changes: It acts as a client of the JDBC connection
pool, as shown in figure 2.3. The application code uses the Hibernate Session
and Query API for persistence operations, and it manages database transactions
(probably) with the Hibernate Transaction API.
Hibernate defines a plug-in architecture that allows integration with any connection-pooling software. However, support for C3P0 is built in, and the software
comes bundled with Hibernate, so you’ll use that (you already copied the c3p0.jar
file into your library directory, right?). Hibernate maintains the pool for you, and
configuration properties are passed through. How do you configure C3P0
through Hibernate?
Nonmanaged JSE environment
Figure 2.3
Hibernate with a connection pool in a nonmanaged environment
Starting a Hibernate project
One way to configure the connection pool is to put the settings into your
hibernate.cfg.xml configuration file, like you did in the previous section.
Alternatively, you can create a file in the classpath root of
the application. An example of a file for C3P0 is shown in
listing 2.5. Note that this file, with the exception of a list of mapping resources, is
equivalent to the configuration shown in listing 2.4.
Listing 2.5
Using for C3P0 connection pool settings
hibernate.connection.driver_class = org.hsqldb.jdbcDriver
hibernate.connection.url = jdbc:hsqldb:hsql://localhost
hibernate.connection.username = sa
hibernate.dialect = org.hibernate.dialect.HSQLDialect
hibernate.c3p0.min_size = 5
hibernate.c3p0.max_size = 20
hibernate.c3p0.timeout = 300
hibernate.c3p0.max_statements = 50
hibernate.c3p0.idle_test_period = 3000
hibernate.show_sql = true
hibernate.format_sql = true
This is the minimum number of JDBC connections that C3P0 keeps ready at all
This is the maximum number of connections in the pool. An exception is thrown
at runtime if this number is exhausted.
You specify the timeout period (in this case, 300 seconds) after which an idle connection is removed from the pool.
A maximum of 50 prepared statements will be cached. Caching of prepared statements is essential for best performance with Hibernate.
This is the idle time in seconds before a connection is automatically validated.
Specifying properties of the form hibernate.c3p0.* selects C3P0 as the connection pool (the c3p0.max_size option is needed—you don’t need any other switch
to enable C3P0 support). C3P0 has more features than shown in the previous
example; refer to the properties file in the etc/ subdirectory of the Hibernate distribution to get a comprehensive example you can copy from.
The Javadoc for the class org.hibernate.cfg.Environment also documents
every Hibernate configuration property. Furthermore, you can find an up-to-date
table with all Hibernate configuration options in the Hibernate reference
Starting a project
documentation. We’ll explain the most important settings throughout the book,
however. You already know all you need to get started.
Can I supply my own connections? Implement the org.hibernate.connection.ConnectionProvider interface, and name your implementation
with the hibernate.connection.provider_class configuration option.
Hibernate will now rely on your custom provider if it needs a database
Now that you’ve completed the Hibernate configuration file, you can move on
and create the SessionFactory in your application.
Handling the SessionFactory
In most Hibernate applications, the SessionFactory should be instantiated once
during application initialization. The single instance should then be used by all
code in a particular process, and any Session should be created using this single
SessionFactory. The SessionFactory is thread-safe and can be shared; a Session is a single-threaded object.
A frequently asked question is where the factory should be stored after creation and how it can be accessed without much hassle. There are more advanced
but comfortable options such as JNDI and JMX, but they’re usually available only
in full Java EE application servers. Instead, we’ll introduce a pragmatic and quick
solution that solves both the problem of Hibernate startup (the one line of code)
and the storing and accessing of the SessionFactory: you’ll use a static global
variable and static initialization.
Both the variable and initialization can be implemented in a single class, which
you’ll call HibernateUtil. This helper class is well known in the Hibernate community—it’s a common pattern for Hibernate startup in plain Java applications
without Java EE services. A basic implementation is shown in listing 2.6.
Listing 2.6
The HibernateUtil class for startup and SessionFactory handling
package persistence;
import org.hibernate.*;
import org.hibernate.cfg.*;
public class HibernateUtil {
private static SessionFactory sessionFactory;
static {
try {
sessionFactory=new Configuration()
Starting a Hibernate project
} catch (Throwable ex) {
throw new ExceptionInInitializerError(ex);
public static SessionFactory getSessionFactory() {
// Alternatively, you could look up in JNDI here
return sessionFactory;
public static void shutdown() {
// Close caches and connection pools
You create a static initializer block to start up Hibernate; this block is executed by
the loader of this class exactly once, on initialization when the class is loaded. The
first call of HibernateUtil in the application loads the class, builds the SessionFactory, and sets the static variable at the same time. If a problem occurs, any
Exception or Error is wrapped and thrown out of the static block (that’s why you
catch Throwable). The wrapping in ExceptionInInitializerError is mandatory
for static initializers.
You’ve created this new class in a new package called persistence. In a fully
featured Hibernate application, you often need such a package—for example, to
wrap up your custom persistence layer interceptors and data type converters as
part of your infrastructure.
Now, whenever you need access to a Hibernate Session in your application,
you can get it easily with HibernateUtil.getSessionFactory().openSession(),
just as you did earlier in the HelloWorld main application code.
You’re almost ready to run and test the application. But because you certainly
want to know what is going on behind the scenes, you’ll first enable logging.
Enabling logging and statistics
You’ve already seen the hibernate.show_sql configuration property. You’ll need
it continually when you develop software with Hibernate; it enables logging of all
generated SQL to the console. You’ll use it for troubleshooting, for performance
tuning, and to see what’s going on. If you also enable hibernate.format_sql, the
output is more readable but takes up more screen space. A third option you
haven’t set so far is hibernate.use_sql_comments—it causes Hibernate to put
Starting a project
comments inside all generated SQL statements to hint at their origin. For example, you can then easily see if a particular SQL statement was generated from an
explicit query or an on-demand collection initialization.
Enabling the SQL output to stdout is only your first logging option. Hibernate (and many other ORM implementations) execute SQL statements asynchronously. An INSERT statement isn’t usually executed when the application calls, nor is an UPDATE immediately issued when the application
calls item.setPrice(). Instead, the SQL statements are usually issued at the end
of a transaction.
This means that tracing and debugging ORM code is sometimes nontrivial. In
theory, it’s possible for the application to treat Hibernate as a black box and
ignore this behavior. However, when you’re troubleshooting a difficult problem,
you need to be able to see exactly what is going on inside Hibernate. Because
Hibernate is open source, you can easily step into the Hibernate code, and occasionally this helps a great deal! Seasoned Hibernate experts debug problems by
looking at the Hibernate log and the mapping files only; we encourage you to
spend some time with the log output generated by Hibernate and familiarize
yourself with the internals.
Hibernate logs all interesting events through Apache commons-logging, a thin
abstraction layer that directs output to either Apache Log4j (if you put log4j.jar in
your classpath) or JDK 1.4 logging (if you’re running under JDK 1.4 or above and
Log4j isn’t present). We recommend Log4j because it’s more mature, more popular, and under more active development.
To see output from Log4j, you need a file named in your classpath (right next to or hibernate.cfg.xml). Also, don’t forget
to copy the log4j.jar library to your lib directory. The Log4j configuration example in listing 2.7 directs all log messages to the console.
Listing 2.7
An example configuration file
# Direct log messages to stdout
➥%5p %c{1}:%L - %m%n
# Root logger option
log4j.rootLogger=INFO, stdout
# Hibernate logging options (INFO only shows startup messages)
Starting a Hibernate project
# Log JDBC bind parameter runtime arguments
The last category in this configuration file is especially interesting: It enables the
logging of JDBC bind parameters if you set it to DEBUG level, providing information
you usually don’t see in the ad hoc SQL console log. For a more comprehensive
example, check the file bundled in the etc/ directory of the
Hibernate distribution, and also look at the Log4j documentation for more information. Note that you should never log anything at DEBUG level in production,
because doing so can seriously impact the performance of your application.
You can also monitor Hibernate by enabling live statistics. Without an application server (that is, if you don’t have a JMX deployment environment), the easiest
way to get statistics out of the Hibernate engine at runtime is the SessionFactory:
Statistics stats =
EntityStatistics itemStats =
The statistics interfaces are Statistics for global information, EntityStatistics for information about a particular entity, CollectionStatistics for
a particular collection role, QueryStatistics for SQL and HQL queries, and SecondLevelCacheStatistics for detailed runtime information about a particular
region in the optional second-level data cache. A convenient method is logSummary(), which prints out a complete summary to the console with a single call. If
you want to enable the collection of statistics through the configuration, and not
programmatically, set the hibernate.generate_statistics configuration property to true. See the API documentation for more information about the various
statistics retrieval methods.
Before you run the “Hello World” application, check that your work directory
has all the necessary files:
Starting a project
<all required libraries>
hibernate.cfg.xml (or
The first file, build.xml, is the Ant build definition. It contains the Ant targets for
building and running the application, which we’ll discuss next. You’ll also add a
target that can generate the database schema automatically.
Running and testing the application
To run the application, you need to compile it first and start the database management system with the right database schema.
Ant is a powerful build system for Java. Typically, you’d write a build.xml file
for your project and call the build targets you defined in this file with the Ant
command-line tool. You can also call Ant targets from your Java IDE, if that is
Compiling the project with Ant
You’ll now add a build.xml file and some targets to the “Hello World” project. The
initial content for the build file is shown in listing 2.8—you create this file directly
in your WORKDIR.
Listing 2.8
A basic Ant build file for “Hello World”
<project name="HelloWorld" default="compile" basedir=".">
<!-- Name of project and version -->
<property name=""
<property name="proj.version"
<!-- Global properties for this
<property name=""
<property name="lib.dir"
<property name="build.dir"
build -->
<!-- Classpath declaration -->
<path id="project.classpath">
<fileset dir="${lib.dir}">
<include name="**/*.jar"/>
<include name="**/*.zip"/>
Starting a Hibernate project
<!-- Useful shortcuts -->
<patternset id="meta.files">
<include name="**/*.xml"/>
<include name="**/*.properties"/>
<!-- Clean up -->
<target name="clean">
<delete dir="${build.dir}"/>
<mkdir dir="${build.dir}"/>
<!-- Compile Java source -->
<target name="compile" depends="clean">
<mkdir dir="${build.dir}"/>
<classpath refid="project.classpath"/>
<!-- Copy metadata to build classpath -->
<target name="copymetafiles">
<copy todir="${build.dir}">
<fileset dir="${}">
<patternset refid="meta.files"/>
<!-- Run HelloWorld -->
<target name="run" depends="compile, copymetafiles"
description="Build and run HelloWorld">
<java fork="true"
<classpath path="${build.dir}"/>
The first half of this Ant build file contains property settings, such as the project
name and global locations of files and directories. You can already see that this
build is based on the existing directory layout, your WORKDIR (for Ant, this is the
same directory as the basedir). The default target, when this build file is called
with no named target, is compile.
Starting a project
Next, a name that can be easily referenced later, project.classpath, is
defined as a shortcut to all libraries in the library directory of the project. Another
shortcut for a pattern that will come in handy is defined as meta.files. You need
to handle configuration and metadata files separately in the processing of the
build, using this filter.
The clean target removes all created and compiled files, and cleans the
project. The last three targets, compile, copymetafiles, and run, should be selfexplanatory. Running the application depends on the compilation of all Java
source files, and the copying of all mapping and property configuration files to
the build directory.
Now, execute ant compile in your WORKDIR to compile the “Hello World”
application. You should see no errors (nor any warnings) during compilation and
find your compiled class files in the bin directory. Also call ant copymetafiles
once, and check whether all configuration and mapping files are copied correctly
into the bin directory.
Before you run the application, start the database management system and
export a fresh database schema.
Starting the HSQL database system
Hibernate supports more than 25 SQL database management systems out of the
box, and support for any unknown dialect can be added easily. If you have an
existing database, or if you know basic database administration, you can also
replace the configuration options (mostly connection and dialect settings) you
created earlier with settings for your own preferred system.
To say hello to the world, you need a lightweight, no-frills database system
that is easy to install and configure. A good choice is HSQLDB, an open source
SQL database management system written in Java. It can run in-process with the
main application, but in our experience, running it stand-alone with a TCP port
listening for connections is usually more convenient. You’ve already copied the
hsqldb.jar file into the library directory of your WORKDIR—this library includes
both the database engine and the JDBC driver required to connect to a running instance.
To start the HSQLDB server, open up a command line, change into your
WORKDIR, and run the command shown in figure 2.4. You should see startup messages and finally a help message that tells you how to shut down the database system (it’s OK to use Ctrl+C). You’ll also find some new files in your WORKDIR,
starting with test—these are the files used by HSQLDB to store your data. If you
want to start with a fresh database, delete the files between restarts of the server.
Starting a Hibernate project
Figure 2.4
Starting the HSQLDB server from the command line
You now have an empty database that has no content, not even a schema. Let’s
create the schema next.
Exporting the database schema
You can create the database schema by hand by writing SQL DDL with CREATE
statements and executing this DDL on your database. Or (and this is much more
convenient) you can let Hibernate take care of this and create a default schema
for your application. The prerequisite in Hibernate for automatic generation of
SQL DDL is always a Hibernate mapping metadata definition, either in XML mapping files or in Java source-code annotations. We assume that you’ve designed and
implemented your domain model classes and written mapping metadata in XML
as you followed the previous sections.
The tool used for schema generation is hbm2ddl; its class is org.hibernate.
tool.hbm2ddl.SchemaExport, so it’s also sometimes called SchemaExport.
There are many ways to run this tool and create a schema:
You can run <hbm2ddl> in an Ant target in your regular build procedure.
You can run SchemaExport programmatically in application code, maybe in
your HibernateUtil startup class. This isn’t common, however, because you
rarely need programmatic control over schema generation.
You can enable automatic export of a schema when your SessionFactory
is built by setting the configuration property to
create or create-drop. The first setting results in DROP statements followed by CREATE statements when the SessionFactory is built. The second
setting adds additional DROP statements when the application is shut down
and the SessionFactory is closed—effectively leaving a clean database
after every run.
Starting a project
Programmatic schema generation is straightforward:
Configuration cfg = new Configuration().configure();
SchemaExport schemaExport = new SchemaExport(cfg);
schemaExport.create(false, true);
A new SchemaExport object is created from a Configuration; all settings (such as
the database driver, connection URL, and so on) are passed to the SchemaExport
constructor. The create(false, true) call triggers the DDL generation process,
without any SQL printed to stdout (because of the false setting), but with DDL
immediately executed in the database (true). See the SchemaExport API for more
information and additional settings.
Your development process determines whether you should enable automatic
schema export with the configuration setting. Many
new Hibernate users find the automatic dropping and re-creation on SessionFactory build a little confusing. Once you’re more familiar with Hibernate, we
encourage you to explore this option for fast turnaround times in integration testing.
An additional option for this configuration property, update, can be useful
during development: it enables the built-in SchemaUpdate tool, which can make
schema evolution easier. If enabled, Hibernate reads the JDBC database metadata
on startup and creates new tables and constraints by comparing the old schema
with the current mapping metadata. Note that this functionality depends on the
quality of the metadata provided by the JDBC driver, an area in which many drivers are lacking. In practice, this feature is therefore less exciting and useful than
it sounds.
We’ve seen Hibernate users trying to use SchemaUpdate to update the
schema of a production database automatically. This can quickly end in
disaster and won’t be allowed by your DBA.
You can also run SchemaUpdate programmatically:
Configuration cfg = new Configuration().configure();
SchemaUpdate schemaUpdate = new SchemaUpdate(cfg);
The false setting at the end again disables printing of the SQL DDL to the console and only executes the statements directly on the database. If you export the
DDL to the console or a text file, your DBA may be able to use it as a starting point
to produce a quality schema-evolution script.
Another setting useful in development is validate. It enables
SchemaValidator to run at startup. This tool can compare your mapping against
Starting a Hibernate project
the JDBC metadata and tell you if the schema and mappings match. You can also
run SchemaValidator programmatically:
Configuration cfg = new Configuration().configure();
new SchemaValidator(cfg).validate();
An exception is thrown if a mismatch between the mappings and the database
schema is detected.
Because you’re basing your build system on Ant, you’ll ideally add a schemaexport target to your Ant build that generates and exports a fresh schema for your
database whenever you need one (see listing 2.9).
Listing 2.9
Ant target for schema export
<taskdef name="hibernatetool"
<target name="schemaexport" depends="compile, copymetafiles"
description="Exports a generated schema to DB and file">
<hibernatetool destdir="${basedir}">
<classpath path="${build.dir}"/>
In this target, you first define a new Ant task that you’d like to use, HibernateToolTask. This is a generic task that can do many things—exporting an SQL
DDL schema from Hibernate mapping metadata is only one of them. You’ll use it
throughout this chapter in all Ant builds. Make sure you include all Hibernate
libraries, required third-party libraries, and your JDBC driver in the classpath of
the task definition. You also need to add the hibernate-tools.jar file, which can be
found in the Hibernate Tools download package.
Starting a project
The schemaexport Ant target uses this task, and it also depends on the compiled classes and copied configuration files in the build directory. The basic use of
the <hibernatetool> task is always the same: A configuration is the starting point
for all code artifact generation. The variation shown here, <configuration>,
understands Hibernate XML configuration files and reads all Hibernate XML
mapping metadata files listed in the given configuration. From that information,
an internal Hibernate metadata model (which is what hbm stands for everywhere)
is produced, and this model data is then processed subsequently by exporters. We
discuss tool configurations that can read annotations or a database for reverse
engineering later in this chapter.
The other element in the target is a so-called exporter. The tool configuration
feeds its metadata information to the exporter you selected; in the preceding
example, it’s the <hbm2ddl> exporter. As you may have guessed, this exporter
understands the Hibernate metadata model and produces SQL DDL. You can control the DDL generation with several options:
The exporter generates SQL, so it’s mandatory that you set an SQL dialect in
your Hibernate configuration file.
If drop is set to true, SQL DROP statements will be generated first, and all
tables and constraints are removed if they exist. If create is set to true, SQL
CREATE statements are generated next, to create all tables and constraints. If
you enable both options, you effectively drop and re-create the database
schema on every run of the Ant target.
If export is set to true, all DDL statements are directly executed in the database. The exporter opens a connection to the database using the connection settings found in your configuration file.
If an outputfilename is present, all DDL statements are written to this file,
and the file is saved in the destdir you configured. The delimiter character is appended to all SQL statements written to the file, and if format is
enabled, all SQL statements are nicely indented.
You can now generate, print, and directly export the schema to a text file and the
database by running ant schemaxport in your WORKDIR. All tables and constraints are dropped and then created again, and you have a fresh database ready.
(Ignore any error message that says that a table couldn’t be dropped because it
didn’t exist.)
Starting a Hibernate project
Check that your database is running and that it has the correct database
schema. A useful tool included with HSQLDB is a simple database browser. You
can call it with the following Ant target:
<target name="dbmanager" description="Start HSQLDB manager">
<arg value="-url"/>
<arg value="jdbc:hsqldb:hsql://localhost/"/>
<arg value="-driver"/>
<arg value="org.hsqldb.jdbcDriver"/>
You should see the schema shown in figure 2.5 after logging in.
Run your application with ant run, and watch the console for Hibernate log
output. You should see your messages being stored, loaded, and printed. Fire an
SQL query in the HSQLDB browser to check the content of your database directly.
You now have a working Hibernate infrastructure and Ant project build. You
could skip to the next chapter and continue writing and mapping more complex
business classes. However, we recommend that you spend some time with the
Figure 2.5
The HSQLDB browser and SQL console
Starting a project
“Hello World” application and extend it with more functionality. You can, for
example, try different HQL queries or logging options. Don’t forget that your
database system is still running in the background, and that you have to either
export a fresh schema or stop it and delete the database files to get a clean and
empty database again.
In the next section, we walk through the “Hello World” example again, with
Java Persistence interfaces and EJB 3.0.
Starting a Java Persistence project
In the following sections, we show you some of the advantages of JPA and the new
EJB 3.0 standard, and how annotations and the standardized programming interfaces can simplify application development, even when compared with Hibernate.
Obviously, designing and linking to standardized interfaces is an advantage if you
ever need to port or deploy an application on a different runtime environment.
Besides portability, though, there are many good reasons to give JPA a closer look.
We’ll now guide you through another “Hello World” example, this time with
Hibernate Annotations and Hibernate EntityManager. You’ll reuse the basic
project infrastructure introduced in the previous section so you can see where JPA
differs from Hibernate. After working with annotations and the JPA interfaces,
we’ll show how an application integrates and interacts with other managed components—EJBs. We’ll discuss many more application design examples later in the
book; however, this first glimpse will let you decide on a particular approach as
soon as possible.
Using Hibernate Annotations
Let’s first use Hibernate Annotations to replace the Hibernate XML mapping files
with inline metadata. You may want to copy your existing “Hello World” project
directory before you make the following changes—you’ll migrate from native
Hibernate to standard JPA mappings (and program code later on).
Copy the Hibernate Annotations libraries to your WORKDIR/lib directory—see
the Hibernate Annotations documentation for a list of required libraries. (At the
time of writing, hibernate-annotations.jar and the API stubs in ejb3-persistence.jar
were required.)
Now delete the src/hello/Message.hbm.xml file. You’ll replace this file with
annotations in the src/hello/ class source, as shown in listing 2.10.
Starting a Java Persistence project
Listing 2.10 Mapping the Message class with annotations
package hello;
import javax.persistence.*;
@Table(name = "MESSAGES")
public class Message {
@Id @GeneratedValue
@Column(name = "MESSAGE_ID")
private Long id;
@Column(name = "MESSAGE_TEXT")
private String text;
@ManyToOne(cascade = CascadeType.ALL)
@JoinColumn(name = "NEXT_MESSAGE_ID")
private Message nextMessage;
private Message() {}
public Message(String text) {
this.text = text;
public Long getId() {
return id;
private void setId(Long id) { = id;
public String getText() {
return text;
public void setText(String text) {
this.text = text;
public Message getNextMessage() {
return nextMessage;
public void setNextMessage(Message nextMessage) {
this.nextMessage = nextMessage;
The first thing you’ll probably notice in this updated business class is the import
of the javax.persistence interfaces. Inside this package are all the standardized
JPA annotations you need to map the @Entity class to a database @Table. You put
Starting a project
annotations on the private fields of the class, starting with @Id and @GeneratedValue for the database identifier mapping. The JPA persistence provider detects
that the @Id annotation is on a field and assumes that it should access properties
on an object directly through fields at runtime. If you placed the @Id annotation
on the getId() method, you’d enable access to properties through getter and setter methods by default. Hence, all other annotations are also placed on either
fields or getter methods, following the selected strategy.
Note that the @Table, @Column, and @JoinColumn annotations aren’t necessary.
All properties of an entity are automatically considered persistent, with default
strategies and table/column names. You add them here for clarity and to get the
same results as with the XML mapping file. Compare the two mapping metadata
strategies now, and you’ll see that annotations are much more convenient and
reduce the lines of metadata significantly. Annotations are also type-safe, they support autocompletion in your IDE as you type (like any other Java interfaces), and
they make refactoring of classes and properties easier.
If you’re worried that the import of the JPA interfaces will bind your code to
this package, you should know that it’s only required on your classpath when the
annotations are used by Hibernate at runtime. You can load and execute this class
without the JPA interfaces on your classpath as long as you don’t want to load and
store instances with Hibernate.
A second concern that developers new to annotations sometimes have relates
to the inclusion of configuration metadata in Java source code. By definition, configuration metadata is metadata that can change for each deployment of the application, such as table names. JPA has a simple solution: You can override or replace
all annotated metadata with XML metadata files. Later in the book, we’ll show you
how this is done.
Let’s assume that this is all you want from JPA—annotations instead of XML.
You don’t want to use the JPA programming interfaces or query language; you’ll
use Hibernate Session and HQL. The only other change you need to make to
your project, besides deleting the now obsolete XML mapping file, is a change in
the Hibernate configuration, in hibernate.cfg.xml:
<!DOCTYPE hibernate-configuration SYSTEM
<!-- ... Many property settings ... -->
<!-- List of annotated classes-->
<mapping class="hello.Message"/>
Starting a Java Persistence project
The Hibernate configuration file previously had a list of all XML mapping files.
This has been replaced with a list of all annotated classes. If you use programmatic
configuration of a SessionFactory, the addAnnotatedClass() method replaces
the addResource() method:
// Load settings from
AnnotationConfiguration cfg = new AnnotationConfiguration();
// ... set other configuration options programmatically
SessionFactory sessionFactory = cfg.buildSessionFactory();
Note that you have now used AnnotationConfiguration instead of the basic
Hibernate Configuration interface—this extension understands annotated
classes. At a minimum, you also need to change your initializer in HibernateUtil
to use that interface. If you export the database schema with an Ant target, replace
<configuration> with <annotationconfiguration> in your build.xml file.
This is all you need to change to run the “Hello World” application with annotations. Try running it again, probably with a fresh database.
Annotation metadata can also be global, although you don’t need this for the
“Hello World” application. Global annotation metadata is placed in a file named in a particular package directory. In addition to listing annotated classes, you need to add the packages that contain global metadata to your
configuration. For example, in a Hibernate XML configuration file, you need to
add the following:
<!DOCTYPE hibernate-configuration SYSTEM
<!-- ... Many property settings ... -->
<!-- List of annotated classes-->
<mapping class="hello.Message"/>
<!-- List of packages with -->
<mapping package="hello"/>
Or you could achieve the same results with programmatic configuration:
Starting a project
// Load settings from
AnnotationConfiguration cfg = new AnnotationConfiguration();
// ... set other configuration options programmatically
SessionFactory sessionFactory = cfg.buildSessionFactory();
Let’s take this one step further and replace the native Hibernate code that loads
and stores messages with code that uses JPA. With Hibernate Annotations and
Hibernate EntityManager, you can create portable and standards-compliant mappings and data access code.
Using Hibernate EntityManager
Hibernate EntityManager is a wrapper around Hibernate Core that provides the
JPA programming interfaces, supports the JPA entity instance lifecycle, and allows
you to write queries with the standardized Java Persistence query language.
Because JPA functionality is a subset of Hibernate’s native capabilities, you may
wonder why you should use the EntityManager package on top of Hibernate.
We’ll present a list of advantages later in this section, but you’ll see one particular
simplification as soon as you configure your project for Hibernate EntityManager:
You no longer have to list all annotated classes (or XML mapping files) in your
configuration file.
Let’s modify the “Hello World” project and prepare it for full JPA compatibility.
Basic JPA configuration
A SessionFactory represents a particular logical data-store configuration in a
Hibernate application. The EntityManagerFactory has the same role in a JPA
application, and you configure an EntityManagerFactory (EMF) either with configuration files or in application code just as you would configure a SessionFactory. The configuration of an EMF, together with a set of mapping metadata
(usually annotated classes), is called the persistence unit.
The notion of a persistence unit also includes the packaging of the application, but we want to keep this as simple as possible for “Hello World”; we’ll assume
that you want to start with a standardized JPA configuration and no special packaging. Not only the content, but also the name and location of the JPA configuration
file for a persistence unit are standardized.
Create a directory named WORKDIR/etc/META-INF and place the basic configuration file named persistence.xml, shown in listing 2.11, in that directory:
Starting a Java Persistence project
Listing 2.11 Persistence unit configuration file
<persistence xmlns=""
<persistence-unit name="helloworld">
<property name="hibernate.ejb.cfgfile"
Every persistence unit needs a name, and in this case it’s helloworld.
The XML header in the preceding persistence unit configuration file
declares what schema should be used, and it’s always the same. We’ll
omit it in future examples and assume that you’ll add it.
A persistence unit is further configured with an arbitrary number of properties,
which are all vendor-specific. The property in the previous example, hibernate.ejb.cfgfile, acts as a catchall. It refers to a hibernate.cfg.xml file (in the
root of the classpath) that contains all settings for this persistence unit—you’re
reusing the existing Hibernate configuration. Later, you’ll move all configuration
details into the persistence.xml file, but for now you’re more interested in running “Hello World” with JPA.
The JPA standard says that the persistence.xml file needs to be present in the
META-INF directory of a deployed persistence unit. Because you aren’t really packaging and deploying the persistence unit, this means that you have to copy persistence.xml into a META-INF directory of the build output directory. Modify your
build.xml, and add the following to the copymetafiles target:
<property name="src.etc.dir" value="etc"/>
<target name="copymetafiles">
<!-- Copy metadata to build -->
<copy todir="${build.dir}">
<fileset dir="${}">
<patternset refid="meta.files"/>
Starting a project
<!-- Copy configuration files from etc/ -->
<copy todir="${build.dir}">
<fileset dir="${src.etc.dir}">
<patternset refid="meta.files"/>
Everything found in WORKDIR/etc that matches the meta.files pattern is
copied to the build output directory, which is part of the classpath at runtime.
Let’s rewrite the main application code with JPA.
“Hello World” with JPA
These are your primary programming interfaces in Java Persistence:
javax.persistence.Persistence —A startup class that provides a static
method for the creation of an EntityManagerFactory.
javax.persistence.EntityManagerFactory —The equivalent to a Hibernate SessionFactory. This runtime object represents a particular persistence unit. It’s thread-safe, is usually handled as a singleton, and provides
methods for the creation of EntityManager instances.
javax.persistence.EntityManager —The equivalent to a Hibernate Session. This single-threaded, nonshared object represents a particular unit of
work for data access. It provides methods to manage the lifecycle of entity
instances and to create Query instances.
javax.persistence.Query —This is the equivalent to a Hibernate Query.
An object is a particular JPA query language or native SQL query representation, and it allows safe binding of parameters and provides various methods
for the execution of the query.
javax.persistence.EntityTransaction —This is the equivalent to a
Hibernate Transaction, used in Java SE environments for the demarcation
of RESOURCE_LOCAL transactions. In Java EE, you rely on the standardized
javax.transaction.UserTransaction interface of JTA for programmatic
transaction demarcation.
To use the JPA interfaces, you need to copy the required libraries to your
WORKDIR/lib directory; check the documentation bundled with Hibernate
EntityManager for an up-to-date list. You can then rewrite the code in WORKDIR/
src/hello/ and switch from Hibernate to JPA interfaces (see
listing 2.12).
Starting a Java Persistence project
Listing 2.12 The “Hello World” main application code with JPA
package hello;
import java.util.*;
import javax.persistence.*;
public class HelloWorld {
public static void main(String[] args) {
// Start EntityManagerFactory
EntityManagerFactory emf =
// First unit of work
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Message message = new Message("Hello World");
// Second unit of work
EntityManager newEm = emf.createEntityManager();
EntityTransaction newTx = newEm.getTransaction();
List messages = newEm
.createQuery("select m from Message m
➥ order by m.text asc")
System.out.println( messages.size() + " message(s) found" );
for (Object m : messages) {
Message loadedMsg = (Message) m;
// Shutting down the application
Starting a project
The first thing you probably notice in this code is that there is no Hibernate
import anymore, only javax.peristence.*. The EntityManagerFactory is created with a static call to Persistence and the name of the persistence unit. The
rest of the code should be self-explanatory—you use JPA just like Hibernate,
though there are some minor differences in the API, and methods have slightly
different names. Furthermore, you didn’t use the HibernateUtil class for static
initialization of the infrastructure; you can write a JPAUtil class and move the creation of an EntityManagerFactory there if you want, or you can remove the now
unused WORKDIR/src/persistence package.
JPA also supports programmatic configuration, with a map of options:
Map myProperties = new HashMap();
myProperties.put("", "create-drop");
EntityManagerFactory emf =
Persistence.createEntityManagerFactory("helloworld", myProperties);
Custom programmatic properties override any property you’ve set in the persistence.xml configuration file.
Try to run the ported HelloWorld code with a fresh database. You should see
the exact same log output on your screen as you did with native Hibernate—the
JPA persistence provider engine is Hibernate.
Automatic detection of metadata
We promised earlier that you won’t have to list all your annotated classes or XML
mapping files in the configuration, but it’s still there, in hibernate.cfg.xml. Let’s
enable the autodetection feature of JPA.
Run the “Hello World” application again after switching to DEBUG logging for
the org.hibernate package. Some additional lines should appear in your log:
- Trying to find persistence unit: helloworld
- Analyse of persistence.xml:
- Persistent Unit name from persistence.xml: helloworld
- Detect class: true; detect hbm: true
- Searching mapped entities in jar/par: file:/helloworld/build
- Filtering: hello.HelloWorld
- Filtering: hello.Message
Starting a Java Persistence project
- Java element filter matched for hello.Message
- Creating Factory: helloworld
On startup, the Persistence.createEntityManagerFactory() method tries to
locate the persistence unit named helloworld. It searches the classpath for all
META-INF/persistence.xml files and then configures the EMF if a match is found.
The second part of the log shows something you probably didn’t expect. The JPA
persistence provider tried to find all annotated classes and all Hibernate XML
mapping files in the build output directory. The list of annotated classes (or the
list of XML mapping files) in hibernate.cfg.xml isn’t needed, because hello.Message, the annotated entity class, has already been found.
Instead of removing only this single unnecessary option from hibernate.cfg.xml, let’s remove the whole file and move all configuration details into
persistence.xml (see listing 2.13).
Listing 2.13 Full persistence unit configuration file
<persistence-unit name="helloworld">
<!-- Not needed, Hibernate supports auto-detection in JSE
<property name="hibernate.archive.autodetection"
value="class, hbm"/>
<property name="hibernate.show_sql" value="true"/>
<property name="hibernate.format_sql" value="true"/>
<property name="hibernate.connection.driver_class"
<property name="hibernate.connection.url"
<property name="hibernate.connection.username"
<property name="hibernate.c3p0.min_size"
<property name="hibernate.c3p0.max_size"
<property name="hibernate.c3p0.timeout"
<property name="hibernate.c3p0.max_statements"
Starting a project
<property name="hibernate.c3p0.idle_test_period"
<property name="hibernate.dialect"
<property name="" value="create"/>
There are three interesting new elements in this configuration file. First, you set
an explicit <provider> that should be used for this persistence unit. This is usually required only if you work with several JPA implementations at the same time,
but we hope that Hibernate will, of course, be the only one. Next, the specification requires that you list all annotated classes with <class> elements if you
deploy in a non-Java EE environment—Hibernate supports autodetection of mapping metadata everywhere, making this optional. Finally, the Hibernate
configuration setting archive.autodetection tells Hibernate what metadata to
scan for automatically: annotated classes (class) and/or Hibernate XML mapping files (hbm). By default, Hibernate EntityManager scans for both. The rest of
the configuration file contains all options we explained and used earlier in this
chapter in the regular hibernate.cfg.xml file.
Automatic detection of annotated classes and XML mapping files is a great feature of JPA. It’s usually only available in a Java EE application server; at least, this is
what the EJB 3.0 specification guarantees. But Hibernate, as a JPA provider, also
implements it in plain Java SE, though you may not be able to use the exact same
configuration with any other JPA provider.
You’ve now created an application that is fully JPA specification-compliant.
Your project directory should look like this (note that we also moved to the etc/ directory):
<all required libraries>
All JPA configuration settings are bundled in persistence.xml, all mapping metadata is included in the Java source code of the Message class, and Hibernate
Starting a Java Persistence project
automatically scans and finds the metadata on startup. Compared to pure Hibernate, you now have these benefits:
Automatic scanning of deployed metadata, an important feature in large
projects. Maintaining a list of annotated classes or mapping files becomes
difficult if hundreds of entities are developed by a large team.
Standardized and simplified configuration, with a standard location for the
configuration file, and a deployment concept—the persistence unit—that
has many more advantages in larger projects that wrap several units (JARs)
in an application archive (EAR).
Standardized data access code, entity instance lifecycle, and queries that are
fully portable. There is no proprietary import in your application.
These are only some of the advantages of JPA. You’ll see its real power if you combine it with the full EJB 3.0 programming model and other managed components.
Introducing EJB components
Java Persistence starts to shine when you also work with EJB 3.0 session beans and
message-driven beans (and other Java EE 5.0 standards). The EJB 3.0 specification
has been designed to permit the integration of persistence, so you can, for example, get automatic transaction demarcation on bean method boundaries, or a persistence context (think Session) that spans the lifecycle of a stateful session EJB.
This section will get you started with EJB 3.0 and JPA in a managed Java EE
environment; you’ll again modify the “Hello World” application to learn the
basics. You need a Java EE environment first—a runtime container that provides
Java EE services. There are two ways you can get it:
You can install a full Java EE 5.0 application server that supports EJB 3.0 and
JPA. Several open source (Sun GlassFish, JBoss AS, ObjectWeb EasyBeans)
and other proprietary licensed alternatives are on the market at the time of
writing, and probably more will be available when you read this book.
You can install a modular server that provides only the services you need,
selected from the full Java EE 5.0 bundle. At a minimum, you probably want
an EJB 3.0 container, JTA transaction services, and a JNDI registry. At the
time of writing, only JBoss AS provided modular Java EE 5.0 services in an
easily customizable package.
To keep things simple and to show you how easy it is to get started with EJB 3.0,
you’ll install and configure the modular JBoss Application Server and enable only
the Java EE 5.0 services you need.
Starting a project
Installing the EJB container
Go to, download the modular embeddable
server, and unzip the downloaded archive. Copy all libraries that come with the
server into your project’s WORKDIR/lib directory, and copy all included configuration files to your WORKDIR/src directory. You should now have the following
directory layout:
<all required libraries>
The JBoss embeddable server relies on Hibernate for Java Persistence, so the file contains default settings for Hibernate that are
needed for all deployments (such as JTA integration settings). The ejb3-interceptors-aop.xml and embedded-jboss-beans.xml configuration files contain the services configuration of the server—you can look at these files, but you don’t need
to modify them now. By default, at the time of writing, the enabled services are
JNDI, JCA, JTA, and the EJB 3.0 container—exactly what you need.
To migrate the “Hello World” application, you need a managed datasource,
which is a database connection that is handled by the embeddable server. The easiest way to configure a managed datasource is to add a configuration file that
deploys the datasource as a managed service. Create the file in listing 2.14 as
Listing 2.14 Datasource configuration file for the JBoss server
<?xml version="1.0" encoding="UTF-8"?>
<deployment xmlns:xsi=""
xsi:schemaLocation="urn:jboss:bean-deployer bean-deployer_1_0.xsd"
<!-- Enable a JCA datasource available through JNDI -->
<bean name="helloWorldDatasourceFactory"
Starting a Java Persistence project
<property name="jndiName">java:/HelloWorldDS</property>
<!-- HSQLDB -->
<property name="driverClass">
<property name="connectionURL">
<property name="userName">sa</property>
<property name="transactionManager">
<inject bean="TransactionManager"/>
<property name="cachedConnectionManager">
<inject bean="CachedConnectionManager"/>
<property name="initialContextProperties">
<inject bean="InitialContextProperties"/>
<bean name="HelloWorldDS" class="java.lang.Object">
<constructor factoryMethod="getDatasource">
<factory bean="helloWorldDatasourceFactory"/>
Again, the XML header and schema declaration aren’t important for this example. You set up two beans: The first is a factory that can produce the second type
of bean. The LocalTxDataSource is effectively now your database connection
pool, and all your connection pool settings are available on this factory. The factory binds a managed datasource under the JNDI name java:/HelloWorldDS.
The second bean configuration declares how the registered object named
HelloWorldDS should be instantiated, if another service looks it up in the JNDI
registry. Your “Hello World” application asks for the datasource under this name,
and the server calls getDatasource() on the LocalTxDataSource factory to
obtain it.
Starting a project
Also note that we added some line breaks in the property values to make this
more readable—you shouldn’t do this in your real configuration file (unless your
database username contains a line break).
Configuring the persistence unit
Next, you need to change the persistence unit configuration of the “Hello World”
application to access a managed JTA datasource, instead of a resource-local connection pool. Change your WORKDIR/etc/META-INF/persistence.xml file as follows:
<persistence ...>
<persistence-unit name="helloworld">
<property name="hibernate.show_sql" value="true"/>
<property name="hibernate.format_sql" value="true"/>
<property name="hibernate.dialect"
<property name="" value="create"/>
You removed many Hibernate configuration options that are no longer relevant,
such as the connection pool and database connection settings. Instead, you set a
<jta-data-source> property with the name of the datasource as bound in JNDI.
Don’t forget that you still need to configure the correct SQL dialect and any other
Hibernate options that aren’t present in
The installation and configuration of the environment is now complete, (we’ll
show you the purpose of the files in a moment) and you can
rewrite the application code with EJBs.
Writing EJBs
There are many ways to design and create an application with managed components. The “Hello World” application isn’t sophisticated enough to show elaborate examples, so we’ll introduce only the most basic type of EJB, a stateless session
bean. (You’ve already seen entity classes—annotated plain Java classes that can
have persistent instances. Note that the term entity bean only refers to the old EJB
2.1 entity beans; EJB 3.0 and Java Persistence standardize a lightweight programming model for plain entity classes.)
Starting a Java Persistence project
Every EJB session bean needs a business interface. This isn’t a special interface
that needs to implement predefined methods or extend existing ones; it’s plain
Java. Create the following interface in the WORKDIR/src/hello package:
package hello;
public interface MessageHandler {
public void saveMessages();
public void showMessages();
A MessageHandler can save and show messages; it’s straightforward. The actual
EJB implements this business interface, which is by default considered a local
interface (that is, remote EJB clients cannot call it); see listing 2.15.
Listing 2.15 The “Hello World” EJB session bean application code
package hello;
import javax.ejb.Stateless;
import javax.persistence.*;
import java.util.List;
public class MessageHandlerBean implements MessageHandler {
EntityManager em;
public void saveMessages() {
Message message = new Message("Hello World");
public void showMessages() {
List messages =
em.createQuery("select m from Message m
➥ order by m.text asc")
System.out.println(messages.size() + " message(s) found:");
for (Object m : messages) {
Message loadedMsg = (Message) m;
Starting a project
There are several interesting things to observe in this implementation. First, it’s a
plain Java class with no hard dependencies on any other package. It becomes an
EJB only with a single metadata annotation, @Stateless. EJBs support containermanaged services, so you can apply the @PersistenceContext annotation, and
the server injects a fresh EntityManager instance whenever a method on this
stateless bean is called. Each method is also assigned a transaction automatically
by the container. The transaction starts when the method is called, and commits
when the method returns. (It would be rolled back when an exception is thrown
inside the method.)
You can now modify the HelloWorld main class and delegate all the work of
storing and showing messages to the MessageHandler.
Running the application
The main class of the “Hello World” application calls the MessageHandler stateless session bean after looking it up in the JNDI registry. Obviously, the managed
environment and the whole application server, including the JNDI registry, must
be booted first. You do all of this in the main() method of (see
listing 2.16).
Listing 2.16 “Hello World” main application code, calling EJBs
package hello;
import org.jboss.ejb3.embedded.EJB3StandaloneBootstrap;
import javax.naming.InitialContext;
public class HelloWorld {
public static void main(String[] args) throws Exception {
// Boot the JBoss Microcontainer with EJB3 settings, automatically
// loads ejb3-interceptors-aop.xml and embedded-jboss-beans.xml
// Deploy custom stateless beans (datasource, mostly)
// Deploy all EJBs found on classpath (slow, scans all)
// EJB3StandaloneBootstrap.scanClasspath();
// Deploy all EJBs found on classpath (fast, scans build directory)
// This is a relative location, matching the substring end of one
// of java.class.path locations. Print out the value of
// System.getProperty("java.class.path") to see all paths.
// Create InitialContext from
Starting a Java Persistence project
InitialContext initialContext = new InitialContext();
// Look up the stateless MessageHandler EJB
MessageHandler msgHandler = (MessageHandler) initialContext
// Call the stateless EJB
// Shut down EJB container
The first command in main() boots the server’s kernel and deploys the base services found in the service configuration files. Next, the datasource factory configuration you created earlier in helloworld-beans.xml is deployed, and the
datasource is bound to JNDI by the container. From that point on, the container is
ready to deploy EJBs. The easiest (but often not the fastest) way to deploy all EJBs
is to let the container search the whole classpath for any class that has an EJB
annotation. To learn about the many other deployment options available, check
the JBoss AS documentation bundled in the download.
To look up an EJB, you need an InitialContext, which is your entry point for
the JNDI registry. If you instantiate an InitialContext, Java automatically looks for
the file on your classpath. You need to create this file in WORKDIR/
etc with settings that match the JBoss server’s JNDI registry configuration:
➥ org.jnp.interfaces.LocalOnlyContextFactory
java.naming.factory.url.pkgs org.jboss.naming:org.jnp.interfaces
You don’t need to know exactly what this configuration means, but it basically
points your InitialContext to a JNDI registry running in the local virtual
machine (remote EJB client calls would require a JNDI service that supports
remote communication).
By default, you look up the MessageHandler bean by the name of an implementation class, with the /local suffix for a local interface. How EJBs are named,
how they’re bound to JNDI, and how you look them up varies and can be customized. These are the defaults for the JBoss server.
Finally, you call the MessageHandler EJB and let it do all the work automatically in two units—each method call will result in a separate transaction.
Starting a project
This completes our first example with managed EJB components and integrated JPA. You can probably already see how automatic transaction demarcation
and EntityManager injection can improve the readability of your code. Later,
we’ll show you how stateful session beans can help you implement sophisticated
conversations between the user and the application, with transactional semantics.
Furthermore, the EJB components don’t contain any unnecessary glue code or
infrastructure methods, and they’re fully reusable, portable, and executable in
any EJB 3.0 container.
Packaging of persistence units —We didn’t talk much about the packaging
of persistence units—you didn’t need to package the “Hello World”
example for any of the deployments. However, if you want to use features
such as hot redeployment on a full application server, you need to package your application correctly. This includes the usual combination of
JARs, WARs, EJB-JARs, and EARs. Deployment and packaging is often also
vendor-specific, so you should consult the documentation of your application server for more information. JPA persistence units can be scoped
to JARs, WARs, and EJB-JARs, which means that one or several of these
archives contains all the annotated classes and a META-INF/persistence.xml configuration file with all settings for this particular unit. You
can wrap one or several JARs, WARs, and EJB-JARs in a single enterprise
application archive, an EAR. Your application server should correctly
detect all persistence units and create the necessary factories automatically. With a unit name attribute on the @PersistenceContext annotation, you instruct the container to inject an EntityManager from a
particular unit.
Full portability of an application isn’t often a primary reason to use JPA or EJB 3.0.
After all, you made a decision to use Hibernate as your JPA persistence provider.
Let’s look at how you can fall back and use a Hibernate native feature from time
to time.
Switching to Hibernate interfaces
You decided to use Hibernate as a JPA persistence provider for several reasons:
First, Hibernate is a good JPA implementation that provides many options that
don’t affect your code. For example, you can enable the Hibernate second-level
data cache in your JPA configuration, and transparently improve the performance
and scalability of your application without touching any code.
Second, you can use native Hibernate mappings or APIs when needed. We discuss the mixing of mappings (especially annotations) in chapter 3, section 3.3,
Starting a Java Persistence project
“Object/relational mapping metadata,” but here we want to show how you can
use a Hibernate API in your JPA application, when needed. Obviously, importing a
Hibernate API into your code makes porting the code to a different JPA provider
more difficult. Hence, it becomes critically important to isolate these parts of your
code properly, or at least to document why and when you used a native Hibernate
You can fall back to Hibernate APIs from their equivalent JPA interfaces and
get, for example, a Configuration, a SessionFactory, and even a Session whenever needed.
For example, instead of creating an EntityManagerFactory with the Persistence static class, you can use a Hibernate Ejb3Configuration:
Ejb3Configuration cfg = new Ejb3Configuration();
EntityManagerFactory emf =
.setProperty("hibernate.show_sql", "false")
.setInterceptor( new MyInterceptor() )
.addAnnotatedClass( hello.Message.class )
.addResource( "/Foo.hbm.xml")
hibCfg = cfg.getHibernateConfiguration();
The Ejb3Configuration is a new interface that duplicates the regular Hibernate
Configuration instead of extending it (this is an implementation detail). This
means you can get a plain AnnotationConfiguration object from an
Ejb3Configuration, for example, and pass it to a SchemaExport instance programmatically.
The SessionFactory interface is useful if you need programmatic control over
the second-level cache regions. You can get a SessionFactory by casting the
EntityManagerFactory first:
HibernateEntityManagerFactory hibEMF =
(HibernateEntityManagerFactory) emf;
SessionFactory sf = hibEMF.getSessionFactory();
The same technique can be applied to get a Session from an EntityManager:
HibernateEntityManager hibEM =
(HibernateEntityManager) em;
Session session = hibEM.getSession();
This isn’t the only way to get a native API from the standardized EntityManager.
The JPA specification supports a getDelegate() method that returns the underlying implementation:
Starting a project
Session session = (Session) entityManager.getDelegate();
Or you can get a Session injected into an EJB component (although this only
works in the JBoss Application Server):
public class MessageHandlerBean implements MessageHandler {
Session session;
In rare cases, you can fall back to plain JDBC interfaces from the Hibernate Session:
Connection jdbcConnection = session.connection();
This last option comes with some caveats: You aren’t allowed to close the JDBC
Connection you get from Hibernate—this happens automatically. The exception
to this rule is that in an environment that relies on aggressive connection releases,
which means in a JTA or CMT environment, you have to close the returned connection in application code.
A better and safer way to access a JDBC connection directly is through resource
injection in a Java EE 5.0. Annotate a field or setter method in an EJB, an EJB listener, a servlet, a servlet filter, or even a JavaServer Faces backing bean, like this:
@Resource(mappedName="java:/HelloWorldDS") DataSource ds;
So far, we’ve assumed that you work on a new Hibernate or JPA project that
involves no legacy application code or existing database schema. We now switch
perspectives and consider a development process that is bottom-up. In such a scenario, you probably want to automatically reverse-engineer artifacts from an existing database schema.
Reverse engineering a legacy database
Your first step when mapping a legacy database likely involves an automatic
reverse-engineering procedure. After all, an entity schema already exists in your
database system. To make this easier, Hibernate has a set of tools that can read a
schema and produce various artifacts from this metadata, including XML mapping files and Java source code. All of this is template-based, so many customizations are possible.
You can control the reverse-engineering process with tools and tasks in your
Ant build. The HibernateToolTask you used earlier to export SQL DDL from
Reverse engineering a legacy database
Hibernate mapping metadata has many more options, most of which are
related to reverse engineering, as to how XML mapping files, Java code, or even
whole application skeletons can be generated automatically from an existing
database schema.
We’ll first show you how to write an Ant target that can load an existing database into a Hibernate metadata model. Next, you’ll apply various exporters and
produce XML files, Java code, and other useful artifacts from the database tables
and columns.
Creating a database configuration
Let’s assume that you have a new WORKDIR with nothing but the lib directory
(and its usual contents) and an empty src directory. To generate mappings and
code from an existing database, you first need to create a configuration file that
contains your database connection settings:
hibernate.dialect = org.hibernate.dialect.HSQLDialect
hibernate.connection.driver_class = org.hsqldb.jdbcDriver
hibernate.connection.url = jdbc:hsqldb:hsql://localhost
hibernate.connection.username = sa
Store this file directly in WORKDIR, and name it The
four lines shown here are the minimum that is required to connect to the database and read the metadata of all tables and columns. You could have created a
Hibernate XML configuration file instead of, but there is no
reason to make this more complex than necessary.
Write the Ant target next. In a build.xml file in your project, add the following
<taskdef name="hibernatetool"
<target name="reveng.hbmxml"
description="Produces XML mapping files in src directory">
<hibernatetool destdir="${basedir}/src">
<hbm2hbmxml/> <!-- Export Hibernate XML files -->
<hbm2cfgxml/> <!-- Export a hibernate.cfg.xml file -->
Starting a project
The HibernateToolTask definition for Ant is the same as before. We assume that
you’ll reuse most of the build file introduced in previous sections, and that references such as project.classpath are the same. The <hibernatetool> task is set
with WORKDIR/src as the default destination directory for all generated artifacts.
A <jdbconfiguration> is a Hibernate tool configuration that can connect to a
database via JDBC and read the JDBC metadata from the database catalog. You usually configure it with two options: database connection settings (the properties
file) and an optional reverse-engineering customization file.
The metadata produced by the tool configuration is then fed to exporters. The
example Ant target names two such exporters: the hbm2hbmxml exporter, as you
can guess from its name, takes Hibernate metadata (hbm) from a configuration,
and generates Hibernate XML mapping files; the second exporter can prepare a
hibernate.cfg.xml file that lists all the generated XML mapping files.
Before we talk about these and various other exporters, let’s spend a minute
on the reverse-engineering customization file and what you can do with it.
Customizing reverse engineering
JDBC metadata—that is, the information you can read from a database about itself
via JDBC—often isn’t sufficient to create a perfect XML mapping file, let alone
Java application code. The opposite may also be true: Your database may contain
information that you want to ignore (such as particular tables or columns) or that
you wish to transform with nondefault strategies. You can customize the reverseengineering procedure with a reverse-engineering configuration file, which uses an
XML syntax.
Let’s assume that you’re reverse-engineering the “Hello World” database you
created earlier in this chapter, with its single MESSAGES table and only a few columns. With a helloworld.reveng.xml file, as shown in listing 2.17, you can customize this reverse engineering.
Listing 2.17 Configuration for customized reverse engineering
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE hibernate-reverse-engineering SYSTEM
➥ hibernate-reverse-engineering-3.0.dtd">
<table-filter match-name=".*" package="hello"/>
<table name="MESSAGES" schema="PUBLIC" class="Message">
Reverse engineering a legacy database
<generator class="increment"/>
<key-column name="MESSAGE_ID" property="id" type="long"/>
<column name="MESSAGE_TEXT" property="text"/>
<foreign-key constraint-name="FK_NEXT_MESSAGE">
<many-to-one property="nextMessage"/>
<set exclude="true"/>
This XML file has its own DTD for validation and autocompletion.
You can customize individual tables by name. The schema name is usually
optional, but HSQLDB assigns the PUBLIC schema to all tables by default so this
setting is needed to identify the table when the JDBC metadata is retrieved. You
can also set a custom class name for the generated entity here.
The primary key column generates a property named id, the default would be
messageId. You also explicitly declare which Hibernate identifier generator
should be used.
An individual column can be excluded or, in this case, the name of the generated
property can be specified—the default would be messageText.
If the foreign key constraint FK_NEXT_MESSAGE is retrieved from JDBC metadata, a
many-to-one association is created by default to the target entity of that class. By
matching the foreign key constraint by name, you can specify whether an inverse
collection (one-to-many) should also be generated (the example excludes this)
and what the name of the many-to-one property should be.
A table filter can exclude tables by name with a regular expression. However, in
this example, you define a a default package for all classes produced for the tables
matching the regular expression.
If you now run the Ant target with this customization, it generates a Message.hbm.xml file in the hello package in your source directory. (You need to
copy the Freemarker and jTidy JAR files into your library directory first.) The
customizations you made result in the same Hibernate mapping file you wrote
earlier by hand, shown in listing 2.2.
In addition to the XML mapping file, the Ant target also generates a Hibernate
XML configuration file in the source directory:
Starting a project
<property name="hibernate.connection.driver_class">
<property name="hibernate.connection.url">
<property name="hibernate.connection.username">
<property name="hibernate.dialect">
<mapping resource="hello/Message.hbm.xml" />
The exporter writes all the database connection settings you used for reverse engineering into this file, assuming that this is the database you want to connect to
when you run the application. It also adds all generated XML mapping files to the
What is your next step? You can start writing the source code for the Message
Java class. Or you can let the Hibernate Tools generate the classes of the domain
model for you.
Generating Java source code
Let’s assume you have an existing Hibernate XML mapping file for the Message
class, and you’d like to generate the source for the class. As discussed in chapter 3,
a plain Java entity class ideally implements Serializable, has a no-arguments
constructor, has getters and setters for all properties, and has an encapsulated
Source code for entity classes can be generated with the Hibernate Tools and
the hbm2java exporter in your Ant build. The source artifact can be anything that
can be read into a Hibernate metadata model—Hibernate XML mapping files are
best if you want to customize the Java code generation.
Add the following target to your Ant build:
<target name="reveng.pojos"
description="Produces Java classes from XML mappings">
<hibernatetool destdir="${basedir}/src">
Reverse engineering a legacy database
<fileset dir="${basedir}/src">
<include name="**/*.hbm.xml"/>
<hbm2java/> <!-- Generate entity class source -->
The <configuration> reads all Hibernate XML mapping files, and the <hbm2java> exporter produces Java source code with the default strategy.
Customizing entity class generation
By default, hbm2java generates a simple entity class for each mapped entity. The
class implements the Serializable marker interface, and it has accessor methods
for all properties and the required constructor. All attributes of the class have private visibility for fields, although you can change that behavior with the <meta>
element and attributes in the XML mapping files.
The first change to the default reverse engineering behavior you make is to
restrict the visibility scope for the Message’s attributes. By default, all accessor
methods are generated with public visibility. Let’s say that Message objects are
immutable; you wouldn’t expose the setter methods on the public interface, but
only the getter methods. Instead of enhancing the mapping of each property with
a <meta> element, you can declare a meta-attribute at the class level, thus applying
the setting to all properties in that class:
<class name="Message"
<meta attribute="scope-set">private</meta>
The scope-set attribute defines the visibility of property setter methods.
The hbm2java exporter also accepts meta-attributes on the next higher-level,
in the root <hibernate-mapping> element, which are then applied to all classes
mapped in the XML file. You can also add fine-grained meta-attributes to single
property, collection, or component mappings.
One (albeit small) improvement of the generated entity class is the inclusion
of the text of the Message in the output of the generated toString() method.
The text is a good visual control element in the log output of the application. You
can change the mapping of Message to include it in the generated code:
Starting a project
<property name="text" type="string">
<meta attribute="use-in-tostring">true</meta>
<column name="MESSAGE_TEXT" />
The generated code of the toString() method in looks like this:
public String toString() {
StringBuffer buffer = new StringBuffer();
.append( Integer.toHexString(hashCode()) )
.append(" [");
.append("text").append("='").append(getText()).append("' ");
return buffer.toString();
Meta-attributes can be inherited; that is, if you declare a use-in-tostring at the
level of a <class> element, all properties of that class are included in the
toString() method. This inheritance mechanism works for all hbm2java metaattributes, but you can turn it off selectively:
<meta attribute="scope-class" inherit="false">public abstract</meta>
Setting inherit to false in the scope-class meta-attribute creates only the parent class of this <meta> element as public abstract, but not any of the (possibly)
nested subclasses.
The hbm2java exporter supports, at the time of writing, 17 meta-attributes for
fine-tuning code generation. Most are related to visibility, interface implementation, class extension, and predefined Javadoc comments. Refer to the Hibernate
Tools documentation for a complete list.
If you use JDK 5.0, you can switch to automatically generated static imports and
generics with the jdk5="true" setting on the <hbm2java> task. Or, you can produce EJB 3.0 entity classes with annotations.
Generating Java Persistence entity classes
Normally, you use either Hibernate XML mapping files or JPA annotations in your
entity class source code to define your mapping metadata, so generating Java
Persistence entity classes with annotations from XML mapping files doesn’t seem
reasonable. However, you can create entity class source code with annotations
directly from JDBC metadata, and skip the XML mapping step. Look at the following Ant target:
Reverse engineering a legacy database
<target name="reveng.entities"
description="Produces Java entity classes in src directory">
<hibernatetool destdir="${basedir}/src">
<hbm2java jdk5="true" ejb3="true"/>
<hbm2cfgxml ejb3="true"/>
This target generates entity class source code with mapping annotations and a
hibernate.cfg.xml file that lists these mapped classes. You can edit the Java source
directly to customize the mapping, if the customization in helloworld.reveng.xml
is too limited.
Also note that all exporters rely on templates written in the FreeMarker template language. You can customize the templates in whatever way you like, or
even write your own. Even programmatic customization of code generation is
possible. The Hibernate Tools reference documentation shows you how these
options are used.
Other exporters and configurations are available with the Hibernate Tools:
An <annotationconfiguration> replaces the regular <configuration> if
you want to read mapping metadata from annotated Java classes, instead of
XML mapping files. Its only argument is the location and name of a hibernate.cfg.xml file that contains a list of annotated classes. Use this approach
to export a database schema from annotated classes.
An <ejb3configuration> is equivalent to an <annotationconfiguration>,
except that it can scan for annotated Java classes automatically on the classpath; it doesn’t need a hibernate.cfg.xml file.
The <hbm2dao> exporter can create additional Java source for a persistence
layer, based on the data access object pattern. At the time of writing, the
templates for this exporter are old and need updating. We expect that the
finalized templates will be similar to the DAO code shown in chapter 16,
section 16.2, “Creating a persistence layer.”
The <hbm2doc> exporter generates HTML files that document the tables
and Java entities.
Starting a project
The <hbmtemplate> exporter can be parameterized with a set of custom
FreeMarker templates, and you can generate anything you want with this
approach. Templates that produce a complete runable skeleton application
with the JBoss Seam framework are bundled in the Hibernate Tools.
You can get creative with the import and export functionality of the tools. For
example, you can read annotated Java classes with <annotationconfiguration>
and export them with <hbm2hbmxml>. This allows you to develop with JDK 5.0 and
the more convenient annotations but deploy Hibernate XML mapping files in
production (on JDK 1.4).
Let’s finish this chapter with some more advanced configuration options and
integrate Hibernate with Java EE services.
Integration with Java EE services
We assume that you’ve already tried the “Hello World” example shown earlier
in this chapter and that you’re familiar with basic Hibernate configuration and
how Hibernate can be integrated with a plain Java application. We’ll now discuss more advanced native Hibernate configuration options and how a regular
Hibernate application can utilize the Java EE services provided by a Java EE
application server.
If you created your first JPA project with Hibernate Annotations and Hibernate
EntityManager, the following configuration advice isn’t really relevant for you—
you’re already deep inside Java EE land if you’re using JPA, and no extra integration steps are required. Hence, you can skip this section if you use Hibernate
Java EE application servers such as JBoss AS, BEA WebLogic, and IBM WebSphere implement the standard (Java EE-specific) managed environment for
Java. The three most interesting Java EE services Hibernate can be integrated with
are JTA, JNDI, and JMX.
JTA allows Hibernate to participate in transactions on managed resources.
Hibernate can look up managed resources (database connections) via JNDI and
also bind itself as a service to JNDI. Finally, Hibernate can be deployed via JMX and
then be managed as a service by the JMX container and monitored at runtime
with standard JMX clients.
Let’s look at each service and how you can integrate Hibernate with it.
Integration with Java EE services
Integration with JTA
The Java Transaction API (JTA) is the standardized service interface for transaction
control in Java enterprise applications. It exposes several interfaces, such as the
UserTransaction API for transaction demarcation and the TransactionManager
API for participation in the transaction lifecycle. The transaction manager can
coordinate a transaction that spans several resources—imagine working in two
Hibernate Sessions on two databases in a single transaction.
A JTA transaction service is provided by all Java EE application servers. However, many Java EE services are usable stand-alone, and you can deploy a JTA provider along with your application, such as JBoss Transactions or ObjectWeb JOTM.
We won’t have much to say about this part of your configuration but focus on the
integration of Hibernate with a JTA service, which is the same in full application
servers or with stand-alone JTA providers.
Look at figure 2.6. You use the Hibernate Session interface to access your
database(s), and it’s Hibernate’s responsibility to integrate with the Java EE services of the managed environment.
Figure 2.6
Hibernate in an environment with managed resources
In such a managed environment, Hibernate no longer creates and maintains a
JDBC connection pool—Hibernate obtains database connections by looking up a
Datasource object in the JNDI registry. Hence, your Hibernate configuration
needs a reference to the JNDI name where managed connections can be
<property name="hibernate.connection.datasource">
Starting a project
<property name="hibernate.dialect">
With this configuration file, Hibernate looks up database connections in JNDI
using the name java:/MyDatasource. When you configure your application server
and deploy your application, or when you configure your stand-alone JTA provider,
this is the name to which you should bind the managed datasource. Note that a
dialect setting is still required for Hibernate to produce the correct SQL.
Hibernate with Tomcat—Tomcat isn’t a Java EE application server; it’s just
a servlet container, albeit a servlet container with some features usually
found only in application servers. One of these features may be used
with Hibernate: the Tomcat connection pool. Tomcat uses the DBCP
connection pool internally but exposes it as a JNDI datasource, just like a
real application server. To configure the Tomcat datasource, you need
to edit server.xml, according to instructions in the Tomcat JNDI/JDBC
documentation. Hibernate can be configured to use this datasource by
setting hibernate.connection.datasource. Keep in mind that Tomcat
doesn’t ship with a transaction manager, so you still have plain JDBC
transaction semantics, which Hibernate can hide with its optional
Transaction API. Alternatively, you can deploy a JTA-compatible standalone transaction manager along with your web application, which you
should consider to get the standardized UserTransaction API. On the
other hand, a regular application server (especially if it’s modular like
JBoss AS) may be easier to configure than Tomcat plus DBCP plus JTA,
and it provides better services.
To fully integrate Hibernate with JTA, you need to tell Hibernate a bit more about
your transaction manager. Hibernate has to hook into the transaction lifecycle,
for example, to manage its caches. First, you need to tell Hibernate what transaction manager you’re using:
<property name="hibernate.connection.datasource">
Integration with Java EE services
<property name="hibernate.dialect">
<property name="hibernate.transaction.manager_lookup_class">
<property name="hibernate.transaction.factory_class">
You need to pick the appropriate lookup class for your application server, as you
did in the preceding code—Hibernate comes bundled with classes for the most
popular JTA providers and application servers. Finally, you tell Hibernate that you
want to use the JTA transaction interfaces in the application to set transaction
boundaries. The JTATransactionFactory does several things:
It enables correct Session scoping and propagation for JTA if you decide to
use the SessionFactory.getCurrentSession() method instead of opening
and closing every Session manually. We discuss this feature in more detail
in chapter 11, section 11.1, “Propagating the Hibernate session.”
It tells Hibernate that you’re planning to call the JTA UserTransaction interface in your application to start, commit, or roll back system transactions.
It also switches the Hibernate Transaction API to JTA, in case you don’t
want to work with the standardized UserTransaction. If you now begin a
transaction with the Hibernate API, it checks whether an ongoing JTA transaction is in progress and, if possible, joins this transaction. If no JTA transaction is in progress, a new transaction is started. If you commit or roll back
with the Hibernate API, it either ignores the call (if Hibernate joined an
existing transaction) or sets the system transaction to commit or roll back.
We don’t recommend using the Hibernate Transaction API if you deploy
in an environment that supports JTA. However, this setting keeps existing
code portable between managed and nonmanaged environments, albeit
with possibly different transactional behavior.
There are other built-in TransactionFactory options, and you can write your
own by implementing this interface. The JDBCTransactionFactory is the default
in a nonmanaged environment, and you have used it throughout this chapter in
Starting a project
the simple “Hello World” example with no JTA. The CMTTransactionFactory
should be enabled if you’re working with JTA and EJBs, and if you plan to set transaction boundaries declaratively on your managed EJB components—in other
words, if you deploy your EJB application on a Java EE application server but don’t
set transaction boundaries programmatically with the UserTransaction interface
in application code.
Our recommended configuration options, ordered by preference, are as
If your application has to run in managed and nonmanaged environments,
you should move the responsibility for transaction integration and resource
management to the deployer. Call the JTA UserTransaction API in your
application code, and let the deployer of the application configure the
application server or a stand-alone JTA provider accordingly. Enable
JTATransactionFactory in your Hibernate configuration to integrate with
the JTA service, and set the right lookup class.
Consider setting transaction boundaries declaratively, with EJB components.
Your data access code then isn’t bound to any transaction API, and the CMTTransactionFactory integrates and handles the Hibernate Session for you
behind the scenes. This is the easiest solution—of course, the deployer now
has the responsibility to provide an environment that supports JTA and EJB
Write your code with the Hibernate Transaction API and let Hibernate
switch between the different deployment environments by setting either
JDBCTransactionFactory or JTATransactionFactory. Be aware that transaction semantics may change, and the start or commit of a transaction may
result in a no-op you may not expect. This is always the last choice when
portability of transaction demarcation is needed.
How can I use several databases with Hibernate? If you want to work with
several databases, you create several configuration files. Each database is
assigned its own SessionFactory, and you build several SessionFactory
instances from distinct Configuration objects. Each Session that is
opened, from any SessionFactory, looks up a managed datasource in
JNDI. It’s now the responsibility of the transaction and resource manager
to coordinate these resources—Hibernate only executes SQL statements
on these database connections. Transaction boundaries are either set
programmatically with JTA or handled by the container with EJBs and a
declarative assembly.
Integration with Java EE services
Hibernate can not only look up managed resources in JNDI, it can also bind itself
to JNDI. We’ll look at that next.
JNDI-bound SessionFactory
We already touched on a question that every new Hibernate user has to deal with:
How should a SessionFactory be stored, and how should it be accessed in application code? Earlier in this chapter, we addressed this problem by writing a
HibernateUtil class that held a SessionFactory in a static field and provided the
static getSessionFactory() method. However, if you deploy your application in
an environment that supports JNDI, Hibernate can bind a SessionFactory to
JNDI, and you can look it up there when needed.
The Java Naming and Directory Interface API (JNDI) allows objects to be
stored to and retrieved from a hierarchical structure (directory tree).
JNDI implements the Registry pattern. Infrastructural objects (transaction
contexts, datasources, and so on), configuration settings (environment
settings, user registries, and so on) and even application objects (EJB references, object factories, and so on) can all be bound to JNDI.
The Hibernate SessionFactory automatically binds itself to JNDI if the hibernate.session_factory_name property is set to the name of the JNDI node. If your
runtime environment doesn’t provide a default JNDI context (or if the default
JNDI implementation doesn’t support instances of Referenceable), you need to
specify a JNDI initial context using the hibernate.jndi.url and hibernate.jndi.class properties.
Here is an example Hibernate configuration that binds the SessionFactory to
the name java:/hibernate/MySessionFactory using Sun’s (free) file-systembased JNDI implementation, fscontext.jar:
hibernate.connection.datasource = java:/MyDatasource
hibernate.transaction.factory_class = \
hibernate.transaction.manager_lookup_class = \
hibernate.dialect = org.hibernate.dialect.PostgreSQLDialect
hibernate.session_factory_name = java:/hibernate/MySessionFactory
hibernate.jndi.class = com.sun.jndi.fscontext.RefFSContextFactory
hibernate.jndi.url = file:/auction/jndi
You can, of course, also use the XML-based configuration for this. This example
isn’t realistic, because most application servers that provide a connection pool
through JNDI also have a JNDI implementation with a writable default context.
Starting a project
JBoss AS certainly has, so you can skip the last two properties and just specify a
name for the SessionFactory.
JNDI with Tomcat —Tomcat comes bundled with a read-only JNDI context,
which isn’t writable from application-level code after the startup of the
servlet container. Hibernate can’t bind to this context: You have to either
use a full context implementation (like the Sun FS context) or disable
JNDI binding of the SessionFactory by omitting the session_
factory_name property in the configuration.
The SessionFactory is bound to JNDI when you build it, which means when
Configuration.buildSessionFactory() is called. To keep your application
code portable, you may want to implement this build and the lookup in
HibernateUtil, and continue using that helper class in your data access code,
as shown in listing 2.18.
Listing 2.18 HibernateUtil for JNDI lookup of SessionFactory
public class HibernateUtil {
private static Context jndiContext;
static {
try {
// Build it and bind it to JNDI
new Configuration().buildSessionFactory();
// Get a handle to the registry (reads
jndiContext = new InitialContext();
} catch (Throwable ex) {
throw new ExceptionInInitializerError(ex);
public static SessionFactory getSessionFactory(String sfName) {
SessionFactory sf;
try {
sf = (SessionFactory) jndiContext.lookup(sfName);
} catch (NamingException ex) {
throw new RuntimeException(ex);
return sf;
Integration with Java EE services
Alternatively, you can look up the SessionFactory directly in application code
with a JNDI call. However, you still need at least the new Configuration().buildSessionFactory() line of startup code somewhere in your application. One way
to remove this last line of Hibernate startup code, and to completely eliminate the
HibernateUtil class, is to deploy Hibernate as a JMX service (or by using JPA and
Java EE).
JMX service deployment
The Java world is full of specifications, standards, and implementations of these. A
relatively new, but important, standard is in its first version: the Java Management
Extensions (JMX). JMX is about the management of systems components or, better,
of system services.
Where does Hibernate fit into this new picture? Hibernate, when deployed in
an application server, makes use of other services, like managed transactions and
pooled datasources. Also, with Hibernate JMX integration, Hibernate can be a
managed JMX service, depended on and used by others.
The JMX specification defines the following components:
The JMX MBean—A reusable component (usually infrastructural) that
exposes an interface for management (administration)
The JMX container—Mediates generic access (local or remote) to the MBean
The JMX client—May be used to administer any MBean via the JMX container
An application server with support for JMX (such as JBoss AS) acts as a JMX container and allows an MBean to be configured and initialized as part of the application server startup process. Your Hibernate service may be packaged and
deployed as a JMX MBean; the bundled interface for this is org.hibernate.jmx
.HibernateService. You can start, stop, and monitor the Hibernate core through
this interface with any standard JMX client. A second MBean interface that can be
deployed optionally is org.hibernate.jmx.StatisticsService, which lets you
enable and monitor Hibernate’s runtime behavior with a JMX client.
How JMX services and MBeans are deployed is vendor-specific. For example,
on JBoss Application Server, you only have to add a jboss-service.xml file to your
application’s EAR to deploy Hibernate as a managed JMX service.
Instead of explaining every option here, see the reference documentation for
JBoss Application Server. It contains a section that shows Hibernate integration
and deployment step by step ( Configuration and
Starting a project
deployment on other application servers that support JMX should be similar, and
you can adapt and port the JBoss configuration files.
In this chapter, you have completed a first Hibernate project. We looked at how
Hibernate XML mapping files are written and what APIs you can call in Hibernate
to interact with the database.
We then introduced Java Persistence and EJB 3.0 and explained how it can simplify even the most basic Hibernate application with automatic metadata scanning, standardized configuration and packaging, and dependency injection in
managed EJB components.
If you have to get started with a legacy database, you can use the Hibernate
toolset to reverse engineer XML mapping files from an existing schema. Or, if you
work with JDK 5.0 and/or EJB 3.0, you can generate Java application code directly
from an SQL database.
Finally, we looked at more advanced Hibernate integration and configuration
options in a Java EE environment—integration that is already done for you if you
rely on JPA or EJB 3.0.
A high-level overview and comparison between Hibernate functionality and
Java Persistence is shown in table 2.1. (You can find a similar comparison table at
the end of each chapter.)
Table 2.1
Hibernate and JPA comparison
Hibernate Core
Java Persistence and EJB 3.0
Integrates with everything, everywhere. Flexible, but sometimes configuration is complex.
Works in Java EE and Java SE. Simple and standardized configuration; no extra integration or special configuration is necessary in Java EE environments.
Configuration requires a list of XML mapping
files or annotated classes.
JPA provider scans for XML mapping files and annotated classes automatically.
Proprietary but powerful. Continually improved
native programming interfaces and query
Standardized and stable interfaces, with a sufficient
subset of Hibernate functionality. Easy fallback to
Hibernate APIs is possible.
In the next chapter, we introduce a more complex example application that we’ll
work with throughout the rest of the book. You’ll see how to design and implement a domain model, and which mapping metadata options are the best choices
in a larger project.
Domain models
and metadata
This chapter covers
The CaveatEmptor example application
POJO design for rich domain models
Object/relational mapping metadata options
Domain models and metadata
The “Hello World” example in the previous chapter introduced you to Hibernate;
however, it isn’t useful for understanding the requirements of real-world applications with complex data models. For the rest of the book, we use a much more
sophisticated example application—CaveatEmptor, an online auction system—to
demonstrate Hibernate and Java Persistence.
We start our discussion of the application by introducing a programming
model for persistent classes. Designing and implementing the persistent classes is
a multistep process that we’ll examine in detail.
First, you’ll learn how to identify the business entities of a problem domain.
You create a conceptual model of these entities and their attributes, called a
domain model, and you implement it in Java by creating persistent classes. We
spend some time exploring exactly what these Java classes should look like, and
we also look at the persistence capabilities of the classes, and how this aspect influences the design and implementation.
We then explore mapping metadata options—the ways you can tell Hibernate
how your persistent classes and their properties relate to database tables and columns. This can involve writing XML documents that are eventually deployed
along with the compiled Java classes and are read by Hibernate at runtime.
Another option is to use JDK 5.0 metadata annotations, based on the EJB 3.0 standard, directly in the Java source code of the persistent classes. After reading this
chapter, you’ll know how to design the persistent parts of your domain model in
complex real-world projects, and what mapping metadata option you’ll primarily
prefer and use.
Finally, in the last (probably optional) section of this chapter, we look at Hibernate’s capability for representation independence. A relatively new feature in
Hibernate allows you to create a domain model in Java that is fully dynamic, such
as a model without any concrete classes but only HashMaps. Hibernate also supports a domain model representation with XML documents.
Let’s start with the example application.
The CaveatEmptor application
The CaveatEmptor online auction application demonstrates ORM techniques and
Hibernate functionality; you can download the source code for the application
from We won’t pay much attention to the
user interface in this book (it could be web based or a rich client); we’ll concentrate instead on the data access code. However, when a design decision about data
The CaveatEmptor application
access code that has consequences for the user interface has to be made, we’ll naturally consider both.
In order to understand the design issues involved in ORM, let’s pretend the
CaveatEmptor application doesn’t yet exist, and that you’re building it from
scratch. Our first task would be analysis.
Analyzing the business domain
A software development effort begins with analysis of the problem domain
(assuming that no legacy code or legacy database already exists).
At this stage, you, with the help of problem domain experts, identify the main
entities that are relevant to the software system. Entities are usually notions
understood by users of the system: payment, customer, order, item, bid, and so
forth. Some entities may be abstractions of less concrete things the user thinks
about, such as a pricing algorithm, but even these would usually be understandable to the user. All these entities are found in the conceptual view of the business, which we sometimes call a business model. Developers and architects of
object-oriented software analyze the business model and create an object-oriented model, still at the conceptual level (no Java code). This model may be as
simple as a mental image existing only in the mind of the developer, or it may be
as elaborate as a UML class diagram created by a computer-aided software engineering (CASE) tool like ArgoUML or TogetherJ. A simple model expressed in
UML is shown in figure 3.1.
This model contains entities that you’re bound to find in any typical auction
system: category, item, and user. The entities and their relationships (and perhaps
their attributes) are all represented by this model of the problem domain. We call
this kind of object-oriented model of entities from the problem domain, encompassing only those entities that are of interest to the user, a domain model. It’s an
abstract view of the real world.
The motivating goal behind the analysis and design of a domain model is to
capture the essence of the business information for the application’s purpose.
Developers and architects may, instead of an object-oriented model, also start the
application design with a data model (possibly expressed with an Entity-Relationship diagram). We usually say that, with regard to persistence, there is little
Figure 3.1
A class diagram of a typical online auction model
Domain models and metadata
difference between the two; they’re merely different starting points. In the end,
we’re most interested in the structure and relationships of the business entities,
the rules that have to be applied to guarantee the integrity of data (for example,
the multiplicity of relationships), and the logic used to manipulate the data.
In object modeling, there is a focus on polymorphic business logic. For our
purpose and top-down development approach, it’s helpful if we can implement
our logical model in polymorphic Java; hence the first draft as an object-oriented
model. We then derive the logical relational data model (usually without additional diagrams) and implement the actual physical database schema.
Let’s see the outcome of our analysis of the problem domain of the CaveatEmptor application.
The CaveatEmptor domain model
The CaveatEmptor site auctions many different kinds of items, from electronic
equipment to airline tickets. Auctions proceed according to the English auction
strategy: Users continue to place bids on an item until the bid period for that item
expires, and the highest bidder wins.
In any store, goods are categorized by type and grouped with similar goods
into sections and onto shelves. The auction catalog requires some kind of hierarchy of item categories so that a buyer can browse these categories or arbitrarily
search by category and item attributes. Lists of items appear in the category
browser and search result screens. Selecting an item from a list takes the buyer to
an item-detail view.
An auction consists of a sequence of bids, and one is the winning bid. User
details include name, login, address, email address, and billing information.
A web of trust is an essential feature of an online auction site. The web of trust
allows users to build a reputation for trustworthiness (or untrustworthiness). Buyers can create comments about sellers (and vice versa), and the comments are visible to all other users.
A high-level overview of our domain model is shown in figure 3.2. Let’s briefly
discuss some interesting features of this model.
Each item can be auctioned only once, so you don’t need to make Item distinct from any auction entities. Instead, you have a single auction item entity
named Item. Thus, Bid is associated directly with Item. Users can write Comments
about other users only in the context of an auction; hence the association
between Item and Comment. The Address information of a User is modeled as a
separate class, even though the User may have only one Address; they may alternatively have three, for home, billing, and shipping. You do allow the user to have
The CaveatEmptor application
inspectionPeriodDays : int
state : ShipmentState
created : Date
0..* amount : BigDecimal
created : Date
name : String
name : String
description : String
initialPrice : BigDecimal
reservePrice : BigDecimal
startDate : Date
endDate : Date
state : ItemState
approvalDatetime : Date
sold by
firstname : String
lastname : String
username : String
password : String
email : String
ranking : int
admin : boolean
street : String
zipcode : String
city : String
rating : Rating
text : String
created : Date
ownername : String
type : CreditCardType
number : String
expMonth : String
expYear : String
Figure 3.2
number : String
bankname : String
swift : String
Persistent classes of the CaveatEmptor domain model and their relationships
many BillingDetails. The various billing strategies are represented as subclasses
of an abstract class (allowing future extension).
A Category may be nested inside another Category. This is expressed by a
recursive association, from the Category entity to itself. Note that a single Category may have multiple child categories but at most one parent. Each Item
belongs to at least one Category.
The entities in a domain model should encapsulate state and behavior. For
example, the User entity should define the name and address of a customer and
the logic required to calculate the shipping costs for items (to this particular customer). The domain model is a rich object model, with complex associations,
interactions, and inheritance relationships. An interesting and detailed discussion
of object-oriented techniques for working with domain models can be found in
Patterns of Enterprise Application Architecture (Fowler, 2003) or in Domain-Driven
Design (Evans, 2003).
Domain models and metadata
In this book, we won’t have much to say about business rules or about the
behavior of our domain model. This isn’t because we consider it unimportant;
rather, this concern is mostly orthogonal to the problem of persistence. It’s the
state of our entities that is persistent, so we concentrate our discussion on how to
best represent state in our domain model, not on how to represent behavior. For
example, in this book, we aren’t interested in how tax for sold items is calculated
or how the system may approve a new user account. We’re more interested in how
the relationship between users and the items they sell is represented and made
persistent. We’ll revisit this issue in later chapters, whenever we have a closer look
at layered application design and the separation of logic and data access.
ORM without a domain model—We stress that object persistence with full
ORM is most suitable for applications based on a rich domain model. If
your application doesn’t implement complex business rules or complex
interactions between entities (or if you have few entities), you may not
need a domain model. Many simple and some not-so-simple problems
are perfectly suited to table-oriented solutions, where the application is
designed around the database data model instead of around an objectoriented domain model, often with logic executed in the database
(stored procedures). However, the more complex and expressive your
domain model, the more you’ll benefit from using Hibernate; it shines
when dealing with the full complexity of object/relational persistence.
Now that you have a (rudimentary) application design with a domain model,
the next step is to implement it in Java. Let’s look at some of the things you
need to consider.
Implementing the domain model
Several issues typically must be addressed when you implement a domain model
in Java. For instance, how do you separate the business concerns from the crosscutting concerns (such as transactions and even persistence)? Do you need automated or transparent persistence? Do you have to use a specific programming
model to achieve this? In this section, we examine these types of issues and how to
address them in a typical Hibernate application.
Let’s start with an issue that any implementation must deal with: the separation
of concerns. The domain model implementation is usually a central, organizing
component; it’s reused heavily whenever you implement new application functionality. For this reason, you should be prepared to go to some lengths to ensure
Implementing the domain model
that concerns other than business aspects don’t leak into the domain model
Addressing leakage of concerns
The domain model implementation is such an important piece of code that it
shouldn’t depend on orthogonal Java APIs. For example, code in the domain
model shouldn’t perform JNDI lookups or call the database via the JDBC API. This
allows you to reuse the domain model implementation virtually anywhere. Most
importantly, it makes it easy to unit test the domain model without the need for a
particular runtime environment or container (or the need for mocking any service dependencies). This separation emphasizes the distinction between logical
unit testing and integration unit testing.
We say that the domain model should be concerned only with modeling the
business domain. However, there are other concerns, such as persistence, transaction management, and authorization. You shouldn’t put code that addresses these
crosscutting concerns in the classes that implement the domain model. When
these concerns start to appear in the domain model classes, this is an example of
leakage of concerns.
The EJB standard solves the problem of leaky concerns. If you implement your
domain classes using the entity programming model, the container takes care of
some concerns for you (or at least lets you externalize those concerns into metadata, as annotations or XML descriptors). The EJB container prevents leakage of
certain crosscutting concerns using interception. An EJB is a managed component, executed inside the EJB container; the container intercepts calls to your
beans and executes its own functionality. This approach allows the container to
implement the predefined crosscutting concerns—security, concurrency, persistence, transactions, and remoteness—in a generic way.
Unfortunately, the EJB 2.1 specification imposes many rules and restrictions on
how you must implement a domain model. This, in itself, is a kind of leakage of
concerns—in this case, the concerns of the container implementer have leaked!
This was addressed in the EJB 3.0 specification, which is nonintrusive and much
closer to the traditional JavaBean programming model.
Hibernate isn’t an application server, and it doesn’t try to implement all the
crosscutting concerns of the full EJB specification. Hibernate is a solution for just
one of these concerns: persistence. If you require declarative security and transaction management, you should access entity instances via a session bean, taking
advantage of the EJB container’s implementation of these concerns. Hibernate in
Domain models and metadata
an EJB container either replaces (EJB 2.1, entity beans with CMP) or implements
(EJB 3.0, Java Persistence entities) the persistence aspect.
Hibernate persistent classes and the EJB 3.0 entity programming model offer
transparent persistence. Hibernate and Java Persistence also provide automatic
Let’s explore both terms in more detail and find an accurate definition.
Transparent and automated persistence
We use transparent to mean a complete separation of concerns between the persistent classes of the domain model and the persistence logic, where the persistent
classes are unaware of—and have no dependency on—the persistence mechanism. We use automatic to refer to a persistence solution that relieves you of handling low-level mechanical details, such as writing most SQL statements and
working with the JDBC API.
The Item class, for example, doesn’t have any code-level dependency on any
Hibernate API. Furthermore:
Hibernate doesn’t require that any special superclasses or interfaces be
inherited or implemented by persistent classes. Nor are any special classes
used to implement properties or associations. (Of course, the option to use
both techniques is always there.) Transparent persistence improves code
readability and maintenance, as you’ll soon see.
Persistent classes can be reused outside the context of persistence, in unit
tests or in the user interface (UI) tier, for example. Testability is a basic
requirement for applications with rich domain models.
In a system with transparent persistence, objects aren’t aware of the underlying data store; they need not even be aware that they are being persisted
or retrieved. Persistence concerns are externalized to a generic persistence
manager interface—in the case of Hibernate, the Session and Query. In
JPA, the EntityManager and Query (which has the same name, but a different package and slightly different API) play the same roles.
Transparent persistence fosters a degree of portability; without special interfaces,
the persistent classes are decoupled from any particular persistence solution. Our
business logic is fully reusable in any other application context. You could easily
change to another transparent persistence mechanism. Because JPA follows the
same basic principles, there is no difference between Hibernate persistent classes
and JPA entity classes.
Implementing the domain model
By this definition of transparent persistence, certain nonautomated persistence layers are transparent (for example, the DAO pattern) because they decouple the persistence-related code with abstract programming interfaces. Only plain
Java classes without dependencies are exposed to the business logic or contain the
business logic. Conversely, some automated persistence layers (including EJB 2.1
entity instances and some ORM solutions) are nontransparent because they
require special interfaces or intrusive programming models.
We regard transparency as required. Transparent persistence should be one of
the primary goals of any ORM solution. However, no automated persistence solution is completely transparent: Every automated persistence layer, including
Hibernate, imposes some requirements on the persistent classes. For example,
Hibernate requires that collection-valued properties be typed to an interface such
as java.util.Set or java.util.List and not to an actual implementation such
as java.util.HashSet (this is a good practice anyway). Or, a JPA entity class has to
have a special property, called the database identifier.
You now know why the persistence mechanism should have minimal impact
on how you implement a domain model, and that transparent and automated
persistence are required. What kind of programming model should you use?
What are the exact requirements and contracts to observe? Do you need a special programming model at all? In theory, no; in practice, however, you should
adopt a disciplined, consistent programming model that is well accepted by the
Java community.
Writing POJOs and persistent entity classes
As a reaction against EJB 2.1 entity instances, many developers started talking
about Plain Old Java Objects (POJOs),1 a back-to-basics approach that essentially
revives JavaBeans, a component model for UI development, and reapplies it to
the business layer. (Most developers now use the terms POJO and JavaBean almost
synonymously.) The overhaul of the EJB specification brought us new lightweight
entities, and it would be appropriate to call them persistence-capable JavaBeans.
Java developers will soon use all three terms as synonyms for the same basic
design approach.
In this book, we use persistent class for any class implementation that is capable of persistent instances, we use POJO if some Java best practices are relevant,
POJO is sometimes also written Plain Ordinary Java Objects. This term was coined in 2002 by Martin
Fowler, Rebecca Parsons, and Josh Mackenzie.
Domain models and metadata
and we use entity class when the Java implementation follows the EJB 3.0 and JPA
specifications. Again, you shouldn’t be too concerned about these differences,
because the ultimate goal is to apply the persistence aspect as transparently as possible. Almost every Java class can be a persistent class, or a POJO, or an entity class
if some good practices are followed.
Hibernate works best with a domain model implemented as POJOs. The few
requirements that Hibernate imposes on your domain model implementation are
also best practices for the POJO implementation, so most POJOs are Hibernatecompatible without any changes. Hibernate requirements are almost the same as
the requirements for EJB 3.0 entity classes, so a POJO implementation can be easily marked up with annotations and made an EJB 3.0 compatible entity.
A POJO declares business methods, which define behavior, and properties,
which represent state. Some properties represent associations to other userdefined POJOs.
A simple POJO class is shown in listing 3.1. This is an implementation of the
User entity of your domain model.
Listing 3.1
POJO implementation of the User class
public class User
implements Serializable {
private String username;
private Address address;
public User() {}
Declaration of
No-argument class constructor
public String getUsername() {
return username;
public void setUsername(String username) {
this.username = username;
public Address getAddress() {
return address;
public void setAddress(Address address) {
this.address = address;
public MonetaryAmount calcShippingCosts(Address fromLocation) {
Business method
Implementing the domain model
Hibernate doesn’t require that persistent classes implement Serializable. However, when objects are stored in an HttpSession or passed by value using RMI,
serialization is necessary. (This is likely to happen in a Hibernate application.)
The class can be abstract and, if needed, extend a nonpersistent class.
Unlike the JavaBeans specification, which requires no specific constructor,
Hibernate (and JPA) require a constructor with no arguments for every persistent
class. Hibernate calls persistent classes using the Java Reflection API on this constructor to instantiate objects. The constructor may be nonpublic, but it has to be
at least package-visible if runtime-generated proxies will be used for performance
optimization. Proxy generation also requires that the class isn’t declared final
(nor has final methods)! (We’ll come back to proxies in chapter 13, section 13.1,
“Defining the global fetch plan.”)
The properties of the POJO implement the attributes of the business entities—
for example, the username of User. Properties are usually implemented as private
or protected instance variables, together with public property accessor methods: a
method for retrieving the value of the instance variable and a method for changing its value. These methods are known as the getter and setter, respectively. The
example POJO in listing 3.1 declares getter and setter methods for the username
and address properties.
The JavaBean specification defines the guidelines for naming these methods,
and they allow generic tools like Hibernate to easily discover and manipulate the
property value. A getter method name begins with get, followed by the name of
the property (the first letter in uppercase); a setter method name begins with set
and similarly is followed by the name of the property. Getter methods for Boolean
properties may begin with is instead of get.
You can choose how the state of an instance of your persistent classes should
be persisted by Hibernate, either through direct access to its fields or through
accessor methods. Your class design isn’t disturbed by these considerations. You
can make some accessor methods nonpublic or completely remove them. Some
getter and setter methods do something more sophisticated than access instance
variables (validation, for example), but trivial accessor methods are common.
Their primary advantage is providing an additional buffer between the internal
representation and the public interface of the class, allowing independent refactoring of both.
The example in listing 3.1 also defines a business method that calculates the
cost of shipping an item to a particular user (we left out the implementation of
this method).
Domain models and metadata
What are the requirements for JPA entity classes? The good news is that so far,
all the conventions we’ve discussed for POJOs are also requirements for JPA entities. You have to apply some additional rules, but they’re equally simple; we’ll
come back to them later.
Now that we’ve covered the basics of using POJO persistent classes as a programming model, let’s see how to handle the associations between those classes.
Implementing POJO associations
You use properties to express associations between POJO
classes, and you use accessor methods to navigate from
object to object at runtime. Let’s consider the associations
defined by the Category class, as shown in figure 3.3.
As with all our diagrams, we left out the associationrelated attributes (let’s call them parentCategory and Figure 3.3 Diagram
of the Category
childCategories) because they would clutter the illustra- class with associations
tion. These attributes and the methods that manipulate
their values are called scaffolding code.
This is what the scaffolding code for the one-to-many self-association of Category looks like:
public class Category {
private String name;
private Category parentCategory;
private Set childCategories = new HashSet();
public Category() { }
To allow bidirectional navigation of the association, you require two attributes.
The parentCategory field implements the single-valued end of the association
and is declared to be of type Category. The many-valued end, implemented by
the childCategories field, must be of collection type. You choose a Set, because
duplicates are disallowed, and initialize the instance variable to a new instance of
Hibernate requires interfaces for collection-typed attributes, so you must use
java.util.Set or java.util.List rather than HashSet, for example. This is consistent with the requirements of the JPA specification for collections in entities. At
runtime, Hibernate wraps the HashSet instance with an instance of one of Hibernate’s own classes. (This special class isn’t visible to the application code.) It’s
Implementing the domain model
good practice to program to collection interfaces anyway, rather than concrete
implementations, so this restriction shouldn’t bother you.
You now have some private instance variables but no public interface to allow
access from business code or property management by Hibernate (if it shouldn’t
access the fields directly). Let’s add some accessor methods to the class:
public String getName() {
return name;
public void setName(String name) { = name;
public Set getChildCategories() {
return childCategories;
public void setChildCategories(Set childCategories) {
this.childCategories = childCategories;
public Category getParentCategory() {
return parentCategory;
public void setParentCategory(Category parentCategory) {
this.parentCategory = parentCategory;
Again, these accessor methods need to be declared public only if they’re part of
the external interface of the persistent class used by the application logic to create
a relationship between two objects. However, managing the link between two Category instances is more difficult than setting a foreign key value in a database
field. In our experience, developers are often unaware of this complication that
arises from a network object model with bidirectional references. Let’s walk
through the issue step by step.
The basic procedure for adding a child Category to a parent Category looks
like this:
Category aParent = new Category();
Category aChild = new Category();
Whenever a link is created between a parent Category and a child Category, two
actions are required:
Domain models and metadata
The parentCategory of the child must be set, effectively breaking the association between the child and its old parent (there can only be one parent
for any child).
The child must be added to the childCategories collection of the new parent Category.
Managed relationships in Hibernate—Hibernate doesn’t manage persistent
associations. If you want to manipulate an association, you must write
exactly the same code you would write without Hibernate. If an association is bidirectional, both sides of the relationship must be considered.
Programming models like EJB 2.1 entity beans muddled this behavior by
introducing container-managed relationships—the container automatically changes the other side of a relationship if one side is modified by
the application. This is one of the reasons why code that uses EJB 2.1
entity beans couldn’t be reused outside the container. EJB 3.0 entity associations are transparent, just like in Hibernate. If you ever have problems
understanding the behavior of associations in Hibernate, just ask yourself, “What would I do without Hibernate?” Hibernate doesn’t change
the regular Java semantics.
It’s a good idea to add a convenience method to the Category class that groups
these operations, allowing reuse and helping ensure correctness, and in the end
guarantee data integrity:
public void addChildCategory(Category childCategory) {
if (childCategory == null)
throw new IllegalArgumentException("Null child category!");
if (childCategory.getParentCategory() != null)
The addChildCategory() method not only reduces the lines of code when dealing with Category objects, but also enforces the cardinality of the association.
Errors that arise from leaving out one of the two required actions are avoided.
This kind of grouping of operations should always be provided for associations, if
possible. If you compare this with the relational model of foreign keys in a relational database, you can easily see how a network and pointer model complicates
a simple operation: instead of a declarative constraint, you need procedural code
to guarantee data integrity.
Implementing the domain model
Because you want addChildCategory() to be the only externally visible mutator method for the child categories (possibly in addition to a removeChildCategory() method), you can make the setChildCategories() method private or
drop it and use direct field access for persistence. The getter method still returns
a modifiable collection, so clients can use it to make changes that aren’t reflected
on the inverse side. You should consider the static methods Collections.unmodifiableCollection(c) and Collections.unmodifiableSet(s), if you prefer to
wrap the internal collections before returning them in your getter method. The
client then gets an exception if it tries to modify the collection; every modification
is forced to go through the relationship-management method.
A different kind of relationship exists between the Category and Item classes:
a bidirectional many-to-many association, as shown in figure 3.4.
Figure 3.4
Category and the associated Item class
In the case of a many-to-many association, both sides are implemented with collection-valued attributes. Let’s add the new attributes and methods for accessing the
Item relationship to the Category class, as shown in listing 3.2.
Listing 3.2 Category to Item scaffolding code
public class Category {
private Set items = new HashSet();
public Set getItems() {
return items;
public void setItems(Set items) {
this.items = items;
Domain models and metadata
The code for the Item class (the other end of the many-to-many association) is
similar to the code for the Category class. You add the collection attribute, the
standard accessor methods, and a method that simplifies relationship management, as in listing 3.3.
Listing 3.3 Item to Category scaffolding code
public class Item {
private String name;
private String description;
private Set categories = new HashSet();
public Set getCategories() {
return categories;
private void setCategories(Set categories) {
this.categories = categories;
public void addCategory(Category category) {
if (category == null)
throw new IllegalArgumentException("Null category");
The addCategory() method is similar to the addChildCategory() convenience
method of the Category class. It’s used by a client to manipulate the link between
an Item and a Category. For the sake of readability, we won’t show convenience
methods in future code samples and assume you’ll add them according to your
own taste.
Using convenience methods for association handling isn’t the only way to
improve a domain model implementation. You can also add logic to your accessor
Adding logic to accessor methods
One of the reasons we like to use JavaBeans-style accessor methods is that they
provide encapsulation: The hidden internal implementation of a property can be
changed without any changes to the public interface. This lets you abstract the
internal data structure of a class—the instance variables—from the design of the
Implementing the domain model
database, if Hibernate accesses the properties at runtime through accessor methods. It also allows easier and independent refactoring of the public API and the
internal representation of a class.
For example, if your database stores the name of a user as a single NAME column, but your User class has firstname and lastname properties, you can add
the following persistent name property to the class:
public class User {
private String firstname;
private String lastname;
public String getName() {
return firstname + ' ' + lastname;
public void setName(String name) {
StringTokenizer t = new StringTokenizer(name);
firstname = t.nextToken();
lastname = t.nextToken();
Later, you’ll see that a Hibernate custom type is a better way to handle many of
these kinds of situations. However, it helps to have several options.
Accessor methods can also perform validation. For instance, in the following
example, the setFirstName() method verifies that the name is capitalized:
public class User {
private String firstname;
public String getFirstname() {
return firstname;
public void setFirstname(String firstname)
throws InvalidNameException {
if ( !StringUtil.isCapitalizedName(firstname) )
throw new InvalidNameException(firstname);
this.firstname = firstname;
Hibernate may use the accessor methods to populate the state of an instance when
loading an object from a database, and sometimes you’ll prefer that this validation
Domain models and metadata
not occur when Hibernate is initializing a newly loaded object. In that case, it
makes sense to tell Hibernate to directly access the instance variables.
Another issue to consider is dirty checking. Hibernate automatically detects
object state changes in order to synchronize the updated state with the database.
It’s usually safe to return a different object from the getter method than the
object passed by Hibernate to the setter. Hibernate compares the objects by
value—not by object identity—to determine whether the property’s persistent
state needs to be updated. For example, the following getter method doesn’t
result in unnecessary SQL UPDATEs:
public String getFirstname() {
return new String(firstname);
There is one important exception to this: Collections are compared by identity!
For a property mapped as a persistent collection, you should return exactly the
same collection instance from the getter method that Hibernate passed to the setter method. If you don’t, Hibernate will update the database, even if no update is
necessary, every time the state held in memory is synchronized with the database.
This kind of code should almost always be avoided in accessor methods:
public void setNames(List namesList) {
names = (String[]) namesList.toArray();
public List getNames() {
return Arrays.asList(names);
Finally, you have to know how exceptions in accessor methods are handled if you
configure Hibernate to use these methods when loading and storing instances. If
a RuntimeException is thrown, the current transaction is rolled back, and the
exception is yours to handle. If a checked application exception is thrown, Hibernate wraps the exception into a RuntimeException.
You can see that Hibernate doesn’t unnecessarily restrict you with a POJO programming model. You’re free to implement whatever logic you need in accessor
methods (as long as you keep the same collection instance in both getter and setter). How Hibernate accesses the properties is completely configurable. This kind
of transparency guarantees an independent and reusable domain model implementation. And everything we have explained and said so far is equally true for
both Hibernate persistent classes and JPA entities.
Let’s now define the object/relational mapping for the persistent classes.
Object/relational mapping metadata
Object/relational mapping metadata
ORM tools require metadata to specify the mapping between classes and tables,
properties and columns, associations and foreign keys, Java types and SQL types,
and so on. This information is called the object/relational mapping metadata.
Metadata is data about data, and mapping metadata defines and governs the
transformation between the different type systems and relationship representations in object-oriented and SQL systems.
It’s your job as a developer to write and maintain this metadata. We discuss various approaches in this section, including metadata in XML files and JDK 5.0
source code annotations. Usually you decide to use one strategy in a particular
project, and after reading these sections you’ll have the background information
to make an educated decision.
Metadata in XML
Any ORM solution should provide a human-readable, easily hand-editable mapping format, not just a GUI mapping tool. Currently, the most popular object/
relational metadata format is XML. Mapping documents written in and with XML
are lightweight, human readable, easily manipulated by version-control systems
and text editors, and they can be customized at deployment time (or even at runtime, with programmatic XML generation).
But is XML-based metadata really the best approach? A certain backlash
against the overuse of XML can be seen in the Java community. Every framework
and application server seems to require its own XML descriptors.
In our view, there are three main reasons for this backlash:
Metadata-based solutions have often been used inappropriately. Metadata is
not, by nature, more flexible or maintainable than plain Java code.
Many existing metadata formats weren’t designed to be readable and easy
to edit by hand. In particular, a major cause of pain is the lack of sensible
defaults for attribute and element values, requiring significantly more typing than should be necessary. Even worse, some metadata schemas use only
XML elements and text values, without any attributes. Another problem is
schemas that are too generic, where every declaration is wrapped in a
generic extension attribute of a meta element.
Good XML editors, especially in IDEs, aren’t as common as good Java coding
environments. Worst, and most easily fixable, a document type declaration
(DTD) often isn’t provided, preventing autocompletion and validation.
Domain models and metadata
There is no getting around the need for metadata in ORM. However, Hibernate
was designed with full awareness of the typical metadata problems. The XML
metadata format of Hibernate is extremely readable and defines useful default
values. If attribute values are missing, reflection is used on the mapped class to
determine defaults. Hibernate also comes with a documented and complete DTD.
Finally, IDE support for XML has improved lately, and modern IDEs provide
dynamic XML validation and even an autocomplete feature.
Let’s look at the way you can use XML metadata in Hibernate. You created the
Category class in the previous section; now you need to map it to the CATEGORY
table in the database. To do that, you write the XML mapping document in
listing 3.4.
Listing 3.4
Hibernate XML mapping of the Category class
<?xml version="1.0"?>
<!DOCTYPE hibernate-mapping PUBLIC
"-//Hibernate/Hibernate Mapping DTD//EN"
<generator class="native"/>
The Hibernate mapping DTD should be declared in every mapping file—it’s
required for syntactic validation of the XML.
Mappings are declared inside a <hibernate-mapping> element. You may include
as many class mappings as you like, along with certain other special declarations
that we’ll mention later in the book.
Object/relational mapping metadata
The class Category (in the auction.model package) is mapped to the CATEGORY
table. Every row in this table represents one instance of type Category.
We haven’t discussed the concept of object identity, so you may be surprised by
this mapping element. This complex topic is covered in the next chapter. To
understand this mapping, it’s sufficient to know that every row in the CATEGORY
table has a primary key value that matches the object identity of the instance in
memory. The <id> mapping element is used to define the details of object identity.
The property name of type java.lang.String is mapped to a database NAME column. Note that the type declared in the mapping is a built-in Hibernate type
(string), not the type of the Java property or the SQL column type. Think about
this as the converter that represents a bridge between the other two type systems.
We’ve intentionally left the collection and association mappings out of this example. Association and especially collection mappings are more complex, so we’ll
return to them in the second part of the book.
Although it’s possible to declare mappings for multiple classes in one mapping
file by using multiple <class> elements, the recommended practice (and the
practice expected by some Hibernate tools) is to use one mapping file per persistent class. The convention is to give the file the same name as the mapped class,
appending a suffix (for example, Category.hbm.xml), and putting it in the same
package as the Category class.
As already mentioned, XML mapping files aren’t the only way to define mapping metadata in a Hibernate application. If you use JDK 5.0, your best choice is
the Hibernate Annotations based on the EJB 3.0 and Java Persistence standard.
Annotation-based metadata
The basic idea is to put metadata next to the information it describes, instead of
separating it physically into a different file. Java didn’t have this functionality
before JDK 5.0, so an alternative was developed. The XDoclet project introduced
annotation of Java source code with meta-information, using special Javadoc tags
with support for key/value pairs. Through nesting of tags, quite complex structures are supported, but only some IDEs allow customization of Javadoc templates
for autocompletion and validation.
Java Specification Request (JSR) 175 introduced the annotation concept in the
Java language, with type-safe and declared interfaces for the definition of annotations. Autocompletion and compile-time checking are no longer an issue. We
found that annotation metadata is, compared to XDoclet, nonverbose and that it
Domain models and metadata
has better defaults. However, JDK 5.0 annotations are sometimes more difficult to
read than XDoclet annotations, because they aren’t inside regular comment
blocks; you should use an IDE that supports configurable syntax highlighting of
annotations. Other than that, we found no serious disadvantage in working with
annotations in our daily work in the past years, and we consider annotation-metadata support to be one of the most important features of JDK 5.0.
We’ll now introduce mapping annotations and use JDK 5.0. If you have to work
with JDK 1.4 but like to use annotation-based metadata, consider XDoclet, which
we’ll show afterwards.
Defining and using annotations
Before you annotate the first persistent class, let’s see how annotations are created. Naturally, you’ll usually use predefined annotations. However, knowing
how to extend the existing metadata format or how to write your own annotations is a useful skill. The following code example shows the definition of an
Entity annotation:
package javax.persistence;
public @interface Entity {
String name() default "";
The first line defines the package, as always. This annotation is in the package
javax.persistence, the Java Persistence API as defined by EJB 3.0. It’s one of the
most important annotations of the specification—you can apply it on a POJO to
make it a persistent entity class. The next line is an annotation that adds metainformation to the @Entity annotation (metadata about metadata). It specifies
that the @Entity annotation can only be put on type declarations; in other words,
you can only mark up classes with the @Entity annotation, not fields or methods.
The retention policy chosen for this annotation is RUNTIME; other options (for
other use cases) include removal of the annotation metadata during compilation,
or only inclusion in byte-code without possible runtime reflectivity. You want to
preserve all entity meta-information even at runtime, so Hibernate can read it on
startup through Java Reflection. What follows in the example is the actual declaration of the annotation, including its interface name and its attributes (just one in
this case, name, with an empty string default).
Let’s use this annotation to make a POJO persistent class a Java Persistence
Object/relational mapping metadata
package auction.model;
import javax.persistence.*;
@Table(name = "ITEM")
public class Item {
This public class, Item, has been declared as a persistent entity. All of its properties are now automatically persistent with a default strategy. Also shown is a second
annotation that declares the name of the table in the database schema this persistent class is mapped to. If you omit this information, the JPA provider defaults to
the unqualified class name (just as Hibernate will if you omit the table name in an
XML mapping file).
All of this is type-safe, and declared annotations are read with Java Reflection
when Hibernate starts up. You don’t need to write any XML mapping files, Hibernate doesn’t need to parse any XML, and startup is faster. Your IDE can also easily
validate and highlight annotations—they are regular Java types, after all.
One of the clear benefits of annotations is their flexibility for agile development. If you refactor your code, you rename, delete, or move classes and properties all the time. Most development tools and editors can’t refactor XML element
and attribute values, but annotations are part of the Java language and are
included in all refactoring operations.
Which annotations should you apply? You have the choice among several standardized and vendor-specific packages.
Considering standards
Annotation-based metadata has a significant impact on how you write Java applications. Other programming environments, like C# and .NET, had this kind of
support for quite a while, and developers adopted the metadata attributes quickly.
In the Java world, the big rollout of annotations is happening with Java EE 5.0. All
specifications that are considered part of Java EE, like EJB, JMS, JMX, and even the
servlet specification, will be updated and use JDK 5.0 annotations for metadata
needs. For example, web services in J2EE 1.4 usually require significant metadata
in XML files, so we expect to see real productivity improvements with annotations.
Or, you can let the web container inject an EJB handle into your servlet, by adding
an annotation on a field. Sun initiated a specification effort (JSR 250) to take care
of the annotations across specifications, defining common annotations for the
Domain models and metadata
whole Java platform. For you, however, working on a persistence layer, the most
important specification is EJB 3.0 and JPA.
Annotations from the Java Persistence package are available in javax.persistence once you have included the JPA interfaces in your classpath. You can use
these annotations to declare persistent entity classes, embeddable classes (we’ll
discuss these in the next chapter), properties, fields, keys, and so on. The JPA
specification covers the basics and most relevant advanced mappings—everything
you need to write a portable application, with a pluggable, standardized persistence layer that works inside and outside of any runtime container.
What annotations and mapping features aren’t specified in Java Persistence? A
particular JPA engine and product may naturally offer advantages—the so-called
vendor extensions.
Utilizing vendor extensions
Even if you map most of your application’s model with JPA-compatible annotations from the javax.persistence package, you’ll have to use vendor extensions
at some point. For example, almost all performance-tuning options you’d expect
to be available in high-quality persistence software, such as fetching and caching
settings, are only available as Hibernate-specific annotations.
Let’s see what that looks like in an example. Annotate the Item entity source
code again:
package auction.model;
import javax.persistence.*;
@Table(name = "ITEM")
@org.hibernate.annotations.BatchSize(size = 10)
"case when ITEM_IS_SPECIAL is not null then A else B end"
public class Item {
This example contains two Hibernate annotations. The first, @BatchSize, is a
fetching option that can increase performance in situations we’ll examine later
in this book. The second, @DiscriminatorFormula, is a Hibernate mapping
annotation that is especially useful for legacy schemas when class inheritance
can’t be determined with simple literal values (here it maps a legacy column
ITEM_IS_SPECIAL—probably some kind of flag—to a literal value). Both annotations are prefixed with the org.hibernate.annotations package name.
Object/relational mapping metadata
Consider this a good practice, because you can now easily see what metadata of
this entity class is from the JPA specification and which tags are vendor-specific. You can also easily search your source code for “org.hibernate.annotations” and get a complete overview of all nonstandard annotations in your
application in a single search result.
If you switch your Java Persistence provider, you only have to replace the vendor-specific extensions, and you can expect a similar feature set to be available
with most sophisticated solutions. Of course, we hope you’ll never have to do this,
and it doesn’t happen often in practice—just be prepared.
Annotations on classes only cover metadata that is applicable for that particular class. However, you often need metadata at a higher level, for a whole package
or even the whole application. Before we discuss these options, we’d like to introduce another mapping metadata format.
XML descriptors in JPA and EJB 3.0
The EJB 3.0 and Java Persistence standard embraces annotations aggressively.
However, the expert group has been aware of the advantages of XML deployment
descriptors in certain situations, especially for configuration metadata that
changes with each deployment. As a consequence, every annotation in EJB 3.0
and JPA can be replaced with an XML descriptor element. In other words, you
don’t have to use annotations if you don’t want to (although we strongly encourage you to reconsider and give annotations a try, if this is your first reaction to
Let’s look at an example of a JPA XML descriptor for a particular persistence
<?xml version="1.0" encoding="UTF-8"?>
" orm_1_0.xsd"
Domain models and metadata
<entity class="Item" access="PROPERTY"
<id name="id">
<generated-value strategy="AUTO"/>
This XML is automatically picked up by the JPA provider if you place it in a file
called orm.xml in your classpath, in the META-INF directory of the persistence
unit. You can see that you only have to name an identifier property for a class; as
in annotations, all other properties of the entity class are automatically considered persistent with a sensible default mapping.
You can also set default mappings for the whole persistence unit, such as the
schema name and default cascading options. If you include the <xml-mappingmetadata-complete> element, the JPA provider completely ignores all annotations on your entity classes in this persistence unit and relies only on the mappings as defined in the orm.xml file. You can (redundantly in this case) enable
this on an entity level, with metadata-complete="true". If enabled, the JPA provider assumes that all properties of the entity are mapped in XML, and that all
annotations for this entity should be ignored.
If you don’t want to ignore but instead want to override the annotation metadata, first remove the global <xml-mapping-metadata-complete> element from
the orm.xml file. Also remove the metadata-complete="true" attribute from any
entity mapping that should override, not replace, annotations:
<entity-mappings ...>
<entity class="Item">
<basic name="initialPrice" optional="false">
<column name="INIT_PRICE"/>
Here you map the initialPrice property to the INIT_PRICE column and specify
it isn’t nullable. Any annotation on the initialPrice property of the Item class is
Object/relational mapping metadata
ignored, but all other annotations on the Item class are still applied. Also note
that you didn’t specify an access strategy in this mapping, so field or accessor
method access is used depending on the position of the @Id annotation in Item.
(We’ll get back to this detail in the next chapter.)
An obvious problem with XML deployment descriptors in Java Persistence is
their compatibility with native Hibernate XML mapping files. The two formats
aren’t compatible at all, and you should make a decision to use one or the other.
The syntax of the JPA XML descriptor is much closer to the actual JPA annotations
than to the native Hibernate XML mapping files.
You also need to consider vendor extensions when you make a decision for an
XML metadata format. The Hibernate XML format supports all possible Hibernate
mappings, so if something can’t be mapped in JPA/Hibernate annotations, it can
be mapped with native Hibernate XML files. The same isn’t true with JPA XML
descriptors—they only provide convenient externalized metadata that covers the
specification. Sun does not allow vendor extensions with an additional namespace.
On the other hand, you can’t override annotations with Hibernate XML mapping files; you have to define a complete entity class mapping in XML.
For these reasons, we don’t show all possible mappings in all three formats; we
focus on native Hibernate XML metadata and JPA/Hibernate annotations. However, you’ll learn enough about the JPA XML descriptor to use it if you want to.
Consider JPA/Hibernate annotations the primary choice if you’re using JDK
5.0. Fall back to native Hibernate XML mapping files if you want to externalize a
particular class mapping or utilize a Hibernate extension that isn’t available as an
annotation. Consider JPA XML descriptors only if you aren’t planning to use any
vendor extension (which is, in practice, unlikely), or if you want to only override a
few annotations, or if you require complete portability that even includes deployment descriptors.
But what if you’re stuck with JDK 1.4 (or even 1.3) and still want to benefit from
the better refactoring capabilities and reduced lines of code of inline metadata?
Using XDoclet
The XDoclet project has brought the notion of attribute-oriented programming
to Java. XDoclet leverages the Javadoc tag format (@attribute) to specify class-,
field-, or method-level metadata attributes. There is even a book about XDoclet
from Manning Publications, XDoclet in Action (Walls and Richards, 2004).
XDoclet is implemented as an Ant task that generates Hibernate XML metadata (or something else, depending on the plug-in) as part of the build process.
Domain models and metadata
Creating the Hibernate XML mapping document with XDoclet is straightforward;
instead of writing it by hand, you mark up the Java source code of your persistent
class with custom Javadoc tags, as shown in listing 3.5.
Listing 3.5 Using XDoclet tags to mark up Java classes with mapping metadata
* The Category class of the CaveatEmptor auction site domain model.
* @hibernate.class
* table="CATEGORY"
public class Category {
* generator-class="native"
* column="CATEGORY_ID"
public Long getId() {
return id;
public String getName() {
return name;
With the annotated class in place and an Ant task ready, you can automatically
generate the same XML document shown in the previous section (listing 3.4).
The downside to XDoclet is that it requires another build step. Most large Java
projects are using Ant already, so this is usually a nonissue. Arguably, XDoclet
mappings are less configurable at deployment time; but there is nothing stopping
you from hand-editing the generated XML before deployment, so this is probably
not a significant objection. Finally, support for XDoclet tag validation may not be
available in your development environment. However, the latest IDEs support at
least autocompletion of tag names. We won’t cover XDoclet in this book, but you
can find examples on the Hibernate website.
Object/relational mapping metadata
Whether you use XML files, JDK 5.0 annotations, or XDoclet, you’ll often
notice that you have to duplicate metadata in several places. In other words, you
need to add global information that is applicable to more than one property,
more than one persistent class, or even the whole application.
Handling global metadata
Consider the following situation: All of your domain model persistent classes are
in the same package. However, you have to specify class names fully qualified,
including the package, in every XML mapping file. It would be a lot easier to
declare the package name once and then use only the short persistent class name.
Or, instead of enabling direct field access for every single property through the
access="field" mapping attribute, you’d rather use a single switch to enable
field access for all properties. Class- or package-scoped metadata would be much
more convenient.
Some metadata is valid for the whole application. For example, query strings
can be externalized to metadata and called by a globally unique name in the
application code. Similarly, a query usually isn’t related to a particular class, and
sometimes not even to a particular package. Other application-scoped metadata
includes user-defined mapping types (converters) and data filter (dynamic view)
Let’s walk through some examples of global metadata in Hibernate XML mappings and JDK 5.0 annotations.
Global XML mapping metadata
If you check the XML mapping DTD, you’ll see that the <hibernate-mapping>
root element has global options that are applied to the class mapping(s) inside
it—some of these options are shown in the following example:
<class ...>
The schema attribute enables a database schema prefix, AUCTION, used by Hibernate for all SQL statements generated for the mapped classes. By setting defaultlazy to false, you enable default outer-join fetching for some class associations, a
Domain models and metadata
topic we’ll discuss in chapter 13, section 13.1, “Defining the global fetch plan.”
(This default-lazy="true" switch has an interesting side effect: It switches to
Hibernate 2.x default fetching behavior—useful if you migrate to Hibernate 3.x
but don’t want to update all fetching settings.) With default-access, you enable
direct field access by Hibernate for all persistent properties of all classes mapped
in this file. Finally, the auto-import setting is turned off for all classes in this file.
We’ll talk about importing and naming of entities in chapter 4, section 4.3, “Class
mapping options.”
Mapping files with no class declarations—Global metadata is required and
present in any sophisticated application. For example, you may easily
import a dozen interfaces, or externalize a hundred query strings. In
large-scale applications, you often create mapping files without actual
class mappings, and only imports, external queries, or global filter and
type definitions. If you look at the DTD, you can see that <class> mappings are optional inside the <hibernate-mapping> root element. Split
up and organize your global metadata into separate files, such as
AuctionTypes.hbm.xml, AuctionQueries.hbm.xml, and so on, and load
them in Hibernate’s configuration just like regular mapping files.
However, make sure that all custom types and filters are loaded before
any other mapping metadata that applies these types and filters to
class mappings.
Let’s look at global metadata with JDK 5.0 annotations.
Global annotation metadata
Annotations are by nature woven into the Java source code for a particular class.
Although it’s possible to place global annotations in the source file of a class (at
the top), we’d rather keep global metadata in a separate file. This is called package metadata, and it’s enabled with a file named in a particular package directory:
typeClass = MonetaryAmountType.class,
parameters = { @Parameter(name="convertTo", value="USD") }
typeClass = MonetaryAmountType.class,
parameters = { @Parameter(name="convertTo", value="EUR") }
Object/relational mapping metadata
name = "findItemsOrderByPrice",
query = "select i from Item i order by i.initialPrice)"
package auction.persistence.types;
This example of a package metadata file, in the package auction.persistence.types, declares two Hibernate type converters. We’ll discuss the Hibernate type system in chapter 5, section 5.2, “The Hibernate type system.” You
can now refer to the user-defined types in class mappings by their names. The
same mechanism can be used to externalize queries and to define global identifier generators (not shown in the last example).
There is a reason the previous code example only includes annotations from
the Hibernate package and no Java Persistence annotations. One of the (lastminute) changes made to the JPA specification was the removal of package visibility of JPA annotations. As a result, no Java Persistence annotations can be placed in
a file. If you need portable global Java Persistence metadata, put
it in an orm.xml file.
Note that you have to name a package that contains a metadata file in your
Hibernate or JPA persistence unit configuration if you aren’t using automatic
detection—see chapter 2, section 2.2.1, “Using Hibernate Annotations.”
Global annotations (Hibernate and JPA) can also be placed in the source code
of a particular class, right after the import section. The syntax for the annotations
is the same as in the file, so we won’t repeat it here.
You now know how to write local and global mapping metadata. Another issue
in large-scale applications is the portability of metadata.
Using placeholders
In any larger Hibernate application, you’ll face the problem of native code in
your mapping metadata—code that effectively binds your mapping to a particular
database product. For example, SQL statements, such as in formula, constraint, or
filter mappings, aren’t parsed by Hibernate but are passed directly through to the
database management system. The advantage is flexibility—you can call any native
SQL function or keyword your database system supports. The disadvantage of putting native SQL in your mapping metadata is lost database portability, because
your mappings, and hence your application, will work only for a particular DBMS
(or even DBMS version).
Domain models and metadata
Even simple things, such as primary key generation strategies, usually aren’t
portable across all database systems. In the next chapter, we discuss a special identifier generator called native, which is a built-in smart primary key generator. On
Oracle, it uses a database sequence to generate primary key values for rows in a
table; on IBM DB2, it uses a special identity primary key column by default. This is
how you map it in XML:
<class name="Category" table="CATEGORY">
<id name="id" column="CATEGORY_ID" type="long">
<generator class="native"/>
We’ll discuss the details of this mapping later. The interesting part is the declaration class="native" as the identifier generator. Let’s assume that the portability
this generator provides isn’t what you need, perhaps because you use a custom
identifier generator, a class you wrote that implements the Hibernate
IdentifierGenerator interface:
<id name="id" column="CATEGORY_ID" type="long">
<generator class="auction.custom.MyOracleGenerator"/>
The XML mapping file is now bound to a particular database product, and you
lose the database portability of the Hibernate application. One way to deal with
this issue is to use a placeholder in your XML file that is replaced during build
when the mapping files are copied to the target directory (Ant supports this).
This mechanism is recommended only if you have experience with Ant or already
need build-time substitution for other parts of your application.
A much more elegant variation is to use custom XML entities (not related to
our application’s business entities). Let’s assume you need to externalize an element or attribute value in your XML files to keep it portable:
<id name="id" column="CATEGORY_ID" type="long">
<generator class="&idgenerator;"/>
The &idgenerator; value is called an entity placeholder. You can define its value
at the top of the XML file as an entity declaration, as part of the document type
<?xml version="1.0"?>
<!DOCTYPE hibernate-mapping SYSTEM
Object/relational mapping metadata
<!ENTITY idgenerator
The XML parser will now substitute the placeholder on Hibernate startup, when
mapping files are read.
You can take this one step further and externalize this addition to the DTD in a
separate file and include the global options in all other mapping files:
<?xml version="1.0"?>
<!DOCTYPE hibernate-mapping SYSTEM
<!ENTITY % globals SYSTEM "classpath://persistence/globals.dtd">
This example shows the inclusion of an external file as part of the DTD. The syntax, as often in XML, is rather crude, but the purpose of each line should be clear.
All global settings are added to the globals.dtd file in the persistence package on
the classpath:
<!ENTITY idgenerator
<!-- Add more options if needed... -->
To switch from Oracle to a different database system, just deploy a different globals.dtd file.
Often, you need not only substitute an XML element or attribute value but also
to include whole blocks of mapping metadata in all files, such as when many of
your classes share some common properties, and you can’t use inheritance to capture them in a single location. With XML entity replacement, you can externalize
an XML snippet to a separate file and include it in other XML files.
Let’s assume all the persistent classes have a dateModified property. The first
step is to put this mapping in its own file, say, DateModified.hbm.xml:
<property name="dateModified"
This file needs no XML header or any other tags. Now you include it in the mapping file for a persistent class:
<?xml version="1.0"?>
<!DOCTYPE hibernate-mapping SYSTEM
Domain models and metadata
<!ENTITY datemodified
SYSTEM "classpath://model/DateModified.hbm.xml">
<class name="Item" table="ITEM"
<id ...>
The content of DateModified.hbm.xml will be included and be substituted for the
&datemodified; placeholder. This, of course, also works with larger XML snippets.
When Hibernate starts up and reads mapping files, XML DTDs have to be
resolved by the XML parser. The built-in Hibernate entity resolver looks for the
hibernate-mapping-3.0.dtd on the classpath; it should find the DTD in the
hibernate3.jar file before it tries to look it up on the Internet, which happens
automatically whenever an entity URL is prefixed with The Hibernate entity resolver can also detect the classpath:// prefix, and the resource is then searched for in the classpath, where you can copy it
on deployment. We have to repeat this FAQ: Hibernate never looks up the DTD on
the Internet if you have a correct DTD reference in your mapping and the right
JAR on the classpath.
The approaches we have described so far—XML, JDK 5.0 annotations, and
XDoclet attributes—assume that all mapping information is known at development (or deployment) time. Suppose, however, that some information isn’t
known before the application starts. Can you programmatically manipulate the
mapping metadata at runtime?
Manipulating metadata at runtime
It’s sometimes useful for an application to browse, manipulate, or build new mappings at runtime. XML APIs like DOM, dom4j, and JDOM allow direct runtime
manipulation of XML documents, so you could create or manipulate an XML document at runtime, before feeding it to the Configuration object.
On the other hand, Hibernate also exposes a configuration-time metamodel
that contains all the information declared in your static mapping metadata.
Direct programmatic manipulation of this metamodel is sometimes useful, especially for applications that allow for extension by user-written code. A more drastic approach would be complete programmatic and dynamic definition of the
mapping metadata, without any static mapping. However, this is exotic and
Object/relational mapping metadata
should be reserved for a particular class of fully dynamic applications, or application building kits.
The following code adds a new property, motto, to the User class:
// Get the existing mapping for User from Configuration
PersistentClass userMapping =
// Define a new column for the USER table
Column column = new Column();
// Wrap the column in a Value
SimpleValue value = new SimpleValue();
value.setTable( userMapping.getTable() );
// Define a new property of the User class
Property prop = new Property();
// Build a new session factory, using the new mapping
SessionFactory sf = cfg.buildSessionFactory();
A PersistentClass object represents the metamodel for a single persistent class,
and you retrieve it from the Configuration object. Column, SimpleValue, and
Property are all classes of the Hibernate metamodel and are available in the
org.hibernate.mapping package.
Keep in mind that adding a property to an existing persistent class mapping, as shown here, is quite easy, but programmatically creating a new
mapping for a previously unmapped class is more involved.
Once a SessionFactory is created, its mappings are immutable. The SessionFactory uses a different metamodel internally than the one used at configuration
time. There is no way to get back to the original Configuration from the SessionFactory or Session. (Note that you can get the SessionFactory from a Session if you wish to access a global setting.) However, the application can read the
SessionFactory’s metamodel by calling getClassMetadata() or getCollectionMetadata(). Here’s an example:
Domain models and metadata
Item item = ...;
ClassMetadata meta = sessionFactory.getClassMetadata(Item.class);
String[] metaPropertyNames =
Object[] propertyValues =
meta.getPropertyValues(item, EntityMode.POJO);
This code snippet retrieves the names of persistent properties of the Item class
and the values of those properties for a particular instance. This helps you write
generic code. For example, you may use this feature to label UI components or
improve log output.
Although you’ve seen some mapping constructs in the previous sections, we
haven’t introduced any more sophisticated class and property mappings so far.
You should now decide which mapping metadata option you’d like to use in
your project and then read more about class and property mappings in the
next chapter.
Or, if you’re already an experienced Hibernate user, you can read on and find
out how the latest Hibernate version allows you to represent a domain model
without Java classes.
Alternative entity representation
In this book, so far, we’ve always talked about a domain model implementation
based on Java classes—we called them POJOs, persistent classes, JavaBeans, or
entities. An implementation of a domain model that is based on Java classes with
regular properties, collections, and so on, is type-safe. If you access a property of
a class, your IDE offers autocompletion based on the strong types of your model,
and the compiler checks whether your source is correct. However, you pay for
this safety with more time spent on the domain model implementation—and
time is money.
In the following sections, we introduce Hibernate’s ability to work with domain
models that aren’t implemented with Java classes. We’re basically trading typesafety for other benefits and, because nothing is free, more errors at runtime
whenever we make a mistake. In Hibernate, you can select an entity mode for your
application, or even mix entity modes for a single model. You can even switch
between entity modes in a single Session.
These are the three built-in entity modes in Hibernate:
POJO—A domain model implementation based on POJOs, persistent classes.
This is what you have seen so far, and it’s the default entity mode.
Alternative entity representation
MAP—No Java classes are required; entities are represented in the Java application with HashMaps. This mode allows quick prototyping of fully dynamic
DOM4J—No Java classes are required; entities are represented as XML elements, based on the dom4j API. This mode is especially useful for exporting
or importing data, or for rendering and transforming data through XSLT
There are two reasons why you may want to skip the next section and come back
later: First, a static domain model implementation with POJOs is the common
case, and dynamic or XML representation are features you may not need right
now. Second, we’re going to present some mappings, queries, and other operations that you may not have seen so far, not even with the default POJO entity
mode. However, if you feel confident enough with Hibernate, read on.
Let’s start with the MAP mode and explore how a Hibernate application can be
fully dynamically typed.
Creating dynamic applications
A dynamic domain model is a model that is dynamically typed. For example,
instead of a Java class that represents an auction item, you work with a bunch of
values in a Java Map. Each attribute of an auction item is represented by a key (the
name of the attribute) and its value.
Mapping entity names
First, you need to enable this strategy by naming your business entities. In a Hibernate XML mapping file, you use the entity-name attribute:
<class entity-name="ItemEntity" table="ITEM_ENTITY">
<id name="id" type="long" column="ITEM_ID">
<generator class="native"/>
<property name="initialPrice"
<property name="description"
<many-to-one name="seller"
Domain models and metadata
<class entity-name="UserEntity" table="USER_ENTITY">
<id name="id" type="long" column="USER_ID">
<generator class="native"/>
<property name="username"
<bag name="itemsForSale" inverse="true" cascade="all">
<key column="USER_ID"/>
<one-to-many entity-name="ItemEntity"/>
There are three interesting things to observe in this mapping file.
First, you mix several class mappings in one, something we didn’t recommend
earlier. This time you aren’t really mapping Java classes, but logical names of entities. You don’t have a Java source file and an XML mapping file with the same name
next to each other, so you’re free to organize your metadata in any way you like.
Second, the <class name="..."> attribute has been replaced with <class
entity-name="...">. You also append ...Entity to these logical names for clarity and to distinguish them from other nondynamic mappings that you made earlier with regular POJOs.
Finally, all entity associations, such as <many-to-one> and <one-to-many>, now
also refer to logical entity names. The class attribute in the association mappings
is now entity-name. This isn’t strictly necessary—Hibernate can recognize that
you’re referring to a logical entity name even if you use the class attribute. However, it avoids confusion when you later mix several representations.
Let’s see what working with dynamic entities looks like.
Working with dynamic maps
To create an instance of one of your entities, you set all attribute values in a Java
Map user = new HashMap();
user.put("username", "johndoe");
Map item1 = new HashMap();
item1.put("description", "An item for auction");
item1.put("initialPrice", new BigDecimal(99));
item1.put("seller", user);
Alternative entity representation
Map item2 = new HashMap();
item2.put("description", "Another item for auction");
item2.put("initialPrice", new BigDecimal(123));
item2.put("seller", user);
Collection itemsForSale = new ArrayList();
user.put("itemsForSale", itemsForSale);"UserEntity", user);
The first map is a UserEntity, and you set the username attribute as a key/value
pair. The next two maps are ItemEntitys, and here you set the link to the seller
of each item by putting the user map into the item1 and item2 maps. You’re
effectively linking maps—that’s why this representation strategy is sometimes also
called “representation with maps of maps.”
The collection on the inverse side of the one-to-many association is initialized
with an ArrayList, because you mapped it with bag semantics (Java doesn’t have
a bag implementation, but the Collection interface has bag semantics). Finally,
the save() method on the Session is given a logical entity name and the user
map as an input parameter.
Hibernate knows that UserEntity refers to the dynamically mapped entity,
and that it should treat the input as a map that has to be saved accordingly. Hibernate also cascades to all elements in the itemsForSale collection; hence, all item
maps are also made persistent. One UserEntity and two ItemEntitys are
inserted into their respective tables.
Can I map a Set in dynamic mode? Collections based on sets don’t work
with dynamic entity mode. In the previous code example, imagine that
itemsForSale was a Set. A Set checks its elements for duplicates, so
when you call add(item1) and add(item2), the equals() method on
these objects is called. However, item1 and item2 are Java Map instances,
and the equals() implementation of a map is based on the key sets of
the map. So, because both item1 and item2 are maps with the same keys,
they aren’t distinct when added to a Set. Use bags or lists only if you
require collections in dynamic entity mode.
Hibernate handles maps just like POJO instances. For example, making a map persistent triggers identifier assignment; each map in persistent state has an identifier
attribute set with the generated value. Furthermore, persistent maps are automatically checked for any modifications inside a unit of work. To set a new price on an
item, for example, you can load it and then let Hibernate do all the work:
Domain models and metadata
Long storedItemId = (Long) item1.get("id");
Session session = getSessionFactory().openSession();
Map loadedItemMap = (Map) session.load("ItemEntity", storedItemId);
loadedItemMap.put("initialPrice", new BigDecimal(100));
All Session methods that have class parameters such as load() also come in an
overloaded variation that accepts entity names. After loading an item map, you set
a new price and make the modification persistent by committing the transaction,
which, by default, triggers dirty checking and flushing of the Session.
You can also refer to entity names in HQL queries:
List queriedItemMaps =
session.createQuery("from ItemEntity where initialPrice >= :p")
.setParameter("p", new BigDecimal(100))
This query returns a collection of ItemEntity maps. They are in persistent state.
Let’s take this one step further and mix a POJO model with dynamic maps.
There are two reasons why you would want to mix a static implementation of your
domain model with a dynamic map representation:
You want to work with a static model based on POJO classes by default, but
sometimes you want to represent data easily as maps of maps. This can be
particularly useful in reporting, or whenever you have to implement a
generic user interface that can represent various entities dynamically.
You want to map a single POJO class of your model to several tables and
then select the table at runtime by specifying a logical entity name.
You may find other use cases for mixed entity modes, but they’re so rare that we
want to focus on the most obvious.
First, therefore, you’ll mix a static POJO model and enable dynamic map representation for some of the entities, some of the time.
Mixing dynamic and static entity modes
To enable a mixed model representation, edit your XML mapping metadata and
declare a POJO class name and a logical entity name:
<class name="model.ItemPojo"
Alternative entity representation
<many-to-one name="seller"
<class name="model.UserPojo"
<bag name="itemsForSale" inverse="true" cascade="all">
<key column="USER_ID"/>
<one-to-many entity-name="ItemEntity"/>
Obviously, you also need the two classes, model.ItemPojo and model.UserPojo,
that implement the properties of these entities. You still base the many-to-one and
one-to-many associations between the two entities on logical names.
Hibernate will primarily use the logical names from now on. For example, the
following code does not work:
UserPojo user = new UserPojo();
ItemPojo item1 = new ItemPojo();
ItemPojo item2 = new ItemPojo();
Collection itemsForSale = new ArrayList();
The preceding example creates a few objects, sets their properties, and links
them, and then tries to save the objects through cascading by passing the user
instance to save(). Hibernate inspects the type of this object and tries to figure
out what entity it is, and because Hibernate now exclusively relies on logical entity
names, it can’t find a mapping for model.UserPojo. You need to tell Hibernate
the logical name when working with a mixed representation mapping:
..."UserEntity", user);
Once you change this line, the previous code example works. Next, consider
loading, and what is returned by queries. By default, a particular SessionFactory
Domain models and metadata
is in POJO entity mode, so the following operations return instances of
Long storedItemId = item1.getId();
ItemPojo loadedItemPojo =
(ItemPojo) session.load("ItemEntity", storedItemId);
List queriedItemPojos =
session.createQuery("from ItemEntity where initialPrice >= :p")
.setParameter("p", new BigDecimal(100))
You can switch to a dynamic map representation either globally or temporarily, but
a global switch of the entity mode has serious consequences. To switch globally,
add the following to your Hibernate configuration; e.g., in hibernate.cfg.xml:
<property name="default_entity_mode">dynamic-map</property>
All Session operations now either expect or return dynamically typed maps! The
previous code examples that stored, loaded, and queried POJO instances no
longer work; you need to store and load maps.
It’s more likely that you want to switch to another entity mode temporarily, so
let’s assume that you leave the SessionFactory in the default POJO mode. To
switch to dynamic maps in a particular Session, you can open up a new temporary Session on top of the existing one. The following code uses such a temporary Session to store a new auction item for an existing seller:
Session dynamicSession = session.getSession(EntityMode.MAP);
Map seller = (Map) dynamicSession.load("UserEntity", user.getId() );
Map newItemMap = new HashMap();
newItemMap.put("description", "An item for auction");
newItemMap.put("initialPrice", new BigDecimal(99));
newItemMap.put("seller", seller);"ItemEntity", newItemMap);
Long storedItemId = (Long) newItemMap.get("id");
Map loadedItemMap =
(Map) dynamicSession.load("ItemEntity", storedItemId);
List queriedItemMaps =
.createQuery("from ItemEntity where initialPrice >= :p")
.setParameter("p", new BigDecimal(100))
The temporary dynamicSession that is opened with getSession() doesn’t need
to be flushed or closed; it inherits the context of the original Session. You use it
Alternative entity representation
only to load, query, or save data in the chosen representation, which is the EntityMode.MAP in the previous example. Note that you can’t link a map with a POJO
instance; the seller reference has to be a HashMap, not an instance of UserPojo.
We mentioned that another good use case for logical entity names is the mapping of one POJO to several tables, so let’s look at that.
Mapping a class several times
Imagine that you have several tables with some columns in common. For example, you could have ITEM_AUCTION and ITEM_SALE tables. Usually you map each
table to an entity persistent class, ItemAuction and ItemSale respectively. With
the help of entity names, you can save work and implement a single persistent
To map both tables to a single persistent class, use different entity names (and
usually different property mappings):
<class name="model.Item"
<id name="id" column="ITEM_AUCTION_ID">...</id>
<property name="description" column="DESCRIPTION"/>
<property name="initialPrice" column="INIT_PRICE"/>
<class name="model.Item"
<id name="id" column="ITEM_SALE_ID">...</id>
<property name="description" column="DESCRIPTION"/>
<property name="salesPrice" column="SALES_PRICE"/>
The model.Item persistent class has all the properties you mapped: id, description, initialPrice, and salesPrice. Depending on the entity name you use at
runtime, some properties are considered persistent and others transient:
Item itemForAuction = new Item();
itemForAuction.setDescription("An item for auction");
itemForAuction.setInitialPrice( new BigDecimal(99) );"ItemAuction", itemForAuction);
Item itemForSale = new Item();
itemForSale.setDescription("An item for sale");
Domain models and metadata
itemForSale.setSalesPrice( new BigDecimal(123) );"ItemSale", itemForSale);
Thanks to the logical entity name, Hibernate knows into which table it should
insert the data. Depending on the entity name you use for loading and querying
entities, Hibernate selects from the appropriate table.
Scenarios in which you need this functionality are rare, and you’ll probably
agree with us that the previous use case isn’t good or common.
In the next section, we introduce the third built-in Hibernate entity mode, the
representation of domain entities as XML documents.
Representing data in XML
XML is nothing but a text file format; it has no inherent capabilities that qualify it
as a medium for data storage or data management. The XML data model is weak,
its type system is complex and underpowered, its data integrity is almost completely procedural, and it introduces hierarchical data structures that were outdated decades ago. However, data in XML format is attractive to work with in Java;
we have nice tools. For example, we can transform XML data with XSLT, which we
consider one of the best use cases.
Hibernate has no built-in functionality to store data in an XML format; it relies
on a relational representation and SQL, and the benefits of this strategy should be
clear. On the other hand, Hibernate can load and present data to the application
developer in an XML format. This allows you to use a sophisticated set of tools
without any additional transformation steps.
Let’s assume that you work in default POJO mode and that you quickly want to
obtain some data represented in XML. Open a temporary Session with the EntityMode.DOM4J:
Session dom4jSession = session.getSession(EntityMode.DOM4J);
Element userXML =
(Element) dom4jSession.load(User.class, storedUserId);
What is returned here is a dom4j Element, and you can use the dom4j API to read
and manipulate it. For example, you can pretty-print it to your console with the
following snippet:
try {
OutputFormat format = OutputFormat.createPrettyPrint();
XMLWriter writer = new XMLWriter( System.out, format);
writer.write( userXML );
} catch (IOException ex) {
throw new RuntimeException(ex);
Alternative entity representation
If we assume that you reuse the POJO classes and data from the previous examples, you see one User instance and two Item instances (for clarity, we no longer
name them UserPojo and ItemPojo):
<description>An item for auction</description>
<description>Another item for auction</description>
Hibernate assumes default XML element names—the entity and property names.
You can also see that collection elements are embedded, and that circular references are resolved through identifiers (the <seller> element).
You can change this default XML representation by adding node attributes to
your Hibernate mapping metadata:
<class name="Item" table="ITEM_ENTITY" node="item">
<id name="id" type="long" column="ITEM_ID" node="@id">
<generator class="native"/>
<property name="initialPrice"
<property name="description"
<many-to-one name="seller"
Domain models and metadata
<class name="User" table="USERS" node="user">
<id name="id" type="long" column="USER_ID" node="@id">
<generator class="native"/>
<property name="username"
<bag name="itemsForSale" inverse="true" cascade="all"
embed-xml="true" node="items-for-sale">
<key column="USER_ID"/>
<one-to-many class="Item"/>
Each node attribute defines the XML representation:
A node="name" attribute on a <class> mapping defines the name of the
XML element for that entity.
A node="name" attribute on any property mapping specifies that the property content should be represented as the text of an XML element of the
given name.
A node="@name" attribute on any property mapping specifies that the property content should be represented as an XML attribute value of the given
A node="name/@attname" attribute on any property mapping specifies that
the property content should be represented as an XML attribute value of
the given name, on a child element of the given name.
The embed-xml option is used to trigger embedding or referencing of associated
entity data. The updated mapping results in the following XML representation of
the same data you’ve seen before:
<user id="1" username="johndoe">
<item id="2" seller-id="1">
<item-details initial-price="99"
description="An item for auction"/>
Alternative entity representation
<item id="3" seller-id="1">
<item-details initial-price="123"
description="Another item for auction"/>
Be careful with the embed-xml option—you can easily create circular references
that result in an endless loop!
Finally, data in an XML representation is transactional and persistent, so you
can modify queried XML elements and let Hibernate take care of updating the
underlying tables:
Element itemXML =
(Element) dom4jSession.get(Item.class, storedItemId);
session.flush(); // Hibernate executes UPDATEs
Element userXML =
(Element) dom4jSession.get(User.class, storedUserId);
Element newItem = DocumentHelper.createElement("item");
Element newItemDetails = newItem.addElement("item-details");
userXml.attribute("id").getValue() );
newItemDetails.addAttribute("initial-price", "123");
newItemDetails.addAttribute("description", "A third item");, newItem);
dom4jSession.flush(); // Hibernate executes INSERTs
There is no limit to what you can do with the XML that is returned by Hibernate.
You can display, export, and transform it in any way you like. See the dom4j documentation for more information.
Finally, note that you can use all three built-in entity modes simultaneously, if
you like. You can map a static POJO implementation of your domain model, switch
to dynamic maps for your generic user interface, and export data into XML. Or,
you can write an application that doesn’t have any domain classes, only dynamic
maps and XML. We have to warn you, though, that prototyping in the software
industry often means that customers end up with the prototype that nobody
wanted to throw away—would you buy a prototype car? We highly recommend
that you rely on static domain models if you want to create a maintainable system.
Domain models and metadata
We won’t consider dynamic models or XML representation again in this book.
Instead, we’ll focus on static persistent classes and how they are mapped.
In this chapter, we focused on the design and implementation of a rich domain
model in Java.
You now understand that persistent classes in a domain model should to be
free of crosscutting concerns, such as transactions and security. Even persistencerelated concerns should not leak into the domain model implementation. You
also know how important transparent persistence is if you want to execute and test
your business objects independently and easily.
You have learned the best practices and requirements for the POJO and JPA
entity programming model, and what concepts they have in common with the old
JavaBean specification. We had a closer look at the implementation of persistent
classes, and how attributes and relationships are best represented.
To be prepared for the next part of the book, and to learn all the object/relational mapping options, you needed to make an educated decision to use either
XML mapping files or JDK 5.0 annotations, or possibly a combination of both.
You’re now ready to write more complex mappings in both formats.
For convenience, table 3.1 summarizes the differences between Hibernate and
Java Persistence related to concepts discussed in this chapter.
Table 3.1
Hibernate and JPA comparison chart for chapter 3
Hibernate Core
Java Persistence and EJB 3.0
Persistent classes require a no-argument constructor with public or protected visibility if proxybased lazy loading is used.
The JPA specification mandates a no-argument
constructor with public or protected visibility for all
entity classes.
Persistent collections must be typed to interfaces.
Hibernate supports all JDK interfaces.
Persistent collections must be typed to interfaces.
Only a subset of all interfaces (no sorted collections, for example) is considered fully portable.
Persistent properties can be accessed through
fields or accessor methods at runtime, or a completely customizable strategy can be applied.
Persistent properties of an entity class are
accessed through fields or accessor methods, but
not both if full portability is required.
Table 3.1
Hibernate and JPA comparison chart for chapter 3 (continued)
Hibernate Core
Java Persistence and EJB 3.0
The XML metadata format supports all possible
Hibernate mapping options.
JPA annotations cover all basic and most advanced
mapping options. Hibernate Annotations are
required for exotic mappings and tuning.
XML mapping metadata can be defined globally,
and XML placeholders are used to keep metadata
free from dependencies.
Global metadata is only fully portable if declared in
the standard orm.xml metadata file.
In the next part of the book, we show you all possible basic and some advanced
mapping techniques, for classes, properties, inheritance, collections, and associations. You’ll learn how to solve the structural object/relational mismatch.
Part 2
Mapping concepts
and strategies
his part is all about actual object/relational mapping, from classes and
properties to tables and columns. Chapter 4 starts with regular class and
property mappings, and explains how you can map fine-grained Java domain
models. Next, in chapter 5, you’ll see how to map more complex class inheritance hierarchies and how to extend Hibernate's functionality with the powerful custom mapping type system. In chapters 6 and 7, we show you how to
map Java collections and associations between classes, with many sophisticated examples. Finally, you’ll find chapter 8 most interesting if you need to
introduce Hibernate in an existing applications, or if you have to work with
legacy database schemas and hand-written SQL. We also talk about customized SQL DDL for schema generation in this chapter.
After reading this part of the book, you’ll be ready to create even the
most complex mappings quickly and with the right strategy. You’ll understand how the problem of inheritance mapping can be solved, and how collections and associations can be mapped. You’ll also be able to tune and
customize Hibernate for integration with any existing database schema or
persistent classes
This chapter covers
Understanding the entity and value-type concept
Mapping classes with XML and annotations
Fine-grained property and component mappings
Mapping persistent classes
This chapter presents the fundamental mapping options, explaining how classes
and properties are mapped to tables and columns. We show and discuss how you
can handle database identity and primary keys, and how various other metadata
settings can be used to customize how Hibernate loads and stores objects. All
mapping examples are done in Hibernate’s native XML format, and with JPA
annotations and XML descriptors, side by side. We also look closely at the mapping of fine-grained domain models, and at how properties and embedded components are mapped.
First, though, we define the essential distinction between entities and value
types, and explain how you should approach the object/relational mapping of
your domain model.
Understanding entities and value types
Entities are persistent types that represent first-class business objects (the term
object is used here in its natural sense). In other words, some of the classes and
types you have to deal with in an application are more important, which naturally
makes others less important. You probably agree that in CaveatEmptor, Item is a
more important class than String. User is probably more important than
Address. What makes something important? Let’s look at the issue from a different perspective.
Fine-grained domain models
A major objective of Hibernate is support for fine-grained domain models, which
we isolated as the most important requirement for a rich domain model. It’s one
reason why we work with POJOs. In crude terms, fine-grained means more classes
than tables.
For example, a user may have both a billing address and a home address. In the
database, you may have a single USERS table with the columns BILLING_STREET,
HOME_ZIPCODE. (Remember the problem of SQL types we discussed in chapter 1?)
In the domain model, you could use the same approach, representing the two
addresses as six string-valued properties of the User class. But it’s much better to
model this using an Address class, where User has the billingAddress and
homeAddress properties, thus using three classes for one table.
This domain model achieves improved cohesion and greater code reuse,
and it’s more understandable than SQL systems with inflexible type systems. In
Understanding entities and value types
the past, many ORM solutions didn’t provide especially good support for this
kind of mapping.
Hibernate emphasizes the usefulness of fine-grained classes for implementing
type safety and behavior. For example, many people model an email address as a
string-valued property of User. A more sophisticated approach is to define an
EmailAddress class, which adds higher-level semantics and behavior—it may provide a sendEmail() method.
This granularity problem leads us to a distinction of central importance in
ORM. In Java, all classes are of equal standing—all objects have their own identity
and lifecycle.
Let’s walk through an example.
Defining the concept
Two people live in the same apartment, and they both register user accounts in
CaveatEmptor. Naturally, each account is represented by one instance of User, so
you have two entity instances. In the CaveatEmptor model, the User class has a
homeAddress association with the Address class. Do both User instances have a
runtime reference to the same Address instance or does each User instance have
a reference to its own Address? If Address is supposed to support shared runtime
references, it’s an entity type. If not, it’s likely a value type and hence is dependent
on a single reference by an owning entity instance, which also provides identity.
We advocate a design with more classes than tables: One row represents multiple instances. Because database identity is implemented by primary key value,
some persistent objects won’t have their own identity. In effect, the persistence
mechanism implements pass-by-value semantics for some classes! One of the
objects represented in the row has its own identity, and others depend on that. In
the previous example, the columns in the USERS table that contain address information are dependent on the identifier of the user, the primary key of the table.
An instance of Address is dependent on an instance of User.
Hibernate makes the following essential distinction:
An object of entity type has its own database identity (primary key value).
An object reference to an entity instance is persisted as a reference in the
database (a foreign key value). An entity has its own lifecycle; it may exist
independently of any other entity. Examples in CaveatEmptor are User,
Item, and Category.
An object of value type has no database identity; it belongs to an entity
instance and its persistent state is embedded in the table row of the owning
Mapping persistent classes
entity. Value types don’t have identifiers or identifier properties. The
lifespan of a value type instance is bounded by the lifespan of the owning
entity instance. A value type doesn’t support shared references: If two users
live in the same apartment, they each have a reference to their own homeAddress instance. The most obvious value types are classes like Strings and
Integers, but all JDK classes are considered value types. User-defined classes
can also be mapped as value types; for example, CaveatEmptor has Address
and MonetaryAmount.
Identification of entities and value types in your domain model isn’t an ad hoc
task but follows a certain procedure.
Identifying entities and value types
You may find it helpful to add stereotype information to your UML class diagrams
so you can immediately see and distinguish entities and value types. This practice
also forces you to think about this distinction for all your classes, which is a first
step to an optimal mapping and well-performing persistence layer. See figure 4.1
for an example.
The Item and User classes are obvious entities. They each have their own identity, their instances have references from many other instances (shared references), and they have independent lifecycles.
Identifying the Address as a value type is also easy: A particular Address
instance is referenced by only a single User instance. You know this because the
association has been created as a composition, where the User instance has been
made fully responsible for the lifecycle of the referenced Address instance.
Therefore, Address objects can’t be referenced by anyone else and don’t need
their own identity.
The Bid class is a problem. In object-oriented modeling, you express a composition (the association between Item and Bid with the diamond), and an Item
manages the lifecycles of all the Bid objects to which it has a reference (it’s a collection of references). This seems reasonable, because the bids would be useless if
Figure 4.1
Stereotypes for entities and value types have been added to the diagram.
Mapping entities with identity
an Item no longer existed. But at the same time, there is another association to
Bid: An Item may hold a reference to its successfulBid. The successful bid must
also be one of the bids referenced by the collection, but this isn’t expressed in the
UML. In any case, you have to deal with possible shared references to Bid
instances, so the Bid class needs to be an entity. It has a dependent lifecycle, but it
must have its own identity to support shared references.
You’ll often find this kind of mixed behavior; however, your first reaction
should be to make everything a value-typed class and promote it to an entity only
when absolutely necessary. Try to simplify your associations: Collections, for example, sometimes add complexity without offering any advantages. Instead of mapping a persistent collection of Bid references, you can write a query to obtain all
the bids for an Item (we’ll come back to this point again in chapter 7).
As the next step, take your domain model diagram and implement POJOs for
all entities and value types. You have to take care of three things:
Shared references—Write your POJO classes in a way that avoids shared references to value type instances. For example, make sure an Address object
can be referenced by only one User. For example, make it immutable and
enforce the relationship with the Address constructor.
Lifecycle dependencies—As discussed, the lifecycle of a value-type instance is
bound to that of its owning entity instance. If a User object is deleted, its
Address dependent object(s) have to be deleted as well. There is no notion
or keyword for this in Java, but your application workflow and user interface
must be designed to respect and expect lifecycle dependencies. Persistence
metadata includes the cascading rules for all dependencies.
Identity—Entity classes need an identifier property in almost all cases. Userdefined value-type classes (and JDK classes) don’t have an identifier property, because instances are identified through the owning entity.
We’ll come back to class associations and lifecycle rules when we discuss more
advanced mappings later in the book. However, object identity is a subject you
have to understand at this point.
Mapping entities with identity
It’s vital to understand the difference between object identity and object equality
before we discuss terms like database identity and the way Hibernate manages
identity. Next, we explore how object identity and equality relate to database (primary key) identity.
Mapping persistent classes
Understanding Java identity and equality
Java developers understand the difference between Java object identity and equality. Object identity, ==, is a notion defined by the Java virtual machine. Two object
references are identical if they point to the same memory location.
On the other hand, object equality is a notion defined by classes that implement the equals() method, sometimes also referred to as equivalence. Equivalence means that two different (nonidentical) objects have the same value. Two
different instances of String are equal if they represent the same sequence of
characters, even though they each have their own location in the memory space
of the virtual machine. (If you’re a Java guru, we acknowledge that String is a special case. Assume we used a different class to make the same point.)
Persistence complicates this picture. With object/relational persistence, a persistent object is an in-memory representation of a particular row of a database
table. Along with Java identity (memory location) and object equality, you pick up
database identity (which is the location in the persistent data store). You now have
three methods for identifying objects:
Objects are identical if they occupy the same memory location in the JVM.
This can be checked by using the == operator. This concept is known as
object identity.
Objects are equal if they have the same value, as defined by the
equals(Object o) method. Classes that don’t explicitly override this
method inherit the implementation defined by java.lang.Object, which
compares object identity. This concept is known as equality.
Objects stored in a relational database are identical if they represent the
same row or, equivalently, if they share the same table and primary key
value. This concept is known as database identity.
We now need to look at how database identity relates to object identity in Hibernate, and how database identity is expressed in the mapping metadata.
Handling database identity
Hibernate exposes database identity to the application in two ways:
The value of the identifier property of a persistent instance
The value returned by Session.getIdentifier(Object entity)
Mapping entities with identity
Adding an identifier property to entities
The identifier property is special—its value is the primary key value of the database row represented by the persistent instance. We don’t usually show the identifier property in the domain model diagrams. In the examples, the identifier
property is always named id. If myCategory is an instance of Category, calling
myCategory.getId() returns the primary key value of the row represented by
myCategory in the database.
Let’s implement an identifier property for the Category class:
public class Category {
private Long id;
public Long getId() {
private void setId(Long id) { = id;
Should you make the accessor methods for the identifier property private scope or
public? Well, database identifiers are often used by the application as a convenient
handle to a particular instance, even outside the persistence layer. For example,
it’s common for web applications to display the results of a search screen to the
user as a list of summary information. When the user selects a particular element,
the application may need to retrieve the selected object, and it’s common to use a
lookup by identifier for this purpose—you’ve probably already used identifiers
this way, even in applications that rely on JDBC. It’s usually appropriate to fully
expose the database identity with a public identifier property accessor.
On the other hand, you usually declare the setId() method private and let
Hibernate generate and set the identifier value. Or, you map it with direct field
access and implement only a getter method. (The exception to this rule is
classes with natural keys, where the value of the identifier is assigned by the
application before the object is made persistent instead of being generated by
Hibernate. We discuss natural keys in chapter 8.) Hibernate doesn’t allow you to
change the identifier value of a persistent instance after it’s first assigned. A primary key value never changes—otherwise the attribute wouldn’t be a suitable
primary key candidate!
Mapping persistent classes
The Java type of the identifier property, java.lang.Long in the previous example, depends on the primary key type of the CATEGORY table and how it’s mapped
in Hibernate metadata.
Mapping the identifier property
A regular (noncomposite) identifier property is mapped in Hibernate XML files
with the <id> element:
<class name="Category" table="CATEGORY">
<id name="id" column="CATEGORY_ID" type="long">
<generator class="native"/>
The identifier property is mapped to the primary key column CATEGORY_ID of the
table CATEGORY. The Hibernate type for this property is long, which maps to a
BIGINT column type in most databases and which has also been chosen to match
the type of the identity value produced by the native identifier generator. (We
discuss identifier generation strategies in the next section.)
For a JPA entity class, you use annotations in the Java source code to map the
identifier property:
public class Category {
private Long id;
@GeneratedValue(strategy = GenerationType.AUTO)
@Column(name = "CATEGORY_ID")
public Long getId() {
private void setId(Long id) { = id;
The @Id annotation on the getter method marks it as the identifier property, and
@GeneratedValue with the GenerationType.AUTO option translates into a native
identifier generation strategy, like the native option in XML Hibernate mappings. Note that if you don’t define a strategy, the default is also Generation-
Mapping entities with identity
Type.AUTO, so you could have omitted this attribute altogether. You also specify a
database column—otherwise Hibernate would use the property name. The mapping type is implied by the Java property type, java.lang.Long.
Of course, you can also use direct field access for all properties, including the
database identifier:
public class Category {
@Id @GeneratedValue
@Column(name = "CATEGORY_ID")
private Long id;
public Long getId() {
Mapping annotations are placed on the field declaration when direct field access
is enabled, as defined by the standard.
Whether field or property access is enabled for an entity depends on the position of the mandatory @Id annotation. In the preceding example, it’s present on a
field, so all attributes of the class are accessed by Hibernate through fields. The
example before that, annotated on the getId() method, enables access to all
attributes through getter and setter methods.
Alternatively, you can use JPA XML descriptors to create your identifier
<entity class="auction.model.Category" access="FIELD">
<table name="CATEGORY"/>
<id name="id">
<generated-value strategy="AUTO"/>
In addition to operations for testing Java object identity, (a == b), and object
equality, ( a.equals(b) ), you may now use a.getId().equals( b.getId() )
to test database identity. What do these notions have in common? In what situations do they all return true? The time when all are true is called the scope of
Mapping persistent classes
guaranteed object identity; and we’ll come back to this subject in chapter 9, section 9.2, “Object identity and equality.”
Using database identifiers in Hibernate is easy and straightforward. Choosing a
good primary key (and key-generation strategy) may be more difficult. We discuss
this issue next.
Database primary keys
Hibernate needs to know your preferred strategy for generating primary keys.
First, though, let’s define primary key.
Selecting a primary key
The candidate key is a column or set of columns that could be used to identify a
particular row in a table. To become a primary key, a candidate key must satisfy
the following properties:
Its value (for any column of the candidate key) is never null.
Each row has a unique value.
The value of a particular row never changes.
If a table has only one identifying attribute, it’s, by definition, the primary key.
However, several columns or combinations of columns may satisfy these properties for a particular table; you choose between candidate keys to decide the best
primary key for the table. Candidate keys not chosen as the primary key should be
declared as unique keys in the database.
Many legacy SQL data models use natural primary keys. A natural key is a key
with business meaning: an attribute or combination of attributes that is unique by
virtue of its business semantics. Examples of natural keys are the U.S. Social Security Number and Australian Tax File Number. Distinguishing natural keys is simple: If a candidate key attribute has meaning outside the database context, it’s a
natural key, whether or not it’s automatically generated. Think about the application users: If they refer to a key attribute when talking about and working with the
application, it’s a natural key.
Experience has shown that natural keys almost always cause problems in the
long run. A good primary key must be unique, constant, and required (never null
or unknown). Few entity attributes satisfy these requirements, and some that do
can’t be efficiently indexed by SQL databases (although this is an implementation
detail and shouldn’t be the primary motivation for or against a particular key). In
Mapping entities with identity
addition, you should make certain that a candidate key definition can never
change throughout the lifetime of the database before making it a primary key.
Changing the value (or even definition) of a primary key, and all foreign keys that
refer to it, is a frustrating task. Furthermore, natural candidate keys can often be
found only by combining several columns in a composite natural key. These composite keys, although certainly appropriate for some relations (like a link table in
a many-to-many relationship), usually make maintenance, ad-hoc queries, and
schema evolution much more difficult.
For these reasons, we strongly recommend that you consider synthetic identifiers, also called surrogate keys. Surrogate keys have no business meaning—they’re
unique values generated by the database or application. Application users ideally
don’t see or refer to these key values; they’re part of the system internals. Introducing a surrogate key column is also appropriate in a common situation: If there
are no candidate keys, a table is by definition not a relation as defined by the relational model—it permits duplicate rows—and so you have to add a surrogate key
column. There are a number of well-known approaches to generating surrogate
key values.
Selecting a key generator
Hibernate has several built-in identifier-generation strategies. We list the most useful options in table 4.1.
Table 4.1
Hibernate’s built-in identifier-generator modules
The native identity generator picks other
identity generators like identity,
sequence, or hilo, depending on the capabilities of the underlying database. Use this
generator to keep your mapping metadata portable to different database management systems.
This generator supports identity columns in
DB2, MySQL, MS SQL Server, Sybase, and
HypersonicSQL. The returned identifier is of
type long, short, or int.
Mapping persistent classes
Table 4.1
Hibernate’s built-in identifier-generator modules (continued)
This generator creates a sequence in DB2,
PostgreSQL, Oracle, SAP DB, or Mckoi; or a
generator in InterBase is used. The returned
identifier is of type long, short, or int.
Use the sequence option to define a catalog
name for the sequence (hibernate_
sequence is the default) and parameters
if you need additional settings creating a
sequence to be added to the DDL.
(Not available)
At Hibernate startup, this generator reads the
maximum (numeric) primary key column value
of the table and increments the value by one
each time a new row is inserted. The generated identifier is of type long, short, or
int. This generator is especially efficient if
the single-server Hibernate application has
exclusive access to the database but should
not be used in any other scenario.
(Not available)
table, column,
A high/low algorithm is an efficient way to generate identifiers of type long, given a table
and column (by default
hibernate_unique_key and next,
respectively) as a source of high values. The
high/low algorithm generates identifiers that
are unique only for a particular database. High
values are retrieved from a global source and
are made unique by adding a local low value.
This algorithm avoids congestion when a single source for identifier values has to be
accessed for many inserts. See “Data Modeling 101” (Ambler, 2002) for more information
about the high/low approach to unique identifiers. This generator needs to use a separate
database connection from time to time to
retrieve high values, so it isn’t supported with
user-supplied database connections. In other
words, don’t use it with
nnection). The max_lo option defines
how many low values are added until a new
high value is fetched. Only settings greater
than 1 are sensible; the default is 32767
Mapping entities with identity
Table 4.1
Hibernate’s built-in identifier-generator modules (continued)
(Not available)
This generator works like the regular hilo
generator, except it uses a named database
sequence to generate high values.
table, catalog,
Much like Hibernate’s hilo strategy, TABLE
relies on a database table that holds the lastgenerated integer primary key value, and each
generator is mapped to one row in this table.
Each row has two columns: pkColumnName
and valueColumnName. The pkColumnValue assigns each row to a particular generator, and the value column holds the last
retrieved primary key. The persistence provider
allocates up to allocationSize integers
in each turn.
(Not available)
This generator is a 128-bit UUID (an algorithm
that generates identifiers of type string,
unique within a network). The IP address is
used in combination with a unique timestamp.
The UUID is encoded as a string of hexadecimal digits of length 32, with an optional
separator string between each component
of the UUID representation. Use this generator
strategy only if you need globally unique identifiers, such as when you have to merge two
databases regularly.
(Not available)
This generator provides a database-generated
globally unique identifier string on MySQL and
SQL Server.
(Not available)
This generator retrieves a primary key
assigned by a database trigger by selecting
the row by some unique key and retrieving the
primary key value. An additional unique candidate key column is required for this strategy,
and the key option has to be set to the name
of the unique key column.
Mapping persistent classes
Some of the built-in identifier generators can be configured with options. In a
native Hibernate XML mapping, you define options as pairs of keys and values:
<id column="MY_ID">
<generator class="sequence">
<parameter name="sequence">MY_SEQUENCE</parameter>
<parameter name="parameters">
You can use Hibernate identifier generators with annotations, even if no direct
annotation is available:
name = "hibernate-uuid",
strategy = "uuid"
class name MyEntity {
@GeneratedValue(generator = "hibernate-uuid")
@Column(name = "MY_ID")
String id;
The @GenericGenerator Hibernate extension can be used to give a Hibernate
identifier generator a name, in this case hibernate-uuid. This name is then referenced by the standardized generator attribute.
This declaration of a generator and its assignment by name also must be
applied for sequence- or table-based identifier generation with annotations. Imagine that you want to use a customized sequence generator in all your entity classes.
Because this identifier generator has to be global, it’s declared in orm.xml:
<sequence-generator name="mySequenceGenerator"
This declares that a database sequence named MY_SEQUENCE with an initial value
of 123 can be used as a source for database identifier generation, and that the persistence engine should obtain 20 values every time it needs identifiers. (Note,
though, that Hibernate Annotations, at the time of writing, ignores the initialValue setting.)
To apply this identifier generator for a particular entity, use its name:
Class mapping options
class name MyEntity {
@Id @GeneratedValue(generator = "mySequenceGenerator")
String id;
If you declared another generator with the same name at the entity level, before
the class keyword, it would override the global identifier generator. The same
approach can be used to declare and apply a @TableGenerator.
You aren’t limited to the built-in strategies; you can create your own identifier
generator by implementing Hibernate’s IdentifierGenerator interface. As
always, it’s a good strategy to look at the Hibernate source code of the existing
identifier generators for inspiration.
It’s even possible to mix identifier generators for persistent classes in a single
domain model, but for nonlegacy data we recommend using the same identifier
generation strategy for all entities.
For legacy data and application-assigned identifiers, the picture is more complicated. In this case, we’re often stuck with natural keys and especially composite
keys. A composite key is a natural key that is composed of multiple table columns.
Because composite identifiers can be a bit more difficult to work with and often
only appear on legacy schemas, we only discuss them in the context of chapter 8,
section 8.1, “Integrating legacy databases.”
We assume from now on that you’ve added identifier properties to the entity
classes of your domain model, and that after you completed the basic mapping of
each entity and its identifier property, you continued to map value-typed properties of the entities. However, some special options can simplify or enhance your
class mappings.
Class mapping options
If you check the <hibernate-mapping> and <class> elements in the DTD (or the
reference documentation), you’ll find a few options we haven’t discussed so far:
Dynamic generation of CRUD SQL statements
Entity mutability control
Naming of entities for querying
Mapping package names
Quoting keywords and reserved database identifiers
Implementing database naming conventions
Mapping persistent classes
Dynamic SQL generation
By default, Hibernate creates SQL statements for each persistent class on startup.
These statements are simple create, read, update, and delete operations for reading a single row, deleting a row, and so on.
How can Hibernate create an UPDATE statement on startup? After all, the columns to be updated aren’t known at this time. The answer is that the generated
SQL statement updates all columns, and if the value of a particular column isn’t
modified, the statement sets it to its old value.
In some situations, such as a legacy table with hundreds of columns where the
SQL statements will be large for even the simplest operations (say, only one column needs updating), you have to turn off this startup SQL generation and switch
to dynamic statements generated at runtime. An extremely large number of entities can also impact startup time, because Hibernate has to generate all SQL statements for CRUD upfront. Memory consumption for this query statement cache
will also be high if a dozen statements must be cached for thousands of entities
(this isn’t an issue, usually).
Two attributes for disabling CRUD SQL generation on startup are available on
the <class> mapping element:
<class name="Item"
The dynamic-insert attribute tells Hibernate whether to include null property
values in an SQL INSERT, and the dynamic-update attribute tells Hibernate
whether to include unmodified properties in the SQL UPDATE.
If you’re using JDK 5.0 annotation mappings, you need a native Hibernate
annotation to enable dynamic SQL generation:
dynamicInsert = true, dynamicUpdate = true
public class Item { ...
The second @Entity annotation from the Hibernate package extends the JPA
annotation with additional options, including dynamicInsert and dynamicUpdate.
Sometimes you can avoid generating any UPDATE statement, if the persistent
class is mapped immutable.
Class mapping options
Making an entity immutable
Instances of a particular class may be immutable. For example, in CaveatEmptor,
a Bid made for an item is immutable. Hence, no UPDATE statement ever needs to
be executed on the BID table. Hibernate can also make a few other optimizations,
such as avoiding dirty checking, if you map an immutable class with the mutable
attribute set to false:
<hibernate-mapping default-access="field">
<class name="Bid" mutable="false">
A POJO is immutable if no public setter methods for any properties of the class are
exposed—all values are set in the constructor. Instead of private setter methods,
you often prefer direct field access by Hibernate for immutable persistent classes,
so you don’t have to write useless accessor methods. You can map an immutable
entity using annotations:
@org.hibernate.annotations.Entity(mutable = false)
public class Bid { ...
Again, the native Hibernate @Entity annotation extends the JPA annotation with
additional options. We have also shown the Hibernate extension annotation
@AccessType here—this is an annotation you’ll rarely use. As explained earlier,
the default access strategy for a particular entity class is implicit from the position
of the mandatory @Id property. However, you can use @AccessType to force a
more fine-grained strategy; it can be placed on class declarations (as in the preceding example) or even on particular fields or accessor methods.
Let’s have a quick look at another issue, the naming of entities for queries.
Naming entities for querying
By default, all class names are automatically “imported” into the namespace of the
Hibernate query language, HQL. In other words, you can use the short class
names without a package prefix in HQL, which is convenient. However, this autoimport can be turned off if two classes with the same name exist for a given SessionFactory, maybe in different packages of the domain model.
If such a conflict exists, and you don’t change the default settings, Hibernate
won’t know which class you’re referring to in HQL. You can turn off auto-import
Mapping persistent classes
of names into the HQL namespace for particular mapping files with the autoimport="false" setting on the <hibernate-mapping> root element.
Entity names can also be imported explicitly into the HQL namespace. You can
even import classes and interfaces that aren’t explicitly mapped, so a short name
can be used in polymorphic HQL queries:
<import class="auction.model.Auditable" rename="IAuditable"/>
You can now use an HQL query such as from IAuditable to retrieve all persistent
instances of classes that implement the auction.model.Auditable interface.
(Don’t worry if you don’t know whether this feature is relevant to you at this
point; we’ll get back to queries later in the book.) Note that the <import> element, like all other immediate child elements of <hibernate-mapping>, is an
application-wide declaration, so you don’t have to (and can’t) duplicate this in
other mapping files.
With annotations, you can give an entity an explicit name, if the short name
would result in a collision in the JPA QL or HQL namespace:
public class Item { ... }
Now let’s consider another aspect of naming: the declaration of packages.
Declaring a package name
All the persistent classes of the CaveatEmptor application are declared in the Java
package auction.model. However, you don’t want to repeat the full package
name whenever this or any other class is named in an association, subclass, or
component mapping. Instead, specify a package attribute:
<hibernate-mapping package="auction.model">
<classname="Item" table="ITEM">
Now all unqualified class names that appear in this mapping document will be
prefixed with the declared package name. We assume this setting in all mapping
examples in this book and use unqualified names for CaveatEmptor model classes.
Names of classes and tables must be selected carefully. However, a name you’ve
chosen may be reserved by the SQL database system, so the name has to be quoted.
Class mapping options
Quoting SQL identifiers
By default, Hibernate doesn’t quote table and column names in the generated
SQL. This makes the SQL slightly more readable, and it also allows you to take
advantage of the fact that most SQL databases are case insensitive when comparing unquoted identifiers. From time to time, especially in legacy databases, you
encounter identifiers with strange characters or whitespace, or you wish to force
case sensitivity. Or, if you rely on Hibernate’s defaults, a class or property name in
Java may be automatically translated to a table or column name that isn’t allowed
in your database management system. For example, the User class is mapped to a
USER table, which is usually a reserved keyword in SQL databases. Hibernate
doesn’t know the SQL keywords of any DBMS product, so the database system
throws an exception at startup or runtime.
If you quote a table or column name with backticks in the mapping document,
Hibernate always quotes this identifier in the generated SQL. The following property declaration forces Hibernate to generate SQL with the quoted column name
"DESCRIPTION". Hibernate also knows that Microsoft SQL Server needs the variation [DESCRIPTION] and that MySQL requires `DESCRIPTION`.
<property name="description"
There is no way, apart from quoting all table and column names in backticks, to
force Hibernate to use quoted identifiers everywhere. You should consider renaming tables or columns with reserved keyword names whenever possible. Quoting
with backticks works with annotation mappings, but it’s an implementation detail
of Hibernate and not part of the JPA specification.
Implementing naming conventions
We often encounter organizations with strict conventions for database table and
column names. Hibernate provides a feature that allows you to enforce naming
standards automatically.
Suppose that all table names in CaveatEmptor should follow the pattern
CE_<table name>. One solution is to manually specify a table attribute on all
<class> and collection elements in the mapping files. However, this approach is
time-consuming and easily forgotten. Instead, you can implement Hibernate’s
NamingStrategy interface, as in listing 4.1.
Mapping persistent classes
Listing 4.1 NamingStrategy implementation
public class CENamingStrategy extends ImprovedNamingStrategy {
public String classToTableName(String className) {
return StringHelper.unqualify(className);
public String propertyToColumnName(String propertyName) {
return propertyName;
public String tableName(String tableName) {
return "CE_" + tableName;
public String columnName(String columnName) {
return columnName;
public String propertyToTableName(String className,
String propertyName) {
return "CE_"
+ classToTableName(className)
+ '_'
+ propertyToColumnName(propertyName);
You extend the ImprovedNamingStrategy, which provides default implementations for all methods of NamingStrategy you don’t want to implement from
scratch (look at the API documentation and source). The classToTableName()
method is called only if a <class> mapping doesn’t specify an explicit table
name. The propertyToColumnName() method is called if a property has no
explicit column name. The tableName() and columnName() methods are called
when an explicit name is declared.
If you enable this CENamingStrategy, the class mapping declaration
<class name="BankAccount">
results in CE_BANKACCOUNT as the name of the table.
However, if a table name is specified, like this,
<class name="BankAccount" table="BANK_ACCOUNT">
then CE_BANK_ACCOUNT is the name of the table. In this case, BANK_ACCOUNT is
passed to the tableName() method.
Fine-grained models and mappings
The best feature of the NamingStrategy interface is the potential for dynamic
behavior. To activate a specific naming strategy, you can pass an instance to the
Hibernate Configuration at startup:
Configuration cfg = new Configuration();
cfg.setNamingStrategy( new CENamingStrategy() );
SessionFactory sessionFactory sf =
This allows you to have multiple SessionFactory instances based on the same
mapping documents, each using a different NamingStrategy. This is extremely
useful in a multiclient installation, where unique table names (but the same data
model) are required for each client. However, a better way to handle this kind of
requirement is to use an SQL schema (a kind of namespace), as already discussed
in chapter 3, section 3.3.4, “Handling global metadata.”
You can set a naming strategy implementation in Java Persistence in your persistence.xml file with the hibernate.ejb.naming_strategy option.
Now that we have covered the concepts and most important mappings for entities, let’s map value types.
Fine-grained models and mappings
After spending the first half of this chapter almost exclusively on entities and
the respective basic persistent class-mapping options, we’ll now focus on value
types in their various forms. Two different kinds come to mind immediately:
value-typed classes that came with the JDK, such as String or primitives, and
value-typed classes defined by the application developer, such as Address and
First, you map persistent class properties that use JDK types and learn the basic
mapping elements and attributes. Then you attack custom value-typed classes and
map them as embeddable components.
Mapping basic properties
If you map a persistent class, no matter whether it’s an entity or a value type, all
persistent properties have to be mapped explicitly in the XML mapping file. On
the other hand, if a class is mapped with annotations, all of its properties are considered persistent by default. You can mark properties with the @javax.persistence.Transient annotation to exclude them, or use the transient Java
keyword (which usually only excludes fields for Java serialization).
In a JPA XML descriptor, you can exclude a particular field or property:
Mapping persistent classes
<entity class="auction.model.User" access="FIELD">
<transient name="age"/>
A typical Hibernate property mapping defines a POJO’s property name, a database column name, and the name of a Hibernate type, and it’s often possible to
omit the type. So, if description is a property of (Java) type java.lang.String,
Hibernate uses the Hibernate type string by default (we come back to the
Hibernate type system in the next chapter).
Hibernate uses reflection to determine the Java type of the property. Thus, the
following mappings are equivalent:
<property name="description" column="DESCRIPTION" type="string"/>
<property name="description" column="DESCRIPTION"/>
It’s even possible to omit the column name if it’s the same as the property name,
ignoring case. (This is one of the sensible defaults we mentioned earlier.)
For some more unusual cases, which you’ll see more about later, you may need
to use a <column> element instead of the column attribute in your XML mapping.
The <column> element provides more flexibility: It has more optional attributes
and may appear more than once. (A single property can map to more than one
column, a technique we discuss in the next chapter.) The following two property
mappings are equivalent:
<property name="description" column="DESCRIPTION" type="string"/>
<property name="description" type="string">
<column name="DESCRIPTION"/>
The <property> element (and especially the <column> element) also defines certain attributes that apply mainly to automatic database schema generation. If you
aren’t using the hbm2ddl tool (see chapter 2, section 2.1.4, “Running and testing
the application”) to generate the database schema, you may safely omit these.
However, it’s preferable to include at least the not-null attribute, because Hibernate can then report illegal null property values without going to the database:
<property name="initialPrice" column="INITIAL_PRICE" not-null="true"/>
JPA is based on a configuration by exception model, so you could rely on defaults.
If a property of a persistent class isn’t annotated, the following rules apply:
Fine-grained models and mappings
If the property is of a JDK type, it’s automatically persistent. In other words,
it’s handled like <property name="propertyName"/> in a Hibernate XML
mapping file.
Otherwise, if the class of the property is annotated as @Embeddable, it’s
mapped as a component of the owning class. We’ll discuss embedding of
components later in this chapter.
Otherwise, if the type of the property is Serializable, its value is stored in
its serialized form. This usually isn’t what you want, and you should always
map Java classes instead of storing a heap of bytes in the database. Imagine
maintaining a database with this binary information when the application is
gone in a few years.
If you don’t want to rely on these defaults, apply the @Basic annotation on a particular property. The @Column annotation is the equivalent of the XML <column>
element. Here is an example of how you declare a property’s value as required:
@Basic(optional = false)
@Column(nullable = false)
public BigDecimal getInitialPrice { return initialPrice; }
The @Basic annotation marks the property as not optional on the Java object
level. The second setting, nullable = false on the column mapping, is only
responsible for the generation of a NOT NULL database constraint. The Hibernate
JPA implementation treats both options the same way in any case, so you may as
well use only one of the annotations for this purpose.
In a JPA XML descriptor, this mapping looks the same:
<entity class="auction.model.Item" access="PROPERTY">
<basic name="initialPrice" optional="false">
<column nullable="false"/>
Quite a few options in Hibernate metadata are available to declare schema constraints, such as NOT NULL on a column. Except for simple nullability, however,
they’re only used to produce DDL when Hibernate exports a database schema
from mapping metadata. We’ll discuss customization of SQL, including DDL, in
chapter 8, section 8.3, “Improving schema DDL.” On the other hand, the Hibernate Annotations package includes a more advanced and sophisticated data validation framework, which you can use not only to define database schema
Mapping persistent classes
constraints in DDL, but also for data validation at runtime. We’ll discuss it in
chapter 17.
Are annotations for properties always on the accessor methods?
Customizing property access
Properties of a class are accessed by the persistence engine either directly
(through fields) or indirectly (through getter and setter property accessor methods). In XML mapping files, you control the default access strategy for a class with
the default-access="field|property|noop|custom.Class" attribute of the
hibernate-mapping root element. An annotated entity inherits the default from
the position of the mandatory @Id annotation. For example, if @Id has been
declared on a field, not a getter method, all other property mapping annotations,
like the name of the column for the item’s description property, are also
declared on fields:
@Column(name = "ITEM_DESCR")
private String description;
public String getDescription() { return description; }
This is the default behavior as defined by the JPA specification. However, Hibernate allows flexible customization of the access strategy with the @org.hibernate.annotations.AccessType(<strategy>) annotation:
If AccessType is set on the class/entity level, all attributes of the class are
accessed according to the selected strategy. Attribute-level annotations are
expected on either fields or getter methods, depending on the strategy.
This setting overrides any defaults from the position of the standard @Id
If an entity defaults or is explicitly set for field access, the AccessType("property") annotation on a field switches this particular attribute to
runtime access through property getter/setter methods. The position of the
AccessType annotation is still the field.
If an entity defaults or is explicitly set for property access, the
AccessType("field") annotation on a getter method switches this particular attribute to runtime access through a field of the same name. The position of the AccessType annotation is still the getter method.
Any @Embedded class inherits the default or explicitly declared access strategy of the owning root entity class.
Any @MappedSuperclass properties are accessed with the default or explicitly declared access strategy of the mapped entity class.
Fine-grained models and mappings
You can also control access strategies on the property level in Hibernate XML
mappings with the access attribute:
<property name="description"
Or, you can set the access strategy for all class mappings inside a root <hibernatemapping> element with the default-access attribute.
Another strategy besides field and property access that can be useful is noop. It
maps a property that doesn’t exist in the Java persistent class. This sounds strange,
but it lets you refer to this “virtual” property in HQL queries (in other words, to
use the database column in HQL queries only).
If none of the built-in access strategies are appropriate, you can define your
own customized property-access strategy by implementing the interface Set the (fully qualified) class
name on the access mapping attribute or @AccessType annotation. Have a look
at the Hibernate source code for inspiration; it’s a straightforward exercise.
Some properties don’t map to a column at all. In particular, a derived property
takes its value from an SQL expression.
Using derived properties
The value of a derived property is calculated at runtime by evaluating an expression that you define using the formula attribute. For example, you may map a
totalIncludingTax property to an SQL expression:
<property name="totalIncludingTax"
formula="TOTAL + TAX_RATE * TOTAL"
The given SQL formula is evaluated every time the entity is retrieved from the
database (and not at any other time, so the result may be outdated if other
properties are modified). The property doesn’t have a column attribute (or subelement) and never appears in an SQL INSERT or UPDATE, only in SELECTs. Formulas may refer to columns of the database table, they can call SQL functions,
and they may even include SQL subselects. The SQL expression is passed to the
underlying database as is; this is a good chance to bind your mapping file to a
particular database product, if you aren’t careful and rely on vendor-specific
operators or keywords.
Formulas are also available with a Hibernate annotation:
@org.hibernate.annotations.Formula("TOTAL + TAX_RATE * TOTAL")
public BigDecimal getTotalIncludingTax() {
Mapping persistent classes
return totalIncludingTax;
The following example uses a correlated subselect to calculate the average
amount of all bids for an item:
"( select AVG(b.AMOUNT) from
BID b where b.ITEM_ID = ITEM_ID )"/>
Notice that unqualified column names refer to columns of the table of the class to
which the derived property belongs.
Another special kind of property relies on database-generated values.
Generated and default property values
Imagine a particular property of a class has its value generated by the database,
usually when the entity row is inserted for the first time. Typical database-generated values are timestamp of creation, a default price for an item, and a trigger
that runs for every modification.
Typically, Hibernate applications need to refresh objects that contain any
properties for which the database generates values. Marking properties as generated, however, lets the application delegate this responsibility to Hibernate. Essentially, whenever Hibernate issues an SQL INSERT or UPDATE for an entity that has
defined generated properties, it immediately does a SELECT afterwards to retrieve
the generated values. Use the generated switch on a property mapping to enable
this automatic refresh:
<property name="lastModified"
Properties marked as database-generated must additionally be noninsertable and
nonupdateable, which you control with the insert and update attributes. If both
are set to false, the property’s columns never appear in the INSERT or UPDATE
statements—the property value is read-only. Also, you usually don’t add a public
setter method in your class for an immutable property (and switch to field access).
With annotations, declare immutability (and automatic refresh) with the
@Generated Hibernate annotation:
Fine-grained models and mappings
@Column(updatable = false, insertable = false)
private Date lastModified;
The settings available are GenerationTime.ALWAYS and GenerationTime.INSERT,
and the equivalent options in XML mappings are generated="always" and generated="insert".
A special case of database-generated property values are default values. For
example, you may want to implement a rule that every auction item costs at least
$1. First, you’d add this to your database catalog as the default value for the
create table ITEM (
INITIAL_PRICE number(10,2) default '1',
If you use Hibernate’s schema export tool, hbm2ddl, you can enable this output
by adding a default attribute to the property mapping:
<class name="Item" table="ITEM"
dynamic-insert="true" dynamic-update="true">
<property name="initialPrice" type="big_decimal">
<column name="INITIAL_PRICE"
Note that you also have to enable dynamic insertion and update statement generation, so that the column with the default value isn’t included in every statement
if its value is null (otherwise a NULL would be inserted instead of the default
value). Furthermore, an instance of Item that has been made persistent but not
yet flushed to the database and not refreshed again won’t have the default value
set on the object property. In other words, you need to execute an explicit flush:
Item newItem = new Item(...);;
// is null
// Trigger an INSERT
// Hibernate does a SELECT automatically
// is $1
Mapping persistent classes
Because you set generated="insert", Hibernate knows that an immediate additional SELECT is required to read the database-generated property value.
You can map default column values with annotations as part of the DDL definition for a column:
@Column(name = "INITIAL_PRICE",
columnDefinition = "number(10,2) default '1'")
private BigDecimal initalPrice;
The columnDefinition attribute includes the complete properties for the column DDL, with datatype and all constraints. Keep in mind that an actual nonportable SQL datatype may bind your annotation mapping to a particular database
management system.
We’ll come back to the topic of constraints and DDL customization in chapter
8, section 8.3, “Improving schema DDL.”
Next, you’ll map user-defined value-typed classes. You can easily spot them in
your UML class diagrams if you search for a composition relationship between two
classes. One of them is a dependent class, a component.
Mapping components
So far, the classes of the object model have all been entity classes, each with its
own lifecycle and identity. The User class, however, has a special kind of association with the Address class, as shown in figure 4.2.
In object-modeling terms, this association is a kind of aggregation—a part-of relationship. Aggregation is a strong form of association; it has some additional
semantics with regard to the lifecycle of objects. In this case, you have an even
stronger form, composition, where the lifecycle of the part is fully dependent
upon the lifecycle of the whole.
Object modeling experts and UML designers claim that there is no difference
between this composition and other weaker styles of association when it comes to
the actual Java implementation. But in the context of ORM, there is a big difference: A composed class is often a candidate value type.
firstname : String
lastname : String
username : String
password : String
email : String
ranking : int
admin : boolean
street : String
zipcode : String
city : String
Figure 4.2
Relationships between User and
Address using composition
Fine-grained models and mappings
You map Address as a value type and User as an entity. Does this affect the
implementation of the POJO classes?
Java has no concept of composition—a class or attribute can’t be marked as a
component or composition. The only difference is the object identifier: A component has no individual identity, hence the persistent component class requires no
identifier property or identifier mapping. It’s a simple POJO:
public class Address {
private String street;
private String zipcode;
private String city;
public Address() {}
public String getStreet() { return street; }
public void setStreet(String street) { this.street = street; }
public String getZipcode() { return zipcode; }
public void setZipcode(String zipcode) {
this.zipcode = zipcode; }
public String getCity() { return city; }
public void setCity(String city) { = city; }
The composition between User and Address is a metadata-level notion; you only
have to tell Hibernate that the Address is a value type in the mapping document
or with annotations.
Component mapping in XML
Hibernate uses the term component for a user-defined class that is persisted to
the same table as the owning entity, an example of which is shown in listing 4.2.
(The use of the word component here has nothing to do with the architecturelevel concept, as in software component.)
Listing 4.2 Mapping of the User class with a component Address
<class name="User" table="USER">
<id name="id" column="USER_ID" type="long">
<generator class="native"/>
<property name="loginName" column="LOGIN" type="string"/>
<component name="homeAddress" class="Address">
<property name="street" type="string"
column="HOME_STREET" not-null="true"/>
<property name="city" type="string"
column="HOME_CITY" not-null="true"/>
Mapping persistent classes
<property name="zipcode" type="string"
column="HOME_ZIPCODE" not-null="true"/>
<component name="billingAddress" class="Address">
<property name="street" type="string"
column="BILLING_STREET" not-null="true"/>
<property name="city" type="string"
column="BILLING_CITY" not-null="true"/>
<property name="zipcode" type="string"
column="BILLING_ZIPCODE" not-null="true"/>
You declare the persistent attributes of Address
inside the <component> element. The property
of the User class is named homeAddress.
You reuse the same component class to map
another property of this type to the same table.
Figure 4.3 shows how the attributes of the
Address class are persisted to the same table as
the User entity.
Notice that, in this example, you model the
composition association as unidirectional. You
can’t navigate from Address to User. Hibernate
supports both unidirectional and bidirectional
compositions, but unidirectional composition
is far more common. An example of a bidirectional mapping is shown in listing 4.3.
Figure 4.3 Table attributes of User with
Address component
Listing 4.3 Adding a back-pointer to a composition
<component name="homeAddress" class="Address">
<parent name="user"/>
<property name="street" type="string"
column="HOME_STREET" not-null="true"/>
<property name="city" type="string"
column="HOME_CITY" not-null="true"/>
<property name="zipcode" type="stringshort"
column="HOME_ZIPCODE" not-null="true"/>
Fine-grained models and mappings
In listing 4.3, the <parent> element maps a property of type User to the owning
entity, which in this example is the property named user. You can then call
Address.getUser() to navigate in the other direction. This is really a simple
A Hibernate component can own other components and even associations to
other entities. This flexibility is the foundation of Hibernate’s support for finegrained object models. For example, you can create a Location class with detailed
information about the home address of an Address owner:
<component name="homeAddress" class="Address">
<parent name="user"/>
<component name="location" class="Location">
<property name="streetname" column="HOME_STREETNAME"/>
<property name="streetside" column="HOME_STREETSIDE"/>
<property name="housenumber" column="HOME_HOUSENR"/>
<property name="floor" column="HOME_FLOOR"/>
<property name="city" type="string" column="HOME_CITY"/>
<property name="zipcode" type="string" column="HOME_ZIPCODE"/>
The design of the Location class is equivalent to the Address class. You now have
three classes, one entity, and two value types, all mapped to the same table.
Now let’s map components with JPA annotations.
Annotating embedded classes
The Java Persistence specification calls components embedded classes. To map an
embedded class with annotations, you can declare a particular property in the
owning entity class as @Embedded, in this case the homeAddress of User:
@Table(name = "USERS")
public class User {
private Address homeAddress;
If you don’t declare a property as @Embedded, and it isn’t of a JDK type, Hibernate
looks into the associated class for the @Embeddable annotation. If it’s present, the
property is automatically mapped as a dependent component.
Mapping persistent classes
This is what the embeddable class looks like:
public class Address {
@Column(name = "ADDRESS_STREET", nullable = false)
private String street;
@Column(name = "ADDRESS_ZIPCODE", nullable = false)
private String zipcode;
@Column(name = "ADDRESS_CITY", nullable = false)
private String city;
You can further customize the individual property mappings in the embeddable
class, such as with the @Column annotation. The USERS table now contains, among
other entity table that contains component fields (say, an Order class that also has
an Address) uses the same column options. You can also add a back-pointer property to the Address embeddable class and map it with @org.hibernate.annotations.Parent.
Sometimes you’ll want to override the settings you made inside the
embeddable class from outside for a particular entity. For example, here is how
you can rename the columns:
@Table(name = "USERS")
public class User {
@AttributeOverrides( {
private Address homeAddress;
@Column(name="HOME_STREET") ),
@Column(name="HOME_ZIPCODE") ),
@Column(name="HOME_CITY") )
The new @Column declarations in the User class override the settings of the
embeddable class. Note that all attributes on the embedded @Column annotation
are replaced, so they’re no longer nullable = false.
In a JPA XML descriptor, a mapping of an embeddable class and a composition
looks like the following:
<embeddable class="auction.model.Address access-type="FIELD"/>
<entity class="auction.model.User" access="FIELD">
<embedded name="homeAddress">
<attribute-override name="street">
<column name="HOME_STREET"/>
<attribute-override name="zipcode">
<column name="HOME_ZIPCODE"/>
<attribute-override name="city">
<column name="HOME_CITY"/>
There are two important limitations to classes mapped as components. First,
shared references, as for all value types, aren’t possible. The component homeAddress doesn’t have its own database identity (primary key) and so can’t be
referred to by any object other than the containing instance of User.
Second, there is no elegant way to represent a null reference to an Address. In
lieu of any elegant approach, Hibernate represents a null component as null values in all mapped columns of the component. This means that if you store a component object with all null property values, Hibernate returns a null component
when the owning entity object is retrieved from the database.
You’ll find many more component mappings (even collections of them)
throughout the book.
In this chapter, you learned the essential distinction between entities and value
types and how these concepts influence the implementation of your domain
model as persistent Java classes.
Entities are the coarser-grained classes of your system. Their instances have an
independent lifecycle and their own identity, and they can be referenced by many
Mapping persistent classes
other instances. Value types, on the other hand, are dependent on a particular
entity class. An instance of a value type has a lifecycle bound by its owning entity
instance, and it can be referenced by only one entity—it has no individual identity.
We looked at Java identity, object equality, and database identity, and at what
makes good primary keys. You learned which generators for primary key values
are built into Hibernate, and how you can use and extend this identifier system.
You also learned various (mostly optional) class mapping options and, finally,
how basic properties and value-type components are mapped in XML mappings
and annotations.
For convenience, table 4.2 summarizes the differences between Hibernate and
Java Persistence related to concepts discussed in this chapter.
Table 4.2
Hibernate and JPA comparison chart for chapter 4
Hibernate Core
Java Persistence and EJB 3.0
Entity- and value-typed classes are the essential
concepts for the support of rich and fine-grained
domain models.
The JPA specification makes the same distinction,
but calls value types “embeddable classes.” However, nested embeddable classes are considered a
nonportable feature.
Hibernate supports 10 identifier generation strategies out-of-the-box.
JPA standardizes a subset of 4 identifier generators, but allows vendor extension.
Hibernate can access properties through fields,
accessor methods, or with any custom
PropertyAccessor implementation. Strategies can be mixed for a particular class.
JPA standardizes property access through fields or
access methods, and strategies can’t be mixed for
a particular class without Hibernate extension
Hibernate supports formula properties and database-generated values.
JPA doesn’t include these features, a Hibernate
extension is needed.
In the next chapter, we’ll attack inheritance and how hierarchies of entity classes
can be mapped with various strategies. We’ll also talk about the Hibernate mapping type system, the converters for value types we’ve shown in a few examples.
Inheritance and
custom types
This chapter covers
Inheritance mapping strategies
The Hibernate mapping type system
Customization of mapping types
Inheritance and custom types
We deliberately didn’t talk much about inheritance mapping so far. Mapping a
hierarchy of classes to tables can be a complex issue, and we’ll present various strategies in this chapter. You’ll learn which strategy to choose in a particular scenario.
The Hibernate type system, with all its built-in converters and transformers for
Java value-typed properties to SQL datatypes, is the second big topic we discuss in
this chapter.
Let’s start with the mapping of entity inheritance.
Mapping class inheritance
A simple strategy for mapping classes to database tables might be “one table for
every entity persistent class.” This approach sounds simple enough and, indeed,
works well until we encounter inheritance.
Inheritance is such a visible structural mismatch between the object-oriented
and relational worlds because object-oriented systems model both is a and has a
relationships. SQL-based models provide only has a relationships between entities;
SQL database management systems don’t support type inheritance—and even
when it’s available, it’s usually proprietary or incomplete.
There are four different approaches to representing an inheritance hierarchy:
Table per concrete class with implicit polymorphism—Use no explicit
inheritance mapping, and default runtime polymorphic behavior.
Table per concrete class—Discard polymorphism and inheritance relationships completely from the SQL schema.
Table per class hierarchy—Enable polymorphism by denormalizing the SQL
schema, and utilize a type discriminator column that holds type information.
Table per subclass—Represent is a (inheritance) relationships as has a (foreign key) relationships.
This section takes a top-down approach; it assumes that you’re starting with a
domain model and trying to derive a new SQL schema. However, the mapping
strategies described are just as relevant if you’re working bottom up, starting with
existing database tables. We’ll show some tricks along the way that help you dealing with nonperfect table layouts.
Table per concrete class with implicit polymorphism
Suppose we stick with the simplest approach suggested. You can use exactly one
table for each (nonabstract) class. All properties of a class, including inherited
properties, can be mapped to columns of this table, as shown in figure 5.1.
Mapping class inheritance
Figure 5.1
Mapping all concrete classes to an independent table
You don’t have to do anything special in Hibernate to enable polymorphic behavior. The mapping for CreditCard and BankAccount is straightforward, each in its
own entity <class> element, as we have done already for classes without a superclass (or persistent interfaces). Hibernate still knows about the superclass (or any
interface) because it scans the persistent classes on startup.
The main problem with this approach is that it doesn’t support polymorphic
associations very well. In the database, associations are usually represented as
foreign key relationships. In figure 5.1, if the subclasses are all mapped to different tables, a polymorphic association to their superclass (abstract BillingDetails in this example) can’t be represented as a simple foreign key
relationship. This would be problematic in our domain model, because BillingDetails is associated with User; both subclass tables would need a foreign
key reference to the USERS table. Or, if User had a many-to-one relationship with
BillingDetails, the USERS table would need a single foreign key column,
which would have to refer both concrete subclass tables. This isn’t possible with
regular foreign key constraints.
Polymorphic queries (queries that return objects of all classes that match the
interface of the queried class) are also problematic. A query against the superclass
must be executed as several SQL SELECTs, one for each concrete subclass. For a
query against the BillingDetails class Hibernate uses the following SQL:
Notice that a separate query is needed for each concrete subclass. On the other
hand, queries against the concrete classes are trivial and perform well—only one
of the statements is needed.
Inheritance and custom types
(Also note that here, and in other places in this book, we show SQL that is conceptually identical to the SQL executed by Hibernate. The actual SQL may look
superficially different.)
A further conceptual problem with this mapping strategy is that several different columns, of different tables, share exactly the same semantics. This makes
schema evolution more complex. For example, a change to a superclass property
results in changes to multiple columns. It also makes it much more difficult to
implement database integrity constraints that apply to all subclasses.
We recommend this approach (only) for the top level of your class hierarchy,
where polymorphism isn’t usually required, and when modification of the superclass in the future is unlikely.
Also, the Java Persistence interfaces don’t support full polymorphic queries;
only mapped entities (@Entity) can be officially part of a Java Persistence query
(note that the Hibernate query interfaces are polymorphic, even if you map with
If you’re relying on this implicit polymorphism, you map concrete classes with
@Entity, as usual. However, you also have to duplicate the properties of the superclass to map them to all concrete class tables. By default, properties of the superclass are ignored and not persistent! You need to annotate the superclass to
enable embedding of its properties in the concrete subclass tables:
public abstract class BillingDetails {
@Column(name = "OWNER", nullable = false)
private String owner;
Now map the concrete subclasses:
@AttributeOverride(name = "owner", column =
@Column(name = "CC_OWNER", nullable = false)
public class CreditCard extends BillingDetails {
@Id @GeneratedValue
@Column(name = "CREDIT_CARD_ID")
private Long id = null;
@Column(name = "NUMBER", nullable = false)
private String number;
Mapping class inheritance
You can override column mappings from the superclass in a subclass with the
@AttributeOverride annotation. You rename the OWNER column to CC_OWNER in
the CREDIT_CARD table. The database identifier can also be declared in the superclass, with a shared column name and generator strategy for all subclasses.
Let’s repeat the same mapping in a JPA XML descriptor:
<mapped-superclass class="auction.model.BillingDetails"
<entity class="auction.model.CreditCard" access="FIELD">
<attribute-override name="owner">
<column name="CC_OWNER" nullable="false"/>
A component is a value type; hence, the normal entity inheritance rules
presented in this chapter don’t apply. However, you can map a subclass
as a component by including all the properties of the superclass (or
interface) in your component mapping. With annotations, you use the
@MappedSuperclass annotation on the superclass of the embeddable
component you’re mapping just like you would for an entity. Note that
this feature is available only in Hibernate Annotations and isn’t standardized or portable.
With the help of the SQL UNION operation, you can eliminate most of the issues
with polymorphic queries and associations, which are present with this mapping strategy.
Table per concrete class with unions
First, let’s consider a union subclass mapping with BillingDetails as an abstract
class (or interface), as in the previous section. In this situation, we again have two
tables and duplicate superclass columns in both: CREDIT_CARD and BANK_ACCOUNT.
What’s new is a special Hibernate mapping that includes the superclass, as you
can see in listing 5.1.
Inheritance and custom types
Listing 5.1 Using the <union-subclass> inheritance strategy
<generator class="native"/>
name="CreditCard" table="CREDIT_CARD">
<property name="number" column=”NUMBER”/>
<property name="expMonth" column="EXP_MONTH"/>
<property name="expYear" column="EXP_YEAR"/>
name="BankAccount" table="BANK_ACCOUNT">
An abstract superclass or an interface has to be declared as abstract="true"; otherwise a separate table for instances of the superclass is needed.
The database identifier mapping is shared for all concrete classes in the hierarchy.
primary key column. The database identifier property now has to be shared for all
subclasses; hence you have to move it into BillingDetails and remove it from
CreditCard and BankAccount.
Properties of the superclass (or interface) are declared here and inherited by all
concrete class mappings. This avoids duplication of the same mapping.
A concrete subclass is mapped to a table; the table inherits the superclass (or
interface) identifier and other property mappings.
Mapping class inheritance
The first advantage you may notice with this strategy is the shared declaration of
superclass (or interface) properties. No longer do you have to duplicate these
mappings for all concrete classes—Hibernate takes care of this. Keep in mind that
the SQL schema still isn’t aware of the inheritance; effectively, we’ve mapped two
unrelated tables to a more expressive class structure. Except for the different primary key column name, the tables look exactly alike, as shown in figure 5.1.
In JPA annotations, this strategy is known as TABLE_PER_CLASS:
@Inheritance(strategy = InheritanceType.TABLE_PER_CLASS)
public abstract class BillingDetails {
@Id @GeneratedValue
@Column(name = "BILLING_DETAILS_ID")
private Long id = null;
@Column(name = "OWNER", nullable = false)
private String owner;
The database identifier and its mapping have to be present in the superclass, to
be shared across all subclasses and their tables. An @Entity annotation on each
subclass is all that is required:
@Table(name = "CREDIT_CARD")
public class CreditCard extends BillingDetails {
@Column(name = "NUMBER", nullable = false)
private String number;
Note that TABLE_PER_CLASS is specified in the JPA standard as optional, so not all
JPA implementations may support it. The actual implementation is also vendor
dependent—in Hibernate, it’s equivalent to a <union-subclass> mapping in
XML files.
The same mapping looks like this in a JPA XML descriptor:
<entity class="auction.model.BillingDetails" access="FIELD">
<inheritance strategy="TABLE_PER_CLASS"/>
<entity class="auction.model.CreditCard" access="FIELD"/>
Inheritance and custom types
<entity class="auction.model.BankAccount" access="FIELD"/>
If your superclass is concrete, then an additional table is needed to hold
instances of that class. We have to emphasize again that there is still no relationship between the database tables, except for the fact that they share some similar
columns. The advantages of this mapping strategy are clearer if we examine polymorphic queries. For example, a query for BillingDetails executes the following SQL statement:
( select
null as ACCOUNT, null as BANKNAME, null as SWIFT,
1 as CLAZZ_
null as NUMBER, null as EXP_MONTH, null as EXP_YEAR, ...
2 as CLAZZ_
This SELECT uses a FROM-clause subquery to retrieve all instances of BillingDetails from all concrete class tables. The tables are combined with a UNION operator, and a literal (in this case, 1 and 2) is inserted into the intermediate result;
Hibernate reads this to instantiate the correct class given the data from a particular row. A union requires that the queries that are combined project over the
same columns; hence, we have to pad and fill up nonexistent columns with NULL.
You may ask whether this query will really perform better than two separate statements. Here we can let the database optimizer find the best execution plan to
combine rows from several tables, instead of merging two result sets in memory as
Hibernate’s polymorphic loader engine would do.
Mapping class inheritance
Another much more important advantage is the ability to handle polymorphic
associations; for example, an association mapping from User to BillingDetails
would now be possible. Hibernate can use a UNION query to simulate a single table
as the target of the association mapping. We cover this topic in detail in chapter 7,
section 7.3, “Polymorphic associations.”
So far, the inheritance mapping strategies we’ve discussed don’t require extra
consideration with regard to the SQL schema. No foreign keys are needed, and
relations are properly normalized. This situation changes with the next strategy.
Table per class hierarchy
An entire class hierarchy can be mapped to a single table. This table includes columns for all properties of all classes in the hierarchy. The concrete subclass represented by a particular row is identified by the value of a type discriminator
column. This approach is shown in figure 5.2.
This mapping strategy is a winner in terms of both performance and simplicity.
It’s the best-performing way to represent polymorphism—both polymorphic and
nonpolymorphic queries perform well—and it’s even easy to implement by hand.
Ad-hoc reporting is possible without complex joins or unions. Schema evolution is
Figure 5.2
Mapping a whole class hierarchy to a single table
Inheritance and custom types
There is one major problem: Columns for properties declared by subclasses
must be declared to be nullable. If your subclasses each define several nonnullable properties, the loss of NOT NULL constraints may be a serious problem from
the point of view of data integrity. Another important issue is normalization.
We’ve created functional dependencies between nonkey columns, violating the
third normal form. As always, denormalization for performance can be misleading, because it sacrifices long-term stability, maintainability, and the integrity of
data for immediate gains that may be also achieved by proper optimization of the
SQL execution plans (in other words, ask your DBA).
In Hibernate, you use the <subclass> element to create a table per class hierarchy mapping, as in listing 5.2.
Listing 5.2
Hibernate <subclass> mapping
<generator class="native"/>
<property name="number" column="CC_NUMBER"/>
<property name="expMonth" column="CC_EXP_MONTH"/>
<property name="expYear" column="CC_EXP_YEAR"/>
Mapping class inheritance
The root class BillingDetails of the inheritance hierarchy is mapped to the
You have to add a special column to distinguish between persistent classes: the discriminator. This isn’t a property of the persistent class; it’s used internally by
Hibernate. The column name is BILLING_DETAILS_TYPE, and the values are
strings—in this case, “CC” or “BA”. Hibernate automatically sets and retrieves the
discriminator values.
Properties of the superclass are mapped as always, with a simple <property> element.
Every subclass has its own <subclass> element. Properties of a subclass are
mapped to columns in the BILLING_DETAILS table. Remember that NOT NULL constraints aren’t allowed, because a BankAccount instance won’t have an expMonth
property, and the CC_EXP_MONTH field must be NULL for that row.
The <subclass> element can in turn contain other nested <subclass> elements,
until the whole hierarchy is mapped to the table.
Hibernate generates the following SQL when querying the BillingDetails
To query the CreditCard subclass, Hibernate adds a restriction on the discriminator column:
This mapping strategy is also available in JPA, as SINGLE_TABLE:
@Inheritance(strategy = InheritanceType.SINGLE_TABLE)
discriminatorType = DiscriminatorType.STRING
Inheritance and custom types
public abstract class BillingDetails {
@Id @GeneratedValue
@Column(name = "BILLING_DETAILS_ID")
private Long id = null;
@Column(name = "OWNER", nullable = false)
private String owner;
If you don’t specify a discriminator column in the superclass, its name defaults to
DTYPE and its type to string. All concrete classes in the inheritance hierarchy can
have a discriminator value; in this case, BillingDetails is abstract, and CreditCard is a concrete class:
public class CreditCard extends BillingDetails {
@Column(name = "CC_NUMBER")
private String number;
Without an explicit discriminator value, Hibernate defaults to the fully qualified
class name if you use Hibernate XML files and the entity name if you use annotations or JPA XML files. Note that no default is specified in Java Persistence for nonstring discriminator types; each persistence provider can have different defaults.
This is the equivalent mapping in JPA XML descriptors:
<entity class="auction.model.BillingDetails" access="FIELD">
<inheritance strategy="SINGLE_TABLE"/>
<discriminator-column name="BILLING_DETAILS_TYPE"
<entity class="auction.model.CreditCard" access="FIELD">
Sometimes, especially in legacy schemas, you don’t have the freedom to include
an extra discriminator column in your entity tables. In this case, you can apply a
formula to calculate a discriminator value for each row:
Mapping class inheritance
formula="case when CC_NUMBER is not null then 'CC' else 'BA' end"
This mapping relies on an SQL CASE/WHEN expression to determine whether a particular row represents a credit card or a bank account (many developers never
used this kind of SQL expression; check the ANSI standard if you aren’t familiar
with it). The result of the expression is a literal, CC or BA, which in turn is declared
on the <subclass> mappings. Formulas for discrimination aren’t part of the JPA
specification. However, you can apply a Hibernate annotation:
@Inheritance(strategy = InheritanceType.SINGLE_TABLE)
"case when CC_NUMBER is not null then 'CC' else 'BA' end"
public abstract class BillingDetails {
The disadvantages of the table per class hierarchy strategy may be too serious for
your design—after all, denormalized schemas can become a major burden in the
long run. Your DBA may not like it at all. The next inheritance mapping strategy
doesn’t expose you to this problem.
Table per subclass
The fourth option is to represent inheritance relationships as relational foreign
key associations. Every class/subclass that declares persistent properties—including abstract classes and even interfaces—has its own table.
Unlike the table per concrete class strategy we mapped first, the table here
contains columns only for each noninherited property (each property declared
by the subclass itself) along with a primary key that is also a foreign key of the
superclass table. This approach is shown in figure 5.3.
If an instance of the CreditCard subclass is made persistent, the values of properties declared by the BillingDetails superclass are persisted to a new row of the
BILLING_DETAILS table. Only the values of properties declared by the subclass are
persisted to a new row of the CREDIT_CARD table. The two rows are linked together
Inheritance and custom types
Figure 5.3
Mapping all classes of the hierarchy to their own table
by their shared primary key value. Later, the subclass instance may be retrieved
from the database by joining the subclass table with the superclass table.
The primary advantage of this strategy is that the SQL schema is normalized.
Schema evolution and integrity constraint definition are straightforward. A polymorphic association to a particular subclass may be represented as a foreign key
referencing the table of that particular subclass.
In Hibernate, you use the <joined-subclass> element to create a table per
subclass mapping. See listing 5.3.
Listing 5.3
Hibernate <joined-subclass> mapping
<generator class="native"/>
Mapping class inheritance
<key column="CREDIT_CARD_ID"/>
<property name="number" column="NUMBER"/>
<property name="expMonth" column="EXP_MONTH"/>
<property name="expYear" column="EXP_YEAR"/>
The root class BillingDetails is mapped to the table BILLING_DETAILS. Note
that no discriminator is required with this strategy.
The new <joined-subclass> element maps a subclass to a new table—in this
example, CREDIT_CARD. All properties declared in the joined subclass are mapped
to this table.
A primary key is required for the CREDIT_CARD table. This column also has a foreign key constraint to the primary key of the BILLING_DETAILS table. A CreditCard object lookup requires a join of both tables. A <joined-subclass> element
may contain other nested <joined-subclass> elements, until the whole hierarchy has been mapped.
Hibernate relies on an outer join when querying the BillingDetails class:
when CC.CREDIT_CARD_ID is not null then 1
when BA.BANK_ACCOUNT_ID is not null then 2
when BD.BILLING_DETAILS_ID is not null then 0
end as CLAZZ_
Inheritance and custom types
left join CREDIT_CARD CC
The SQL CASE statement detects the existence (or absence) of rows in the subclass
tables CREDIT_CARD and BANK_ACCOUNT, so Hibernate can determine the concrete
subclass for a particular row of the BILLING_DETAILS table.
To narrow the query to the subclass, Hibernate uses an inner join:
As you can see, this mapping strategy is more difficult to implement by hand—
even ad-hoc reporting is more complex. This is an important consideration if you
plan to mix Hibernate code with handwritten SQL.
Furthermore, even though this mapping strategy is deceptively simple, our
experience is that performance can be unacceptable for complex class hierarchies.
Queries always require either a join across many tables or many sequential reads.
Let’s map the hierarchy with the same strategy and annotations, here called
the JOINED strategy:
@Inheritance(strategy = InheritanceType.JOINED)
public abstract class BillingDetails {
@Id @GeneratedValue
@Column(name = "BILLING_DETAILS_ID")
private Long id = null;
In subclasses, you don’t need to specify the join column if the primary key column
of the subclass table has (or is supposed to have) the same name as the primary
key column of the superclass table:
public class BankAccount {
This entity has no identifier property; it automatically inherits the BILLING_
DETAILS_ID property and column from the superclass, and Hibernate knows how
Mapping class inheritance
to join the tables together if you want to retrieve instances of BankAccount. Of
course, you can specify the column name explicitly:
@PrimaryKeyJoinColumn(name = "CREDIT_CARD_ID")
public class CreditCard {
Finally, this is the equivalent mapping in JPA XML descriptors:
<entity class="auction.model.BillingDetails" access="FIELD">
<inheritance strategy="JOINED"/>
<entity class="auction.model.BankAccount" access="FIELD"/>
<entity class="auction.model.CreditCard" access="FIELD">
<primary-key-join-column name="CREDIT_CARD_ID"/>
Before we show you when to choose which strategy, let’s consider mixing inheritance mapping strategies in a single class hierarchy.
Mixing inheritance strategies
You can map whole inheritance hierarchies by nesting <union-subclass>, <subclass>, and <joined-subclass> mapping elements. You can’t mix them—for
example, to switch from a table-per-class hierarchy with a discriminator to a normalized table-per-subclass strategy. Once you’ve made a decision for an inheritance strategy, you have to stick to it.
This isn’t completely true, however. With some Hibernate tricks, you can
switch the mapping strategy for a particular subclass. For example, you can map a
class hierarchy to a single table, but for a particular subclass, switch to a separate
table with a foreign key mapping strategy, just as with table per subclass. This is
possible with the <join> mapping element:
<class name="BillingDetails"
Inheritance and custom types
<join table="CREDIT_CARD">
<key column="CREDIT_CARD_ID"/>
<property name="number" column="CC_NUMBER"/>
<property name="expMonth" column="CC_EXP_MONTH"/>
<property name="expYear" column="CC_EXP_YEAR"/>
<property name=account" column="BA_ACCOUNT"/>
The <join> element groups some properties and tells Hibernate to get them from
a secondary table. This mapping element has many uses, and you’ll see it again
later in the book. In this example, it separates the CreditCard properties from the
table per hierarchy into the CREDIT_CARD table. The CREDIT_CARD_ID column of
this table is at the same time the primary key, and it has a foreign key constraint
referencing the BILLING_DETAILS_ID of the hierarchy table. The BankAccount
subclass is mapped to the hierarchy table. Look at the schema in figure 5.4.
At runtime, Hibernate executes an outer join to fetch BillingDetails and all
subclass instances polymorphically:
left outer join
Mapping class inheritance
Figure 5.4
Breaking out a subclass to its own secondary table
You can also use the <join> trick for other subclasses in your class hierarchy. However, if you have an exceptionally wide class hierarchy, the outer join can become
a problem. Some database systems (Oracle, for example) limit the number of
tables in an outer join operation. For a wide hierarchy, you may want to switch to a
different fetching strategy that executes an immediate second select instead of an
outer join:
<join table="CREDIT_CARD" fetch="select">
<key column="CREDIT_CARD_ID"/>
Java Persistence also supports this mixed inheritance mapping strategy with annotations. Map the superclass BillingDetails with InheritanceType.SINGLE_
TABLE, as you did before. Now map the subclass you want to break out of the single table to a secondary table.
Inheritance and custom types
name = "CREDIT_CARD",
pkJoinColumns = @PrimaryKeyJoinColumn(name = "CREDIT_CARD_ID")
public class CreditCard extends BillingDetails {
@Column(table = "CREDIT_CARD",
name = "CC_NUMBER",
nullable = false)
private String number;
If you don’t specify a primary key join column for the secondary table, the name
of the primary key of the single inheritance table is used—in this case,
BILLING_DETAILS_ID. Also note that you need to map all properties that are
moved into the secondary table with the name of that secondary table.
You also want more tips about how to choose an appropriate combination of
mapping strategies for your application’s class hierarchies.
Choosing a strategy
You can apply all mapping strategies to abstract classes and interfaces. Interfaces
may have no state but may contain accessor method declarations, so they can be
treated like abstract classes. You can map an interface with <class>, <union-subclass>, <subclass>, or <joined-subclass>, and you can map any declared or
inherited property with <property>. Hibernate won’t try to instantiate an abstract
class, even if you query or load it.
Note that the JPA specification doesn’t support any mapping annotation
on an interface! This will be resolved in a future version of the specification; when you read this book, it will probably be possible with Hibernate
Here are some rules of thumb:
If you don’t require polymorphic associations or queries, lean toward tableper-concrete-class—in other words, if you never or rarely query for BillingDetails and you have no class that has an association to BillingDetails (our model has). An explicit UNION-based mapping should be
preferred, because (optimized) polymorphic queries and associations will
then be possible later. Implicit polymorphism is mostly useful for queries
utilizing non-persistence-related interfaces.
If you do require polymorphic associations (an association to a superclass,
hence to all classes in the hierarchy with dynamic resolution of the concrete
Mapping class inheritance
class at runtime) or queries, and subclasses declare relatively few properties
(particularly if the main difference between subclasses is in their behavior),
lean toward table-per-class-hierarchy. Your goal is to minimize the number
of nullable columns and to convince yourself (and your DBA) that a denormalized schema won’t create problems in the long run.
If you do require polymorphic associations or queries, and subclasses
declare many properties (subclasses differ mainly by the data they hold),
lean toward table-per-subclass. Or, depending on the width and depth of
your inheritance hierarchy and the possible cost of joins versus unions, use
By default, choose table-per-class-hierarchy only for simple problems. For more
complex cases (or when you’re overruled by a data modeler insisting on the
importance of nullability constraints and normalization), you should consider the
table-per-subclass strategy. But at that point, ask yourself whether it may not be
better to remodel inheritance as delegation in the object model. Complex inheritance is often best avoided for all sorts of reasons unrelated to persistence or ORM.
Hibernate acts as a buffer between the domain and relational models, but that
doesn’t mean you can ignore persistence concerns when designing your classes.
When you start thinking about mixing inheritance strategies, remember that
implicit polymorphism in Hibernate is smart enough to handle more exotic cases.
For example, consider an additional interface in our application, ElectronicPaymentOption. This is a business interface that doesn’t have a persistence
aspect—except that in our application, a persistent class such as CreditCard will
likely implement this interface. No matter how you map the BillingDetails
hierarchy, Hibernate can answer a query from ElectronicPaymentOption correctly. This even works if other classes, which aren’t part of the BillingDetails
hierarchy, are mapped persistent and implement this interface. Hibernate always
know what tables to query, which instances to construct, and how to return a polymorphic result.
Finally, you can also use <union-subclass>, <subclass>, and <joined-subclass> mapping elements in a separate mapping file (as a top-level element
instead of <class>). You then have to declare the class that is extended, such as
<subclass name="CreditCard" extends="BillingDetails">, and the superclass mapping must be loaded programmatically before the subclass mapping file
(you don’t have to worry about this order when you list mapping resources in the
XML configuration file). This technique allows you to extend a class hierarchy
without modifying the mapping file of the superclass.
Inheritance and custom types
You now know everything you need to know about the mapping of entities,
properties, and inheritance hierarchies. You can already map complex domain
models. In the second half of this chapter, we discuss another important feature that you should know by heart as a Hibernate user: the Hibernate mapping type system.
The Hibernate type system
In chapter 4, we first distinguished between entity and value types—a central
concept of ORM in Java. We must elaborate on that distinction in order for you
to fully understand the Hibernate type system of entities, value types, and mapping types.
Recapitulating entity and value types
Entities are the coarse-grained classes in your system. You usually define the features of a system in terms of the entities involved. The user places a bid for an item is
a typical feature definition; it mentions three entities. Classes of value types often
don’t even appear in the business requirements—they’re usually the fine-grained
classes representing strings, numbers, and monetary amounts. Occasionally, value
types do appear in feature definitions: the user changes billing address is one example, assuming that Address is a value type.
More formally, an entity is any class whose instances have their own persistent
identity. A value type is a class that doesn’t define some kind of persistent identity.
In practice, this means that entity types are classes with identifier properties, and
value type classes depend on an entity.
At runtime, you have a network of entity instances interleaved with value type
instances. The entity instances may be in any of the three persistent lifecycle
states: transient, detached, or persistent. We don’t consider these lifecycle states to
apply to the value type instances. (We’ll come back to this discussion of object
states in chapter 9.)
Therefore, entities have their own lifecycle. The save() and delete() methods
of the Hibernate Session interface apply to instances of entity classes, never to
value type instances. The persistence lifecycle of a value type instance is completely
tied to the lifecycle of the owning entity instance. For example, the username
becomes persistent when the user is saved; it never becomes persistent independently of the user.
The Hibernate type system
In Hibernate, a value type may define associations; it’s possible to navigate
from a value type instance to some other entity. However, it’s never possible to
navigate from the other entity back to the value type instance. Associations always
point to entities. This means that a value type instance is owned by exactly one
entity when it’s retrieved from the database; it’s never shared.
At the level of the database, any table is considered an entity. However, Hibernate provides certain constructs to hide the existence of a database-level entity
from the Java code. For example, a many-to-many association mapping hides the
intermediate association table from the application. A collection of strings (more
accurately, a collection of value-typed instances) behaves like a value type from
the point of view of the application; however, it’s mapped to its own table.
Although these features seem nice at first (they simplify the Java code), we have
over time become suspicious of them. Inevitably, these hidden entities end up
needing to be exposed to the application as business requirements evolve. The
many-to-many association table, for example, often has additional columns added
as the application matures. We’re almost prepared to recommend that every database-level entity be exposed to the application as an entity class. For example, we
would be inclined to model the many-to-many association as two one-to-many
associations to an intervening entity class. We’ll leave the final decision to you,
however, and come back to the topic of many-to-many entity associations in the
future chapters.
Entity classes are always mapped to the database using <class>, <union-subclass>, <subclass>, and <joined-subclass> mapping elements. How are value
types mapped?
You’ve already met two different kinds of value type mappings: <property>
and <component>. The value type of a component is obvious: It’s the class that is
mapped as embeddable. However, the type of a property is a more generic notion.
Consider this mapping of the CaveatEmptor User and email address:
<property name="email"
Let’s focus on that type="string" attribute. You know that in ORM you have to
deal with Java types and SQL datatypes. The two different type systems must be
bridged. This is the job of the Hibernate mapping types, and string is the name
of a built-in Hibernate mapping type.
Inheritance and custom types
The string mapping type isn’t the only one built into Hibernate. Hibernate
comes with various mapping types that define default persistence strategies for
primitive Java types and certain JDK classes.
Built-in mapping types
Hibernate’s built-in mapping types usually share the name of the Java type they
map. However, there may be more than one Hibernate mapping type for a particular Java type.
The built-in types may not be used to perform arbitrary conversions, such as
mapping a VARCHAR database value to a Java Integer property value. You may
define your own custom value types for this kind of conversation, as shown later in
this chapter.
We now discuss the basic, date and time, locator object, and various other
built-in mapping types and show you what Java and SQL datatype they handle.
Java primitive mapping types
The basic mapping types in table 5.1 map Java primitive types (or their wrapper
types) to appropriate built-in SQL standard types.
Table 5.1
Primitive types
Mapping type
Java type
Standard SQL built-in type
int or java.lang.Integer
long or java.lang.Long
short or java.lang.Short
float or java.lang.Float
double or java.lang.Double
byte or java.lang.Byte
boolean or java.lang.Boolean
boolean or java.lang.Boolean
CHAR(1) ('Y' or 'N')
boolean or java.lang.Boolean
CHAR(1) ('T' or 'F')
The Hibernate type system
You’ve probably noticed that your database doesn’t support some of the SQL
types mentioned in table 5.1. The listed type names are names of ANSI-standard
datatypes. Most database vendors ignore this part of the SQL standard (because
their legacy type systems often predate the standard). However, the JDBC driver
provides a partial abstraction of vendor-specific SQL datatypes, allowing Hibernate to work with ANSI-standard types when executing DML. For database-specific
DDL generation, Hibernate translates from the ANSI-standard type to an appropriate vendor-specific type, using the built-in support for specific SQL dialects. (This
means you usually don’t have to worry about SQL datatypes if you’re using Hibernate for data access and SQL schema definition.)
Furthermore, the Hibernate type system is smart and can switch SQL datatypes
depending on the defined length of a value. The most obvious case is string: If
you declare a string property mapping with a length attribute, Hibernate picks
the correct SQL datatype depending on the selected dialect. For MySQL, for
example, a length of up to 65535 results in a regular VARCHAR(length) column
when Hibernate exports the schema. For a length of up to 16777215, a MEDIUMTEXT datatype is used. Larger string mappings result in a LONGTEXT. Check your
SQL dialect (the source code comes with Hibernate) if you want to know the
ranges for this and other mapping types. You can customize this behavior by subclassing your dialect and overriding these settings.
Most dialects also support setting the scale and precision of decimal SQL
datatypes. For example, a precision or scale setting in your mapping of a BigDecimal creates a NUMERIC(precision, scale) datatype for MySQL.
Finally, the yes_no and true_false mapping types are converters that are
mostly useful for legacy schemas and Oracle users; Oracle DBMS products don’t
have a built-in boolean or truth-valued type (the only built-in datatype actually
required by the relational data model).
Date and time mapping types
Table 5.2 lists Hibernate types associated with dates, times, and timestamps. In
your domain model, you may choose to represent date and time data using
java.util.Date, java.util.Calendar, or the subclasses of java.util.Date
defined in the java.sql package. This is a matter of taste, and we leave the decision to you—make sure you’re consistent, however. (In practice, binding your
domain model to types from the JDBC package isn’t the best idea.)
A caveat: If you map a java.util.Date property with timestamp (the most
common case), Hibernate returns a java.sql.Timestamp when loading the property from the database. Hibernate has to use the JDBC subclass because it includes
Inheritance and custom types
Table 5.2
Date and time types
Mapping type
Standard SQL
built-in type
Java type
java.util.Date or java.sql.Date
java.util.Date or java.sql.Time
java.util.Date or java.sql.Timestamp
nanosecond information that may be present in the database. Hibernate can’t just
cut off this information. This can lead to problems if you try to compare your
java.util.Date properties with the equals() method, because it isn’t symmetric
with the java.sql.Timestamp subclass equals() method. First, the right way (in
any case) to compare two java.util.Date objects, which also works for any subclass, is aDate.getTime() > bDate.getTime() (for a greater-than comparison).
Second, you can write a custom mapping type that cuts off the database nanosecond information and returns a java.util.Date in all cases. Currently (although
this may change in the future), no such mapping type is built into Hibernate.
Binary and large value mapping types
Table 5.3 lists Hibernate types for handling binary data and large values. Note that
only binary is supported as the type of an identifier property.
If a property in your persistent Java class is of type byte[], Hibernate can map
it to a VARBINARY column with the binary mapping type. (Note that the real SQL
Table 5.3
Binary and large value types
Mapping type
Java type
Standard SQL
built-in type
Any Java class that implements
The Hibernate type system
type depends on the dialect; for example, in PostgreSQL, the SQL type is BYTEA,
and in Oracle it’s RAW.) If a property in your persistent Java class is of type
java.lang.String, Hibernate can map it to an SQL CLOB column, with the text
mapping type.
Note that in both cases, Hibernate initializes the property value right away,
when the entity instance that holds the property variable is loaded. This is inconvenient when you have to deal with potentially large values.
One solution is lazy loading through interception of field access, on demand.
However, this approach requires bytecode instrumentation of your persistent
classes for the injection of extra code. We’ll discuss lazy loading through bytecode
instrumentation and interception in chapter 13, section 13.1.6, “Lazy loading
with interception.”
A second solution is a different kind of property in your Java class. JDBC supports locator objects (LOBs) directly.1 If your Java property is of type
java.sql.Clob or java.sql.Blob, you can map it with the clob or blob mapping
type to get lazy loading of large values without bytecode instrumentation. When
the owner of the property is loaded, the property value is a locator object—effectively, a pointer to the real value that isn’t yet materialized. Once you access the
property, the value is materialized. This on-demand loading works only as long as
the database transaction is open, so you need to access any property of such a type
when the owning entity instance is in a persistent and transactional state, not in
detached state. Your domain model is now also bound to JDBC, because the
import of the java.sql package is required. Although domain model classes are
executable in isolated unit tests, you can’t access LOB properties without a database connection.
Mapping properties with potentially large values is slightly different if you rely
on Java Persistence annotations. By default, a property of type java.lang.String
is mapped to an SQL VARCHAR column (or equivalent, depending on the SQL dialect). If you want to map a java.lang.String, char[], Character[], or even a
java.sql.Clob typed property to a CLOB column, you need to map it with the
@Lob annotation:
@Column(name = "ITEM_DESCRIPTION")
private String description;
Jim Starkey, who came up with the idea of LOBs, says that the terms BLOB and CLOB don’t mean anything but were created by the marketing department. You can interpret them any way you like. We prefer locator objects, as a hint that they work like pointers.
Inheritance and custom types
@Column(name = "ITEM_IMAGE")
private byte[] image;
The same is true for any property that is of type byte[], Byte[], or java.
sql.Blob. Note that for all cases, except properties that are of java.sql.Clob or
java.sql.Blob type, the values are again loaded immediately by Hibernate, and
not lazily on demand. Instrumenting bytecode with interception code is again an
option to enable lazy loading of individual properties transparently.
To create and set a java.sql.Blob or java.sql.Clob value, if you have these
property types in your domain model, use the static Hibernate.createBlob()
and Hibernate.createClob() methods and provide a byte array, an input stream,
or a string.
Finally, note that both Hibernate and JPA provide a serialization fallback for
any property type that is Serializable. This mapping type converts the value of a
property to a byte stream that is then stored in a VARBINARY (or equivalent) column. When the owner of the property is loaded, the property value is deserialized. Naturally, you should use this strategy with extreme caution (data lives
longer than an application), and it may be useful only for temporary data (user
preferences, login session data, and so on).
JDK mapping types
Table 5.4 lists Hibernate types for various other Java types of the JDK that may be
represented as a VARCHAR in the database.
You may have noticed that <property> isn’t the only Hibernate mapping element that has a type attribute.
Table 5.4
Other JDK-related types
Mapping type
Java type
Standard SQL
built-in type
The Hibernate type system
Using mapping types
All of the basic mapping types may appear almost anywhere in the Hibernate
mapping document, on normal property, identifier property, and other mapping
elements. The <id>, <property>, <version>, <discriminator>, <index> and
<element> elements all define an attribute named type.
You can see how useful the built-in mapping types are in this mapping for the
BillingDetails class:
<class name="BillingDetails" table="BILLING_DETAILS">
<id name="id" type="long" column="BILLING_DETAILS_ID">
<generator class="native"/>
<discriminator type="character" column="BILLING_DETAILS_TYPE"/>
<property name="number" type="string"/>
The BillingDetails class is mapped as an entity. Its discriminator, identifier,
and name properties are value typed, and we use the built-in Hibernate mapping
types to specify the conversion strategy.
It isn’t often necessary to explicitly specify a built-in mapping type in the XML
mapping document. For instance, if you have a property of Java type
java.lang.String, Hibernate discovers this using reflection and selects string
by default. We can easily simplify the previous mapping example:
<class name="BillingDetails" table="BILLING_DETAILS">
<id name="id" column="BILLING_DETAILS_ID">
<generator class="native"/>
<discriminator type="character" column="BILLING_DETAILS_TYPE"/>
<property name="number"/>
Hibernate also understands type="java.lang.String"; it doesn’t have to use
reflection then. The most important case where this approach doesn’t work well is
a java.util.Date property. By default, Hibernate interprets a java.util.Date as
a timestamp mapping. You need to explicitly specify type="time" or type="date"
if you don’t wish to persist both date and time information.
With JPA annotations, the mapping type of a property is automatically
detected, just like in Hibernate. For a java.util.Date or java.util.Calendar
property, the Java Persistence standard requires that you select the precision with
a @Temporal annotation:
Inheritance and custom types
@Column(nullable = false, updatable = false)
private Date startDate;
On the other hand, Hibernate Annotations, relaxing the rules of the standard,
defaults to TemporalType.TIMESTAMP—options are TemporalType.TIME and TemporalType.DATE.
In other rare cases, you may want to add the @org.hibernate.annotations.Type annotation to a property and declare the name of a built-in or custom
Hibernate mapping type explicitly. This is a much more common extension as
soon as you start writing your own custom mapping types, which you’ll do later in
this chapter.
The equivalent JPA XML descriptor is as follows:
<entity class="auction.model.Item" access="FIELD">
<basic name="startDate">
<column nullable="false" updatable="false"/>
For each of the built-in mapping types, a constant is defined by the class
org.hibernate.Hibernate. For example, Hibernate.STRING represents the
string mapping type. These constants are useful for query parameter binding, as
discussed in more detail in chapters 14 and 15:
session.createQuery("from Item i where i.description like :desc")
.setParameter("desc", d, Hibernate.STRING)
Note that you may as well use the setString() argument binding method in this
case. Type constants are also useful for programmatic manipulation of the Hibernate mapping metamodel, as discussed in chapter 3.
Hibernate isn’t limited to the built-in mapping types. We consider the extensible mapping-type system one of the core features and an important aspect that
makes Hibernate so flexible.
Creating custom mapping types
Object-oriented languages like Java make it easy to define new types by writing
new classes. This is a fundamental part of the definition of object-orientation. If
we were then limited to the predefined built-in Hibernate mapping types when
Creating custom mapping types
declaring properties of our persistent classes, we would lose much of Java’s
expressiveness. Furthermore, our domain model implementation would be
tightly coupled to the physical data model, because new type conversions would
be impossible.
Most ORM solutions that we have seen provide support for user-defined strategies for performing type conversions. These are often called converters. For
example, the user can create a new strategy for persisting a property of JDK type
Integer to a VARCHAR column. Hibernate provides a similar, much more powerful, feature called custom mapping types.
First you need to understand when it’s appropriate to write your own custom
mapping type, and which Hibernate extension point is relevant for you. We’ll
then write some custom mapping types and explore the options.
Considering custom mapping types
As an example, take the mapping of the Address class from previous chapters, as a
<component name="homeAddress" class="Address">
<property name="street" type="string" column="HOME_STREET"/>
<property name="city" type="string"
<property name="zipcode" type="string" column="HOME_ZIPCODE"/>
This value type mapping is straightforward; all properties of the new user-defined
Java type are mapped to individual columns of a built-in SQL datatype. However,
you can alternatively map it as a simple property, with a custom mapping type:
<property name="homeAddress"
<column name="HOME_STREET"/>
<column name="HOME_CITY"/>
<column name="HOME_ZIPCODE"/>
This is also probably the first time you’ve seen a single <property> element with
several <column> elements nested inside. We’re moving the responsibility for
translating and converting between an Address value type (it isn’t even named
anywhere) and the named three columns to a separate class: auction.persistence.CustomAddressType. This class is now responsible for loading and saving
this property. Note that no Java code changes in the domain model implementation—the homeAddress property is of type Address.
Inheritance and custom types
Granted, the benefit of replacing a component mapping with a custom mapping type is dubious in this case. As long as you require no special conversion
when loading and saving this object, the CustomAddressType you now have to
write is just additional work. However, you can already see that custom mapping
types provide an additional buffer—something that may come in handy in the
long run when extra conversion is required. Of course, there are better use cases
for custom mapping types, as you’ll soon see. (Many examples of useful Hibernate
mapping types can be found on the Hibernate community website.)
Let’s look at the Hibernate extension points for the creation of custom mapping types.
The extension points
Hibernate provides several interfaces that applications may use when defining
custom mapping types. These interfaces reduce the work involved in creating new
mapping types and insulate the custom type from changes to the Hibernate core.
This allows you to easily upgrade Hibernate and keep your existing custom mapping types.
The extension points are as follows:
org.hibernate.usertype.UserType —The basic extension point, which is
useful in many situations. It provides the basic methods for custom loading
and storing of value type instances.
org.hibernate.usertype.CompositeUserType —An interface with more
methods than the basic UserType, used to expose internals about your value
type class to Hibernate, such as the individual properties. You can then
refer to these properties in Hibernate queries.
org.hibernate.usertype.UserCollectionType —A rarely needed interface that’s used to implement custom collections. A custom mapping type
implementing this interface isn’t declared on a property mapping but is
useful only for custom collection mappings. You have to implement this
type if you want to persist a non-JDK collection and preserve additional
semantics persistently. We discuss collection mappings and this extension
point in the next chapter.
org.hibernate.usertype.EnhancedUserType —An interface that extends
UserType and provides additional methods for marshalling value types to
and from XML representations, or enables a custom mapping type for use
in identifier and discriminator mappings.
Creating custom mapping types
org.hibernate.usertype.UserVersionType —An interface that extends
UserType and provides additional methods enabling the custom mapping
type for usage in entity version mappings.
org.hibernate.usertype.ParameterizedType —A useful interface that
can be combined with all others to provide configuration settings—that is,
parameters defined in metadata. For example, you can write a single MoneyConverter that knows how to translate values into Euro or US dollars,
depending on a parameter in the mapping.
We’ll now create some custom mapping types. You shouldn’t consider this an
unnecessary exercise, even if you’re happy with the built-in Hibernate mapping
types. In our experience, every sophisticated application has many good use cases
for custom mapping types.
The case for custom mapping types
The Bid class defines an amount property, and the Item class defines an initialPrice property; both are monetary values. So far, we’ve used only a simple
BigDecimal to represent the value, mapped with big_decimal to a single
NUMERIC column.
Suppose you want to support multiple currencies in the auction application
and that you have to refactor the existing domain model for this (customerdriven) change. One way to implement this change would be to add new properties to Bid and Item: amountCurrency and initialPriceCurrency. You could
then map these new properties to additional VARCHAR columns with the built-in
currency mapping type. We hope you never use this approach!
Instead, you should create a new MonetaryAmount class that encapsulates both
currency and amount. Note that this is a class of your domain model; it doesn’t
have any dependency on Hibernate interfaces:
public class MonetaryAmount implements Serializable {
private final BigDecimal amount;
private final Currency currency;
public MonetaryAmount(BigDecimal amount, Currency currency) {
this.amount = amount;
this.currency = currency;
public BigDecimal getAmount() { return amount; }
public Currency getCurrency() { return currency; }
Inheritance and custom types
public boolean equals(Object o) { ... }
public int hashCode() { ...}
We have made MonetaryAmount an immutable class. This is a good practice in Java
because it simplifies coding. Note that you have to implement equals() and
hashCode() to finish the class (there is nothing special to consider here). You use
this new MonetaryAmount to replace the BigDecimal of the initialPrice property in Item. You can and should use it for all other BigDecimal prices in any persistent classes, such as the Bid.amount, and in business logic—for example, in the
billing system.
Let’s map the refactored initialPrice property of Item, with its new MonetaryAmount type to the database.
Creating a UserType
Imagine that you’re working with a legacy database that represents all monetary
amounts in USD. The application is no longer restricted to a single currency (that
was the point of the refactoring), but it takes some time for the database team to
make the changes. You need to convert the amount to USD when persisting MonetaryAmount objects. When you load from the database, you convert it back to the
currency the user selected in his or her preferences.
Create a new MonetaryAmountUserType class that implements the Hibernate
interface UserType. This is your custom mapping type, shown in listing 5.4.
Listing 5.4
Custom mapping type for monetary amounts in USD
public class MonetaryAmountUserType
implements UserType {
public int[] sqlTypes() {
return new int[]{ Hibernate.BIG_DECIMAL.sqlType() };
public Class returnedClass() { return MonetaryAmount.class; }
public boolean isMutable() { return false; }
public Object deepCopy(Object value) {
public Serializable disassemble(Object value)
{ return (Serializable) value; }
public Object assemble(Serializable cached, Object owner)
{ return cached; }
public Object replace(Object original,
Object target,
return value; }
Creating custom mapping types
Object owner)
{ return original; }
public boolean equals(Object x, Object y) {
if (x == y) return true;
if (x == null || y == null) return false;
return x.equals(y);
public int hashCode(Object x) {
return x.hashCode();
public Object nullSafeGet(ResultSet resultSet,
String[] names,
Object owner)
throws SQLException {
BigDecimal valueInUSD = resultSet.getBigDecimal(names[0]);
// Deferred check after first read
if (resultSet.wasNull()) return null;
Currency userCurrency = User.getPreferences().getCurrency();
MonetaryAmount amount = new MonetaryAmount(valueInUSD, "USD");
return amount.convertTo(userCurrency);
public void nullSafeSet(PreparedStatement statement,
Object value,
int index)
throws HibernateException, SQLException {
if (value == null) {
statement.setNull(index, Hibernate.BIG_DECIMAL.sqlType());
} else {
MonetaryAmount anyCurrency = (MonetaryAmount)value;
MonetaryAmount amountInUSD =
MonetaryAmount.convert( anyCurrency,
Currency.getInstance("USD") );
statement.setBigDecimal(index, amountInUSD.getAmount ());
The sqlTypes() method tells Hibernate what SQL column types to use for
DDL schema generation. Notice that this method returns an array of type
codes. A UserType may map a single property to multiple columns, but this
legacy data model has only a single numeric column. By using the Hibernate.BIG_DECIMAL.sqlType() method, you let Hibernate decide the exact SQL
Inheritance and custom types
datatype for the given database dialect. Alternatively, return a constant from
The returnedClass() method tells Hibernate what Java value type class is
mapped by this UserType.
Hibernate can make some minor performance optimizations for immutable types
like this one, for example, when comparing snapshots during dirty checking. The
isMutable() method tells Hibernate that this type is immutable.
The UserType is also partially responsible for creating a snapshot of a value in the
first place. Because MonetaryAmount is an immutable class, the deepCopy()
method returns its argument. In the case of a mutable type, it would need to
return a copy of the argument to be used as the snapshot value.
The disassemble() method is called when Hibernate puts a MonetaryAmount
into the second-level cache. As you’ll learn later, this is a cache of data that stores
information in a serialized form.
The assemble() method does the opposite of disassembly: It can transform
cached data into an instance of MonetaryAmount. As you can see, implementation
of both routines is easy for immutable types.
Implement replace() to handle merging of detached object state. As you’ll see
later in the book, the process of merging involves an original and a target object,
whose state must be combined. Again, for immutable value types, return the first
argument. For mutable types, at least return a deep copy of the first argument.
For mutable types that have component fields, you probably want to apply a recursive merging routine.
The UserType is responsible for dirty checking property values. The equals()
method compares the current property value to a previous snapshot and determines whether the property is dirty and must by saved to the database. The hashCode() of two equal value typed instances has to be the same. We usually delegate
this method to the actual value type class—in this case, the hashCode() method of
the given MonetaryAmount object.
The nullSafeGet() method retrieves the property value from the JDBC ResultSet. You can also access the owner of the component if you need it for the conversion. All database values are in USD, so you convert it to the currency the user has
currently set in his preferences. (Note that it’s up to you to implement this conversion and preference handling.)
Creating custom mapping types
The nullSafeSet() method writes the property value to the JDBC PreparedStatement. This method takes whatever currency is set and converts it to a simple
BigDecimal USD amount before saving.
You now map the initialPrice property of Item as follows:
<property name="initialPrice"
Note that you place the custom user type into the persistence package; it’s part
of the persistence layer of the application, not the domain model or business layer.
To use a custom type in annotations, you have to add a Hibernate extension:
type = " persistence.MonetaryAmountUserType"
@Column(name = "INITIAL_PRICE")
private MonetaryAmount initialPrice;
This is the simplest kind of transformation that a UserType can perform. Much
more sophisticated things are possible. A custom mapping type can perform validation; it can read and write data to and from an LDAP directory; it can even
retrieve persistent objects from a different database. You’re limited mainly by
your imagination.
In reality, we’d prefer to represent both the amount and currency of monetary amounts in the database, especially if the schema isn’t legacy but can be
defined (or updated quickly). Let’s assume you now have two columns available
and can store the MonetaryAmount without much conversion. A first option may
again be a simple <component> mapping. However, let’s try to solve it with a custom mapping type.
(Instead of writing a new custom type, try to adapt the previous example for
two columns. You can do this without changing the Java domain model classes—
only the converter needs to be updated for this new requirement and the additional column named in the mapping.)
The disadvantage of a simple UserType implementation is that Hibernate
doesn’t know anything about the individual properties inside a MonetaryAmount.
All it knows is the custom type class and the column names. The Hibernate query
engine (discussed in more detail later) doesn’t know how to query for amount or a
particular currency.
Inheritance and custom types
You write a CompositeUserType if you need the full power of Hibernate queries. This (slightly more complex) interface exposes the properties of the
MonetaryAmount to Hibernate queries. We’ll now map it again with this more flexible customization interface to two columns, effectively producing an equivalent
to a component mapping.
Creating a CompositeUserType
To demonstrate the flexibility of custom mappings types, you don’t change the
MonetaryAmount class (and other persistent classes) at all—you change only the
custom mapping type, as shown in listing 5.5.
Listing 5.5 Custom mapping type for monetary amounts in new database schemas
public class MonetaryAmountCompositeUserType
implements CompositeUserType {
// public int[] sqlTypes()...
public Class returnedClass...
public boolean isMutable...
public Object deepCopy...
public Serializable disassemble...
public Object assemble...
public Object replace...
public boolean equals...
public int hashCode...
public Object nullSafeGet(ResultSet resultSet,
String[] names,
SessionImplementor session,
Object owner)
throws SQLException {
BigDecimal value = resultSet.getBigDecimal( names[0] );
if (resultSet.wasNull()) return null;
Currency currency =
Currency.getInstance(resultSet.getString( names[1] ) );
return new MonetaryAmount(value, currency);
public void nullSafeSet(PreparedStatement statement,
Object value,
int index,
SessionImplementor session)
throws SQLException {
if (value==null) {
statement.setNull(index, Hibernate.BIG_DECIMAL.sqlType());
statement.setNull(index+1, Hibernate.CURRENCY.sqlType());
} else {
Creating custom mapping types
MonetaryAmount amount = (MonetaryAmount) value;
String currencyCode =
statement.setBigDecimal( index, amount.getAmount() );
statement.setString( index+1, currencyCode );
public String[] getPropertyNames() {
return new String[] { "amount", "currency" };
public Type[] getPropertyTypes() {
return new Type[] { Hibernate.BIG_DECIMAL,
Hibernate.CURRENCY };
public Object getPropertyValue(Object component, int property) {
MonetaryAmount monetaryAmount = (MonetaryAmount) component;
if (property == 0)
return monetaryAmount.getAmount();
return monetaryAmount.getCurrency();
public void setPropertyValue(Object component,
int property,
Object value) {
throw new
UnsupportedOperationException("Immutable MonetaryAmount!");
The CompositeUserType interface requires the same housekeeping methods as
the UserType you created earlier. However, the sqlTypes() method is no longer
Loading a value now is straightforward: You transform two column values in the
result set to two property values in a new MonetaryAmount instance.
Saving a value involves setting two parameters on the prepared statement.
The properties each have their own type, as defined by getPropertyTypes(). The
types of the SQL columns are now implicit from this method.
The getPropertyValue() method returns the value of an individual property of
the MonetaryAmount.
A CompositeUserType exposes the properties of the value type through getPropertyNames().
Inheritance and custom types
The setPropertyValue() method sets the value of an individual property of the
The initialPrice property now maps to two columns, so you need to declare
both in the mapping file. The first column stores the value; the second stores the
currency of the MonetaryAmount:
<property name="initialPrice"
<column name="INITIAL_PRICE"/>
If Item is mapped with annotations, you have to declare several columns for this
property. You can’t use the javax.persistence.Column annotation several times,
so a new, Hibernate-specific annotation is needed:
type = "persistence.MonetaryAmountUserType"
@org.hibernate.annotations.Columns(columns = {
@Column(name="INITIAL_PRICE_CURRENCY", length = 2)
private MonetaryAmount initialPrice;
In a Hibernate query, you can now refer to the amount and currency properties of
the custom type, even though they don’t appear anywhere in the mapping document as individual properties:
from Item i
where i.initialPrice.amount > 100.0
and i.initialPrice.currency = 'AUD'
You have extended the buffer between the Java object model and the SQL database schema with the new custom composite type. Both representations are now
more robust to changes. Note that the number of columns isn’t relevant for your
choice of UserType versus CompositeUserType—only your desire to expose value
type properties for Hibernate queries.
Parameterization is a helpful feature for all custom mapping types.
Parameterizing custom types
Let’s assume that you face the initial problem again: conversion of money to a different currency when storing it to the database. Often, problems are more subtle
than a generic conversion; for example, you may store US dollars in some tables
Creating custom mapping types
and Euros in others. You still want to write a single custom mapping type for this,
which can do arbitrary conversions. This is possible if you add the ParameterizedType interface to your UserType or CompositeUserType classes:
public class MonetaryAmountConversionType
implements UserType, ParameterizedType {
// Configuration parameter
private Currency convertTo;
public void setParameterValues(Properties parameters) {
this.convertTo = Currency.getInstance(
// ... Housekeeping methods
public Object nullSafeGet(ResultSet resultSet,
String[] names,
SessionImplementor session,
Object owner)
throws SQLException {
BigDecimal value = resultSet.getBigDecimal( names[0] );
if (resultSet.wasNull()) return null;
// When loading, take the currency from the database
Currency currency = Currency.getInstance(
resultSet.getString( names[1] )
return new MonetaryAmount(value, currency);
public void nullSafeSet(PreparedStatement statement,
Object value,
int index,
SessionImplementor session)
throws SQLException {
if (value==null) {
statement.setNull(index, Types.NUMERIC);
} else {
MonetaryAmount amount = (MonetaryAmount) value;
// When storing, convert the amount to the
// currency this converter was parameterized with
MonetaryAmount dbAmount =
MonetaryAmount.convert(amount, convertTo);
statement.setBigDecimal( index, dbAmount.getAmount() );
statement.setString( index+1,
dbAmount.getCurrencyCode() );
Inheritance and custom types
We left out the usual mandatory housekeeping methods in this example. The
important additional method is setParameterValues() of the ParameterizedType interface. Hibernate calls this method on startup to initialize this class with a
convertTo parameter. The nullSafeSet() methods uses this setting to convert to
the target currency when saving a MonetaryAmount. The nullSafeGet() method
takes the currency that is present in the database and leaves it to the client to deal
with the currency of a loaded MonetaryAmount (this asymmetric implementation
isn’t the best idea, naturally).
You now have to set the configuration parameters in your mapping file when
you apply the custom mapping type. A simple solution is the nested <type> mapping on a property:
<property name="initialPrice">
<column name="INITIAL_PRICE"/>
<column name="INITIAL_PRICE_CUR"/>
<type name="persistence.MonetaryAmountConversionType">
<param name="convertTo">USD</param>
However, this is inconvenient and requires duplication if you have many monetary
amounts in your domain model. A better strategy uses a separate definition of the
type, including all parameters, under a unique name that you can then reuse
across all your mappings. You do this with a separate <typedef>, an element (you
can also use it without parameters):
<typedef class="persistence.MonetaryAmountConversionType"
<param name="convertTo">USD</param>
<typedef class="persistence.MonetaryAmountConversionType"
<param name="convertTo">EUR</param>
What we show here is a binding of a custom mapping type with some arguments
to the names monetary_amount_usd and monetary_amount_eur. This definition
can be placed anywhere in your mapping files; it’s a child element of <hibernatemapping> (as mentioned earlier in the book, larger applications have often one or
several MyCustomTypes.hbm.xml files with no class mappings). With Hibernate
extensions, you can define named custom types with parameters in annotations:
Creating custom mapping types
typeClass = persistence.MonetaryAmountConversionType.class,
parameters = { @Parameter(name="convertTo", value="USD") }
typeClass = persistence.MonetaryAmountConversionType.class,
parameters = { @Parameter(name="convertTo", value="EUR") }
This annotation metadata is global as well, so it can be placed outside any Java
class declaration (right after the import statements) or in a separate file,, as discussed in chapter 2, section 2.2.1, “Using Hibernate Annotations.” A good location in this system is in a file in the
persistence package.
In XML mapping files and annotation mappings, you now refer to the defined
type name instead of the fully qualified class name of your custom type:
<property name="initialPrice"
<column name="INITIAL_PRICE"/>
<column name="INITIAL_PRICE_CUR"/>
@org.hibernate.annotations.Type(type = "monetary_amount_eur")
@Column(name = "BID_AMOUNT"),
@Column(name = "BID_AMOUNT_CUR")
private MonetaryAmount bidAmount;
Let’s look at a different, extremely important, application of custom mapping
types. The type-safe enumeration design pattern can be found in almost all applications.
Mapping enumerations
An enumeration type is a common Java idiom where a class has a constant (small)
number of immutable instances. In CaveatEmptor, this can be applied to credit
cards: for example, to express the possible types a user can enter and the application offers (Mastercard, Visa, and so on). Or, you can enumerate the possible ratings a user can submit in a Comment, about a particular auction.
Inheritance and custom types
In older JDKs, you had to implement such classes (let’s call them CreditCardType and Rating) yourself, following the type-safe enumeration pattern. This is
still the right way to do it if you don’t have JDK 5.0; the pattern and compatible
custom mapping types can be found on the Hibernate community website.
Using enumerations in JDK 5.0
If you use JDK 5.0, you can use the built-in language support for type-safe enumerations. For example, a Rating class looks as follows:
package auction.model;
public enum Rating {
The Comment class has a property of this type:
public class Comment {
private Rating rating;
private Item auction;
This is how you use the enumeration in the application code:
Comment goodComment =
new Comment(Rating.EXCELLENT, thisAuction);
You now have to persist this Comment instance and its Rating. One approach is to
use the actual name of the enumeration and save it to a VARCHAR column in the
COMMENTS table. This RATING column will then contain EXCELLENT, OK, or BAD,
depending on the Rating given.
Let’s write a Hibernate UserType that can load and store VARCHAR-backed enumerations, such as the Rating.
Writing a custom enumeration handler
Instead of the most basic UserType interface, we now want to show you the
EnhancedUserType interface. This interface allows you to work with the Comment
entity in XML representation mode, not only as a POJO (see the discussion of
data representations in chapter 3, section 3.4, “Alternative entity representation”). Furthermore, the implementation you’ll write can support any VARCHARbacked enumeration, not only Rating, thanks to the additional ParameterizedType interface.
Look at the code in listing 5.6.
Creating custom mapping types
Listing 5.6
Custom mapping type for string-backed enumerations
public class StringEnumUserType
implements EnhancedUserType, ParameterizedType {
private Class<Enum> enumClass;
public void setParameterValues(Properties parameters) {
String enumClassName =
try {
enumClass = ReflectHelper.classForName(enumClassName);
} catch (ClassNotFoundException cnfe) {
throw new
HibernateException("Enum class not found", cnfe);
public Class returnedClass() {
return enumClass;
public int[] sqlTypes() {
return new int[] { Hibernate.STRING.sqlType() };
boolean isMutable...
Object deepCopy...
Serializable disassemble...
Object replace...
Object assemble...
boolean equals...
int hashCode...
public Object fromXMLString(String xmlValue) {
return Enum.valueOf(enumClass, xmlValue);
public String objectToSQLString(Object value) {
return '\'' + ( (Enum) value ).name() + '\'';
public String toXMLString(Object value) {
return ( (Enum) value ).name();
public Object nullSafeGet(ResultSet rs,
String[] names,
Object owner)
throws SQLException {
String name = rs.getString( names[0] );
return rs.wasNull() ? null : Enum.valueOf(enumClass, name);
public void nullSafeSet(PreparedStatement st,
Inheritance and custom types
Object value,
int index)
throws SQLException {
if (value == null) {
st.setNull(index, Hibernate.STRING.sqlType());
} else {
st.setString( index, ( (Enum) value ).name() );
The configuration parameter for this custom mapping type is the name of the
enumeration class it’s used for, such as Rating.
It’s also the class that is returned from this method.
A single VARCHAR column is needed in the database table. You keep it portable by
letting Hibernate decide the SQL datatype.
These are the usual housekeeping methods for an immutable type.
The following three methods are part of the EnhancedUserType and are used for
XML marshalling.
When you’re loading an enumeration, you get its name from the database and
create an instance.
When you’re saving an enumeration, you store its name.
Next, you’ll map the rating property with this new custom type.
Mapping enumerations with XML and annotations
In the XML mapping, first create a custom type definition:
<typedef class="persistence.StringEnumUserType"
<param name="enumClassname">auction.model.Rating</param>
You can now use the type named rating in the Comment class mapping:
Creating custom mapping types
Because ratings are immutable, you map it as update="false" and enable direct
field access (no setter method for immutable properties). If other classes besides
Comment have a Rating property, use the defined custom mapping type again.
The definition and declaration of this custom mapping type in annotations
looks the same as the one you did in the previous section.
On the other hand, you can rely on the Java Persistence provider to persist
enumerations. If you have a property in one of your annotated entity classes of
type java.lang.Enum (such as the rating in your Comment), and it isn’t marked as
@Transient or transient (the Java keyword), the Hibernate JPA implementation
must persist this property out of the box without complaining; it has a built-in
type that handles this. This built-in mapping type has to default to a representation of an enumeration in the database. The two common choices are string representation, as you implemented for native Hibernate with a custom type, or
ordinal representation. An ordinal representation saves the position of the
selected enumeration option: for example, 1 for EXCELLENT, 2 for OK, and 3 for
BAD. The database column also defaults to a numeric column. You can change this
default enumeration mapping with the Enumerated annotation on your property:
public class Comment {
@Column(name = "RATING", nullable = false, updatable = false)
private Rating rating;
You’ve now switched to a string-based representation, effectively the same representation your custom type can read and write. You can also use a JPA XML
<entity class="auction.model.Item" access="PROPERTY">
<basic name="rating">
<column name="RATING" nullable="false" updatable="false"/>
You may (rightfully) ask why you have to write your own custom mapping type for
enumerations when obviously Hibernate, as a Java Persistence provider, can persist and load enumerations out of the box. The secret is that Hibernate Annotations includes several custom mapping types that implement the behavior defined
Inheritance and custom types
by Java Persistence. You could use these custom types in XML mappings; however,
they aren’t user friendly (they need many parameters) and weren’t written for
that purpose. You can check the source (such as org.hibernate.type.EnumType
in Hibernate Annotations) to learn their parameters and decide if you want to use
them directly in XML.
Querying with custom mapping types
One further problem you may run into is using enumerated types in Hibernate
queries. For example, consider the following query in HQL that retrieves all comments that are rated “bad”:
Query q =
"from Comment c where c.rating = auction.model.Rating.BAD"
Although this query works if you persist your enumeration as a string (the query
parser uses the enumeration value as a constant), it doesn’t work if you selected
ordinal representation. You have to use a bind parameter and set the rating value
for the comparison programmatically:
Query q =
session.createQuery("from Comment c where c.rating = :rating");
Properties params = new Properties();
q.setParameter("rating", Rating.BAD,
Hibernate.custom(StringEnumUserType.class, params)
The last line in this example uses the static helper method Hibernate.custom()
to convert the custom mapping type to a Hibernate Type; this is a simple way to
tell Hibernate about your enumeration mapping and how to deal with the
Rating.BAD value. Note that you also have to tell Hibernate about any initialization properties the parameterized type may need.
Unfortunately, there is no API in Java Persistence for arbitrary and custom
query parameters, so you have to fall back to the Hibernate Session API and create a Hibernate Query object.
We recommend that you become intimately familiar with the Hibernate type
system and that you consider the creation of custom mapping types an essential
skill—it will be useful in every application you develop with Hibernate or JPA.
In this chapter, you learned how inheritance hierarchies of entities can be
mapped to the database with the four basic inheritance mapping strategies: table
per concrete class with implicit polymorphism, table per concrete class with
unions, table per class hierarchy, and the normalized table per subclass strategy.
You’ve seen how these strategies can be mixed for a particular hierarchy and
when each strategy is most appropriate.
We also elaborated on the Hibernate entity and value type distinction, and
how the Hibernate mapping type system works. You used various built-in types
and wrote your own custom types by utilizing the Hibernate extension points such
as UserType and ParameterizedType.
Table 5.5 shows a summary you can use to compare native Hibernate features
and Java Persistence.
Table 5.5
Hibernate and JPA comparison chart for chapter 5
Hibernate Core
Java Persistence and EJB 3.0
Supports four inheritance mapping
strategies. Mixing of inheritance
strategies is possible.
Four inheritance mapping strategies are standardized; mixing
strategies in one hierarchy isn’t considered portable. Only table
per class hierarchy and table per subclass are required for JPAcompliant providers.
A persistent supertype can be an
abstract class or an interface (with
property accessor methods only).
A persistent supertype can be an abstract class; mapped interfaces aren’t considered portable.
Provides flexible built-in mapping
types and converters for value typed
There is automatic detection of mapping types, with standardized override for temporal and enum mapping types. Hibernate
extension annotation is used for any custom mapping type declaration.
Powerful extendable type system.
The standard requires built-in types for enumerations, LOBs, and
many other value types for which you’d have to write or apply a
custom mapping type in native Hibernate.
The next chapter introduces collection mappings and discusses how you can handle collections of value typed objects (for example, a collection of Strings) and
collections that contain references to entity instances.
Mapping collections
and entity associations
This chapter covers
Basic collection mapping strategies
Mapping collections of value types
Mapping a parent/children entity relationship
Sets, bags, lists, and maps of value types
Two important (and sometimes difficult to understand) topics didn’t appear in
the previous chapters: the mapping of collections, and the mapping of associations between entity classes.
Most developers new to Hibernate are dealing with collections and entity associations for the first time when they try to map a typical parent/child relationship.
But instead of jumping right into the middle, we start this chapter with basic collection mapping concepts and simple examples. After that, you’ll be prepared for
the first collection in an entity association—although we’ll come back to more
complicated entity association mappings in the next chapter. To get the full picture, we recommend you read both chapters.
Sets, bags, lists, and maps of value types
An object of value type has no database identity; it belongs to an entity instance,
and its persistent state is embedded in the table row of the owning entity—at least,
if an entity has a reference to a single instance of a valuetype. If an entity class has
a collection of value types (or a collection of references to value-typed instances),
you need an additional table, the so-called collection table.
Before you map collections of value types to collection tables, remember that
value-typed classes don’t have identifiers or identifier properties. The lifespan of a
value-type instance is bounded by the lifespan of the owning entity instance. A
value type doesn’t support shared references.
Java has a rich collection API, so you can choose the collection interface and
implementation that best fits your domain model design. Let’s walk through the
most common collection mappings.
Suppose that sellers in CaveatEmptor are able to attach images to Items. An
image is accessible only via the containing item; it doesn’t need to support associations from any other entity in your system. The application manages the collection of images through the Item class, adding and removing elements. An image
object has no life outside of the collection; it’s dependent on an Item entity.
In this case, it isn’t unreasonable to model the image class as a value type.
Next. you need to decide what collection to use.
Selecting a collection interface
The idiom for a collection property in the Java domain model is always the same:
private <<Interface>> images = new <<Implementation>>();
// Getter and setter methods
Mapping collections and entity associations
Use an interface to declare the type of the property, not an implementation. Pick
a matching implementation, and initialize the collection right away; doing so
avoids uninitialized collections (we don’t recommend initializing collections late,
in constructors or setter methods).
If you work with JDK 5.0, you’ll likely code with the generic versions of the JDK
collections. Note that this isn’t a requirement; you can also specify the contents of
the collection explicitly in mapping metadata. Here’s a typical generic Set with a
type parameter:
private Set<String> images = new HashSet<String>();
// Getter and setter methods
Out of the box, Hibernate supports the most important JDK collection interfaces.
In other words, it knows how to preserve the semantics of JDK collections, maps,
and arrays in a persistent fashion. Each interface has a matching implementation
supported by Hibernate, and it’s important that you use the right combination.
Hibernate only wraps the collection object you’ve already initialized on declaration of the field (or sometimes replaces it, if it’s not the right one).
Without extending Hibernate, you can choose from the following collections:
A java.util.Set is mapped with a <set> element. Initialize the collection
with a java.util.HashSet. The order of its elements isn’t preserved, and
duplicate elements aren’t allowed. This is the most common persistent collection in a typical Hibernate application.
A java.util.SortedSet can be mapped with <set>, and the sort attribute
can be set to either a comparator or natural ordering for in-memory sorting. Initialize the collection with a java.util.TreeSet instance.
A java.util.List can be mapped with <list>, preserving the position of
each element with an additional index column in the collection table. Initialize with a java.util.ArrayList.
A java.util.Collection can be mapped with <bag> or <idbag>. Java
doesn’t have a Bag interface or an implementation; however, java.util.
Collection allows bag semantics (possible duplicates, no element order is
preserved). Hibernate supports persistent bags (it uses lists internally but
ignores the index of the elements). Use a java.util.ArrayList to initialize a bag collection.
A java.util.Map can be mapped with <map>, preserving key and value
pairs. Use a java.util.HashMap to initialize a property.
Sets, bags, lists, and maps of value types
A java.util.SortedMap can be mapped with <map> element, and the sort
attribute can be set to either a comparator or natural ordering for in-memory sorting. Initialize the collection with a java.util.TreeMap instance.
Arrays are supported by Hibernate with <primitive-array> (for Java primitive value types) and <array> (for everything else). However, they’re rarely
used in domain models, because Hibernate can’t wrap array properties.
You lose lazy loading without bytecode instrumentation, and optimized
dirty checking, essential convenience and performance features for persistent collections.
The JPA standard doesn’t name all these options. The possible standard collection
property types are Set, List, Collection, and Map. Arrays aren’t considered.
Furthermore, the JPA specification only specifies that collection properties
hold references to entity objects. Collections of value types, such as simple String
instances, aren’t standardized. However, the specification document already mentions that future versions of JPA will support collection elements of embeddable
classes (in other words, value types). You’ll need vendor-specific support if you
want to map collections of value types with annotations. Hibernate Annotations
include that support, and we’d expect many other JPA vendors support the same.
If you want to map collection interfaces and implementations not directly supported by Hibernate, you need to tell Hibernate about the semantics of your custom collections. The extension point in Hibernate is called PersistentCollection; usually you extend one of the existing PersistentSet, PersistentBag, or PersistentList classes. Custom persistent collections are not very easy to
write and we don’t recommend doing this if you aren’t an experienced Hibernate
user. An example can be found in the Hibernate test suite source code, as part of
your Hibernate download package.
We now go through several scenarios, always implementing the collection of
item images. You map it first in XML and then with Hibernate’s support for collection annotations. For now, assume that the image is stored somewhere on the filesystem and that you keep just the filename in the database. How images are stored
and loaded with this approach isn’t discussed; we focus on the mapping.
Mapping a set
The simplest implementation is a Set of String image filenames. First, add a collection property to the Item class:
Mapping collections and entity associations
private Set images = new HashSet();
public Set getImages() {
return this.images;
public void setImages(Set images) {
this.images = images;
Now, create the following mapping in the Item’s XML metadata:
<set name="images" table="ITEM_IMAGE">
<key column="ITEM_ID"/>
<element type="string" column="FILENAME" not-null="true"/>
The image filenames are stored in a table named ITEM_IMAGE, the collection
table. From the point of view of the database, this table is a separate entity, a separate table, but Hibernate hides this for you. The <key> element declares the foreign key column in the collection table that references the primary key ITEM_ID
of the owning entity. The <element> tag declares this collection as a collection of
value type instances—in this case, of strings.
A set can’t contain duplicate elements, so the primary key of the ITEM_IMAGE
collection table is a composite of both columns in the <set> declaration: ITEM_ID
and FILENAME. You can see the schema in figure 6.1.
Figure 6.1
Table structure and example data for a
collection of strings
It doesn’t seem likely that you would allow the user to attach the same image more
than once, but let’s suppose you did. What kind of mapping would be appropriate
in that case?
Mapping an identifier bag
An unordered collection that permits duplicate elements is called a bag. Curiously, the Java Collections framework doesn’t include a bag implementation.
However, the java.util.Collection interface has bag semantics, so you only
need a matching implementation. You have two choices:
Sets, bags, lists, and maps of value types
Write the collection property with the java.util.Collection interface,
and, on declaration, initialize it with an ArrayList of the JDK. Map the
collection in Hibernate with a standard <bag> or <idbag> element. Hibernate has a built-in PersistentBag that can deal with lists; however, consistent with the contract of a bag, it ignores the position of elements in the
ArrayList. In other words, you get a persistent Collection.
Write the collection property with the java.util.List interface, and, on
declaration, initialize it with an ArrayList of the JDK. Map it like the previous option, but expose a different collection interface in the domain model
class. This approach works but isn’t recommended, because clients using
this collection property may think the order of elements is always preserved,
which isn’t the case if it’s mapped as a <bag> or <idbag>.
We recommend the first option. Change the type of images in the Item class from
Set to Collection, and initialize it with an ArrayList:
private Collection images = new ArrayList();
public Collection getImages() {
return this.images;
public void setImages(Collection images) {
this.images = images;
Note that the setter method accepts a Collection, which can be anything in the
JDK collection interface hierarchy. However, Hibernate is smart enough to replace
this when persisting the collection. (It also relies on an ArrayList internally, like
you did in the declaration of the field.)
You also have to modify the collection table to permit duplicate FILENAMEs;
the table needs a different primary key. An <idbag> mapping adds a surrogate
key column to the collection table, much like the synthetic identifiers you use for
entity classes:
<idbag name="images" table="ITEM_IMAGE">
<collection-id type="long" column="ITEM_IMAGE_ID">
<generator class="sequence"/>
<key column="ITEM_ID"/>
<element type="string" column="FILENAME" not-null="true"/>
Mapping collections and entity associations
Figure 6.2
A surrogate primary key allows duplicate bag elements.
In this case, the primary key is the generated ITEM_IMAGE_ID, as you can see in figure 6.2. Note that the native generator for primary keys isn’t supported for
<idbag> mappings; you have to name a concrete strategy. This usually isn’t a
problem, because real-world applications often use a customized identifier generator anyway. You can also isolate your identifier generation strategy with placeholders; see chapter 3, section, “Using placeholders.”
Also note that the ITEM_IMAGE_ID column isn’t exposed to the application in
any way. Hibernate manages it internally.
A more likely scenario is one in which you wish to preserve the order in which
images are attached to the Item. There are a number of good ways to do this; one
way is to use a real list, instead of a bag.
Mapping a list
First, let’s update the Item class:
private List images = new ArrayList();
public List getImages() {
return this.images;
public void setImages(List images) {
this.images = images;
A <list> mapping requires the addition of an index column to the collection table.
The index column defines the position of the element in the collection. Thus,
Hibernate is able to preserve the ordering of the collection elements. Map the
collection as a <list>:
<list name="images" table="ITEM_IMAGE">
<key column="ITEM_ID"/>
<list-index column="POSITION"/>
Sets, bags, lists, and maps of value types
<element type="string" column="FILENAME" not-null="true"/>
(There is also an index element in the XML DTD, for compatibility with Hibernate 2.x. The new list-index is recommended; it’s less confusing and does the
same thing.)
The primary key of the collection table is a composite of ITEM_ID and POSITION. Notice that duplicate elements (FILENAME) are now allowed, which is consistent with the semantics of a list, see figure 6.3.
Figure 6.3
The collection table preserves the position of each element.
The index of the persistent list starts at zero. You could change this, for example,
with <list-index base="1".../> in your mapping. Note that Hibernate adds null
elements to your Java list if the index numbers in the database aren’t continuous.
Alternatively, you could map a Java array instead of a list. Hibernate supports
this; an array mapping is virtually identical to the previous example, except with
different element and attribute names (<array> and <array-index>). However,
for reasons explained earlier, Hibernate applications rarely use arrays.
Now, suppose that the images for an item have user-supplied names in addition to the filename. One way to model this in Java is a map, with names as keys
and filenames as values of the map.
Mapping a map
Again, make a small change to the Java class:
private Map images = new HashMap();
public Map getImages() {
return this.images;
public void setImages(Map images) {
this.images = images;
Mapping a <map> (pardon us) is similar to mapping a list.
Mapping collections and entity associations
Figure 6.4
Tables for a map, using strings as indexes and elements
<map name="images" table="ITEM_IMAGE">
<key column="ITEM_ID"/>
<map-key column="IMAGENAME" type="string"/>
<element type="string" column="FILENAME" not-null="true"/>
The primary key of the collection table is a composite of ITEM_ID and IMAGENAME.
The IMAGENAME column holds the keys of the map. Again, duplicate elements are
allowed; see figure 6.4 for a graphical view of the tables.
This map is unordered. What if you want to always sort your map by the name
of the image?
Sorted and ordered collections
In a startling abuse of the English language, the words sorted and ordered mean different things when it comes to Hibernate persistent collections. A sorted collection is
sorted in memory using a Java comparator. An ordered collection is ordered at the
database level using an SQL query with an order by clause.
Let’s make the map of images a sorted map. First, you need to change the initialization of the Java property to a java.util.TreeMap and switch to the
java.util.SortedMap interface:
private SortedMap images = new TreeMap();
public SortedMap getImages() {
return this.images;
public void setImages(SortedMap images) {
this.images = images;
Hibernate handles this collection accordingly, if you map it as sorted:
Sets, bags, lists, and maps of value types
<map name="images"
<key column="ITEM_ID"/>
<map-key column="IMAGENAME" type="string"/>
<element type="string" column="FILENAME" not-null="true"/>
By specifying sort="natural", you tell Hibernate to use a SortedMap and to sort
the image names according to the compareTo() method of java.lang.String. If
you need some other sort algorithm (for example, reverse alphabetical order),
you may specify the name of a class that implements java.util.Comparator in
the sort attribute. For example:
<map name="images"
<key column="ITEM_ID"/>
<map-key column="IMAGENAME" type="string"/>
<element type="string" column="FILENAME" not-null="true"/>
A java.util.SortedSet (with a java.util.TreeSet implementation) is mapped
like this:
<set name="images"
<key column="ITEM_ID"/>
<element type="string" column="FILENAME" not-null="true"/>
Bags may not be sorted (there is no TreeBag, unfortunately), nor may lists; the
order of list elements is defined by the list index.
Alternatively, instead of switching to the Sorted* interfaces and the (Tree*
implementations), you may want to work with a linked map and to sort elements
on the database side, not in memory. Keep the Map/HashMap declaration in the
Java class, and create the following mapping:
<map name="images"
order-by="IMAGENAME asc">
Mapping collections and entity associations
<key column="ITEM_ID"/>
<map-key column="IMAGENAME" type="string"/>
<element type="string" column="FILENAME" not-null="true"/>
The expression in the order-by attribute is a fragment of an SQL order by
clause. In this case, Hibernate orders the collection elements by the IMAGENAME
column in ascending order during loading of the collection. You can even include
an SQL function call in the order-by attribute:
<map name="images"
order-by="lower(FILENAME) asc">
<key column="ITEM_ID"/>
<map-key column="IMAGENAME" type="string"/>
<element type="string" column="FILENAME" not-null="true"/>
You can order by any column of the collection table. Internally, Hibernate uses a
LinkedHashMap, a variation of a map that preserves the insertion order of key elements. In other words, the order that Hibernate uses to add the elements to the
collection, during loading of the collection, is the iteration order you see in your
application. The same can be done with a set: Hibernate internally uses a
LinkedHashSet. In your Java class, the property is a regular Set/HashSet, but
Hibernate’s internal wrapping with a LinkedHashSet is again enabled with the
order-by attribute:
<set name="images"
order-by="FILENAME asc">
<key column="ITEM_ID"/>
<element type="string" column="FILENAME" not-null="true"/>
You can also let Hibernate order the elements of a bag for you during collection
loading. Your Java collection property is either Collection/ArrayList or List/
ArrayList. Internally, Hibernate uses an ArrayList to implement a bag that preserves insertion-iteration order:
<idbag name="images"
order-by="ITEM_IMAGE_ID desc">
Collections of components
<collection-id type="long" column="ITEM_IMAGE_ID">
<generator class="sequence"/>
<key column="ITEM_ID"/>
<element type="string" column="FILENAME" not-null="true"/>
The linked collections Hibernate uses internally for sets and maps are available
only in JDK 1.4 or later; older JDKs don’t come with a LinkedHashMap and
LinkedHashSet. Ordered bags are available in all JDK versions; internally, an
ArrayList is used.
In a real system, it’s likely that you’ll need to keep more than just the image
name and filename. You’ll probably need to create an Image class for this extra
information. This is the perfect use case for a collection of components.
Collections of components
You could map Image as an entity class and create a one-to-many relationship
from Item to Image. However, this isn’t necessary, because Image can be modeled
as a value type: Instances of this class have a dependent lifecycle, don’t need their
own identity, and don’t have to support shared references.
As a value type, the Image class defines the properties name, filename, sizeX,
and sizeY. It has a single association with its owner, the Item entity class, as shown
in figure 6.5.
As you can see from the composition association style (the black diamond),
Image is a component of Item, and Item is the entity that is responsible for the
lifecycle of Image instances. The multiplicity of the association further declares
this association as many-valued—that is, many (or zero) Image instances for the
same Item instance.
Let’s walk through the implementation of this in Java and through a mapping
in XML.
name : String
description : String
initialPrice : BigDecimal
reservePrice : BigDecimal
startDate : Date
endDate : Date
state : ItemState
approvalDatetime : Date
0..* name : String
filename : String
sizeX : int
sizeY : int
Figure 6.5
Collection of Image components
in Item
Mapping collections and entity associations
Writing the component class
First, implement the Image class as a regular POJO. As you know from chapter 4,
component classes don’t have an identifier property. You must implement
equals() (and hashCode()) and compare the name, filename, sizeX, and sizeY
properties. Hibernate relies on this equality routine to check instances for
modifications. A custom implementation of equals() and hashCode() isn’t
required for all component classes (we would have mentioned this earlier). However, we recommend it for any component class because the implementation is
straightforward, and “better safe than sorry” is a good motto.
The Item class may have a Set of images, with no duplicates allowed. Let’s map
this to the database.
Mapping the collection
Collections of components are mapped similarly to collections of JDK value type.
The only difference is the use of <composite-element> instead of an <element>
tag. An ordered set of images (internally, a LinkedHashSet) can be mapped like
<set name="images"
order-by="IMAGENAME asc">
<key column="ITEM_ID"/>
<composite-element class="Image">
<property name="name" column="IMAGENAME" not-null="true"/>
<property name="filename" column="FILENAME" not-null="true"/>
<property name="sizeX" column="SIZEX" not-null="true"/>
<property name="sizeY" column="SIZEY" not-null="true"/>
The tables with example data are shown in figure 6.6.
This is a set, so the primary key of the collection table is a composite of the
key column and all element columns: ITEM_ID, IMAGENAME, FILENAME, SIZEX, and
SIZEY. Because these columns all appear in the primary key, you needed to
declare them with not-null="true" (or make sure they’re NOT NULL in any existing schema). No column in a composite primary key can be nullable—you can’t
identify what you don’t know. This is probably a disadvantage of this particular
mapping. Before you improve this (as you may guess, with an identifier bag), let’s
enable bidirectional navigation.
Collections of components
Figure 6.6
Example data tables for a collection
of components mapping
Enabling bidirectional navigation
The association from Item to Image is unidirectional. You can navigate to the
images by accessing the collection through an Item instance and iterating:
anItem.getImages().iterator(). This is the only way you can get these image
objects; no other entity holds a reference to them (value type again).
On the other hand, navigating from an image back to an item doesn’t make
much sense. However, it may be convenient to access a back pointer like anImage.getItem() in some cases. Hibernate can fill in this property for you if you
add a <parent> element to the mapping:
<set name="images"
order-by="IMAGE_NAME asc">
<key column="ITEM_ID"/>
<composite-element class="Image">
<parent name="item"/>
<property name="name" column="IMAGENAME" not-null="true"/>
<property name="filename" column="FILENAME" not-null="true"/>
<property name="sizeX" column="SIZEX" not-null="true"/>
<property name="sizeY" column="SIZEY" not-null="true"/>
True bidirectional navigation is impossible, however. You can’t retrieve an Image
independently and then navigate back to its parent Item. This is an important
issue: You can load Image instances by querying for them. But these Image objects
won’t have a reference to their owner (the property is null) when you query in
HQL or with a Criteria. They’re retrieved as scalar values.
Mapping collections and entity associations
Finally, declaring all properties as not-null is something you may not want.
You need a different primary key for the IMAGE collection table, if any of the property columns are nullable.
Avoiding not-null columns
Analogous to the additional surrogate identifier property an <idbag> offers, a
surrogate key column would come in handy now. As a side effect, an <idset>
would also allow duplicates—a clear conflict with the notion of a set. For this and
other reasons (including the fact that nobody ever asked for this feature), Hibernate doesn’t offer an <idset> or any surrogate identifier collection other than
an <idbag>. Hence, you need to change the Java property to a Collection with
bag semantics:
private Collection images = new ArrayList();
public Collection getImages() {
return this.images;
public void setImages(Collection images) {
this.images = images;
This collection now also allows duplicate Image elements—it’s the responsibility of
your user interface, or any other application code, to avoid these duplicate elements if you require set semantics. The mapping adds the surrogate identifier column to the collection table:
<idbag name="images"
order-by="IMAGE_NAME asc">
<collection-id type="long" column="ITEM_IMAGE_ID">
<generator class="sequence"/>
<key column="ITEM_ID"/>
<composite-element class="Image">
<property name="name" column="IMAGENAME"/>
<property name="filename" column="FILENAME" not-null="true"/>
<property name="sizeX" column="SIZEX"/>
<property name="sizeY" column="SIZEY"/>
The primary key of the collection table is now the ITEM_IMAGE_ID column, and it
isn’t important that you implement equals() and hashCode() on the Image class
Collections of components
Figure 6.7
Collection of Image components using a bag with surrogate key
(at least, Hibernate doesn’t require it). Nor do you have to declare the properties
with not-null="true". They may be nullable, as can be seen in figure 6.7.
We should point out that there isn’t a great deal of difference between this bag
mapping and a standard parent/child entity relationship like the one you map
later in this chapter. The tables are identical. The choice is mainly a matter of
taste. A parent/child relationship supports shared references to the child entity
and true bidirectional navigation. The price you’d pay is more complex lifecycles
of objects. Value-typed instances can be created and associated with the persistent
Item by adding a new element to the collection. They can be disassociated and
permanently deleted by removing an element from the collection. If Image would
be an entity class that supports shared references, you’d need more code in your
application for the same operations, as you’ll see later.
Another way to switch to a different primary key is a map. You can remove the
name property from the Image class and use the image name as the key of a map:
<map name="images"
order-by="IMAGENAME asc">
<key column="ITEM_ID"/>
<map-key type="string" column="IMAGENAME"/>
<composite-element class="Image">
<property name="filename" column="FILENAME" not-null="true"/>
<property name="sizeX" column="SIZEX"/>
<property name="sizeY" column="SIZEY"/>
The primary key of the collection table is now a composite of ITEM_ID and IMAGENAME.
A composite element class like Image isn’t limited to simple properties of basic
type like filename. It may contain other components, mapped with <nestedcomposite-element>, and even <many-to-one> associations to entities. It can’t
Mapping collections and entity associations
own collections, however. A composite element with a many-to-one association is
useful, and we come back to this kind of mapping in the next chapter.
This wraps up our discussion of basic collection mappings in XML. As we mentioned at the beginning of this section, mapping collections of value types with
annotations is different compared with mappings in XML; at the time of writing, it
isn’t part of the Java Persistence standard but is available in Hibernate.
Mapping collections with annotations
The Hibernate Annotations package supports nonstandard annotations for the
mapping of collections that contain value-typed elements, mainly org.hibernate.annotations.CollectionOfElements. Let’s walk through some of the most
common scenarios again.
Basic collection mapping
The following maps a simple collection of String elements:
targetElement = java.lang.String.class
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
@Column(name = "FILENAME", nullable = false)
private Set<String> images = new HashSet<String>();
The collection table ITEM_IMAGE has two columns; together, they form the composite primary key. Hibernate can automatically detect the type of the element if
you use generic collections. If you don’t code with generic collections, you need
to specify the element type with the targetElement attribute—in the previous
example it’s therefore optional.
To map a persistent List, add @org.hibernate.annotations.IndexColumn
with an optional base for the index (default is zero):
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
name="POSITION", base = 1
Mapping collections with annotations
@Column(name = "FILENAME")
private List<String> images = new ArrayList<String>();
If you forget the index column, this list would be treated as a bag collection,
equivalent to a <bag> in XML.
For collections of value types, you'd usually use <idbag> to get a surrogate primary key on the collection table. A <bag> of value typed elements doesn’t really
work; duplicates would be allowed at the Java level, but not in the database. On
the other hand, pure bags are great for one-to-many entity associations, as you’ll
see in chapter 7.
To map a persistent map, use @org.hibernate.annotations.MapKey:
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
columns = @Column(name="IMAGENAME")
@Column(name = "FILENAME")
private Map<String, String> images = new HashMap<String, String>();
If you forget the map key, the keys of this map would be automatically mapped to
the column MAPKEY.
If the keys of the map are not simple strings but of an embeddable class, you
can specify multiple map key columns that hold the individual properties of the
embeddable component. Note that @org.hibernate.annotations.MapKey is a
more powerful replacement for @javax.persistence.MapKey, which isn’t very
useful (see chapter 7, section 7.2.4 “Mapping maps”).
Sorted and ordered collections
A collection can also be sorted or ordered with Hibernate annotations:
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
@Column(name = "FILENAME", nullable = false)
type = org.hibernate.annotations.SortType.NATURAL
private SortedSet<String> images = new TreeSet<String>();
Mapping collections and entity associations
(Note that without the @JoinColumn and/or @Column, Hibernate applies the
usual naming conventions and defaults for the schema.) The @Sort annotation
supports various SortType attributes, with the same semantics as the XML mapping options. The shown mapping uses a java.util.SortedSet (with a java.
util.TreeSet implementation) and natural sort order. If you enable SortType.
COMPARATOR, you also need to set the comparator attribute to a class that implements your comparison routine. Maps can also be sorted; however, as in XML
mappings, there is no sorted Java bag or a sorted list (which has a persistent
ordering of elements, by definition).
Maps, sets, and even bags, can be ordered on load, by the database, through an
SQL fragment in the ORDER BY clause:
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
@Column(name = "FILENAME", nullable = false)
clause = "FILENAME asc"
private Set<String> images = new HashSet<String>();
The clause attribute of the Hibernate-specific @OrderBy annotation is an SQL
fragment that is passed on directly to the database; it can even contain function
calls or any other native SQL keyword. See our explanation earlier for details
about the internal implementation of sorting and ordering; the annotations are
equivalent to the XML mappings.
Mapping a collection of embedded objects
Finally, you can map a collection of components, of user-defined value-typed elements. Let’s assume that you want to map the same Image component class you’ve
seen earlier in this chapter, with image names, sizes, and so on.
You need to add the @Embeddable component annotation on that class to
enable embedding:
public class Image {
Item item;
@Column(length = 255, nullable = false)
private String name;
Mapping collections with annotations
@Column(length = 255, nullable = false)
private String filename;
@Column(nullable = false)
private int sizeX;
@Column(nullable = false)
private int sizeY;
... // Constructor, accessor methods, equals()/hashCode()
Note that you again map a back pointer with a Hibernate annotation; anImage.getItem() can be useful. You can leave out this property if you don’t need
this reference. Because the collection table needs all the component columns as
the composite primary key, it’s important that you map these columns as NOT
NULL. You can now embed this component in a collection mapping and even override column definitions (in the following example you override the name of a single column of the component collection table; all others are named with the
default strategy):
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
name = "",
column = @Column(name = "IMAGENAME",
length = 255,
nullable = false)
private Set<Image> images = new HashSet<Image>();
To avoid the non-nullable component columns you need a surrogate primary key
on the collection table, like <idbag> provides in XML mappings. With annotations, use the @CollectionId Hibernate extension:
name = "ITEM_IMAGE",
joinColumns = @JoinColumn(name = "ITEM_ID")
columns = @Column(name = "ITEM_IMAGE_ID"),
type = @org.hibernate.annotations.Type(type = "long"),
generator = "sequence"
private Collection<Image> images = new ArrayList<Image>();
Mapping collections and entity associations
You’ve now mapped all the basic and some more complex collections with XML
mapping metadata, and annotations. Switching focus, we now consider collections with elements that aren’t value types, but references to other entity
instances. Many Hibernate users try to map a typical parent/children entity relationship, which involves a collection of entity references.
Mapping a parent/children relationship
From our experience with the Hibernate user community, we know that the first
thing many developers try to do when they begin using Hibernate is a mapping of
a parent/children relationship. This is usually the first time you encounter
collections. It’s also the first time you have to think about the differences between
entities and value types, or get lost in the complexity of ORM.
Managing the associations between classes and the relationships between
tables is at the heart of ORM. Most of the difficult problems involved in implementing an ORM solution relate to association management.
You mapped relationships between classes of value type in the previous section
and earlier in the book, with varying multiplicity of the relationship ends. You
map a one multiplicity with a simple <property> or as a <component>. The many
association multiplicity requires a collection of value types, with <element> or
<composite-element> mappings.
Now you want to map one- and many-valued relationships between entity
classes. Clearly, entity aspects such as shared references and independent lifecycle complicate this relationship mapping. We’ll approach these issues step by step; and, in
case you aren’t familiar with the term multiplicity, we’ll also discuss that.
The relationship we show in the following sections is always the same, between
the Item and Bid entity classes, as can be seen in figure 6.8.
Memorize this class diagram. But first, there’s something we need to explain
up front.
If you’ve used EJB CMP 2.0, you’re familiar with the concept of a managed
association (or managed relationship). CMP associations are called container
managed relationships (CMRs) for a reason. Associations in CMP are inherently
bidirectional. A change made to one side of an association is instantly reflected at
the other side. For example, if you call aBid.setItem(anItem), the container
automatically calls anItem.getBids().add(aBid).
Figure 6.8
Relationship between Item and Bid
Mapping a parent/children relationship
POJO-oriented persistence engines such as Hibernate don’t implement managed associations, and POJO standards such as EJB 3.0 and Java Persistence don’t
require managed associations. Contrary to EJB 2.0 CMR, Hibernate and JPA associations are all inherently unidirectional. As far as Hibernate is concerned, the association from Bid to Item is a different association than the association from Item to
Bid! This is a good thing—otherwise your entity classes wouldn’t be usable outside
of a runtime container (CMR was a major reason why EJB 2.1 entities were considered problematic).
Because associations are so important, you need a precise language for classifying them.
In describing and classifying associations, we’ll almost always use the term multiplicity. In our example, the multiplicity is just two bits of information:
Can there be more than one Bid for a particular Item?
Can there be more than one Item for a particular Bid?
After glancing at the domain model (see figure 6.8), you can conclude that the
association from Bid to Item is a many-to-one association. Recalling that associations are directional, you classify the inverse association from Item to Bid as a oneto-many association.
There are only two more possibilities: many-to-many and one-to-one. We’ll get
back to these in the next chapter.
In the context of object persistence, we aren’t interested in whether many
means two or a maximum of five or unrestricted. And we’re only barely interested in optionality of most associations; we don’t especially care whether an associated instance is required or if the other end in an association can be NULL
(meaning zero-to-many and to-zero association) However, these are important
aspects in your relational data schema that influence your choice of integrity
rules and the constraints you define in SQL DDL (see chapter 8, section 8.3,
“Improving schema DDL”).
The simplest possible association
The association from Bid to Item (and vice versa) is an example of the simplest
possible kind of entity association. You have two properties in two classes. One is a
collection of references, and the other a single reference.
First, here’s the Java class implementation of Bid:
Mapping collections and entity associations
public class Bid {
private Item item;
public void setItem(Item item) {
this.item = item;
public Item getItem() {
return item;
Next, this is the Hibernate mapping for this association:
This mapping is called a unidirectional many-to-one association. (Actually, because it’s
unidirectional, you don’t know what is on the other side, and you could just as
well call this mapping a unidirectional to-one association mapping.) The column
ITEM_ID in the BID table is a foreign key to the primary key of the ITEM table.
You name the class Item, which is the target of this association, explicitly. This
is usually optional, because Hibernate can determine the target type with reflection on the Java property.
You added the not-null attribute because you can’t have a bid without an
item—a constraint is generated in the SQL DDL to reflect this. The foreign key
column ITEM_ID in the BID can never be NULL, the association is not to-zero-orone. The table structure for this association mapping is shown in figure 6.9.
Figure 6.9
Table relationships and keys for a
one-to-many mapping
Mapping a parent/children relationship
In JPA, you map this association with the @ManyToOne annotation, either on the
field or getter method, depending on the access strategy for the entity (determined by the position of the @Id annotation):
public class Bid {
@ManyToOne( targetEntity = auction.model.Item.class )
@JoinColumn(name = "ITEM_ID", nullable = false)
private Item item;
There are two optional elements in this mapping. First, you don’t have to include
the targetEntity of the association; it’s implicit from the type of the field. An
explicit targetEntity attribute is useful in more complex domain models—for
example, when you map a @ManyToOne on a getter method that returns a delegate
class, which mimics a particular target entity interface.
The second optional element is the @JoinColumn. If you don’t declare the
name of the foreign key column, Hibernate automatically uses a combination of
the target entity name and the database identifier property name of the target
entity. In other words, if you don’t add a @JoinColumn annotation, the default
name for the foreign key column is item plus id, separated with an underscore.
However, because you want to make the foreign key column NOT NULL, you need
the annotation anyway to set nullable = false. If you generate the schema with
the Hibernate Tools, the optional="false" attribute on the @ManyToOne would
also result in a NOT NULL constraint on the generated column.
This was easy. It’s critically important to realize that you can write a complete
application without using anything else. (Well, maybe a shared primary key oneto-one mapping from time to time, as shown in the next chapter.) You don’t
need to map the other side of this class association, and you’ve already mapped
everything present in the SQL schema (the foreign key column). If you need the
Item instance for which a particular Bid was made, call aBid.getItem(), utilizing the entity association you created. On the other hand, if you need all bids
that have been made for an item, you can write a query (in whatever language
Hibernate supports).
One of the reasons you use a full object/relational mapping tool like Hibernate is, of course, that you don’t want to write that query.
Mapping collections and entity associations
Making the association bidirectional
You want to be able to easily fetch all the bids for a particular item without an
explicit query, by navigating and iterating through the network of persistent
objects. The most convenient way to do this is with a collection property on Item:
anItem.getBids().iterator(). (Note that there are other good reasons to map
a collection of entity references, but not many. Always try to think of these kinds
of collection mappings as a feature, not a requirement. If it gets too difficult,
don’t do it.)
You now map a collection of entity references by making the relationship
between Item and Bid bidirectional.
First add the property and scaffolding code to the Item class:
public class Item {
private Set bids = new HashSet();
public void setBids(Set bids) {
this.bids = bids;
public Set getBids() {
return bids;
public void addBid(Bid bid) {
You can think of the code in addBid() (a convenience method) as implementing
a managed association in the object model! (We had more to say about these
methods in chapter 3, section 3.2, “Implementing the domain model.” You may
want to review the code examples there.)
A basic mapping for this one-to-many association looks like this:
<set name="bids">
<key column="ITEM_ID"/>
Mapping a parent/children relationship
<one-to-many class="Bid"/>
If you compare this with the collection mappings earlier in this chapter, you see
that you map the content of the collection with a different element, <one-tomany>. This indicates that the collection contains not value type instances, but references to entity instances. Hibernate now knows how to treat shared references
and the lifecycle of the associated objects (it disables all the implicit dependent
lifecycle of value type instances). Hibernate also knows that the table used for the
collection is the same table the target entity class is mapped to—the <set> mapping needs no table attribute.
The column mapping defined by the <key> element is the foreign key column
ITEM_ID of the BID table, the same column you already mapped on the other side
of the relationship.
Note that the table schema didn’t change; it’s the same as it was before you
mapped the many side of the association. There is, however, one difference: The
not null="true" attribute is missing. The problem is that you now have two different unidirectional associations mapped to the same foreign key column. What
side controls that column?
At runtime, there are two different in-memory representations of the same foreign key value: the item property of Bid and an element of the bids collection
held by an Item. Suppose the application modifies the association, by, for example, adding a bid to an item in this fragment of the addBid() method:
This code is fine, but in this situation, Hibernate detects two changes to the inmemory persistent instances. From the point of view of the database, only one
value has to be updated to reflect these changes: the ITEM_ID column of the
BID table.
Hibernate doesn’t transparently detect the fact that the two changes refer to
the same database column, because at this point you’ve done nothing to indicate
that this is a bidirectional association. In other words, you’ve mapped the same
column twice (it doesn’t matter that you did this in two mapping files), and Hibernate always needs to know about this because it can’t detect this duplicate automatically (there is no reasonable default way it could be handled).
Mapping collections and entity associations
You need one more thing in the association mapping to make this a real bidirectional association mapping. The inverse attribute tells Hibernate that the collection is a mirror image of the <many-to-one> association on the other side:
<set name="bids"
<key column="ITEM_ID"/>
<one-to-many class="Bid"/>
Without the inverse attribute, Hibernate tries to execute two different SQL statements, both updating the same foreign key column, when you manipulate the
link between two instances. By specifying inverse="true", you explicitly tell
Hibernate which end of the link it should not synchronize with the database. In
this example, you tell Hibernate that it should propagate changes made at the
Bid end of the association to the database, ignoring changes made only to the
bids collection.
If you only call anItem.getBids().add(bid), no changes are made persistent!
You get what you want only if the other side, aBid.setItem(anItem), is set correctly. This is consistent with the behavior in Java without Hibernate: If an association is bidirectional, you have to create the link with pointers on two sides, not just
one. It’s the primary reason why we recommend convenience methods such as
addBid()—they take care of the bidirectional references in a system without container-managed relationships.
Note that an inverse side of an association mapping is always ignored for the
generation of SQL DDL by the Hibernate schema export tools. In this case, the
ITEM_ID foreign key column in the BID table gets a NOT NULL constraint, because
you’ve declared it as such in the noninverse <many-to-one> mapping.
(Can you switch the inverse side? The <many-to-one> element doesn’t have an
inverse attribute, but you can map it with update="false" and insert="false"
to effectively ignore it for any UPDATE or INSERT statements. The collection side is
then noninverse and considered for insertion or updating of the foreign key column. We’ll do this in the next chapter.)
Mapping a parent/children relationship
Let’s map this inverse collection side again, with JPA annotations:
public class Item {
@OneToMany(mappedBy = "item")
private Set<Bid> bids = new HashSet<Bid>();
The mappedBy attribute is the equivalent of the inverse attribute in XML mappings; however, it has to name the inverse property of the target entity. Note that
you don’t specify the foreign key column again here (it’s mapped by the other
side), so this isn’t as verbose as the XML.
You now have a working bidirectional many-to-one association (which could also
be called a bidirectional one-to-many association). One final option is missing if
you want to make it a true parent/children relationship.
Cascading object state
The notion of a parent and a child implies that one takes care of the other. In
practice, this means you need fewer lines of code to manage a relationship
between a parent and a child, because some things can be taken care of automatically. Let’s explore the options.
The following code creates a new Item (which we consider the parent) and a
new Bid instance (the child):
Item newItem = new Item();
Bid newBid = new Bid();
newItem.addBid(newBid); // Set both sides of the association;;
The second call to seems redundant, if we’re talking about a true
parent/children relationship. Hold that thought, and think about entities and
value types again: If both classes are entities, their instances have a completely
independent lifecycle. New objects are transient and have to be made persistent if
you want to store them in the database. Their relationship doesn’t influence their
lifecycle, if they’re entities. If Bid would be a value type, the state of a Bid instance
is the same as the state of its owning entity. In this case, however, Bid is a separate
entity with its own completely independent state. You have three choices:
Mapping collections and entity associations
Take care of the independent instances yourself, and execute additional
save() and delete() calls on the Bid objects when needed—in addition to
the Java code needed to manage the relationship (adding and removing references from collections, and so on).
Make the Bid class a value type (a component). You can map the collection with a <composite-element> and get the implicit lifecycle. However,
you lose other aspects of an entity, such as possible shared references to
an instance.
Do you need shared references to Bid objects? Currently, a particular Bid
instance isn’t referenced by more than one Item. However, imagine that a
User entity also has a collection of bids, made by the user. To support
shared references, you have to map Bid as an entity. Another reason you
need shared references is the successfulBid association from Item in the
full CaveatEmptor model. In this case, Hibernate offers transitive persistence,
a feature you can enable to save lines of code and to let Hibernate manage
the lifecycle of associated entity instances automatically.
You don’t want to execute more persistence operations than absolutely necessary,
and you don’t want to change your domain model—you need shared references
to Bid instances. The third option is what you’ll use to simplify this parent/children example.
Transitive persistence
When you instantiate a new Bid and add it to an Item, the bid should become persistent automatically. You’d like to avoid making the Bid persistent explicitly with
an extra save() operation.
To enable this transitive state across the association, add a cascade option to
the XML mapping:
<set name="bids"
<key column="ITEM_ID"/>
<one-to-many class="Bid"/>
Mapping a parent/children relationship
The cascade="save-update" attribute enables transitive persistence for Bid
instances, if a particular Bid is referenced by a persistent Item, in the collection.
The cascade attribute is directional: It applies to only one end of the association. You could also add cascade="save-update" to the <many-to-one> association in the mapping of Bid, but because bids are created after items, doing so
doesn’t make sense.
JPA also supports cascading entity instance state on associations:
public class Item {
@OneToMany(cascade = { CascadeType.PERSIST, CascadeType.MERGE },
mappedBy = "item")
private Set<Bid> bids = new HashSet<Bid>();
Cascading options are per operation you’d like to be transitive. For native Hibernate, you cascade the save and update operations to associated entities with
cascade="save-update". Hibernate’s object state management always bundles
these two things together, as you’ll learn in future chapters. In JPA, the (almost)
equivalent operations are persist and merge.
You can now simplify the code that links and saves an Item and a Bid, in native
Item newItem = new Item();
Bid newBid = new Bid();
newItem.addBid(newBid); // Set both sides of the association;
All entities in the bids collection are now persistent as well, just as they would be
if you called save() on each Bid manually. With the JPA EntityManager API, the
equivalent to a Session, the code is as follows:
Item newItem = new Item();
Bid newBid = new Bid();
newItem.addBid(newBid); // Set both sides of the association
Don’t worry about the update and merge operations for now; we’ll come back to
them later in the book.
Mapping collections and entity associations
What is the effect of cascade on inverse? Many new Hibernate users ask
this question. The answer is simple: The cascade attribute has nothing to
do with the inverse attribute. They often appear on the same collection
mapping. If you map a collection of entities as inverse="true", you’re
controlling the generation of SQL for a bidirectional association mapping. It’s a hint that tells Hibernate you mapped the same foreign key
column twice. On the other hand, cascading is used as a convenience feature. If you decide to cascade operations from one side of an entity relationship to associated entities, you save the lines of code needed to
manage the state of the other side manually. We say that object state
becomes transitive. You can cascade state not only on collections of entities, but on all entity association mappings. cascade and inverse have in
common the fact that they don’t appear on collections of value types or
on any other value-type mappings. The rules for these are implied by the
nature of value types.
Are you finished now? Well, perhaps not quite.
Cascading deletion
With the previous mapping, the association between Bid and Item is fairly loose.
So far, we have only considered making things persistent as a transitive state. What
about deletion?
It seems reasonable that deletion of an item implies deletion of all bids for the
item. In fact, this is what the composition (the filled out diamond) in the UML
diagram means. With the current cascading operations, you have to write the following code to make that happen:
Item anItem = // Load an item
// Delete all the referenced bids
for ( Iterator<Bid> it = anItem.getBids().iterator();
it.hasNext(); ) {
Bid bid =;
// Remove reference from collection
// Delete it from the database
// Finally, delete the item
First you remove the references to the bids by iterating the collection. You delete
each Bid instance in the database. Finally, the Item is deleted. Iterating and
removing the references in the collection seems unnecessary; after all, you’ll
delete the Item at the end anyway. If you can guarantee that no other object (or
Mapping a parent/children relationship
row in any other table) holds a reference to these bids, you can make the deletion transitive.
Hibernate (and JPA) offer a cascading option for this purpose. You can enable
cascading for the delete operation:
<set name="bids"
cascade="save-update, delete">
The operation you cascade in JPA is called remove:
public class Item {
@OneToMany(cascade = { CascadeType.PERSIST,
CascadeType.REMOVE },
mappedBy = "item")
private Set<Bid> bids = new HashSet<Bid>();
The same code to delete an item and all its bids is reduced to the following, in
Hibernate or with JPA:
Item anItem = // Load an item
The delete operation is now cascaded to all entities referenced in the collection.
You no longer have to worry about removal from the collection and manually
deleting those entities one by one.
Let’s consider one further complication. You may have shared references to
the Bid objects. As suggested earlier, a User may have a collection of references to
the Bid instances they made. You can’t delete an item and all its bids without
removing these references first. You may get an exception if you try to commit this
transaction, because a foreign key constraint may be violated.
You have to chase the pointers. This process can get ugly, as you can see in the following code, which removes all references from all users who have references
before deleting the bids and finally the item:
Item anItem = // Load an item
// Delete all the referenced bids
for ( Iterator<Bid> it = anItem.getBids().iterator();
it.hasNext(); ) {
Mapping collections and entity associations
Bid bid =;
// Remove references from users who have made this bid
Query q = session.createQuery(
"from User u where :bid in elements(u.bids)"
q.setParameter("bid", bid);
Collection usersWithThisBid = q.list();
for (Iterator itUsers = usersWithThisBid.iterator();
itUsers.hasNext();) {
User user = (User);
// Finally, delete the item and the associated bids
Obviously, the additional query (in fact, many queries) isn’t what you want. However, in a network object model, you don’t have any choice other than executing
code like this if you want to correctly set pointers and references—there is no
persistent garbage collector or other automatic mechanism. No Hibernate cascading option helps you; you have to chase all references to an entity before you
finally delete it.
(This isn’t the whole truth: Because the BIDDER_ID foreign key column that
represents the association from User to Bid is in the BID table, these references
are automatically removed at the database level if a row in the BID table is deleted.
This doesn’t affect any objects that are already present in memory in the current
unit of work, and it also doesn’t work if BIDDER_ID is mapped to a different (intermediate) table. To make sure all references and foreign key columns are nulled
out, you need to chase pointers in Java.)
On the other hand, if you don’t have shared references to an entity, you
should rethink your mapping and map the bids as a collection components (with
the Bid as a <composite-element>). With an <idbag> mapping, even the tables
look the same:
<idbag name="bids" table="BID">
<collection-id type="long" column="BID_ID">
<generator class="sequence"/>
Mapping a parent/children relationship
<key column="ITEM_ID" not-null="true"/>
<composite-element class="Bid">
<parent name="item"/>
<property .../>
The separate mapping for Bid is no longer needed.
If you really want to make this a one-to-many entity association, Hibernate
offers another convenience option you may be interested in.
Enabling orphan deletion
The cascading option we explain now is somewhat difficult to understand. If you
followed the discussion in the previous section, you should be prepared.
Imagine you want to delete a Bid from the database. Note that you aren’t deleting the parent (the Item) in this case. The goal is to remove a row in the BID
table. Look at this code:
If the collection has the Bid mapped as a collection of components, as in the previous section, this code triggers several operations:
The aBid instance is removed from the collection Item.bids.
Because Bid is mapped as a value type, and no other object can hold a reference to the aBid instance, the row representing this bid is deleted from the
BID table by Hibernate.
In other words, Hibernate assumes that aBid is an orphan if it’s removed from its
owning entity’s collection. No other in-memory persistent object is holding a reference to it. No foreign key value that references this row can be present in the
database. Obviously, you designed your object model and mapping this way by
making the Bid class an embeddable component.
However, what if Bid is mapped as an entity and the collection is a <one-tomany>? The code changes to
The aBid instance has its own lifecycle, so it can exist outside of the collection. By
deleting it manually, you guarantee that nobody else will hold a reference to it,
Mapping collections and entity associations
and the row can be removed safely. You may have removed all other references
manually. Or, if you didn’t, the database constraints prevent any inconsistency,
and you see a foreign key constraint exception.
Hibernate offers you a way to declare this guarantee for collections of entity
references. You can tell Hibernate, “If I remove an element from this collection, it
will be an entity reference, and it’s going to be the only reference to that entity
instance. You can safely delete it.” The code that worked for deletion with a collection of components works with collections of entity references.
This option is called cascade orphan delete. You can enable it on a collection
mapping in XML as follows:
<set name="bids"
cascade="save-update, delete, delete-orphan">
With annotations, this feature is available only as a Hibernate extension:
public class Item {
@OneToMany(cascade = { CascadeType.PERSIST,
CascadeType.REMOVE },
mappedBy = "item")
value = org.hibernate.annotations.CascadeType.DELETE_ORPHAN
private Set<Bid> bids = new HashSet<Bid>();
Also note that this trick works only for collections of entity references in a one-tomany association; conceptually, no other entity association mapping supports it.
You should ask yourself at this point, with so many cascading options set on your
collection, whether a simple collection of components may be easier to handle.
After all, you’ve enabled a dependent lifecycle for objects referenced in this collection, so you may as well switch to the implicit and fully dependent lifecycle of
Finally, let’s look at the mapping in a JPA XML descriptor:
<entity class="auction.model.Item" access="FIELD">
<one-to-many name="bids" mapped-by="item">
<entity class="auction.model.Bid" access="FIELD">
<many-to-one name="item">
<join-column name="ITEM_ID"/>
Note that the Hibernate extension for cascade orphan deletion isn’t available in
this case.
You’re probably a little overwhelmed by all the new concepts we introduced in
this chapter. You may have to read it a few times, and we encourage you to try the
code (and watch the SQL log). Many of the strategies and techniques we’ve shown
in this chapter are key concepts of object/relational mapping. If you master collection mappings, and once you’ve mapped your first parent/children entity association, you’ll have the worst behind you. You’ll already be able to build entire
Table 6.1 summarizes the differences between Hibernate and Java Persistence
related to concepts discussed in this chapter.
Table 6.1
Hibernate and JPA comparison chart for chapter 6
Hibernate Core
Java Persistence and EJB 3.0
Hibernate provides mapping support for sets, lists,
maps, bags, identifier bags, and arrays. All JDK collection interfaces are supported, and extension points for
custom persistent collections are available.
Standardized persistent sets, lists, maps,
and bags are supported.
Collections of value types and components are
Hibernate Annotations is required for collections of value types and embeddable
Parent/children entity relationships are supported,
with transitive state cascading on associations per
You can map entity associations and enable
transitive state cascading on associations
per operation.
Mapping collections and entity associations
Table 6.1
Hibernate and JPA comparison chart for chapter 6 (continued)
Hibernate Core
Automatic deletion of orphaned entity instances is
built in.
Java Persistence and EJB 3.0
Hibernate Annotations is required for automatic deletion of orphaned entity instances.
We’ve covered only a tiny subset of the entity association options in this chapter.
The remaining options we explore in detail in the next chapter are either rare or
variations of the techniques we’ve just described.
Advanced entity
association mappings
This chapter covers
Mapping one-to-one and many-to-one entity
Mapping one-to-many and many-to-many
entity associations
Polymorphic entity associations
Advanced entity association mappings
When we use the word associations, we always refer to relationships between entities. In the previous chapter, we demonstrated a unidirectional many-to-one association, made it bidirectional, and finally turned it into a parent/children
relationship (one-to-many and many-to-one with cascading options).
One reason we discuss more advanced entity mappings in a separate chapter is
that quite a few of them are considered rare, or at least optional.
It’s absolutely possible to only use component mappings and many-to-one
(occasionally one-to-one) entity associations. You can write a sophisticated application without ever mapping a collection! Of course, efficient and easy access to
persistent data, by iterating a collection for example, is one of the reasons why you
use full object/relational mapping and not a simple JDBC query service. However,
some exotic mapping features should be used with care and even avoided most of
the time.
We’ll point out recommended and optional mapping techniques in this chapter, as we show you how to map entity associations with all kinds of multiplicity,
with and without collections.
Single-valued entity associations
Let’s start with one-to-one entity associations.
We argued in chapter 4 that the relationships between User and Address (the
user has a billingAddress, homeAddress, and shippingAddress) are best represented with a <component> mapping. This is usually the simplest way to represent
one-to-one relationships, because the lifecycle is almost always dependent in such
a case, it’s either an aggregation or a composition in UML.
But what if you want a dedicated table for Address, and you map both User
and Address as entities? One benefit of this model is the possibility for shared references—another entity class (let’s say Shipment) can also have a reference to a
particular Address instance. If a User has a reference to this instance, as their
shippingAddress, the Address instance has to support shared references and
needs its own identity.
In this case, User and Address classes have a true one-to-one association. Look at
the revised class diagram in figure 7.1.
The first change is a mapping of the Address class as a stand-alone entity:
<class name="Address" table="ADDRESS">
<id name="id" column="ADDRESS_ID">
<generator .../>
<property name="street" column="STREET"/>
Single-valued entity associations
<property name="city" column="CITY"/>
<property name="zipcode" column="ZIPCODE"/>
We assume you won’t have any difficulty creating the same mapping with annotations or changing the Java class to an entity, with an identifier property—this is
the only change you have to make.
Now let’s create the association mappings from other entities to that class.
There are several choices, the first being a primary key one-to-one association.
Figure 7.1
Address as an entity with two associations referencing the same instance
Shared primary key associations
Rows in two tables related by a primary key association share the same primary key
values. The main difficulty with this approach is ensuring that associated instances
are assigned the same primary key value when the objects are saved. Before we try
to solve this problem, let’s see how you map the primary key association.
Mapping a primary key association with XML
The XML mapping element that maps an entity association to a shared primary
key entity is <one-to-one>. First you need a new property in the User class:
public class User {
private Address shippingAddress;
// Getters and setters
Next, map the association in User.hbm.xml:
<one-to-one name="shippingAddress"
You add a cascading option that is natural for this model: If a User instance is
made persistent, you usually also want its shippingAddress to become persistent.
Hence, the following code is all that is needed to save both objects:
Advanced entity association mappings
User newUser = new User();
Address shippingAddress = new Address();
Hibernate inserts a row into the USERS table and a row into the ADDRESS table. But
wait, this doesn’t work! How can Hibernate possibly know that the record in the
ADDRESS table needs to get the same primary key value as the USERS row? At the
beginning of this section, we intentionally didn’t show you any primary-key generator in the mapping of Address.
You need to enable a special identifier generator.
The foreign identifier generator
If an Address instance is saved, it needs to get the primary key value of a User
object. You can’t enable a regular identifier generator, let’s say a database
sequence. The special foreign identifier generator for Address has to know
where to get the right primary key value.
The first step to create this identifier binding between Address and User is a
bidirectional association. Add a new user property to the Address entity:
public class Address {
private User user;
// Getters and setters
Map the new user property of an Address in Address.hbm.xml:
<one-to-one name="user"
This mapping not only makes the association bidirectional, but also, with constrained="true", adds a foreign key constraint linking the primary key of the
ADDRESS table to the primary key of the USERS table. In other words, the database
guarantees that an ADDRESS row’s primary key references a valid USERS primary
key. (As a side effect, Hibernate can now also enable lazy loading of users when a
shipping address is loaded. The foreign key constraint means that a user has to
exist for a particular shipping address, so a proxy can be enabled without hitting
the database. Without this constraint, Hibernate has to hit the database to find
out if there is a user for the address; the proxy would then be redundant. We’ll
come back to this in later chapters.)
You can now use the special foreign identifier generator for Address objects:
Single-valued entity associations
<class name="Address" table="ADDRESS">
<id name="id" column="ADDRESS_ID">
<generator class="foreign">
<param name="property">user</param>
<one-to-one name="user"
This mapping seems strange at first. Read it as follows: When an Address is saved,
the primary key value is taken from the user property. The user property is a reference to a User object; hence, the primary key value that is inserted is the same
as the primary key value of that instance. Look at the table structure in figure 7.2.
Figure 7.2
The USERS and ADDRESS tables
have the same primary keys.
The code to save both objects now has to consider the bidirectional relationship,
and it finally works:
User newUser = new User();
Address shippingAddress = new Address();
// Bidirectional;
Let’s do the same with annotations.
Shared primary key with annotations
JPA supports one-to-one entity associations with the @OneToOne annotation. To
map the association of shippingAddress in the User class as a shared primary key
association, you also need the @PrimaryKeyJoinColumn annotation:
private Address shippingAddress;
This is all that is needed to create a unidirectional one-to-one association on a
shared primary key. Note that you need @PrimaryKeyJoinColumns (plural)
Advanced entity association mappings
instead if you map with composite primary keys. In a JPA XML descriptor, a one-toone mapping looks like this:
<entity class="auction.model.User" access="FIELD">
<one-to-one name="shippingAddress">
The JPA specification doesn’t include a standardized method to deal with the
problem of shared primary key generation, which means you’re responsible for
setting the identifier value of an Address instance correctly before you save it (to
the identifier value of the linked User instance). Hibernate has an extension
annotation for custom identifier generators which you can use with the Address
entity (just like in XML):
@Table(name = "ADDRESS")
public class Address {
@Id @GeneratedValue(generator = "myForeignGenerator")
name = "myForeignGenerator",
strategy = "foreign",
parameters = @Parameter(name = "property", value = "user")
@Column(name = "ADDRESS_ID")
private Long id;
private User user;
Shared primary key one-to-one associations aren’t uncommon but are relatively
rare. In many schemas, a to-one association is represented with a foreign key field
and a unique constraint.
One-to-one foreign key associations
Instead of sharing a primary key, two rows can have a foreign key relationship.
One table has a foreign key column that references the primary key of the
associated table. (The source and target of this foreign key constraint can even be
the same table: This is called a self-referencing relationship.)
Single-valued entity associations
Let’s change the mapping from a User to an Address. Instead of the shared
primary key, you now add a SHIPPING_ADDRESS_ID column in the USERS table:
<class name="User" table="USERS">
<many-to-one name="shippingAddress"
The mapping element in XML for this association is <many-to-one>—not <oneto-one>, as you might have expected. The reason is simple: You don’t care what’s
on the target side of the association, so you can treat it like a to-one association
without the many part. All you want is to express “This entity has a property that is
a reference to an instance of another entity” and use a foreign key field to represent that relationship. The database schema for this mapping is shown in
figure 7.3.
Figure 7.3
A one-to-one foreign key association between USERS and ADDRESS
An additional constraint enforces this relationship as a real one to one. By making
the SHIPPING_ADDRESS_ID column unique, you declare that a particular address
can be referenced by at most one user, as a shipping address. This isn’t as strong as
the guarantee from a shared primary key association, which allows a particular
address to be referenced by at most one user, period. With several foreign key columns (let’s say you also have unique HOME_ADDRESS_ID and BILLING_ADDRESS_ID),
you can reference the same address target row several times. But in any case, two
users can’t share the same address for the same purpose.
Let’s make the association from User to Address bidirectional.
Inverse property reference
The last foreign key association was mapped from User to Address with <many-toone> and a unique constraint to guarantee the desired multiplicity. What mapping
Advanced entity association mappings
element can you add on the Address side to make this association bidirectional, so
that access from Address to User is possible in the Java domain model?
In XML, you create a <one-to-one> mapping with a property reference
<one-to-one name="user"
You tell Hibernate that the user property of the Address class is the inverse of a
property on the other side of the association. You can now call anAddress.getUser() to access the user who’s shipping address you’ve given. There is
no additional column or foreign key constraint; Hibernate manages this pointer
for you.
Should you make this association bidirectional? As always, the decision is up to
you and depends on whether you need to navigate through your objects in that
direction in your application code. In this case, we’d probably conclude that the
bidirectional association doesn’t make much sense. If you call anAddress.getUser(), you are saying “give me the user who has this address has its
shipping address,” not a very reasonable request. We recommend that a foreign
key-based one-to-one association, with a unique constraint on the foreign key column—is almost always best represented without a mapping on the other side.
Let’s repeat the same mapping with annotations.
Mapping a foreign key with annotations
The JPA mapping annotations also support a one-to-one relationship between
entities based on a foreign key column. The main difference compared to the
mappings earlier in this chapter is the use of @JoinColumn instead of @PrimaryKeyJoinColumn.
First, here’s the to-one mapping from User to Address with the unique constraint on the SHIPPING_ADDRESS_ID foreign key column. However, instead of a
@ManyToOne annotation, this requires a @OneToOne annotation:
public class User {
private Address shippingAddress;
Single-valued entity associations
Hibernate will now enforce the multiplicity with the unique constraint. If you
want to make this association bidirectional, you need another @OneToOne mapping in the Address class:
public class Address {
@OneToOne(mappedBy = "shippingAddress")
private User user;
The effect of the mappedBy attribute is the same as the property-ref in XML mapping: a simple inverse declaration of an association, naming a property on the target entity side.
The equivalent mapping in JPA XML descriptors is as follows:
<entity class="auction.model.User" access="FIELD">
<one-to-one name="shippingAddress">
<join-column name="SHIPPING_ADDRESS_ID"/>
<entity class="auction.model.Address" access="FIELD">
<one-to-one name="user" mapped-by="shippingAddress"/>
You’ve now completed two basic single-ended association mappings: the first with
a shared primary key, the second with a foreign key reference. The last option we
want to discuss is a bit more exotic: mapping a one-to-one association with the
help of an additional table.
Mapping with a join table
Let’s take a break from the complex CaveatEmptor model and consider a different scenario. Imagine you have to model a data schema that represents an office
allocation plan in a company. Common entities include people working at desks.
It seems reasonable that a desk may be vacant and have no person assigned to it.
On the other hand, an employee may work at home, with the same result. You’re
dealing with an optional one-to-one association between Person and Desk.
Advanced entity association mappings
If you apply the mapping techniques we discussed in the previous sections, you
may come to the following conclusions: Person and Desk are mapped to two
tables, with one of them (let’s say the PERSON table) having a foreign key column
that references the other table (such as ASSIGNED_DESK_ID) with an additional
unique constraint (so two people can’t be assigned the same desk). The relationship is optional if the foreign key column is nullable.
On second thought, you realize that the assignment between persons and
desks calls for another table that represents ASSIGNMENT. In the current design,
this table has only two columns: PERSON_ID and DESK_ID. The multiplicity of
these foreign key columns is enforced with a unique constraint on both—a particular person and desk can only be assigned once, and only one such an assignment can exist.
It also seems likely that one day you’ll need to extend this schema and add columns to the ASSIGNMENT table, such as the date when a person was assigned to a
desk. As long as this isn’t the case, however, you can use object/relational mapping to hide the intermediate table and create a one-to-one Java entity association
between only two classes. (This situation changes completely once additional columns are introduced to ASSIGNMENT.)
Where does such an optional one-to-one relationship exist in CaveatEmptor?
The CaveatEmptor use case
Let’s consider the Shipment entity in CaveatEmptor again and discuss its purpose.
Sellers and buyers interact in CaveatEmptor by starting and bidding on auctions.
The shipment of the goods seems to be outside the scope of the application; the
seller and the buyer agree on a method of shipment and payment after the auction ends. They can do this offline, outside of CaveatEmptor. On the other hand,
you could offer an extra escrow service in CaveatEmptor. Sellers would use this service to create a trackable shipment once the auction completed. The buyer would
pay the price of the auction item to a trustee (you), and you’d inform the seller
that the money was available. Once the shipment arrived and the buyer accepted
it, you’d transfer the money to the seller.
If you’ve ever participated in an online auction of significant value, you’ve
probably used such an escrow service. But you want more service in CaveatEmptor. Not only will you provide trust services for completed auctions, but you’ll also
allow users to create a trackable and trusted shipment for any deal they make outside an auction, outside CaveatEmptor.
This scenario calls for a Shipment entity with an optional one-to-one association to an Item. Look at the class diagram for this domain model in figure 7.4.
Single-valued entity associations
Figure 7.4
A shipment has an optional link with a single auction item.
In the database schema, you add an intermediate link table called ITEM_SHIPMENT.
A row in this table represents a Shipment made in the context of an auction. The
tables are shown in figure 7.5.
You now map two classes to three tables: first in XML, and then with annotations.
Mapping a join table in XML
The property that represents the association from Shipment to Item is called
public class Shipment {
private Item auction;
// Getter/setter methods
Because you have to map this association with a foreign key column, you need the
<many-to-one> mapping element in XML. However, the foreign key column isn’t
in the SHIPMENT table, it’s in the ITEM_SHIPMENT join table. With the help of the
<join> mapping element, you move it there.
Figure 7.5
An optional one-to-many relationship mapped to a join table
Advanced entity association mappings
<class name="Shipment" table="SHIPMENT">
<id name="id" column="SHIPMENT_ID">...</id>
<join table="ITEM_SHIPMENT" optional="true">
<key column="SHIPMENT_ID"/>
<many-to-one name="auction"
The join table has two foreign key columns: SHIPMENT_ID, referencing the primary key of the SHIPMENT table; and ITEM_ID, referencing the ITEM table. The
ITEM_ID column is unique; a particular item can be assigned to exactly one shipment. Because the primary key of the join table is SHIPMENT_ID, which makes this
column also unique, you have a guaranteed one-to-one multiplicity between
Shipment and Item.
By setting optional="true" on the <join> mapping, you tell Hibernate that
it should insert a row into the join table only if the properties grouped by this
mapping are non-null. But if a row needs to be inserted (because you called
aShipment.setAuction(anItem)), the NOT NULL constraint on the ITEM_ID column applies.
You could map this association bidirectional, with the same technique on the
other side. However, optional one-to-one associations are unidirectional most of
the time.
JPA also supports association join tables as secondary tables for an entity.
Mapping secondary join tables with annotations
You can map an optional one-to-one association to an intermediate join table with
public class Shipment {
joinColumns = @JoinColumn(name = "SHIPMENT_ID"),
inverseJoinColumns = @JoinColumn(name = "ITEM_ID")
private Item auction;
// Getter/setter methods
Single-valued entity associations
You don’t have to specify the SHIPMENT_ID column because it’s automatically considered to be the join column; it’s the primary key column of the SHIPMENT table.
Alternatively, you can map properties of a JPA entity to more than one table,
as demonstrated in “Moving properties into a secondary table” in chapter 8,
section 8.1.3. First, you need to declare the secondary table for the entity:
@Table(name = "SHIPMENT")
@SecondaryTable(name = "ITEM_SHIPMENT")
public class Shipment {
@Id @GeneratedValue
@Column(name = "SHIPMENT_ID")
private Long id;
Note that the @SecondaryTable annotation also supports attributes to declare the
foreign-key column name—the equivalent of the <key column="..."/> you saw
earlier in XML and the joinColumn(s) in a @JoinTable. If you don’t specify it, the
primary-key column name of the entity is used—in this case, again SHIPMENT_ID.
The auction property mapping is a @OneToOne; and as before, the foreign key
column referencing the ITEM table is moved to the intermediate secondary table:
public class Shipment {
@JoinColumn(table = "ITEM_SHIPMENT", name = "ITEM_ID")
private Item auction;
The table for the target @JoinColumn is named explicitly. Why would you use this
approach instead of the (simpler) @JoinTable strategy? Declaring a secondary
table for an entity is useful if not only one property (the many-to-one in this case)
but several properties must be moved into the secondary table. We don’t have a
great example with Shipment and Item, but if your ITEM_SHIPMENT table would
have additional columns, mapping these columns to properties of the Shipment
entity might be useful.
This completes our discussion of one-to-one association mappings. To summarize, use a shared primary key association if one of the two entities seems more
important and can act as the primary key source. Use a foreign key association in
all other cases, and a hidden intermediate join table when your one-to-one association is optional.
Advanced entity association mappings
We now focus on many-valued entity associations, including more options for
one-to-many, and finally, many-to-many mappings.
Many-valued entity associations
A many-valued entity association is by definition a collection of entity references.
You mapped one of these in the previous chapter, section 6.4, “Mapping a parent/children relationship.” A parent entity instance has a collection of references
to many child objects—hence, one-to-many.
One-to-many associations are the most important kind of entity association
that involves a collection. We go so far as to discourage the use of more exotic
association styles when a simple bidirectional many-to-one/one-to-many will do
the job. A many-to-many association may always be represented as two many-toone associations to an intervening class. This model is usually more easily extensible, so we tend not to use many-to-many associations in applications. Also remember that you don’t have to map any collection of entities, if you don’t want to; you
can always write an explicit query instead of direct access through iteration.
If you decide to map collections of entity references, there are a few options
and more complex situations that we discuss now, including a many-to-many relationship.
One-to-many associations
The parent/children relationship you mapped earlier was a bidirectional association, with a <one-to-many> and a <many-to-one> mapping. The many end of this
association was implemented in Java with a Set; you had a collection of bids in
the Item class.
Let’s reconsider this mapping and focus on some special cases.
Considering bags
It’s possible to use a <bag> mapping instead of a set for a bidirectional one-to-many
association. Why would you do this?
Bags have the most efficient performance characteristics of all the collections
you can use for a bidirectional one-to-many entity association (in other words, if
the collection side is inverse="true"). By default, collections in Hibernate are
loaded only when they’re accessed for the first time in the application. Because a
bag doesn’t have to maintain the index of its elements (like a list) or check for
duplicate elements (like a set), you can add new elements to the bag without triggering the loading. This is an important feature if you’re going to map a possibly
Many-valued entity associations
large collection of entity references. On the other hand, you can’t eager-fetch two
collections of bag type simultaneously (for example, if bids and images of an
Item were one-to-many bags). We’ll come back to fetching strategies in
chapter 13, section 13.1, “Defining the global fetch plan.” In general we would say
that a bag is the best inverse collection for a one-to-many association.
To map a bidirectional one-to-many association as a bag, you have to replace
the type of the bids collection in the Item persistent class with a Collection and
an ArrayList implementation. The mapping for the association between Item
and Bid is left essentially unchanged:
<class name="Bid"
<many-to-one name="item"
<class name="Item"
<bag name="bids"
<key column="ITEM_ID"/>
<one-to-many class="Bid"/>
You rename the <set> element to <bag>, making no other changes. Even the
tables are the same: The BID table has the ITEM_ID foreign key column. In JPA, all
Collection and List properties are considered to have bag semantics, so the following is equivalent to the XML mapping:
public class Item {
@OneToMany(mappedBy = "item")
private Collection<Bid> bids = new ArrayList<Bid>();
A bag also allows duplicate elements, which the set you mapped earlier didn’t. It
turns out that this isn’t relevant in this case, because duplicate means you’ve added
a particular reference to the same Bid instance several times. You wouldn’t do this
Advanced entity association mappings
in your application code. But even if you add the same reference several times to
this collection, Hibernate ignores it—it’s mapped inverse.
Unidirectional and bidirectional lists
If you need a real list to hold the position of the elements in a collection, you have
to store that position in an additional column. For the one-to-many mapping, this
also means you should change the bids property in the Item class to List and initialize the variable with an ArrayList (or keep the Collection interface from the
previous section, if you don’t want to expose this behavior to a client of the class).
The additional column that holds the position of a reference to a Bid instance
is the BID_POSITION, in the mapping of Item:
<class name="Item"
<list name="bids">
<key column="ITEM_ID"/>
<list-index column="BID_POSITION"/>
<one-to-many class="Bid"/>
So far this seems straightforward; you’ve changed the collection mapping to
<list> and added the <list-index> column BID_POSITION to the collection
table (which in this case is the BID table). Verify this with the table shown in
figure 7.6.
This mapping isn’t really complete. Consider the ITEM_ID foreign key column:
It’s NOT NULL (a bid has to reference an item). The first problem is that you don’t
specify this constraint in the mapping. Also, because this mapping is
unidirectional (the collection is noninverse), you have to assume that there is no
opposite side mapped to the same foreign key column (where this constraint
could be declared). You need to add a not-null="true" attribute to the <key>
element of the collection mapping:
Figure 7.6
Storing the position of each bid in
the list collection
Many-valued entity associations
<class name="Item"
<list name="bids">
<key column="ITEM_ID" not-null="true"/>
<list-index column="BID_POSITION"/>
<one-to-many class="Bid"/>
Note that the attribute has to be on the <key> mapping, not on a possible nested
<column> element. Whenever you have a noninverse collection of entity references (most of the time a one-to-many with a list, map, or array) and the foreign
key join column in the target table is not nullable, you need to tell Hibernate
about this. Hibernate needs the hint to order INSERT and UPDATE statements correctly, to avoid a constraint violation.
Let’s make this bidirectional with an item property of the Bid. If you follow the
examples from earlier chapters, you might want to add a <many-to-one> on the
ITEM_ID foreign key column to make this association bidirectional, and enable
inverse="true" on the collection. Remember that Hibernate ignores the state of
an inverse collection! This time, however, the collection contains information that
is needed to update the database correctly: the position of its elements. If only the
state of each Bid instance is considered for synchronization, and the collection is
inverse and ignored, Hibernate has no value for the BID_POSITION column.
If you map a bidirectional one-to-many entity association with an indexed collection (this is also true for maps and arrays), you have to switch the inverse sides.
You can’t make an indexed collection inverse="true". The collection becomes
responsible for state synchronization, and the one side, the Bid, has to be made
inverse. However, there is no inverse="true" for a many-to-one mapping so you
need to simulate this attribute on a <many-to-one>:
<class name="Bid"
<many-to-one name="item"
Advanced entity association mappings
Setting insert and update to false has the desired effect. As we discussed earlier,
these two attributes used together make a property effectively read-only. This side
of the association is therefore ignored for any write operations, and the state of
the collection (including the index of the elements) is the relevant state when the
in-memory state is synchronized with the database. You’ve switched the inverse/
noninverse sides of the association, a requirement if you switch from a set or bag
to a list (or any other indexed collection).
The equivalent in JPA, an indexed collection in a bidirectional one-to-many
mapping, is as follows:
public class Item {
@JoinColumn(name = "ITEM_ID", nullable = false)
@org.hibernate.annotations.IndexColumn(name = "BID_POSITION")
private List<Bid> bids = new ArrayList<Bid>();
This mapping is noninverse because no mappedBy attribute is present. Because JPA
doesn’t support persistent indexed lists (only ordered with an @OrderBy at load
time), you need to add a Hibernate extension annotation for index support.
Here’s the other side of the association in Bid:
public class Bid {
@JoinColumn(name = "ITEM_ID", nullable = false,
updatable = false, insertable = false)
private Item item;
We now discuss one more scenario with a one-to-many relationship: an association
mapped to an intermediate join table.
Optional one-to-many association with a join table
A useful addition to the Item class is a buyer property. You can then call
anItem.getBuyer() to access the User who made the winning bid. (Of course,
anItem.getSuccessfulBid().getBidder() can provide the same access with a
Many-valued entity associations
name : String
description : String
initialPrice : BigDecimal
reservePrice : BigDecimal
startDate : Date
endDate : Date
state : ItemState
approvalDatetime : Date
firstname : String
lastname : String
username : String
password : String
email : String
ranking : int
admin : boolean
Figure 7.7
Items may be bought by users.
different path.) If made bidirectional, this association will also help to render a
screen that shows all auctions a particular user has won: You call aUser.getBoughtItems() instead of writing a query.
From the point of view of the User class, the association is one-to-many. The
classes and their relationship are shown in figure 7.7.
Why is this association different than the one between Item and Bid? The multiplicity 0..* in UML indicates that the reference is optional. This doesn’t influence the Java domain model much, but it has consequences for the underlying
tables. You expect a BUYER_ID foreign key column in the ITEM table. The column
has to be nullable—a particular Item may not have been bought (as long as the
auction is still running).
You can accept that the foreign key column can be NULL and apply additional
constraints (“allowed to be NULL only if the auction end time hasn’t been
reached or if no bid has been made”). We always try to avoid nullable columns in
a relational database schema. Information that is unknown degrades the quality
of the data you store. Tuples represent propositions that are true; you can’t assert
something you don’t know. And, in practice, many developers and DBAs don’t
create the right constraint and rely on (often buggy) application code to provide data integrity.
An optional entity association, be it one-to-one or one-to-many, is best represented in an SQL database with a join table. See figure 7.8 for an example schema.
You added a join table earlier in this chapter, for a one-to-one association. To
guarantee the multiplicity of one-to-one, you applied unique constraints on both
foreign key columns of the join table. In the current case, you have a one-to-many
multiplicity, so only the ITEM_ID column of the ITEM_BUYER table is unique. A particular item can be bought only once.
Let’s map this in XML. First, here’s the boughtItems collection of the User
Advanced entity association mappings
Figure 7.8
An optional relationship with a join table avoids nullable foreign key columns.
<set name="boughtItems" table="ITEM_BUYER">
<key column="USER_ID"/>
<many-to-many class="Item"
You use a Set as the collection type. The collection table is the join table,
ITEM_BUYER; its primary key is a composite of USER_ID and ITEM_ID. The new
mapping element you haven’t seen before is <many-to-many>; it’s required
because the regular <one-to-many> doesn’t know anything about join tables. By
forcing a unique constraint on the foreign key column that references the target
entity table, you effectively force a one-to-many multiplicity.
You can map this association bidirectional with the buyer property of Item.
Without the join table, you’d add a <many-to-one> with a BUYER_ID foreign key
column in the ITEM table. With the join table, you have to move this foreign key
column into the join table. This is possible with a <join> mapping:
<join table="ITEM_BUYER"
<key column="ITEM_ID” unique=”true” not-null="true"/>
<many-to-one name="buyer" column="USER_ID"/>
Two important details: First, the association is optional, and you tell Hibernate
not to insert a row into the join table if the grouped properties (only one here,
buyer) are null. Second, this is a bidirectional entity association. As always, one
side has to be the inverse end. You’ve chosen the <join> to be inverse; Hibernate
now uses the collection state to synchronize the database and ignores the state of
Many-valued entity associations
the Item.buyer property. As long as your collection is not an indexed variation (a
list, map, or array), you can reverse this by declaring the collection
inverse="true". The Java code to create a link between a bought item and a user
object is the same in both cases:
You can map secondary tables in JPA to create a one-to-many association with a
join table. First, map a @ManyToOne to a join table:
public class Item {
name = "ITEM_BUYER",
joinColumns = {@JoinColumn(name = "ITEM_ID")},
inverseJoinColumns = {@JoinColumn(name = "USER_ID")}
private User buyer;
At the time of writing, this mapping has the limitation that you can’t set it to
optional="true"; hence, the USER_ID column is nullable. If you try to add a nullable="false" attribute on the @JoinColumn, Hibernate Annotations thinks that
you want the whole buyer property to never be null. Furthermore, the primary
key of the join table is now the ITEM_ID column only. This is fine, because you
don’t want duplicate items in this table—they can be bought only once.
To make this mapping bidirectional, add a collection on the User class and
make it inverse with mappedBy:
@OneToMany(mappedBy = "buyer")
private Set<Item> boughtItems = new HashSet<Item>();
We showed a <many-to-many> XML mapping element in the previous section for a
one-to-many association on a join table. The @JoinTable annotation is the equivalent in annotations. Let’s map a real many-to-many association.
Many-to-many associations
The association between Category and Item is a many-to-many association, as can
be seen in figure 7.9.
In a real system, you may not have a many-to-many association. Our experience
is that there is almost always other information that must be attached to each link
between associated instances (such as the date and time when an item was added
Advanced entity association mappings
Figure 7.9
A many-to-many valued association between
Category and Item
to a category) and that the best way to represent this information is via an intermediate association class. In Hibernate, you can map the association class as an
entity and map two one-to-many associations for either side. Perhaps more conveniently, you can also map a composite element class, a technique we show later.
It’s the purpose of this section to implement a real many-to-many entity association. Let’s start with a unidirectional example.
A simple unidirectional many-to-many association
If you require only unidirectional navigation, the mapping is straightforward.
Unidirectional many-to-many associations are essentially no more difficult than
the collections of value-type instances we discussed earlier. For example, if the
Category has a set of Items, you can create this mapping:
<set name="items"
<key column="CATEGORY_ID"/>
<many-to-many class="Item" column="ITEM_ID"/>
The join table (or link table, as some developers call it) has two columns: the foreign keys of the CATEGORY and ITEM tables. The primary key is a composite of both
columns. The full table structure is shown in figure 7.10.
In JPA annotations, many-to-many associations are mapped with the @ManyToMany attribute:
joinColumns = {@JoinColumn(name = "CATEGORY_ID")},
inverseJoinColumns = {@JoinColumn(name = "ITEM_ID")}
private Set<Item> items = new HashSet<Item>();
Many-valued entity associations
Figure 7.10
Many-to-many entity association mapped to an association table
In Hibernate XML you can also switch to an <idbag> with a separate primary key
column on the join table:
<idbag name="items"
<collection-id type="long" column="CATEGORY_ITEM_ID">
<generator class="sequence"/>
<key column="CATEGORY_ID"/>
<many-to-many class="Item" column="ITEM_ID"/>
As usual with an <idbag> mapping, the primary key is a surrogate key column,
CATEGORY_ITEM_ID. Duplicate links are therefore allowed; the same Item can be
added twice to a Category. (This doesn’t seem to be a useful feature.) With annotations, you can switch to an identifier bag with the Hibernate @CollectionId:
columns = @Column(name = "CATEGORY_ITEM_ID"),
type = @org.hibernate.annotations.Type(type = "long"),
generator = "sequence"
joinColumns = {@JoinColumn(name = "CATEGORY_ID")},
inverseJoinColumns = {@JoinColumn(name = "ITEM_ID")}
private Collection<Item> items = new ArrayList<Item>();
A JPA XML descriptor for a regular many-to-many mapping with a set (you can’t
use a Hibernate extension for identifier bags) looks like this:
Advanced entity association mappings
<entity class="auction.model.Category" access="FIELD">
<many-to-many name="items">
<join-table name="CATEGORY_ITEM">
<join-column name="CATEGORY_ID"/>
<inverse-join-column name="ITEM_ID"/>
You may even switch to an indexed collection (a map or list) in a many-to-many
association. The following example maps a list in Hibernate XML:
<list name="items"
<key column="CATEGORY_ID"/>
<list-index column="DISPLAY_POSITION"/>
<many-to-many class="Item" column="ITEM_ID"/>
The primary key of the link table is a composite of the CATEGORY_ID and
DISPLAY_POSITION columns; this mapping guarantees that the position of each
Item in a Category is persistent. Or, with annotations:
joinColumns = {@JoinColumn(name = "CATEGORY_ID")},
inverseJoinColumns = {@JoinColumn(name = "ITEM_ID")}
@org.hibernate.annotations.IndexColumn(name = "DISPLAY_POSITION")
private List<Item> items = new ArrayList<Item>();
As discussed earlier, JPA only supports ordered collections (with an optional
@OrderBy annotation or ordered by primary key), so you again have to use a
Hibernate extension for indexed collection support. If you don’t add an @IndexColumn, the List is stored with bag semantics (no guaranteed persistent order of
Creating a link between a Category and an Item is easy:
Bidirectional many-to-many associations are slightly more difficult.
A bidirectional many-to-many association
You know that one side in a bidirectional association has to be mapped as inverse
because you have named the foreign key column(s) twice. The same principle
Many-valued entity associations
applies to bidirectional many-to-many associations: Each row of the link table is
represented by two collection elements, one element at each end of the association. An association between an Item and a Category is represented in memory by
the Item instance in the items collection of the Category, but also by the Category instance in the categories collection of the Item.
Before we discuss the mapping of this bidirectional case, you have to be aware
that the code to create the object association also changes:
As always, a bidirectional association (no matter of what multiplicity) requires that
you set both ends of the association.
When you map a bidirectional many-to-many association, you must declare
one end of the association using inverse="true" to define which side’s state is
used to update the join table. You can choose which side should be inverse.
Recall this mapping of the items collection from the previous section:
<class name="Category" table="CATEGORY">
<set name="items"
<key column="CATEGORY_ID"/>
<many-to-many class="Item" column="ITEM_ID"/>
You may reuse this mapping for the Category end of the bidirectional association
and map the other side as follows:
<class name="Item" table="ITEM">
<set name="categories"
<key column="ITEM_ID"/>
<many-to-many class="Category" column="CATEGORY_ID"/>
Note the inverse="true". Again, this setting tells Hibernate to ignore changes
made to the categories collection and that the other end of the association, the
items collection, is the representation that should be synchronized with the database if you link instances in Java code.
Advanced entity association mappings
You have enabled cascade="save-update" for both ends of the collection.
This isn’t unreasonable, we suppose. On the other hand, the cascading options
all, delete, and delete-orphans aren’t meaningful for many-to-many associations. (This is good point to test if you understand entities and value types—try to
come up with reasonable answers why these cascading options don’t make sense
for a many-to-many association.)
In JPA and with annotations, making a many-to-many association bidirectional
is easy. First, the noninverse side:
joinColumns = {@JoinColumn(name = "CATEGORY_ID")},
inverseJoinColumns = {@JoinColumn(name = "ITEM_ID")}
private Set<Item> items = new HashSet<Item>();
Now the opposite inverse side:
@ManyToMany(mappedBy = "items")
private Set<Category> categories = new HashSet<Category>();
As you can see, you don’t have to repeat the join-table declaration on the inverse
What types of collections may be used for bidirectional many-to-many associations? Do you need the same type of collection at each end? It’s reasonable to
map, for example, a <list> for the noninverse side of the association and a <bag>
on the inverse side.
For the inverse end, <set> is acceptable, as is the following bag mapping:
<class name="Item" table="ITEM">
<bag name="categories"
<key column="ITEM_ID"/>
<many-to-many class="Category" column="CATEGORY_ID"/>
In JPA, a bag is a collection without a persistent index:
@ManyToMany(mappedBy = "items")
private Collection<Category> categories = new ArrayList<Category>();
Many-valued entity associations
No other mappings can be used for the inverse end of a many-to-many association.
Indexed collections (lists and maps) don’t work, because Hibernate won’t initialize or maintain the index column if the collection is inverse. In other words, a
many-to-many association can’t be mapped with indexed collections on both sides.
We already frowned at the use of many-to-many associations, because additional columns on the join table are almost always inevitable.
Adding columns to join tables
In this section, we discuss a question that is asked frequently by Hibernate users:
What do I do if my join table has additional columns, not only two foreign key columns?
Imagine that you need to record some information each time you add an Item
to a Category. For example, you may need to store the date and the name of the
user who added the item to this category. This requires additional columns on the
join table, as you can see in figure 7.11.
Figure 7.11
Additional columns on the
join table in a many-to-many
You can use two common strategies to map such a structure to Java classes. The
first strategy requires an intermediate entity class for the join table and is mapped
with one-to-many associations. The second strategy utilizes a collection of components, with a value-type class for the join table.
Mapping the join table to an intermediate entity
The first option we discuss now resolves the many-to-many relationship between
Category and Item with an intermediate entity class, CategorizedItem.
Listing 7.1 shows this entity class, which represents the join table in Java, including JPA annotations:
Advanced entity association mappings
Listing 7.1
An entity class that represents a link table with additional columns
@Table(name = "CATEGORIZED_ITEM")
public class CategorizedItem {
public static class Id implements Serializable {
@Column(name = "CATEGORY_ID")
private Long categoryId;
@Column(name = "ITEM_ID")
private Long itemId;
public Id() {}
public Id(Long categoryId, Long itemId) {
this.categoryId = categoryId;
this.itemId = itemId;
public boolean equals(Object o) {
if (o != null && o instanceof Id) {
Id that = (Id)o;
return this.categoryId.equals(that.categoryId) &&
} else {
return false;
public int hashCode() {
return categoryId.hashCode() + itemId.hashCode();
private Id id = new Id();
@Column(name = "ADDED_BY_USER")
private String username;
@Column(name = "ADDED_ON")
private Date dateAdded = new Date();
insertable = false,
updatable = false)
private Item item;
insertable = false,
updatable = false)
private Category category;
Many-valued entity associations
public CategorizedItem() {}
public CategorizedItem(String username,
Category category,
Item item) {
// Set fields
this.username = username;
this.category = category;
this.item = item;
// Set identifier values = category.getId(); = item.getId();
// Guarantee referential integrity
// Getter and setter methods
An entity class needs an identifier property. The primary key of the join table is
CATEGORY_ID and ITEM_ID, a composite. Hence, the entity class also has a composite key, which you encapsulate in a static nested class for convenience. You can
also see that constructing a CategorizedItem involves setting the values of the
identifier—composite key values are assigned by the application. Pay extra attention to the constructor and how it sets the field values and guarantees referential
integrity by managing collections on either side of the association.
Let’s map this class to the join table in XML:
<class name="CategorizedItem"
<composite-id name="id" class="CategorizedItem$Id">
<key-property name="categoryId"
<key-property name="itemId"
<property name="dateAdded"
Advanced entity association mappings
<property name="username"
<many-to-one name="category"
<many-to-one name="item"
The entity class is mapped as immutable—you’ll never update any properties after
creation. Hibernate accesses <composite-id> fields directly—you don’t need getters and setters in this nested class. The two <many-to-one> mappings are effectively read-only; insert and update are set to false. This is necessary because the
columns are mapped twice, once in the composite key (which is responsible for
insertion of the values) and again for the many-to-one associations.
The Category and Item entities (can) have a one-to-many association to the
CategorizedItem entity, a collection. For example, in Category:
<set name="categorizedItems"
<key column="CATEGORY_ID"/>
<one-to-many class="CategorizedItem"/>
And here’s the annotation equivalent:
@OneToMany(mappedBy = "category")
private Set<CategorizedItem> categorizedItems =
new HashSet<CategorizedItem>();
There is nothing special to consider here; it’s a regular bidirectional one-to-many
association with an inverse collection. Add the same collection and mapping to
Item to complete the association. This code creates and stores a link between a
category and an item:
CategorizedItem newLink =
new CategorizedItem(aUser.getUsername(), aCategory, anItem);;
Many-valued entity associations
The referential integrity of the Java objects is guaranteed by the constructor of
CategorizedItem, which manages the collection in aCategory and in anItem.
Remove and delete the link between a category and an item:
aCategory.getCategorizedItems().remove( theLink );
anItem.getCategorizedItems().remove( theLink );
The primary advantage of this strategy is the possibility for bidirectional navigation: You can get all items in a category by calling aCategory.getCategorizedItems() and the also navigate from the opposite direction with
anItem.getCategorizedItems(). A disadvantage is the more complex code
needed to manage the CategorizedItem entity instances to create and remove
associations—they have to be saved and deleted independently, and you need
some infrastructure in the CategorizedItem class, such as the composite identifier. However, you can enable transitive persistence with cascading options on the
collections from Category and Item to CategorizedItem, as explained in
chapter 12, section 12.1, “Transitive persistence.”
The second strategy for dealing with additional columns on the join table
doesn’t need an intermediate entity class; it’s simpler.
Mapping the join table to a collection of components
First, simplify the CategorizedItem class, and make it a value type, without an
identifier or any complex constructor:
public class CategorizedItem {
private String username;
private Date dateAdded = new Date();
private Item item;
private Category category;
public CategorizedItem(String username,
Category category,
Item item) {
this.username = username;
this.category = category;
this.item = item;
// Getter and setter methods
// Don't forget the equals/hashCode methods
As for all value types, this class has to be owned by an entity. The owner is the Category, and it has a collection of these components:
Advanced entity association mappings
<class name="Category" table="CATEGORY">
<set name="categorizedItems" table="CATEGORY_ITEM">
<key column="CATEGORY_ID"/>
<composite-element class="CategorizedItem">
<parent name="category"/>
<many-to-one name="item"
<property name="username" column="ADDED_BY_USER"/>
<property name="dateAdded" column="ADDED_ON"/>
This is the complete mapping for a many-to-many association with extra columns
on the join table. The <many-to-one> element represents the association to Item;
the <property> mappings cover the extra columns on the join table. There is only
one change to the database tables: The CATEGORY_ITEM table now has a primary
key that is a composite of all columns, not only CATEGORY_ID and ITEM_ID, as in
the previous section. Hence, all properties should never be nullable—otherwise
you can’t identify a row in the join table. Except for this change, the tables still
look as shown in figure 7.11.
You can enhance this mapping with a reference to the User instead of just the
user’s name. This requires an additional USER_ID column on the join table, with a
foreign key to USERS. This is a ternary association mapping:
<set name="categorizedItems" table="CATEGORY_ITEM">
<key column="CATEGORY_ID"/>
<composite-element class="CategorizedItem">
<parent name="category"/>
<many-to-one name="item"
<many-to-one name="user"
<property name="dateAdded" column="ADDED_ON"/>
Many-valued entity associations
This is a fairly exotic beast!
The advantage of a collection of components is clearly the implicit lifecycle of
the link objects. To create an association between a Category and an Item, add a
new CategorizedItem instance to the collection. To break the link, remove the
element from the collection. No extra cascading settings are required, and the
Java code is simplified:
CategorizedItem aLink =
new CategorizedItem(aUser.getUserName(), aCategory, anItem);
aCategory.getCategorizedItems().add( aLink );
aCategory.getCategorizedItems().remove( aLink );
The downside of this approach is that there is no way to enable bidirectional navigation: A component (such as CategorizedItem) can’t, by definition, have shared
references. You can’t navigate from Item to CategorizedItem. However, you can
write a query to retrieve the objects you need.
Let’s do the same mapping with annotations. First, make the component class
@Embeddable, and add the component column and association mappings:
public class CategorizedItem {
@org.hibernate.annotations.Parent // Optional back-pointer
private Category category;
@JoinColumn(name = "ITEM_ID",
nullable = false,
updatable = false)
private Item item;
@JoinColumn(name = "USER_ID",
nullable = false,
updatable = false)
private User user;
@Column(name = "ADDED_ON", nullable = false, updatable = false)
private Date dateAdded;
// Constructor
// Getter and setter methods
// Don't forget the equals/hashCode methods
Now map this as a collection of components in the Category class:
Advanced entity association mappings
joinColumns = @JoinColumn(name = "CATEGORY_ID")
private Set<CategorizedItem> categorizedItems =
new HashSet<CategorizedItem>();
That’s it: You’ve mapped a ternary association with annotations. What looked
incredibly complex at the beginning has been reduced to a few lines of annotation metadata, most of it optional.
The last collection mapping we’ll explore are Maps of entity references.
Mapping maps
You mapped a Java Map in the last chapter—the keys and values of the Map were
value types, simple strings. You can create more complex maps; not only can the
keys be references to entities, but so can the values. The result can therefore be a
ternary association.
Values as references to entities
First, let’s assume that only the value of each map entry is a reference to another
entity. The key is a value type, a long. Imagine that the Item entity has a map of
Bid instances and that each map entry is a pair of Bid identifier and reference to a
Bid instance. If you iterate through anItem.getBidsByIdentifier(), you iterate
through map entries that look like (1, <reference to Bid with PK 1>), (2,
<reference to Bid with PK 2>), and so on.
The underlying tables for this mapping are nothing special; you again have an
ITEM and a BID table, with an ITEM_ID foreign key column in the BID table. Your
motivation here is a slightly different representation of the data in the application, with a Map.
In the Item class, include a Map:
private Map<Long,Bid> bidsByIdentifier = new HashMap<Long,Bid>();
New here is the @MapKey element of JPA—it maps a property of the target entity as
key of the map.The default if you omit the name attribute is the identifier property
of the target entity (so the name here is redundant). Because the keys of a map
form a set, values are expected to be unique for a particular map—this is the case
for Bid primary keys but likely not for any other property of Bid.
Many-valued entity associations
In Hibernate XML, this mapping is as follows:
<map name="bidsByIdentifier">
<key column="ITEM_ID"/>
<map-key type="long" formula="BID_ID"/>
<one-to-many class="Bid"/>
The formula key for a map makes this column read-only, so it’s never updated
when you modify the map. A more common situation is a map in the middle of a
ternary association.
Ternary associations
You may be a little bored by now, but we promise this is the last time we’ll show
another way to map the association between Category and Item. Let’s summarize
what you already know about this many-to-many association:
It can be mapped with two collections on either side and a join table that
has only two foreign key columns. This is a regular many-to-many association mapping.
It can be mapped with an intermediate entity class that represents the join
table, and any additional columns therein. A one-to-many association is
mapped on either side (Category and Item), and a bidirectional many-toone equivalent is mapped in the intermediate entity class.
It can be mapped unidirectional, with a join table represented as a value
type component. The Category entity has a collection of components. Each
component has a reference to its owning Category and a many-to-one
entity association to an Item. (You can also switch the words Category and
Item in this explanation.)
You previously turned the last scenario into a ternary association by adding
another many-to-one entity association to a User. Let’s do the same with a Map.
A Category has a Map of Item instances—the key of each map entry is a reference to an Item. The value of each map entry is the User who added the Item to
the Category. This strategy is appropriate if there are no additional columns on
the join table; see the schema in figure 7.12.
The advantage of this strategy is that you don’t need any intermediate class, no
entity or value type, to represent the ADDED_BY_USER_ID column of the join table
in your Java application.
First, here’s the Map property in Category with a Hibernate extension
Advanced entity association mappings
Figure 7.12
A ternary association with a join table between three entities
joinColumns = @JoinColumn(name = "ITEM_ID")
joinColumns = @JoinColumn(name = "CATEGORY_ID"),
inverseJoinColumns = @JoinColumn(name = "USER_ID")
private Map<Item,User> itemsAndUser = new HashMap<Item,User>();
The Hibernate XML mapping includes a new element, <map-key-many-to-many>:
<map name="itemsAndUser" table="CATEGORY_ITEM">
<key column="CATEGORY_ID"/>
<map-key-many-to-many column="ITEM_ID" class="Item"/>
<many-to-many column="ADDED_BY_USER_ID" class="User"/>
To create a link between all three entities, if all your instances are already in persistent state, add a new entry to the map:
aCategory.getItemsAndUser().add( anItem, aUser );
To remove the link, remove the entry from the map. As an exercise, you can try to
make this mapping bidirectional, with a collection of categories in Item.
Polymorphic associations
Remember that this has to be an inverse collection mapping, so it doesn’t support
indexed collections.
Now that you know all the association mapping techniques for normal entities,
we still have to consider inheritance and associations to the various levels of an
inheritance hierarchy. What we really want is polymorphic behavior. Let’s see how
Hibernate deals with polymorphic entity associations.
Polymorphic associations
Polymorphism is a defining feature of object-oriented languages like Java. Support for polymorphic associations and polymorphic queries is an absolutely basic
feature of an ORM solution like Hibernate. Surprisingly, we’ve managed to get this
far without needing to talk much about polymorphism. Even more surprisingly,
there is not much to say on the topic—polymorphism is so easy to use in Hibernate that we don’t need to spend a lot of effort explaining it.
To get an overview, we first consider a many-to-one association to a class that
may have subclasses. In this case, Hibernate guarantees that you can create links
to any subclass instance just like you would to instances of the superclass.
Polymorphic many-to-one associations
A polymorphic association is an association that may refer instances of a subclass of
the class that was explicitly specified in the mapping metadata. For this example,
consider the defaultBillingDetails property of User. It references one particular BillingDetails object, which at runtime can be any concrete instance of that
class. The classes are shown in figure 7.13.
You map this association to the abstract class BillingDetails as follows in
Figure 7.13
A user has either a credit card or a bank account as the default.
Advanced entity association mappings
<many-to-one name="defaultBillingDetails"
But because BillingDetails is abstract, the association must refer to an instance
of one of its subclasses—CreditCard or CheckingAccount—at runtime.
You don’t have to do anything special to enable polymorphic associations in
Hibernate; specify the name of any mapped persistent class in your association
mapping (or let Hibernate discover it using reflection), and then, if that class
declares any <union-subclass>, <subclass>, or <joined-subclass> elements,
the association is naturally polymorphic.
The following code demonstrates the creation of an association to an instance
of the CreditCard subclass:
CreditCard cc = new CreditCard();
User user = (User) session.get(User.class, userId);
user.addBillingDetails(cc); // Add it to the one-to-many association
// Complete unit of work
Now, when you navigate the association in a second unit of work, Hibernate automatically retrieves the CreditCard instance:
User user = (User) secondSession.get(User.class, userId);
// Invoke the pay() method on the actual subclass instance
There is just one thing to watch out for: If BillingDetails was mapped with
lazy="true" (which is the default), Hibernate would proxy the defaultBillingDetails association target. In this case, you wouldn’t be able to perform a typecast to the concrete class CreditCard at runtime, and even the instanceof
operator would behave strangely:
User user = (User) session.get(User.class, userid);
BillingDetails bd = user.getDefaultBillingDetails();
System.out.println( bd instanceof CreditCard ); // Prints "false"
CreditCard cc = (CreditCard) bd; // ClassCastException!
In this code, the typecast fails because bd is a proxy instance. When a method is
invoked on the proxy, the call is delegated to an instance of CreditCard that is
fetched lazily (it’s an instance of a runtime-generated subclass, so instanceof also
fails). Until this initialization occurs, Hibernate doesn’t know what the subtype of
Polymorphic associations
the given instance is—this would require a database hit, which you try to avoid
with lazy loading in the first place. To perform a proxy-safe typecast, use load():
User user = (User) session.get(User.class, userId);
BillingDetails bd = user.getDefaultBillingDetails();
// Narrow the proxy to the subclass, doesn't hit the database
CreditCard cc =
(CreditCard) session.load( CreditCard.class, bd.getId() );
expiryDate = cc.getExpiryDate();
After the call to load(), bd and cc refer to two different proxy instances, which
both delegate to the same underlying CreditCard instance. However, the second
proxy has a different interface, and you can call methods (like getExpiryDate())
that apply only to this interface.
Note that you can avoid these issues by avoiding lazy fetching, as in the following code, using an eager fetch query:
User user = (User)session.createCriteria(User.class)
.add(Restrictions.eq("id", uid) )
.setFetchMode("defaultBillingDetails", FetchMode.JOIN)
// The users defaultBillingDetails have been fetched eagerly
CreditCard cc = (CreditCard) user.getDefaultBillingDetails();
expiryDate = cc.getExpiryDate();
Truly object-oriented code shouldn’t use instanceof or numerous typecasts. If
you find yourself running into problems with proxies, you should question your
design, asking whether there is a more polymorphic approach. Hibernate also
offers bytecode instrumentation as an alternative to lazy loading through proxies;
we’ll get back to fetching strategies in chapter 13, section 13.1, “Defining the global fetch plan.”
One-to-one associations are handled the same way. What about many-valued
associations—for example, the collection of billingDetails for each User?
Polymorphic collections
A User may have references to many BillingDetails, not only a single default
(one of the many is the default). You map this with a bidirectional one-to-many
In BillingDetails, you have the following:
<many-to-one name="user"
Advanced entity association mappings
In the Users mapping you have:
<set name="billingDetails"
<key column="USER_ID"/>
<one-to-many class="BillingDetails"/>
Adding a CreditCard is easy:
CreditCard cc = new CreditCard();
User user = (User) session.get(User.class, userId);
// Call convenience method that sets both sides of the association
// Complete unit of work
As usual, addBillingDetails() calls getBillingDetails().add(cc) and cc.setUser(this) to guarantee the integrity of the relationship by setting both pointers.
You may iterate over the collection and handle instances of CreditCard and
CheckingAccount polymorphically (you probably don’t want to bill users several
times in the final system, though):
User user = (User) session.get(User.class, userId);
for( BillingDetails bd : user.getBillingDetails() ) {
// Invoke or;
In the examples so far, we assumed that BillingDetails is a class mapped explicitly and that the inheritance mapping strategy is table per class hierarchy, or normalized with table per subclass.
However, if the hierarchy is mapped with table per concrete class (implicit polymorphism) or explicitly with table per concrete class with union, this scenario requires
a more sophisticated solution.
Polymorphic associations to unions
Hibernate supports the polymorphic many-to-one and one-to-many associations
shown in the previous sections even if a class hierarchy is mapped with the table per
concrete class strategy. You may wonder how this works, because you may not have a
table for the superclass with this strategy; if so, you can’t reference or add a foreign key column to BILLING_DETAILS.
Polymorphic associations
Review our discussion of table per concrete class with union in chapter 5,
section 5.1.2, “Table per concrete class with unions.” Pay extra attention to the polymorphic query Hibernate executes when retrieving instances of BillingDetails.
Now, consider the following collection of BillingDetails mapped for User:
<set name="billingDetails"
<key column="USER_ID"/>
<one-to-many class="BillingDetails"/>
If you want to enable the polymorphic union feature, a requirement for this polymorphic association is that it’s inverse; there must be a mapping on the opposite
side. In the mapping of BillingDetails, with <union-subclass>, you have to
include a <many-to-one> association:
<class name="BillingDetails" abstract="true">
<id name="id" column="BILLING_DETAILS_ID" .../>
<property .../>
<many-to-one name="user"
<union-subclass name="CreditCard" table="CREDIT_CARD">
<property .../>
<union-subclass name="BankAccount" table="BANK_ACCOUNT">
<property .../>
You have two tables for both concrete classes of the hierarchy. Each table has a foreign key column, USER_ID, referencing the USERS table. The schema is shown in
figure 7.14.
Now, consider the following data-access code:
Figure 7.14
Two concrete classes mapped to two separate tables
Advanced entity association mappings
Hibernate executes a UNION query to retrieve all instances that are referenced in
this collection:
( select
null as ACCOUNT, null as BANKNAME, null as SWIFT,
1 as CLAZZ
null as NUMBER, null as EXP_MONTH, null as EXP_YEAR
2 as CLAZZ
) BD
The FROM-clause subselect is a union of all concrete class tables, and it includes the
USER_ID foreign key values for all instances. The outer select now includes a
restriction in the WHERE clause to all rows referencing a particular user.
This magic works great for retrieval of data. If you manipulate the collection
and association, the noninverse side is used to update the USER_ID column(s) in
the concrete table. In other words, the modification of the inverse collection has
no effect: The value of the user property of a CreditCard or BankAccount
instance is taken.
Now consider the many-to-one association defaultBillingDetails again,
mapped with the DEFAULT_BILLING_DETAILS_ID column in the USERS table.
Hibernate executes a UNION query that looks similar to the previous query to
retrieve this instance, if you access the property. However, instead of a restriction
in the WHERE clause to a particular user, the restriction is made on a particular
Important: Hibernate cannot and will not create a foreign key constraint for
DEFAULT_BILLING_DETAILS_ID with this strategy. The target table of this reference
can be any of the concrete tables, which can’t be constrained easily. You should
consider writing a custom integrity rule for this column with a database trigger.
Polymorphic associations
One problematic inheritance strategy remains: table per concrete class with
implicit polymorphism.
Polymorphic table per concrete class
In chapter 5, section 5.1.1, “Table per concrete class with implicit polymorphism,”
we defined the table per concrete class mapping strategy and observed that this mapping strategy makes it difficult to represent a polymorphic association, because
you can’t map a foreign key relationship to a table of the abstract superclass.
There is no table for the superclass with this strategy; you have tables only for concrete classes. You also can’t create a UNION, because Hibernate doesn’t know what
unifies the concrete classes; the superclass (or interface) isn’t mapped anywhere.
Hibernate doesn’t support a polymorphic billingDetails one-to-many collection in User, if this inheritance mapping strategy is applied on the BillingDetails hierarchy. If you need polymorphic many-to-one associations with this
strategy, you’ll have to resort to a hack. The technique we’ll show you in this section should be your last choice. Try to switch to a <union-subclass> mapping first.
Suppose that you want to represent a polymorphic many-to-one association
from User to BillingDetails, where the BillingDetails class hierarchy is
mapped with a table per concrete class strategy and implicit polymorphic behavior in
Hibernate. You have a CREDIT_CARD table and a BANK_ACCOUNT table, but no
BILLING_DETAILS table. Hibernate needs two pieces of information in the USERS
table to uniquely identify the associated default CreditCard or BankAccount:
The name of the table in which the associated instance resides
The identifier of the associated instance
The USERS table requires a DEFAULT_BILLING_DETAILS_TYPE column in addition
to the DEFAULT_BILLING_DETAILS_ID. This extra column works as an additional
discriminator and requires a Hibernate <any> mapping in User.hbm.xml:
<any name="defaultBillingDetails"
<meta-value value="CREDIT_CARD" class="CreditCard"/>
<meta-value value="BANK_ACCOUNT" class="BankAccount"/>
The meta-type attribute specifies the Hibernate type of the DEFAULT_BILLING_
DETAILS_TYPE column; the id-type attribute specifies the type of the DEFAULT_
Advanced entity association mappings
BILLING_DETAILS_ID column (it’s necessary for CreditCard and BankAccount to
have the same identifier type).
The <meta-value> elements tell Hibernate how to interpret the value of the
DEFAULT_BILLING_DETAILS_TYPE column. You don’t need to use the full table
name here—you can use any value you like as a type discriminator. For example,
you can encode the information in two characters:
<any name="defaultBillingDetails"
<meta-value value="CC" class="CreditCard"/>
<meta-value value="CA" class="BankAccount"/>
An example of this table structure is shown in figure 7.15.
Here is the first major problem with this kind of association: You can’t add a
foreign key constraint to the DEFAULT_BILLING_DETAILS_ID column, because
some values refer to the BANK_ACCOUNT table and others to the CREDIT_CARD table.
Thus, you need to come up with some other way to ensure integrity (a trigger, for
example). This is the same issue you’d face with a <union-subclass> strategy.
Furthermore, it’s difficult to write SQL table joins for this association. In particular, the Hibernate query facilities don’t support this kind of association mapping,
nor may this association be fetched using an outer join. We discourage the use of
<any> associations for all but the most special cases. Also note that this mapping
Figure 7.15
Using a discriminator column with an any association
technique isn’t available with annotations or in Java Persistence (this mapping is
so rare that nobody asked for annotation support so far).
As you can see, as long as you don’t plan to create an association to a class hierarchy mapped with implicit polymorphism, associations are straightforward; you
don’t usually need to think about it. You may be surprised that we didn’t show any
JPA or annotation example in the previous sections—the runtime behavior is the
same, and you don’t need any extra mapping to get it.
In this chapter, you learned how to map more complex entity associations. Many
of the techniques we’ve shown are rarely needed and may be unnecessary if you
can simplify the relationships between your classes. In particular, many-to-many
entity associations are often best represented as two one-to-many associations to
an intermediate entity class, or with a collection of components.
Table 7.1 shows a summary you can use to compare native Hibernate features
and Java Persistence.
Table 7.1
Hibernate and JPA comparison chart for chapter 7
Hibernate Core
Java Persistence and EJB 3.0
Hibernate supports key generation for shared
primary key one-to-one association mappings.
Standardized one-to-one mapping is supported. Automatic shared primary key generation is possible
through a Hibernate extension.
Hibernate supports all entity association mappings across join tables.
Standardized association mappings are available
across secondary tables.
Hibernate supports mapping of lists with persistent indexes.
Persistent indexes require a Hibernate extension
Hibernate supports fully polymorphic behavior.
It provides extra support for any association
mappings to an inheritance hierarchy mapped
with implicit polymorphism.
Fully polymorphic behavior is available, but there is
no annotation support for any mappings.
In the next chapter, we’ll focus on legacy database integration and how you can
customize the SQL that Hibernate generates automatically for you. This chapter is
interesting not only if you have to work with legacy schemas, but also if you want
to improve your new schema with custom DDL, for example.
Legacy databases
and custom SQL
This chapter covers
Legacy database integration and tricky mappings
Customization of SQL statements
Improving the SQL schema with custom DDL
Integrating legacy databases
Many examples presented in this chapter are about “difficult” mappings. The first
time you’ll likely have problems creating a mapping is with a legacy database
schema that can’t be modified. We discuss typical issues you encounter in such a
scenario and how you can bend and twist your mapping metadata instead of
changing your application or database schema.
We also show you how you can override the SQL Hibernate generates automatically. This includes SQL queries, DML (create, update, delete) operations, as
well as Hibernate’s automatic DDL-generation feature. You’ll see how to map
stored procedures and user-defined SQL functions, and how to apply the right
integrity rules in your database schema. This section will be especially useful if
your DBA needs full control (or if you’re a DBA and want to optimize Hibernate
at the SQL level).
As you can see, the topics in this chapter are diverse; you don’t have to read
them all at once. You can consider a large part of this chapter to be reference
material and come back when you face a particular issue.
Integrating legacy databases
In this section, we hope to cover all the things you may encounter when you have
to deal with an existing legacy database or (and this is often synonymous) a weird
or broken schema. If your development process is top-down, however, you may
want to skip this section. Furthermore, we recommend that you first read all chapters about class, collection, and association mappings before you attempt to
reverse-engineer a complex legacy schema.
We have to warn you: When your application inherits an existing legacy database schema, you should usually make as few changes to the existing schema as
possible. Every change that you make to the schema could break other existing
applications that access the database. Possibly expensive migration of existing
data is also something you need to evaluate. In general, it isn’t possible to build a
new application and make no changes to the existing data model—a new application usually means additional business requirements that naturally require evolution of the database schema.
We’ll therefore consider two types of problems: problems that relate to the
changing business requirements (which generally can’t be solved without schema
changes) and problems that relate only to how you wish to represent the same
business problem in your new application (these can usually, but not always, be
solved without database schema changes). It should be clear that the first kind of
problem is usually visible by looking at just the logical data model. The second
Legacy databases and custom SQL
more often relates to the implementation of the logical data model as a physical
database schema.
If you accept this observation, you’ll see that the kinds of problems that require
schema changes are those that necessitate addition of new entities, refactoring of
existing entities, addition of new attributes to existing entities, and modification
to the associations between entities. The problems that can be solved without
schema changes usually involve inconvenient table or column definitions for a
particular entity. In this section, we’ll concentrate on these kinds of problems.
We assume that you’ve tried to reverse-engineer your existing schema with the
Hibernate toolset, as described in chapter 2, section 2.3, “Reverse engineering a
legacy database.” The concepts and solutions discussed in the following sections
assume that you have basic object/relational mapping in place and that you need
to make additional changes to get it working. Alternatively, you can try to write the
mapping completely by hand without the reverse-engineering tools.
Let’s start with the most obvious problem: legacy primary keys.
Handling primary keys
We’ve already mentioned that we think natural primary keys can be a bad idea.
Natural keys often make it difficult to refactor the data model when business
requirements change. They may even, in extreme cases, impact performance.
Unfortunately, many legacy schemas use (natural) composite keys heavily and, for
the reason we discourage the use of composite keys, it may be difficult to change
the legacy schema to use noncomposite natural or surrogate keys.
Therefore, Hibernate supports the use of natural keys. If the natural key is a
composite key, support is via the <composite-id> mapping. Let’s map both a
composite and a noncomposite natural primary key.
Mapping a natural key
If you encountered a USERS table in a legacy schema, it’s likely that USERNAME is
the actual primary key. In this case, you have no surrogate identifier that is automatically generated. Instead, you enable the assigned identifier generator strategy to indicate to Hibernate that the identifier is a natural key assigned by the
application before the object is saved:
<class name="User" table="USERS">
<id name="username" column="USERNAME" length="16">
<generator class="assigned"/>
Integrating legacy databases
The code to save a new User is as follows:
User user = new User();
user.setUsername("johndoe"); // Assign a primary key value
session.saveOrUpdate(user); // Will result in an INSERT
// System.out.println( session.getIdentifier(user) );
How does Hibernate know that saveOrUpdate() requires an INSERT and not an
UPDATE? It doesn’t, so a trick is needed: Hibernate queries the USERS table for the
given username, and if it’s found, Hibernate updates the row. If it isn’t found,
insertion of a new row is required and done. This is certainly not the best solution,
because it triggers an additional hit on the database.
Several strategies avoid the SELECT:
Add a <version> or a <timestamp> mapping, and a property, to your entity.
Hibernate manages both values internally for optimistic concurrency control (discussed later in the book). As a side effect, an empty timestamp or a
0 or NULL version indicates that an instance is new and has to be inserted,
not updated.
Implement a Hibernate Interceptor, and hook it into your Session. This
extension interface allows you to implement the method isTransient()
with any custom procedure you may need to distinguish old and new
On the other hand, if you’re happy to use save() and update() explicitly instead
of saveOrUpdate(), Hibernate doesn’t have to distinguish between transient and
detached instances—you do this by selecting the right method to call. (This issue
is, in practice, the only reason to not use saveOrUpdate() all the time, by the way.)
Mapping natural primary keys with JPA annotations is straightforward:
private String username;
If no identifier generator is declared, Hibernate assumes that it has to apply the
regular select-to-determine-state-unless-versioned strategy and expects the application to take care of the primary key value assignment. You can again avoid the
SELECT by extending your application with an interceptor or by adding a versioncontrol property (version number or timestamp).
Composite natural keys extend on the same ideas.
Legacy databases and custom SQL
Mapping a composite natural key
Suppose that the primary key of the USERS table consists of a USERNAME and
DEPARTMENT_NR. You can add a property named departmentNr to the User class
and create the following mapping:
<class name="User" table="USERS">
<key-property name="username"
<key-property name="departmentNr"
The code to save a new User looks like this:
User user = new User();
// Assign a primary key value
// Set property values
Again, keep in mind that Hibernate executes a SELECT to determine what saveOrUpdate() should do—unless you enable versioning control or a custom Interceptor. But what object can/should you use as the identifier when you call load()
or get()? Well, it’s possible to use an instance of the User class, for example:
User user = new User();
// Assign a primary key value
// Load the persistent state into user
session.load(User.class, user);
In this code snippet, User acts as its own identifier class. It’s more elegant to
define a separate composite identifier class that declares just the key properties.
Call this class UserId:
public class UserId implements Serializable {
private String username;
Integrating legacy databases
private Integer departmentNr;
public UserId(String username, Integer departmentNr) {
this.username = username;
this.departmentNr = departmentNr;
// Getters...
public int hashCode() {
int result;
result = username.hashCode();
result = 29 * result + departmentNr.hashCode();
return result;
public boolean equals(Object other) {
if (other==null) return false;
if ( !(other instanceof UserId) ) return false;
UserId that = (UserId) other;
return this.username.equals(that.username) &&
It’s critical that you implement equals() and hashCode() correctly, because
Hibernate relies on these methods for cache lookups. Identifier classes are also
expected to implement Serializable.
You now remove the username and departmentNr properties from User and
add a userId property. Create the following mapping:
<class name="User" table="USERS">
<composite-id name="userId" class="UserId">
<key-property name="username"
<key-property name="departmentNr"
Save a new instance of User with this code:
UserId id = new UserId("johndoe", 42);
User user = new User();
// Assign a primary key value
// Set property values
Legacy databases and custom SQL
Again, a SELECT is needed for saveOrUpdate() to work. The following code shows
how to load an instance:
UserId id = new UserId("johndoe", 42);
User user = (User) session.load(User.class, id);
Now, suppose that the DEPARTMENT_NR is a foreign key referencing the DEPARTMENT table, and that you wish to represent this association in the Java domain
model as a many-to-one association.
Foreign keys in composite primary keys
We recommend that you map a foreign key column that is also part of a composite primary key with a regular <many-to-one> element, and disable any Hibernate inserts or updates of this column with insert="false" update="false", as
<class name="User" table="USER">
<composite-id name="userId" class="UserId">
<key-property name="username"
<key-property name="departmentId"
<many-to-one name="department"
insert="false" update="false"/>
Hibernate now ignores the department property when updating or inserting a
User, but you can of course read it with johndoe.getDepartment(). The relationship between a User and Department is now managed through the departmentId
property of the UserId composite key class:
UserId id = new UserId("johndoe", department.getId() );
User user = new User();
// Assign a primary key value
Integrating legacy databases
// Set property values
Only the identifier value of the department has any effect on the persistent state;
the setDepartment(department) call is done for consistency: Otherwise, you’d
have to refresh the object from the database to get the department set after the
flush. (In practice you can move all these details into the constructor of your composite identifier class.)
An alternative approach is a <key-many-to-one>:
<class name="User" table="USER">
<composite-id name="userId" class="UserId">
<key-property name="username"
<key-many-to-one name="department"
However, it’s usually inconvenient to have an association in a composite identifier
class, so this approach isn’t recommended except in special circumstances. The
<key-many-to-one> construct also has limitations in queries: You can’t restrict a
query result in HQL or Criteria across a <key-many-to-one> join (although it’s
possible these features will be implemented in a later Hibernate version).
Foreign keys to composite primary keys
Because USERS has a composite primary key, any referencing foreign key is also
composite. For example, the association from Item to User (the seller) is now
mapped with a composite foreign key.
Hibernate can hide this detail from the Java code with the following association mapping from Item to User:
<many-to-one name="seller" class="User">
<column name="USERNAME"/>
<column name="DEPARTMENT_ID"/>
Legacy databases and custom SQL
Any collection owned by the User class also has a composite foreign key—for
example, the inverse association, items, sold by this user:
<set name="itemsForAuction" inverse="true">
<column name="USERNAME"/>
<column name="DEPARTMENT_ID"/>
<one-to-many class="Item"/>
Note that the order in which columns are listed is important and should match
the order in which they appear in the <composite-id> element of the primary
key mapping of User.
This completes our discussion of the basic composite key mapping technique
in Hibernate. Mapping composite keys with annotations is almost the same, but as
always, small differences are important.
Composite keys with annotations
The JPA specification covers strategies for handling composite keys. You have
three options:
Encapsulate the identifier properties in a separate class and mark it
@Embeddable, like a regular component. Include a property of this component type in your entity class, and map it with @Id for an applicationassigned strategy.
Encapsulate the identifier properties in a separate class without any annotations on it. Include a property of this type in your entity class, and map it
with @EmbeddedId.
Encapsulate the identifier properties in a separate class. Now—and this is
different that what you usually do in native Hibernate—duplicate all the
identifier properties in the entity class. Then, annotate the entity class with
@IdClass and specify the name of your encapsulated identifier class.
The first option is straightforward. You need to make the UserId class from the
previous section embeddable:
public class UserId implements Serializable {
private String username;
private String departmentNr;
Integrating legacy databases
As for all component mappings, you can define extra mapping attributes on the
fields (or getter methods) of this class. To map the composite key of User, set the
generation strategy to application assigned by omitting the @GeneratedValue
private UserId userId;
@Column(name="USERNAME") ),
@Column(name="DEP_NR") )
Just as you did with regular component mappings earlier in the book, you can
override particular attribute mappings of the component class, if you like.
The second composite-key mapping strategy doesn’t require that you mark up
the UserId primary key class. Hence, no @Embeddable and no other annotation
on that class is needed. In the owning entity, you map the composite identifier
property with @EmbeddedId, again, with optional overrides:
private UserId userId;
@Column(name="USERNAME") ),
@Column(name="DEP_NR") )
In a JPA XML descriptor, this mapping looks as follows:
<embeddable class="auction.model.UserId" access ="PROPERTY">
<basic name="username">
<column name="UNAME"/>
<basic name="departmentNr">
<column name="DEPARTMENT_NR"/>
<entity class="auction.model.User" access="FIELD">
<embedded-id name="userId">
<attribute-override name="username">
<column name="USERNAME"/>
Legacy databases and custom SQL
<attribute-override name="departmentNr">
<column name="DEP_NR"/>
The third composite-key mapping strategy is a bit more difficult to understand,
especially for experienced Hibernate users. First, you encapsulate all identifier
attributes in a separate class—as in the previous strategy, no extra annotations
on that class are needed. Now you duplicate all the identifier properties in the
entity class:
@Table(name = "USERS")
public class User {
private String username;
private String departmentNr;
// Accessor methods, etc.
Hibernate inspects the @IdClass and singles out all the duplicate properties (by
comparing name and type) as identifier properties and as part of the primary
key. All primary key properties are annotated with @Id, and depending on the
position of these elements (field or getter method), the entity defaults to field or
property access.
Note that this last strategy is also available in Hibernate XML mappings; however, it’s somewhat obscure:
<composite-id class="UserId" mapped="true">
<key-property name="username"
<key-property name="departmentNr"
You omit the identifier property name of the entity (because there is none), so
Hibernate handles the identifier internally. With mapped="true", you enable the
last JPA mapping strategy, so all key properties are now expected to be present in
both the User and the UserId classes.
Integrating legacy databases
This composite identifier mapping strategy looks as follows if you use JPA XML
<entity class="auction.model.User" access="FIELD">
<id-class class="auction.model.UserId"/>
<id name="username"/>
<id name="departmentNr"/>
Because we didn’t find a compelling case for this last strategy defined in Java Persistence, we have to assume that it was added to the specification to support some
legacy behavior (EJB 2.x entity beans).
Composite foreign keys are also possible with annotations. Let’s first map the
association from Item to User:
@JoinColumn(name="USERNAME", referencedColumnName = "USERNAME"),
@JoinColumn(name="DEP_NR", referencedColumnName = "DEP_NR")
private User seller;
The primary difference between a regular @ManyToOne and this mapping is the
number of columns involved—again, the order is important and should be the
same as the order of the primary key columns. However, if you declare the referencedColumnName for each column, order isn’t important, and both the source
and target tables of the foreign key constraint can have different column names.
The inverse mapping from User to Item with a collection is even more straightforward:
@OneToMany(mappedBy = "seller")
private Set<Item> itemsForAuction = new HashSet<Item>();
This inverse side needs the mappedBy attribute, as usual for bidirectional associations. Because this is the inverse side, it doesn’t need any column declarations.
In legacy schemas, a foreign key often doesn’t reference a primary key.
Foreign key referencing nonprimary keys
Usually, a foreign key constraint references a primary key. A foreign key constraint
is an integrity rule that guarantees that the referenced table has one row with a key
value that matches the key value in the referencing table and given row. Note that
a foreign key constraint can be self-referencing; in other words, a column with a
foreign key constraint can reference the primary key column of the same table.
(The PARENT_CATEGORY_ID in the CaveatEmptor CATEGORY table is one example.)
Legacy databases and custom SQL
Legacy schemas sometimes have foreign key constraints that don’t follow the
simple “FK references PK” rule. Sometimes a foreign key references a nonprimary
key: a simple unique column, a natural nonprimary key. Let’s assume that in CaveatEmptor, you need to handle a legacy natural key column called CUSTOMER_NR on
the USERS table:
<class name="User" table="USERS">
<id name="id" column="USER_ID">...</id>
<property name="customerNr"
The only thing that is probably new to you in this mapping is the unique attribute.
This is one of the SQL customization options in Hibernate; it’s not used at runtime (Hibernate doesn’t do any uniqueness validation) but to export the database
schema with hbm2ddl. If you have an existing schema with a natural key, you
assume that it’s unique. For completeness, you can and should repeat such important constraints in your mapping metadata—maybe you’ll use it one day to export
a fresh schema.
Equivalent to the XML mapping, you can declare a column as unique in JPA
@Column(name = "CUSTOMER_NR", nullable = false, unique=true)
private int customerNr;
The next issue you may discover in the legacy schema is that the ITEM table has a
foreign key column, SELLER_NR. In an ideal world, you would expect this foreign
key to reference the primary key, USER_ID, of the USERS table. However, in a legacy
schema, it may reference the natural unique key, CUSTOMER_NR. You need to map
it with a property reference:
<class name="Item" table="ITEM">
<id name="id" column="ITEM_ID">...</id>
<many-to-one name="seller" column="SELLER_NR"
You’ll encounter the property-ref attribute in more exotic Hibernate mappings.
It’s used to tell Hibernate that “this is a mirror of the named property.” In the previous example, Hibernate now knows the target of the foreign key reference. One
Integrating legacy databases
further thing to note is that property-ref requires the target property to be
unique, so unique="true", as shown earlier, is needed for this mapping.
If you try to map this association with JPA annotations, you may look for an
equivalent to the property-ref attribute. You map the association with an explicit
reference to the natural key column, CUSTOMER_NR:
@JoinColumn(name="SELLER_NR", referencedColumnName = "CUSTOMER_NR")
private User seller;
Hibernate now knows that the referenced target column is a natural key and manages the foreign key relationship accordingly.
To complete this example, you make this association mapping between the two
classes bidirectional, with a mapping of an itemsForAuction collection on the
User class. First, here it is in XML:
<class name="User" table="USERS">
<id name="id" column="USER_ID">...</id>
<property name="customerNr" column="CUSTOMER_NR" unique="true"/>
<set name="itemsForAuction" inverse="true">
<key column="SELLER_NR” property-ref="customerNr"/>
<one-to-many class="Item"/>
Again the foreign key column in ITEM is mapped with a property reference to
customerNr. In annotations, this is a lot easier to map as an inverse side:
@OneToMany(mappedBy = "seller")
private Set<Item> itemsForAuction = new HashSet<Item>();
Composite foreign key referencing nonprimary keys
Some legacy schemas are even more complicated than the one discussed before:
A foreign key might be a composite key and, by design, reference a composite natural nonprimary key!
Let’s assume that USERS has a natural composite key that includes the FIRSTNAME, LASTNAME, and BIRTHDAY columns. A foreign key may reference this natural
key, as shown in figure 8.1.
To map this, you need to group several properties under the same name—otherwise you can’t name the composite in a property-ref. Apply the <properties>
element to group the mappings:
Legacy databases and custom SQL
Figure 8.1
A composite foreign key references a composite primary key.
<class name="User" table="USERS">
<id name="id" column="USER_ID">...</id>
<properties name="nameAndBirthday" unique="true" update="false">
<property name="firstname" column="FIRSTNAME"/>
<property name="lastname" column="LASTNAME"/>
<property name="birthday" column="BIRTHDAY" type="date"/>
<set name="itemsForAuction" inverse="true">
<key property-ref="nameAndBirthday">
<column name="SELLER_FIRSTNAME"/>
<column name="SELLER_LASTNAME"/>
<column name="SELLER_BIRTHDAY"/>
<one-to-many class="Item"/>
As you can see, the <properties> element is useful not only to give several properties a name, but also to define a multicolumn unique constraint or to make several properties immutable. For the association mappings, the order of columns is
again important:
<class name="Item" table="ITEM">
<id name="id" column="ITEM_ID">...</id>
<many-to-one name="seller" property-ref="nameAndBirthday">
<column name="SELLER_FIRSTNAME"/>
<column name="SELLER_LASTNAME"/>
<column name="SELLER_BIRTHDAY"/>
Integrating legacy databases
Fortunately, it’s often straightforward to clean up such a schema by refactoring
foreign keys to reference primary keys—if you can make changes to the database
that don’t disturb other applications sharing the data.
This completes our exploration of natural, composite, and foreign key-related
problems you may have to deal with when you try to map a legacy schema. Let’s
move on to other interesting special mapping strategies.
Sometimes you can’t make any changes to a legacy database—not even creating tables or views. Hibernate can map classes, properties, and even parts of associations to a simple SQL statement or expression. We call these kinds of mappings
formula mappings.
Arbitrary join conditions with formulas
Mapping a Java artifact to an SQL expression is useful for more than integrating a
legacy schema. You created two formula mappings already: The first, “Using
derived properties,” in chapter 4, section 4.4.1, was a simple derived read-only
property mapping. The second formula calculated the discriminator in an inheritance mapping; see chapter 5, section 5.1.3, “Table per class hierarchy.”
You’ll now apply formulas for a more exotic purposes. Keep in mind that some
of the mappings you’ll see now are complex, and you may be better prepared to
understand them after reading all the chapters in part 2 of this book.
Understanding the use case
You now map a literal join condition between two entities. This sounds more complex than it is in practice. Look at the two classes shown in figure 8.2.
A particular Item may have several Bids—this is a one-to-many association. But
it isn’t the only association between the two classes; the other, a unidirectional
Figure 8.2
A single-association that references an
instance in a many-association
Legacy databases and custom SQL
one-to-one, is needed to single out one particular Bid instance as the winning bid.
You map the first association because you’d like to be able to get all the bids for an
auctioned item by calling anItem.getBids(). The second association allows you
to call anItem.getSuccessfulBid(). Logically, one of the elements in the collection is also the successful bid object referenced by getSuccessfulBid().
The first association is clearly a bidirectional one-to-many/many-to-one association, with a foreign key ITEM_ID in the BID table. (If you haven’t mapped this
before, look at chapter 6, section 6.4, “Mapping a parent/children relationship.”)
The one-to-one association is more difficult; you can map it several ways.
The most natural is a uniquely constrained foreign key in the ITEM table referencing a row in the BID table—the winning row, for example a SUCCESSFUL_
BID_ID column.
Legacy schemas often need a mapping that isn’t a simple foreign key relationship.
Mapping a formula join condition
Imagine that each row in the BID table has a flag column to mark the winning bid,
as shown in figure 8.3. One BID row has the flag set to true, and all other rows for
this auction item are naturally false. Chances are good that you won’t find a constraint or an integrity rule for this relationship in a legacy schema, but we ignore
this for now and focus on the mapping to Java classes.
To make this mapping even more interesting, assume that the legacy schema
didn’t use the SQL BOOLEAN datatype but a CHAR(1) field and the values T (for
true) and F (for false) to simulate the boolean switch. Your goal is to map this flag
column to a successfulBid property of the Item class. To map this as an object
reference, you need a literal join condition, because there is no foreign key Hibernate can use for a join. In other words, for each ITEM row, you need to join a row
from the BID table that has the SUCCESSFUL flag set to T. If there is no such row,
the anItem.getSuccessfulBid() call returns null.
Let’s first map the Bid class and a successful boolean property to the SUCCESSFUL database column:
Figure 8.3
The winning bid is marked with the
SUCCESSFUL column flag.
Integrating legacy databases
<class name="Bid" table="BID">
<id name="id" column="BID_ID"...
<property name="amount"
<properties name="successfulReference">
<property name="successful"
<many-to-one name="item"
<many-to-one name="bidder"
The type="true_false" attribute creates a mapping between a Java boolean
primitive (or its wrapper) property and a simple CHAR(1) column with T/F literal
values—it’s a built-in Hibernate mapping type. You again group several properties
with <properties> under a name that you can reference in other mappings. What
is new here is that you can group a <many-to-one>, not only basic properties.
The real trick is happening on the other side, for the mapping of the successfulBid property of the Item class:
<class name="Item" table="ITEM">
<id name="id" column="ITEM_ID"...
<property name="initialPrice"
<one-to-one name="successfulBid"
<set name="bids" inverse="true">
<key column="ITEM_ID”/>
<one-to-many class="Bid"/>
Legacy databases and custom SQL
Ignore the <set> association mapping in this example; this is the regular one-tomany association between Item and Bid, bidirectional, on the ITEM_ID foreign
key column in BID.
Isn’t <one-to-one> used for primary key associations? Usually, a <one-toone> mapping is a primary key relationship between two entities, when
rows in both entity tables share the same primary key value. However, by
using a formula with a property-ref, you can apply it to a foreign key
relationship. In the example shown in this section, you could replace the
<one-to-one> element with <many-to-one>, and it would still work.
The interesting part is the <one-to-one> mapping and how it relies on a property-ref and literal formula values as a join condition when you work with the
Working with the association
The full SQL query for retrieval of an auction item and its successful bid looks like
left outer join
and i.ITEM_ID = b.ITEM_ID
i.ITEM_ID = ?
When you load an Item, Hibernate now joins a row from the BID table by applying
a join condition that involves the columns of the successfulReference property.
Because this is a grouped property, you can declare individual expressions for
each of the columns involved, in the right order. The first one, 'T', is a literal, as
you can see from the quotes. Hibernate now includes 'T' = SUCCESSFUL in the
join condition when it tries to find out whether there is a successful row in the BID
table. The second expression isn’t a literal but a column name (no quotes).
Integrating legacy databases
Hence, another join condition is appended: i.ITEM_ID = b.ITEM_ID. You can
expand this and add more join conditions if you need additional restrictions.
Note that an outer join is generated because the item in question may not have
a successful bid, so NULL is returned for each b.* column. You can now call
anItem.getSuccessfulBid() to get a reference to the successful bid (or null if
none exists).
Finally, with or without database constraints, you can’t just implement an
item.setSuccessfulBid() method that only sets the value on a private field in
the Item instance. You have to implement a small procedure in this setter method
that takes care of this special relationship and the flag property on the bids:
public class Item {
private Bid successfulBid;
private Set<Bid> bids = new HashSet<Bid>();
public Bid getSuccessfulBid() {
return successfulBid;
public void setSuccessfulBid(Bid successfulBid) {
if (successfulBid != null) {
for (Bid bid : bids)
this.successfulBid = successfulBid;
When setSuccessfulBid() is called, you set all bids to not successful. Doing so
may trigger the loading of the collection—a price you have to pay with this strategy. Then, the new successful bid is marked and set as an instance variable. Setting
the flag updates the SUCCESSFUL column in the BID table when you save the
objects. To complete this (and to fix the legacy schema), your database-level constraints need to do the same as this method. (We’ll come back to constraints later
in this chapter.)
One of the things to remember about this literal join condition mapping is
that it can be applied in many other situations, not only for successful or default
relationships. Whenever you need some arbitrary join condition appended to
your queries, a formula is the right choice. For example, you could use it in a
Legacy databases and custom SQL
<many-to-many> mapping to create a literal join condition from the association
table to the entity table(s).
Unfortunately, at the time of writing, Hibernate Annotations doesn’t support
arbitrary join conditions expressed with formulas. The grouping of properties
under a reference name also wasn’t possible. We expect that these features will
closely resemble the XML mapping, once they’re available.
Another issue you may encounter in a legacy schema is that it doesn’t integrate
nicely with your class granularity. Our usual recommendation to have more
classes than tables may not work, and you may have to do the opposite and join
arbitrary tables into one class.
Joining arbitrary tables
We’ve already shown the <join> mapping element in an inheritance mapping in
chapter 5; see section 5.1.5, “Mixing inheritance strategies.” It helped to break out
properties of a particular subclass into a separate table, out of the primary inheritance hierarchy table. This generic functionality has more uses—however, we have
to warn you that <join> can also be a bad idea. Any properly designed system
should have more classes than tables. Splitting a single class into separate tables is
something you should do only when you need to merge several tables in a legacy
schema into a single class.
Moving properties into a secondary table
Suppose that in CaveatEmptor, you aren’t keeping a user’s address information
with the user’s main information in the USERS table, mapped as a component, but
in a separate table. This is shown in figure 8.4. Note that each BILLING_ADDRESS
has a foreign key USER_ID, which is in turn the primary key of the BILLING_
ADDRESS table.
To map this in XML, you need to group the properties of the Address in a
<join> element:
Figure 8.4
Breaking out the billing address
data into a secondary table
Integrating legacy databases
<class name="User" table="USERS">
<join table="BILLING_ADDRESS" optional="true">
<key column="USER_ID"/>
<component name="billingAddress" class="Address">
You don’t have to join a component; you can as well join individual properties or
even a <many-to-one> (we did this in the previous chapter for optional entity
associations). By setting optional="true", you indicate that the component property may also be null for a User with no billingAddress, and that no row should
then be inserted into the secondary table. Hibernate also executes an outer join
instead of an inner join to retrieve the row from the secondary table. If you
declared fetch="select" on the <join> mapping, a secondary select would be
used for that purpose.
The notion of a secondary table is also included in the Java Persistence specification. First, you have to declare a secondary table (or several) for a particular
@Table(name = "USERS")
pkJoinColumns = {
public class User {
Legacy databases and custom SQL
Each secondary table needs a name and a join condition. In this example, a foreign key column references the primary key column of the USERS table, just like
earlier in the XML mapping. (This is the default join condition, so you can only
declare the secondary table name, and nothing else). You can probably see that
the syntax of annotations is starting to become an issue and code is more difficult
to read. The good news is that you won’t have to use secondary tables often.
The actual component property, billingAddress, is mapped as a regular
@Embedded class, just like a regular component. However, you need to override
each component property column and assign it to the secondary table, in the
User class:
@AttributeOverrides( {
= "street",
column = @Column(name="STREET",
= "zipcode",
column = @Column(name="ZIPCODE",
= "city",
column = @Column(name="CITY",
private Address billingAddress;
This is no longer easily readable, but it’s the price you pay for mapping flexibility
with declarative metadata in annotations. Or, you can use a JPA XML descriptor:
<entity class="auction.model.User" access="FIELD">
<table name="USERS"/>
<secondary-table name="BILLING_ADDRESS">
<embedded name="billingAddress">
<attribute-override name="street">
<column name="STREET" table="BILLING_ADDRESS"/>
<attribute-override name="zipcode">
Integrating legacy databases
<column name="ZIPCODE" table="BILLING_ADDRESS"/>
<attribute-override name="city">
<column name="CITY" table="BILLING_ADDRESS"/>
Another, even more exotic use case for the <join> element is inverse joined properties or components.
Inverse joined properties
Let’s assume that in CaveatEmptor you have a legacy table called DAILY_BILLING.
This table contains all the open payments, executed in a nightly batch, for any
auctions. The table has a foreign key column to ITEM, as you can see in figure 8.5.
Each payment includes a TOTAL column with the amount of money that will be
billed. In CaveatEmptor, it would be convenient if you could access the price of a
particular auction by calling anItem.getBillingTotal().
You can map the column from the DAILY_BILLING table into the Item class.
However, you never insert or update it from this side; it’s read-only. For that reason, you map it inverse—a simple mirror of the (supposed, you don’t map it here)
other side that takes care of maintaining the column value:
<class name="Item" table="ITEM">
<join table="DAILY_BILLING" optional="true" inverse="true">
<key column="ITEM_ID"/>
Figure 8.5
The daily billing summary references an item and contains the total sum.
Legacy databases and custom SQL
Note that an alternative solution for this problem is a derived property using a formula expression and a correlated subquery:
<property name="billingTotal"
formula="( select db.TOTAL from DAILY_BILLING db
where db.ITEM_ID = ITEM_ID )"/>
The main difference is the SQL SELECT used to load an ITEM: The first solution
defaults to an outer join, with an optional second SELECT if you enable <join
fetch="select">. The derived property results in an embedded subselect in
the select clause of the original query. At the time of writing, inverse join mappings aren’t supported with annotations, but you can use a Hibernate annotation for formulas.
As you can probably guess from the examples, <join> mappings come in
handy in many situations. They’re even more powerful if combined with formulas, but we hope you won’t have to use this combination often.
One further problem that often arises in the context of working with legacy
data are database triggers.
Working with triggers
There are some reasons for using triggers even in a brand-new database, so legacy
data isn’t the only scenerio in which they can cause problems. Triggers and object
state management with an ORM software are almost always an issue, because triggers may run at inconvenient times or may modify data that isn’t synchronized
with the in-memory state.
Triggers that run on INSERT
Suppose the ITEM table has a CREATED column, mapped to a created property of
type Date, that is initialized by a trigger that executes automatically on insertion.
The following mapping is appropriate:
<property name="created"
Notice that you map this property insert="false" update="false" to indicate
that it isn’t to be included in SQL INSERTs or UPDATEs by Hibernate.
After saving a new Item, Hibernate isn’t aware of the value assigned to this column by the trigger, because it occurred after the INSERT of the item row. If you
Integrating legacy databases
need the generated value in the application, you must explicitly tell Hibernate to
reload the object with an SQL SELECT. For example:
Item item = new Item();
Session session = getSessionFactory().openSession();
Transaction tx = session.beginTransaction();;
session.flush(); // Force the INSERT to occur
session.refresh(item); // Reload the object with a SELECT
System.out.println( item.getCreated() );
Most problems involving triggers may be solved in this way, using an explicit
flush() to force immediate execution of the trigger, perhaps followed by a call to
refresh() to retrieve the result of the trigger.
Before you add refresh() calls to your application, we have to tell you that the
primary goal of the previous section was to show you when to use refresh().
Many Hibernate beginners don’t understand its real purpose and often use it
incorrectly. A more formal definition of refresh() is “refresh an in-memory
instance in persistent state with the current values present in the database.”
For the example shown, a database trigger filling a column value after insertion, a much simpler technique can be used:
<property name="created"
With annotations, use a Hibernate extension:
@Column(name = "CREATED", insertable = false, updatable = false)
private Date created;
We have already discussed the generated attribute in detail in chapter 4,
section, “Generated and default property values.” With generated="insert", Hibernate automatically executes a SELECT after insertion, to
retrieve the updated state.
Legacy databases and custom SQL
There is one further problem to be aware of when your database executes triggers: reassociation of a detached object graph and triggers that run on each
Triggers that run on UPDATE
Before we discuss the problem of ON UPDATE triggers in combination with reattachment of objects, we need to point out an additional setting for the generated
<version name="version"
<timestamp name="lastModified"
<property name="lastModified"
With annotations, the equivalent mappings are as follows:
@Column(name = "OBJ_VERSION")
private int version;
@Column(name = "LAST_MODIFIED")
private Date lastModified;
@Column(name = "LAST_MODIFIED", insertable = false, updatable = false)
private Date lastModified;
With always, you enable Hibernate’s automatic refreshing not only for insertion
but also for updating of a row. In other words, whenever a version, timestamp, or
any property value is generated by a trigger that runs on UPDATE SQL statements,
Integrating legacy databases
you need to enable this option. Again, refer to our earlier discussion of generated
properties in section 4.4.1.
Let’s look at the second issue you may run into if you have triggers running on
updates. Because no snapshot is available when a detached object is reattached to
a new Session (with update() or saveOrUpdate()), Hibernate may execute
unnecessary SQL UPDATE statements to ensure that the database state is synchronized with the persistence context state. This may cause an UPDATE trigger to fire
inconveniently. You avoid this behavior by enabling select-before-update in the
mapping for the class that is persisted to the table with the trigger. If the ITEM
table has an update trigger, add the following attribute to your mapping:
<class name="Item"
This setting forces Hibernate to retrieve a snapshot of the current database state
using an SQL SELECT, enabling the subsequent UPDATE to be avoided if the state of
the in-memory Item is the same. You trade the inconvenient UPDATE for an additional SELECT.
A Hibernate annotation enables the same behavior:
@org.hibernate.annotations.Entity(selectBeforeUpdate = true)
public class Item { ... }
Before you try to map a legacy scheme, note that the SELECT before an update
only retrieves the state of the entity instance in question. No collections or associated instances are eagerly fetched, and no prefetching optimization is active. If
you start enabling selectBeforeUpdate for many entities in your system, you’ll
probably find that the performance issues introduced by the nonoptimized
selects are problematic. A better strategy uses merging instead of reattachment.
Hibernate can then apply some optimizations (outer joins) when retrieving database snapshots. We’ll talk about the differences between reattachment and merging later in the book in more detail.
Let’s summarize our discussion of legacy data models: Hibernate offers several
strategies to deal with (natural) composite keys and inconvenient columns easily.
Before you try to map a legacy schema, our recommendation is to carefully examine whether a schema change is possible. In our experience, many developers
immediately dismiss database schema changes as too complex and time-consuming and look for a Hibernate solution. This sometimes isn’t justified, and you
Legacy databases and custom SQL
should consider schema evolution a natural part of your schema’s lifecycle. If
tables change, then a data export, some transformation, and an import may solve
the problem. One day of work may save many days in the long run.
Legacy schemas often also require customization of the SQL generated by
Hibernate, be it for data manipulation (DML) or schema definition (DDL).
Customizing SQL
SQL started its life in the 1970s but wasn’t (ANSI) standardized until 1986.
Although each update of the SQL standard has seen new (and many controversial) features, every DBMS product that supports SQL does so in its own unique
way. The burden of portability is again on the database application developers.
This is where Hibernate helps: Its built-in query mechanisms, HQL and the Criteria API, produce SQL that depends on the configured database dialect. All
other automatically generated SQL (for example, when a collection has to be
retrieved on demand) is also produced with the help of dialects. With a simple
switch of the dialect, you can run your application on a different DBMS.
To support this portability, Hibernate has to handle three kinds of operations:
Every data-retrieval operation results in SELECT statements being executed.
Many variations are possible; for example, database products may use a different syntax for the join operation or how a result can be limited to a particular number of rows.
Every data modification requires the execution of Data Manipulation Language (DML) statements, such as UPDATE, INSERT, and DELETE. DML often
isn’t as complex as data retrieval, but it still has product-specific variations.
A database schema must be created or altered before DML and data
retrieval can be executed. You use Data Definition Language (DDL) to work
on the database catalog; it includes statements such as CREATE, ALTER, and
DROP. DDL is almost completely vendor specific, but most products have at
least a similar syntax structure.
Another term we use often is CRUD, for create, read, update, and delete. Hibernate generates all this SQL for you, for all CRUD operations and schema definition.
The translation is based on an org.hibernate.dialect.Dialect implementation—Hibernate comes bundled with dialects for all popular SQL database management systems. We encourage you to look at the source code of the dialect
you’re using; it’s not difficult to read. Once you’re more experienced with
Customizing SQL
Hibernate, you may even want to extend a dialect or write your own. For example,
to register a custom SQL function for use in HQL selects, you’d extend an existing
dialect with a new subclass and add the registration code—again, check the existing source code to find out more about the flexibility of the dialect system.
On the other hand, you sometimes need more control than Hibernate APIs (or
HQL) provide, when you need to work on a lower level of abstraction. With Hibernate you can override or completely replace all CRUD SQL statements that will be
executed. You can customize and extend all DDL SQL statements that define your
schema, if you rely on Hibernate’s automatic schema-export tool (you don’t have
Furthermore Hibernate allows you to get a plain JDBC Connection object at all
times through session.connection(). You should use this feature as a last resort,
when nothing else works or anything else would be more difficult than plain
JDBC. With the newest Hibernate versions, this is fortunately exceedingly rare,
because more and more features for typical stateless JDBC operations (bulk
updates and deletes, for example) are built-in, and many extension points for custom SQL already exist.
This custom SQL, both DML and DDL, is the topic of this section. We start with
custom DML for create, read, update, and delete operations. Later, we integrate
stored database procedures to do the same work. Finally, we look at DDL customization for the automatic generation of a database schema and how you can create
a schema that represents a good starting point for the optimization work of a DBA.
Note that at the time of writing this detailed customization of automatically
generated SQL isn’t available in annotations; hence, we use XML metadata exclusively in the following examples. We expect that a future version of Hibernate
Annotations will include better support for SQL customization.
Writing custom CRUD statements
The first custom SQL you’ll write is used to load entities and collections. (Most of
the following code examples show almost the same SQL Hibernate executes by
default, without much customization—this helps you to understand the mapping
technique more quickly.)
Loading entities and collections with custom SQL
For each entity class that requires a custom SQL operation to load an instance, you
define a <loader> reference to a named query:
<class name="User" table="USERS">
<id name="id" column="USER_ID"...
Legacy databases and custom SQL
<loader query-ref="loadUser"/>
The loadUser query can now be defined anywhere in your mapping metadata,
separate and encapsulated from its use. This is an example of a simple query that
retrieves the data for a User entity instance:
<sql-query name="loadUser">
<return alias="u" class="User"/>
as {},
as {u.firstname},
as {u.lastname},
as {u.username},
as {u.password},
as {},
as {u.ranking},
as {u.admin},
as {u.created},
us.HOME_STREET as {u.homeAddress.street},
us.HOME_ZIPCODE as {u.homeAddress.zipcode},
as {},
us.DEFAULT_BILLING_DETAILS_ID as {u.defaultBillingDetails}
us.USER_ID = ?
As you can see, the mapping from column names to entity properties uses a simple aliasing. In a named loader query for an entity, you have to SELECT the following columns and properties:
The primary key columns and primary key property or properties, if a composite primary key is used.
All scalar properties, which must be initialized from their respective column(s).
All composite properties which must be initialized. You can address the
individual scalar elements with the following aliasing syntax: {entityalias.componentProperty.scalarProperty}.
All foreign key columns, which must be retrieved and mapped to the
respective many-to-one property. See the DEFAULT_BILLING_DETAILS_ID
example in the previous snippet.
Customizing SQL
All scalar properties, composite properties, and many-to-one entity references that are inside a <join> element. You use an inner join to the
secondary table if all the joined properties are never NULL; otherwise, an
outer join is appropriate. (Note that this isn’t shown in the example.)
If you enable lazy loading for scalar properties, through bytecode instrumentation, you don’t need to load the lazy properties. See chapter 13, section 13.1.6, “Lazy loading with interception.”
The {propertyName} aliases as shown in the previous example are not absolutely
necessary. If the name of a column in the result is the same as the name of a
mapped column, Hibernate can automatically bind them together.
You can even call a mapped query by name in your application with session.getNamedQuery("loadUser"). Many more things are possible with custom
SQL queries, but we’ll focus on basic SQL customization for CRUD in this section.
We come back to other relevant APIs in chapter 15, section 15.2, “Using native
SQL queries.”
Let’s assume that you also want to customize the SQL that is used to load a collection—for example, the items sold by a User. First, declare a loader reference
in the collection mapping:
<set name="items" inverse="true">
<key column="SELLER_ID" not-null="true"/>
<one-to-many class="Item"/>
<loader query-ref="loadItemsForUser"/>
The named query loadItemsForUser looks almost the same as the entity loader:
<sql-query name="loadItemsForUser">
<load-collection alias="i" role="User.items"/>
i.SELLER_ID = :id
There are two major differences: One is the <load-collection> mapping from
an alias to a collection role; it should be self-explanatory. What is new in this query
is an automatic mapping from the SQL table alias ITEM i to the properties of all
items with {i.*}. You created a connection between the two by using the same
alias: the symbol i. Furthermore, you’re now using a named parameter, :id,
Legacy databases and custom SQL
instead of a simple positional parameter with a question mark. You can use whatever syntax you prefer.
Sometimes, loading an entity instance and a collection is better done in a single query, with an outer join (the entity may have an empty collection, so you can’t
use an inner join). If you want to apply this eager fetch, don’t declare a loader references for the collection. The entity loader takes care of the collection retrieval:
<sql-query name="loadUser">
<return alias="u" class="User"/>
<return-join alias="i" property="u.items"/>
{u.*}, {i.*}
left outer join ITEM i
u.USER_ID = ?
Note how you use the <return-join> element to bind an alias to a collection
property of the entity, effectively linking both aliases together. Further note that
this technique also works if you’d like to eager-fetch one-to-one and many-to-one
associated entities in the original query. In this case, you may want an inner join if
the associated entity is mandatory (the foreign key can’t be NULL) or an outer join
if the target is optional. You can retrieve many single-ended associations eagerly in
one query; however, if you (outer-) join more than one collection, you create a
Cartesian product, effectively multiplying all collection rows. This can generate
huge results that may be slower than two queries. You’ll meet this limitation again
when we discuss fetching strategies in chapter 13.
As mentioned earlier, you’ll see more SQL options for object loading later in
the book. We now discuss customization of insert, update, and delete operations,
to complete the CRUD basics.
Custom insert, update, and delete
Hibernate produces all trivial CRUD SQL at startup. It caches the SQL statements
internally for future use, thus avoiding any runtime cost of SQL generation for the
most common operations. You’ve seen how you can override the R of CRUD, so
let’s do the same for CUD.
For each entity or collection, you can define custom CUD SQL statements
inside the <sql-insert>, <sql-delete>, and <sql-update> element, respectively:
Customizing SQL
<class name="User" table="USERS">
<id name="id" column="USER_ID"...
<join table="BILLING_ADDRESS" optional="true">
<key column="USER_ID"/>
<component name="billingAddress" class="Address">
<property ...
values (?, ?, ?, ?)
values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
This mapping example may look complicated, but it’s really simple. You have two
tables in a single mapping: the primary table for the entity, USERS, and the secondary table BILLING_ADDRESS from your legacy mapping earlier in this chapter.
Whenever you have secondary tables for an entity, you have to include them in
any custom SQL—hence the <sql-insert>, <sql-delete>, and <sql-update>
elements in both the <class> and the <join> sections of the mapping.
The next issue is the binding of arguments for the statements. For CUD SQL
customization, only positional parameters are supported at the time of writing.
But what is the right order for the parameters? There is an internal order to how
Hibernate binds arguments to SQL parameters. The easiest way to figure out the
right SQL statement and parameter order is to let Hibernate generate one for
Legacy databases and custom SQL
you. Remove your custom SQL from the mapping file, enable DEBUG logging for
the org.hibernate.persister.entity package, and watch (or search) the
Hibernate startup log for lines similar to these:
AbstractEntityPersister - Insert 0: insert into USERS (FIRSTNAME,
HOME_CITY, USER_ID) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
AbstractEntityPersister - Update 0: update USERS set
You can now copy the statements you want to customize into your mapping file
and make the necessary changes. For more information on logging in Hibernate,
refer to “Enabling logging statistics” in chapter 2, in section 2.1.3.
You’ve now mapped CRUD operations to custom SQL statements. On the other
hand, dynamic SQL isn’t the only way how you can retrieve and manipulate data.
Predefined and compiled procedures stored in the database can also be mapped
to CRUD operations for entities and collections.
Integrating stored procedures and functions
Stored procedures are common in database application development. Moving
code closer to the data and executing it inside the database has distinct advantages.
First, you don’t have to duplicate functionality and logic in each program that
accesses the data. A different point of view is that a lot of business logic shouldn’t
be duplicated, so it can be applied all the time. This includes procedures that
guarantee the integrity of the data: for example, constraints that are too complex
to be implemented declaratively. You’ll usually also find triggers in a database that
has procedural integrity rules.
Stored procedures have advantages for all processing on large amounts of
data, such as reporting and statistical analysis. You should always try to avoid moving large data sets on your network and between your database and application
servers, so a stored procedure is a natural choice for mass data operations. Or, you
can implement a complex data-retrieval operation that assembles data with several queries before it passes the final result to the application client.
On the other hand, you’ll often see (legacy) systems that implement even the
most basic CRUD operations with a stored procedure. As a variation of this, systems that don’t allow any direct SQL DML, but only stored procedure calls, also
had (and sometimes still have) their place.
Customizing SQL
You may start integrating existing stored procedures for CRUD or for mass data
operations, or you may begin writing your own stored procedure first.
Writing a procedure
Programming languages for stored procedures are usually proprietary. Oracle
PL/SQL, a procedural dialect of SQL, is very popular (and available with variations
in other database products). Some databases even support stored procedures written in Java. Standardizing Java stored procedures was part of the SQLJ effort,
which, unfortunately, hasn’t been successful.
You’ll use the most common stored procedure systems in this section: Oracle
databases and PL/SQL. It turns out that stored procedures in Oracle, like so many
other things, are always different than you expect; we’ll tell you whenever something requires extra attention.
A stored procedure in PL/SQL has to be created in the database catalog as
source code and then compiled. Let’s first write a stored procedure that can load
all User entities that match a particular criterion:
create or replace procedure SELECT_USERS_BY_RANK
in int
) as
open OUT_RESULT for
Legacy databases and custom SQL
left outer join
on us.USER_ID = ba.USER_ID
drop procedure SELECT_USERS_BY_RANK
You embed the DDL for the stored procedure in a <database-object> element
for creation and removal. That way, Hibernate automatically creates and drops
the procedure when the database schema is created and updated with the
hbm2ddl tool. You could also execute the DDL by hand on your database catalog.
Keeping it in your mapping files (in whatever location seems appropriate, such as
in MyStoredProcedures.hbm.xml) is a good choice if you’re working on a nonlegacy system with no existing stored procedures. We’ll come back to other options
for the <database-object> mapping later in this chapter.
As before, the stored procedure code in the example is straightforward: a join
query against the base tables (primary and secondary tables for the User class)
and a restriction by RANKING, an input argument to the procedure.
You must observe a few rules for stored procedures mapped in Hibernate.
Stored procedures support IN and OUT parameters. If you use stored procedures
with Oracle’s own JDBC drivers, Hibernate requires that the first parameter of the
stored procedure is an OUT; and for stored procedures that are supposed to be
used for queries, the query result is supposed to be returned in this parameter. In
Oracle 9 or newer, the type of the OUT parameter has to be a SYS_REFCURSOR. In
older versions of Oracle, you must define your own reference cursor type first,
called REF CURSOR—examples can be found in Oracle product documentation.
All other major database management systems (and drivers for the Oracle DBMS
not from Oracle) are JDBC-compliant, and you can return a result directly in the
stored procedure without using an OUT parameter. For example, a similar procedure in Microsoft SQL Server would look as follows:
create procedure SELECT_USERS_BY_RANK
@IN_RANK int
Customizing SQL
where us.RANKING >= @IN_RANK
Let’s map this stored procedure to a named query in Hibernate.
Querying with a procedure
A stored procedure for querying is mapped as a regular named query, with some
minor differences:
<sql-query name="loadUsersByRank" callable="true">
<return alias="u" class="User">
<return-property name="id"
<return-property name="firstname"
<return-property name="lastname"
<return-property name="username"
<return-property name="password"
<return-property name="email"
<return-property name="ranking"
<return-property name="admin"
<return-property name="created"
<return-property name="homeAddress">
<return-column name="HOME_STREET"/>
<return-column name="HOME_ZIPCODE"/>
<return-column name="HOME_CITY"/>
<return-property name="billingAddress">
<return-column name="BILLING_STREET"/>
<return-column name="BILLING_ZIPCODE"/>
<return-column name="BILLING_CITY"/>
<return-property name="defaultBillingDetails"
{ call SELECT_USERS_BY_RANK(?, :rank) }
The first difference, compared to a regular SQL query mapping, is the callable="true" attribute. This enables support for callable statements in Hibernate
and correct handling of the output of the stored procedure. The following mappings bind the column names returned in the procedures result to the properties
of a User object. One special case needs extra consideration: If multicolumn
properties, including components (homeAddress), are present in the class, you
need to map their columns in the right order. For example, the homeAddress
property is mapped as a <component> with three properties, each to its own
Legacy databases and custom SQL
column. Hence, the stored procedure mapping includes three columns bound to
the homeAddress property.
The call of the stored procedure prepares one OUT (the question mark) and a
named input parameter. If you aren’t using the Oracle JDBC drivers (other drivers or a different DBMS), you don’t need to reserve the first OUT parameter; the
result can be returned directly from the stored procedure.
Look at the regular class mapping of the User class. Notice that the column
names returned by the procedure in this example are the same as the column
names you already mapped. You can omit the binding of each property and let
Hibernate take care of the mapping automatically:
<sql-query name="loadUsersByRank" callable="true">
<return class="User"/>
{ call SELECT_USERS_BY_RANK(?, :rank) }
The responsibility for returning the correct columns, for all properties and foreign key associations of the class with the same names as in the regular mappings,
is now moved into the stored procedure code. Because you have aliases in the
stored procedure already (select ... us.FIRSTNAME as FIRSTNAME...), this is
straightforward. Or, if only some of the columns returned in the result of the procedure have different names than the ones you mapped already as your properties, you only need to declare these:
<sql-query name="loadUsersByRank" callable="true">
<return class="User">
<return-property name="firstname"
<return-property name="lastname"
{ call SELECT_USERS_BY_RANK(?, :rank) }
Finally, let’s look at the call of the stored procedure. The syntax you’re using
here, { call PROCEDURE() }, is defined in the SQL standard and portable. A nonportable syntax that works for Oracle is begin PROCEDURE(); end;. It’s recommended that you always use the portable syntax. The procedure has two
parameters. As explained, the first is reserved as an output parameter, so you use a
positional parameter symbol (?). Hibernate takes care of this parameter if you
configured a dialect for an Oracle JDBC driver. The second is an input parameter
you have to supply when executing the call. You can either use only positional
parameters or mix named and positional parameters. We prefer named parameters for readability.
Customizing SQL
Querying with this stored procedure in the application looks like any other
named query execution:
Query q = session.getNamedQuery("loadUsersByRank");
q.setParameter("rank", 12);
List result = q.list();
At the time of writing, mapped stored procedures can be enabled as named queries, as you did in this section, or as loaders for an entity, similar to the loadUser
example you mapped earlier.
Stored procedures can not only query and load data, but also manipulate data.
The first use case for this is mass data operations, executed in the database tier.
You shouldn’t map this in Hibernate but should execute it with plain JDBC: session.connection().prepareCallableStatement(); and so on. The data-manipulation operations you can map in Hibernate are the creation, deletion, and
update of an entity object.
Mapping CUD to a procedure
Earlier, you mapped <sql-insert>, <sql-delete>, and <sql-update> elements
for a class to custom SQL statements. If you’d like to use stored procedures for
these operations, change the mapping to callable statements:
<class name="User">
<sql-update callable="true" check="none">
{ call UPDATE_USER(?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) }
With the current version of Hibernate, you have the same problem as before: the
binding of values to the positional parameters. First, the stored procedure must
have the same number of input parameters as expected by Hibernate (enable the
SQL log as shown earlier to get a generated statement you can copy and paste).
The parameters again must be in the same order as expected by Hibernate.
Consider the check="none" attribute. For correct (and, if you enabled it) optimistic locking, Hibernate needs to know whether this custom update operation
was successful. Usually, for dynamically generated SQL, Hibernate looks at the
number of updated rows returned from an operation. If the operation didn’t or
couldn’t update any rows, an optimistic locking failure occurs. If you write your
own custom SQL operation, you can customize this behavior as well.
With check="none", Hibernate expects your custom procedure to deal internally with failed updates (for example, by doing a version check of the row that
Legacy databases and custom SQL
needs to be updated) and expects your procedure to throw an exception if something goes wrong. In Oracle, such a procedure is as follows:
create or replace procedure UPDATE_USER
(IN_FIRSTNAME in varchar,
in varchar,
in varchar,
rowcount INTEGER;
update USERS set
rowcount := SQL%ROWCOUNT;
if rowcount != 1 then
RAISE_APPLICATION_ERROR( -20001, 'Version check failed');
end if;
drop procedure UPDATE_USER
The SQL error is caught by Hibernate and converted into an optimistic locking
exception you can then handle in application code. Other options for the check
attribute are as follows:
If you enable check="count", Hibernate checks the number of modified
rows using the plain JDBC API. This is the default and used when you write
dynamic SQL without stored procedures.
If you enable check="param", Hibernate reserves an OUT parameter to get
the return value of the stored procedure call. You need to add an additional
question mark to your call and, in your stored procedure, return the row
count of your DML operation on this (first) OUT parameter. Hibernate then
validates the number of modified rows for you.
Customizing SQL
Mappings for insertion and deletion are similar; all of these must declare how
optimistic lock checking is performed. You can copy a template from the Hibernate startup log to get the correct order and number of parameters.
Finally, you can also map stored functions in Hibernate. They have slightly different semantics and use cases.
Mapping stored functions
A stored function only has input parameters—no output parameters. However, it
can return a value. For example, a stored function can return the rank of a user:
create or replace function GET_USER_RANK
(IN_USER_ID int)
return int is
RANK int;
return RANK;
drop function GET_USER_RANK
This function returns a scalar number. The primary use case for stored functions
that return scalars is embedding a call in regular SQL or HQL queries. For example, you can retrieve all users who have a higher rank than a given user:
String q = "from User u where u.ranking > get_user_rank(:userId)";
List result = session.createQuery(q)
.setParameter("userId", 123)
This query is in HQL; thanks to the pass-through functionality for function calls in
the WHERE clause (not in any other clause though), you can call any stored function in your database directly. The return type of the function should match the
Legacy databases and custom SQL
operation: in this case, the greater-than comparison with the ranking property,
which is also numeric.
If your function returns a resultset cursor, as in previous sections, you can
even map it as a named query and let Hibernate marshal the resultset into an
object graph.
Finally, remember that stored procedures and functions, especially in legacy
databases, sometimes can’t be mapped in Hibernate; in such cases you have to fall
back to plain JDBC. Sometimes you can wrap a legacy stored procedure with
another stored procedure that has the parameter interface expected by Hibernate. There are too many varieties and special cases to be covered in a generic
mapping tool. However, future versions of Hibernate will improve mapping capabilities—we expect better handling of parameters (no more counting of question
marks) and support for arbitrary input and output arguments to be available in
the near future.
You’ve now completed customization of runtime SQL queries and DML. Let’s
switch perspective and customize the SQL used for the creation and modification
of the database schema, the DDL.
Improving schema DDL
Customizing the DDL in your Hibernate application is something you’ll usually
consider only when you generate the database schema with Hibernate’s toolset.
If a schema already exists, such customizations won’t affect the runtime behavior
of Hibernate.
You can export DDL to a text file or execute it directly on your database
whenever you run your integration tests. Because DDL is mostly vendor-specific,
every option you put in your mapping metadata has the potential to bind the
metadata to a particular database product—keep this in mind when applying the
following features.
We separate DDL customization into two categories:
Naming automatically generated database objects, such as tables, columns,
and constraints explicitly in mapping metadata, instead of relying on the
automatic naming derived from the Java class and property names by Hibernate. We already discussed the built-in mechanism and options for quoting
and extending names in chapter 4, section 4.3.5, “Quoting SQL identifiers.”
We next look at other options you can enable to beautify your generated
DDL scripts.
Improving schema DDL
Handling additional database objects, such as indexes, constraints, and
stored procedures in your mapping metadata. Earlier in this chapter, you
added arbitrary CREATE and DROP statements to XML mapping files with the
<database-object> element. You can also enable the creation of indexes
and constraints with additional mapping elements inside the regular class
and property mappings.
Custom SQL names and datatypes
In listing 8.1, you add attributes and elements to the mapping of the Item class.
Listing 8.1
Additional elements in the Item mapping for hbm2ddl
<class name="Item" table="ITEMS">
<id name="id" type="string">
<column name="ITEM_ID" sql-type="char(32)"/>
<generator class="uuid"/>
<property name="initialPrice" type="big_decimal">
<column name="INIT_PRICE"
<property name="description" type="string"
column="ITM_DESCRIPTION" length="4000"/>
<set name="categories" table="CATEGORY_ITEM" cascade="none">
<column name="ITEM_ID" sql-type="char(32)"/>
<many-to-many class="Category">
<column name="CATEGORY_ID" sql-type="char(32)”/>
The hbm2ddl exporter generates a VARCHAR typed column if a property (even the
identifier property) is of mapping type string. You know that the identifier generator uuid always generates 32-character strings; therefore you switch to a CHAR
SQL type and also set its size fixed at 32 characters. The <column> element is
Legacy databases and custom SQL
required for this declaration, because no attribute supports the SQL datatype on
the <id> element.
For decimal types, you can declare the precision and scale. This example creates
the column as INIT_PRICE number(10,2) on an Oracle dialect; however, for databases that don’t support types with decimal precision, a simple INIT_PRICE
numeric (this is in HSQL) is produced.
For the description field, you add DDL attributes on the <property> element
instead of a nested <column> element. The DESCRIPTION column is generated as
VARCHAR(4000)—a limitation of a variable character field in an Oracle database
(in Oracle, it would be VARCHAR2(4000) in the DDL, but the dialect takes care
of this).
A <column> element can also be used to declare the foreign key fields in an association mapping. Otherwise, the columns of your association table CATEGORY_ITEM
would be VARCHAR(32) instead of the more appropriate CHAR(32) type.
The same customization is possible in annotations, see listing 8.2.
Listing 8.2
Additional annotations for customization of DDL export
@Table(name = "ITEMS")
public class Item {
@Column(name = "ITEM_ID", columnDefinition = "char(32)")
@GeneratedValue(generator = "hibernate-uuid.hex")
name = "hibernate-uuid.hex",
strategy = "uuid.hex"
Private String id;
@Column(name = "INIT_PRICE", nullable = false,
precision = 10, scale = 2)
BigDecimal initialPrice;
@Column(name = "ITM_DESCRIPTION", length = 4000)
Private String description;
joinColumns =
{ @JoinColumn(name = "ITEM_ID",
columnDefinition = "char(32)")
inverseJoinColumns =
Improving schema DDL
{ @JoinColumn(name = "CATEGORY_ID",
columnDefinition = "char(32)")
Private Set<Category> categories = new HashSet<Category>();
You have to use one Hibernate extension to name the nonstandard identifier generator. All other customizations of the generated SQL DDL are done with annotations of the JPA specification. One attribute deserves special attention: The
columnDefinition isn’t the same as sql-type in a Hibernate mapping file. It’s
more flexible: The JPA persistence provider appends the whole string after the
column name in the CREATE TABLE statement, as in ITEM_ID char(32).
Customization of names and data types is the absolute minimum you should
consider. We recommend that you always improve the quality of your database
schema (and ultimately, the quality of the data that is stored) with the appropriate
integrity rules.
Ensuring data consistency
Integrity rules are an important part of your database schema. The most important
responsibility of your database is to protect your information and to guarantee that
it’s never in an inconsistent state. This is called consistency, and it’s part of the
ACID criteria commonly applied to transactional database management systems.
Rules are part of your business logic, so you usually have a mix of businessrelated rules implemented in your application code and in your database. Your
application is written so as to avoid any violation of the database rules. However,
it’s the job of the database management system to never allow any false (in the
business logic sense) information to be stored permanently—for example, if one
of the applications accessing the database has bugs. Systems that ensure integrity
only in application code are prone to data corruption and often degrade the quality of the database over time. Keep in mind that the primary purpose of most business applications is to produce valuable business data in the long run.
In contrast to ensuring data consistency in procedural (or object-oriented)
application code, database-management systems allow you to implement integrity
rules declaratively as part of your data schema. The advantages of declarative rules
include fewer possible errors in code and a chance for the database-management
system to optimize data access.
Legacy databases and custom SQL
We identify four levels of rules:
Domain constraint—A domain is (loosely speaking, and in the database
world) a datatype in a database. Hence, a domain constraint defines the
range of possible values a particular datatype can handle. For example, an
int datatype is usable for integer values. A char datatype can hold character
strings: for example, all characters defined in ASCII. Because we mostly use
datatypes that are built in to the database management system, we rely on
the domain constraints as defined by the vendor. If you create user-defined
datatypes (UDT), you’ll have to define their constraints. If they’re supported by your SQL database, you can use the (limited) support for custom
domains to add additional constraints for particular datatypes.
Column constraint—Restricting a column to hold values of a particular
domain is equivalent to adding a column constraint. For example, you
declare in DDL that the INITIAL_PRICE column holds values of the domain
MONEY, which internally uses the datatype number(10,2). You use the
datatype directly most of the time, without defining a domain first. A special
column constraint in an SQL database is NOT NULL.
Table constraint—An integrity rule that applies to a single row or several rows
is a table constraint. A typical declarative table constraints is UNIQUE (all
rows are checked for duplicate values). A sample rule affecting only a single
row is “end date of an auction must be later than the start date.”
Database constraint—If a rule applies to more than one table, it has database
scope. You should already be familiar with the most common database constraint, the foreign key. This rule guarantees the integrity of references
between rows, usually in separate tables, but not always (self-referencing foreign key constraints aren’t uncommon).
Most (if not all) SQL database-management systems support the mentioned levels
of constraints and the most important options in each. In addition to simple keywords, such as NOT NULL and UNIQUE, you can usually also declare more complex
rules with the CHECK constraint that applies an arbitrary SQL expression. Still,
integrity constraints are one of the weak areas in the SQL standard, and solutions
from vendors can differ significantly.
Furthermore, nondeclarative and procedural constraints are possible with
database triggers that intercept data-modification operations. A trigger can then
implement the constraint procedure directly or call an existing stored procedure.
Improving schema DDL
Like DDL for stored procedures, you can add trigger declarations to your
Hibernate mapping metadata with the <database-object> element for inclusion
in the generated DDL.
Finally, integrity constraints can be checked immediately when a data-modification statement is executed, or the check can be deferred until the end of a
transaction. The violation response in SQL databases is usually rejection, without
any possibility of customization.
We now have a closer look at the implementation of integrity constraints.
Adding domains and column constraints
The SQL standard includes domains, which, unfortunately, not only are rather
limited but also are often not supported by the DBMS. If your system supports SQL
domains, you can use them to add constraints to datatypes:
create domain EMAILADDRESS as varchar
check ( IS_EMAILADDRESS(value) );
You can now use this domain identifier as a column type when creating a table:
create table USERS (
The (relatively minor) advantage of domains in SQL is the abstraction of common
constraints into a single location. Domain constraints are always checked immediately when data is inserted and modified. To complete the previous example, you
also have to write the stored function IS_EMAILADDRESS (you can find many regular expressions to do this on the Web). Adding the new domain in a Hibernate
mapping is simple as an sql-type:
<property name="email" type="string">
<column name="USER_EMAIL"
With annotations, declare your own columnDefinition:
@Column(name = "USER_EMAIL", length = 255,
columnDefinition = "EMAILADDRESS(255) not null")
String email;
Legacy databases and custom SQL
If you want to create and drop the domain declaration automatically with the rest
of your schema, put it into a <database-object> mapping.
SQL supports additional column constraints. For example, the business rules
allow only alphanumeric characters in user login names:
create table USERS (
USERNAME varchar(16) not null
You may not be able to use this expression in your DBMS unless it supports regular
expressions. Single-column check constraints are declared in Hibernate mappings on the <column> mapping element:
<property name="username" type="string">
<column name="USERNAME"
Check constraints in annotations are available only as a Hibernate extension:
@Column(name = "USERNAME", length = 16,
nullable = false, unique = true)
constraints = "regexp_like(USERNAME,'^[[:alpha:]]+$')"
private String username;
Note that you have a choice: Creating and using a domain or adding a single-column constraint has the same effect. In the long run, domains are usually easier to
maintain and more likely to avoid duplication.
Let’s look at the next level of rules: single and multirow table constraints.
Table-level constraints
Imagine that you want to guarantee that a CaveatEmptor auction can’t end before
it started. You write the application code to prevent users from setting the startDate and endDate properties on an Item to wrong values. You can do this in the
user interface or in the setter methods of the properties. In the database schema,
you add a single-row table constraint:
create table ITEM (
START_DATE timestamp not null,
Improving schema DDL
END_DATE timestamp not null,
Table constraints are appended in the CREATE TABLE DDL and can contain arbitrary SQL expressions. You include the constraint expression in your Hibernate
mapping file on the <class> mapping element:
<class name="Item"
check="START_DATE &lt; END_DATE">
Note that the < character must be escaped as &lt; in XML. With annotations, you
need to add a Hibernate extension annotation to declare check constraints:
constraints = "START_DATE < END_DATE"
public class Item { ... }
Multirow table constraints can be implemented with more complex expressions.
You may need a subselect in the expression to do this, which may not be supported in your DBMS—check your product documentation first. However, there
are common multirow table constraints you can add directly as attributes in
Hibernate mappings. For example, you identify the login name of a User as
unique in the system:
<property name="username" type="string">
<column name="USERNAME"
Unique constraint declaration is also possible in annotation metadata:
@Column(name = "USERNAME", length = 16, nullable = false,
unique = true)
constraints = "regexp_like(USERNAME,'^[[:alpha:]]+$')"
private String username;
And, of course, you can do this in JPA XML descriptors (there is no check constraint, however):
Legacy databases and custom SQL
<entity class="auction.model.User" access="FIELD">
<basic name="username">
<column name="USERNAME"
The exported DDL includes the unique constraint:
create table USERS (
USERNAME varchar(16) not null unique
A unique constraint can also span several columns. For example, CaveatEmptor
supports a tree of nested Category objects. One of the business rules says that a
particular category can’t have the same name as any of its siblings. Hence, you
need a multicolumn multirow constraint that guarantees this uniqueness:
<class name="Category" table="CATEGORY">
<property name="name">
<column name="CAT_NAME"
<many-to-one name="parent" class="Category">
<column name="PARENT_CATEGORY_ID"
You assign an identifier to the constraint with the unique-key attribute so you can
refer to it several times in one class mapping and group columns for the same
constraint. However, the identifier isn’t used in the DDL to name the constraint:
create table CATEGORY (
CAT_NAME varchar(255) not null,
Improving schema DDL
If you want to create a unique constraint with annotations that spans several columns, you need to declare it on the entity, not on a single column:
@Table(name = "CATEGORY",
uniqueConstraints = {
@UniqueConstraint(columnNames =
public class Category { ... }
With JPA XML descriptors, multicolumn constraints are as follows:
<entity class="Category" access="FIELD">
<table name="CATEGORY">
Completely custom constraints, including an identifier for the database catalog,
can be added to your DDL with the <database-object> element:
alter table CATEGORY add constraint UNIQUE_SIBLINGS
drop constraint UNIQUE_SIBLINGS
This functionality isn’t available in annotations. Note that you can add a Hibernate XML metadata file with all your custom database DDL objects in your annotation-based application.
Finally, the last category of constraints includes database-wide rules that span
several tables.
Database constraints
You can create a rule that spans several tables with a join in a subselect in any
check expression. Instead of referring only to the table on which the constraint is
declared, you may query (usually for the existence or nonexistence of a particular
piece of information) a different table.
Legacy databases and custom SQL
Another technique to create a database-wide constraint uses custom triggers
that run on insertion or update of rows in particular tables. This is a procedural
approach that has the already-mentioned disadvantages but is inherently flexible.
By far the most common rules that span several tables are referential integrity
rules. They’re widely known as foreign keys, which are a combination of two
things: a key value copy from a related row and a constraint that guarantees that
the referenced value exists. Hibernate creates foreign key constraints automatically for all foreign key columns in association mappings. If you check the DDL
produced by Hibernate, you may notice that these constraints also have automatically generated database identifiers—names that aren’t easy to read and that
make debugging more difficult:
alter table ITEM add constraint FK1FF7F1F09FA3CB90
foreign key (SELLER_ID) references USERS;
This DDL declares the foreign key constraint for the SELLER_ID column in the
ITEM table. It references the primary key column of the USERS table. You can customize the name of the constraint in the <many-to-one> mapping of the Item
class with the foreign-key attribute:
<many-to-one name="seller"
With annotations, use a Hibernate extension:
@JoinColumn(name = "SELLER_ID")
@org.hibernate.annotations.ForeignKey(name = "FK_SELLER_ID")
private User seller;
And a special syntax is required for foreign keys created for a many-to-many
name = "FK_CATEGORY_ID",
inverseName = "FK_ITEM_ID"
private Set<Category> categories...
If you want to automatically generate DDL that isn’t distinguishable from what a
human DBA would write, customize all your foreign key constraints in all your
mapping metadata. Not only is this good practice, but it also helps significantly
Improving schema DDL
when you have to read exception messages. Note that the hbm2ddl exporter considers constraint names only for foreign keys that have been set on the noninverse
side of a bidirectional association mapping.
Foreign key constraints also have features in SQL that your legacy schema may
already utilize. Instead of immediately rejecting a modification of data that would
violate a foreign key constraint, an SQL database can CASCADE the change to the
referencing rows. For example, if a row that is considered a parent is deleted, all
child rows with a foreign key constraint on the primary key of the parent row may
be deleted as well. If you have or want to use these database-level cascading
options, enable them in your foreign key mapping:
<class name="Item" table="ITEM">
<set name="bids" cascade="save-update, delete">
<key column="ITEM_ID" on-delete="cascade"/>
<one-to-many class="Bid"/>
Hibernate now creates and relies on a database-level ON CASCADE DELETE option
of the foreign key constraint, instead of executing many individual DELETE statements when an Item instance is deleted and all bids have to be removed. Be
aware that this feature bypasses Hibernate’s usual optimistic locking strategy for
versioned data!
Finally, unrelated to integrity rules translated from business logic, database
performance optimization is also part of your typical DDL customization effort.
Creating indexes
Indexes are a key feature when optimizing the performance of a database application. The query optimizer in a database-management system can use indexes to
avoid excessive scans of the data tables. Because they’re relevant only in the physical implementation of a database, indexes aren’t part of the SQL standard, and
the DDL and available indexing options are specific for a particular product. However, the most common DDL for typical indexes can be embedded in a Hibernate
mapping (that is, without the generic <database-object> element).
Many queries in CaveatEmptor will probably involve the endDate property of
an auction Item. You can speed up these queries by creating an index for the column of this property:
Legacy databases and custom SQL
The automatically produced DDL now includes an additional statement:
create index IDX_END_DATE on ITEM (END_DATE);
The same functionality is available with annotations, as a Hibernate extension:
@Column(name = "END_DATE", nullable = false, updatable = false)
@org.hibernate.annotations.Index(name = "IDX_END_DATE")
private Date endDate;
You can create a multicolumn index by setting the same identifier on several
property (or column) mappings. Any other index option, such as UNIQUE INDEX
(which creates an additional multirow table-level constraint), the indexing
method (common are btree, hash, and binary), and any storage clause (for
example, to create the index in a separate tablespace) can be set only in completely custom DDL with <database-object>.
A multicolumn index with annotations is defined at the entity level, with a custom Hibernate annotation that applies additional attributes to table mapping:
appliesTo = "ITEMS", indexes =
public class Item { ... }
Note that @org.hibernate.annotations.Table isn’t a replacement for @javax.
perisistence.Table, so if you need to override the default table name, you still
need the regular @Table.
We recommend that you get the excellent book SQL Tuning by Dan Tow (Tow,
2003) if you want to learn efficient database-optimization techniques and especially how indexes can get you closer to the best-performing execution plan for
your queries.
One mapping we have shown a few times in this chapter is <databaseobject>. It has some other options that we haven’t discussed yet.
Adding auxiliary DDL
Hibernate creates the basic DDL for your tables and constraints automatically; it
even creates sequences if you have a particular identifier generator. However,
Improving schema DDL
there is some DDL that Hibernate can’t create automatically. This includes all
kinds of highly vendor-specific performance options and any other DDL that is relevant only for the physical storage of data (tablespaces, for example).
One reason this kind of DDL has no mapping elements or annotations is that
there are too many variations and possibilities—nobody can or wants to maintain
more than 25 database dialects with all the possible combinations of DDL. A second, much more important reason, is that you should always ask your DBA to
finalize your database schema. For example, did you know that indexes on foreign
key columns can hurt performance in some situations and therefore aren’t automatically generated by Hibernate? We recommend that DBAs get involved early
and verify the automatically generated DDL from Hibernate.
A common process, if you’re starting with a new application and new database,
is to generate DDL with Hibernate automatically during development; database
performance concerns shouldn’t and usually don’t play an important role at that
stage. At the same time (or later, during testing), a professional DBA verifies and
optimizes the SQL DDL and creates the final database schema. You can export the
DDL into a text file and hand it to your DBA.
Or—and this is the option you’ve seen a few times already—you can add customized DDL statements to your mapping metadata:
[CREATE statement]
[DROP statement]
<dialect-scope name="org.hibernate.dialect.Oracle9Dialect"/>
<dialect-scope name="org.hibernate.dialect.OracleDialect"/>
The <dialect-scope> elements restrict the custom CREATE or DROP statements to
a particular set of configured database dialects, which is useful if you’re deploying
on several systems and need different customizations.
If you need more programmatic control over the generated DDL, implement the AuxiliaryDatabaseObject interface. Hibernate comes bundled with a
convenience implementation that you can subclass; you can then override methods selectively:
package auction.persistence;
import org.hibernate.mapping.*;
Legacy databases and custom SQL
import org.hibernate.dialect.Dialect;
import org.hibernate.engine.Mapping;
public class CustomDDLExtension
extends AbstractAuxiliaryDatabaseObject {
public CustomDDLExtension() {
public String sqlCreateString(Dialect dialect,
Mapping mapping,
String defaultCatalog,
String defaultSchema) {
return "[CREATE statement]";
public String sqlDropString(Dialect dialect,
String defaultCatalog,
String defaultSchema) {
return "[DROP statement]";
You can add dialect scopes programmatically and even access some mapping
information in the sqlCreateString() and sqlDropString() methods. This
gives you a lot of flexibility regarding how you create and write your DDL statements. You have to enable this custom class in your mapping metadata:
<definition class="auction.persistence.CustomDDLExtension"/>
<dialect-scope name="org.hibernate.dialect.OracleDialect"/>
Additional dialect scopes are cumulative; the previous examples all apply to two
In this chapter, we looked at issues that you may run into when you have to deal
with a legacy database schema. Natural keys, composite keys, and foreign keys are
often inconvenient and need to be mapped with extra care. Hibernate also offers
formulas, little SQL expressions in your mapping file, that can help you to deal
with a legacy schema you can’t change.
Usually, you also rely on Hibernate’s automatically generated SQL for all create, read, update, and delete operations in your application. In this chapter,
you’ve learned how to customize this SQL with your own statements and how to
integrate Hibernate with stored procedures and stored functions.
In the last section, we explored the generation of database schemas and how
you can customize and extend your mappings to include all kinds of constraints,
indexes, and arbitrary DDL that your DBA may recommend.
Table 8.1 shows a summary you can use to compare native Hibernate features
and Java Persistence.
Table 8.1
Hibernate and JPA comparison chart for chapter 8
Hibernate Core
Java Persistence and EJB 3.0
Hibernate supports any kind of natural and composite primary key, including foreign keys to natural keys, composite primary keys, and foreign
keys in composite primary keys.
Standardized support is provided for natural and
composite keys, equivalent to Hibernate.
Hibernate supports arbitrary association join conditions with formula mappings and property references.
No standard or annotation support is provided for
grouped property references at the time of writing.
Hibernate supports basic joins of secondary
tables for a particular entity class.
Standardized support is provided for secondary
tables and basic joins.
Hibernate supports trigger integration and
generated property settings.
Hibernate Annotations supports generated properties and trigger integration.
Hibernate lets you customize all SQL DML statements with options in XML mapping metadata.
At the time of writing, no support is provided for
SQL DML customization with annotations.
Hibernate lets you customize SQL DDL for automatic schema generation. Arbitrary SQL DDL
statements can be included in XML mapping
JPA standardizes basic DDL declarations, but not
all features of the XML mapping metadata are supported with annotations.
You now know everything (well, as much as we can show in a single book) there is
to know about mapping classes to schemas. In the next part of the book, we’ll discuss how to use the persistence manager APIs to load and store objects, how transactions and conversations are implemented, and how to write queries.
Part 3
object processing
n this part of the book, we explain how to work with persistent objects.
Chapter 9 shows you how to load and store objects with the Hibernate and
Java Persistence programming interfaces. Transactions and concurrency
control are another important topic, discussed in detail in chapter 10. We
then implement conversations in chapter 11 and show you how this concept can improve the design of your system. Chapters 12 and 13 focus on
efficiency and how Hibernate features can make your life easier when you
have to load and modify large and complex datasets. Querying, query languages, and APIs are examined in detail in chapters 14 and 15. In
chapter 16, we bring it all together by designing and testing a layered application with ORM persistence.
After reading this part, you’ll know how to work with Hibernate and Java
Persistence programming interfaces and how to load, modify, and store
objects efficiently. You’ll understand how transactions work and why conversational processing can open up new ways for application design. You’ll be
ready to optimize any object modification scenario, write complex queries,
and apply the best fetching and caching strategy to increase performance
and scalability.
Working with objects
This chapter covers
The lifecycle and states of objects
Working with the Hibernate API
Working with the Java Persistence API
Working with objects
You now have an understanding of how Hibernate and ORM solve the static
aspects of the object/relational mismatch. With what you know so far, it’s possible
to solve the structural mismatch problem, but an efficient solution to the problem
requires something more. You must investigate strategies for runtime data access,
because they’re crucial to the performance of your applications. You basically
have learn how to control the state of objects.
This and the following chapters cover the behavioral aspect of the object/relational mismatch. We consider these problems to be at least as important as the
structural problems discussed in previous chapters. In our experience, many
developers are only really aware of the structural mismatch and rarely pay attention to the more dynamic behavioral aspects of the mismatch.
In this chapter, we discuss the lifecycle of objects—how an object becomes persistent, and how it stops being considered persistent—and the method calls and
other actions that trigger these transitions. The Hibernate persistence manager,
the Session, is responsible for managing object state, so we discuss how to use this
important API. The main Java Persistence interface in EJB 3.0 is called EntityManager, and thanks to its close resemblance with Hibernate APIs, it will be easy to
learn alongside. Of course, you can skip quickly through this material if you aren’t
working with Java Persistence or EJB 3.0—we encourage you to read about both
options and then decide what is better for your application.
Let’s start with persistent objects, their lifecycle, and the events which trigger a
change of persistent state. Although some of the material may be formal, a solid
understanding of the persistence lifecycle is essential.
The persistence lifecycle
Because Hibernate is a transparent persistence mechanism—classes are unaware
of their own persistence capability—it’s possible to write application logic that is
unaware whether the objects it operates on represent persistent state or temporary state that exists only in memory. The application shouldn’t necessarily need
to care that an object is persistent when invoking its methods. You can, for example, invoke the calculateTotalPrice() business method on an instance of the
Item class without having to consider persistence at all; e.g., in a unit test.
Any application with persistent state must interact with the persistence service
whenever it needs to propagate state held in memory to the database (or vice
versa). In other words, you have to call Hibernate (or the Java Persistence) interfaces to store and load objects.
The persistence lifecycle
When interacting with the persistence mechanism in that way, it’s necessary for
the application to concern itself with the state and lifecycle of an object with
respect to persistence. We refer to this as the persistence lifecycle: the states an object
goes through during its life. We also use the term unit of work: a set of operations
you consider one (usually atomic) group. Another piece of the puzzle is the persistence context provided by the persistence service. Think of the persistence context
as a cache that remembers all the modifications and state changes you made to
objects in a particular unit of work (this is somewhat simplified, but it’s a good
starting point).
We now dissect all these terms: object and entity states, persistence contexts,
and managed scope. You’re probably more accustomed to thinking about what
statements you have to manage to get stuff in and out of the database (via JDBC and
SQL). However, one of the key factors of your success with Hibernate (and Java
Persistence) is your understanding of state management, so stick with us through
this section.
Object states
Different ORM solutions use different terminology and define different states and
state transitions for the persistence lifecycle. Moreover, the object states used
internally may be different from those exposed to the client application. Hibernate defines only four states, hiding the complexity of its internal implementation
from the client code.
The object states defined by Hibernate and their transitions in a state chart are
shown in figure 9.1. You can also see the method calls to the persistence manager
API that trigger transitions. This API in Hibernate is the Session. We discuss this
chart in this chapter; refer to it whenever you need an overview.
We’ve also included the states of Java Persistence entity instances in figure 9.1.
As you can see, they’re almost equivalent to Hibernate’s, and most methods of the
Session have a counterpart on the EntityManager API (shown in italics). We say
that Hibernate is a superset of the functionality provided by the subset standardized
in Java Persistence.
Some methods are available on both APIs; for example, the Session has a persist() operation with the same semantics as the EntityManager’s counterpart.
Others, like load() and getReference(), also share semantics, with a different
method name.
During its life, an object can transition from a transient object to a persistent
object to a detached object. Let’s explore the states and transitions in more detail.
Working with objects
Figure 9.1
Object states and their transitions as triggered by persistence manager operations
Transient objects
Objects instantiated using the new operator aren’t immediately persistent. Their
state is transient, which means they aren’t associated with any database table row
and so their state is lost as soon as they’re no longer referenced by any other
object. These objects have a lifespan that effectively ends at that time, and they
become inaccessible and available for garbage collection. Java Persistence doesn’t
include a term for this state; entity objects you just instantiated are new. We’ll continue to refer to them as transient to emphasize the potential for these instances to
become managed by a persistence service.
Hibernate and Java Persistence consider all transient instances to be nontransactional; any modification of a transient instance isn’t known to a persistence context. This means that Hibernate doesn’t provide any roll-back functionality for
transient objects.
Objects that are referenced only by other transient instances are, by default,
also transient. For an instance to transition from transient to persistent state, to
become managed, requires either a call to the persistence manager or the creation of a reference from an already persistent instance.
Persistent objects
A persistent instance is an entity instance with a database identity, as defined in chapter 4, section 4.2, “Mapping entities with identity.” That means a persistent and
The persistence lifecycle
managed instance has a primary key value set as its database identifier. (There are
some variations to when this identifier is assigned to a persistent instance.)
Persistent instances may be objects instantiated by the application and then
made persistent by calling one of the methods on the persistence manager. They
may even be objects that became persistent when a reference was created from
another persistent object that is already managed. Alternatively, a persistent
instance may be an instance retrieved from the database by execution of a query,
by an identifier lookup, or by navigating the object graph starting from another
persistent instance.
Persistent instances are always associated with a persistence context. Hibernate
caches them and can detect whether they have been modified by the application.
There is much more to be said about this state and how an instance is managed in a persistence context. We’ll get back to this later in this chapter.
Removed objects
You can delete an entity instance in several ways: For example, you can remove it
with an explicit operation of the persistence manager. It may also become available for deletion if you remove all references to it, a feature available only in
Hibernate or in Java Persistence with a Hibernate extension setting (orphan deletion for entities).
An object is in the removed state if it has been scheduled for deletion at the end
of a unit of work, but it’s still managed by the persistence context until the unit of
work completes. In other words, a removed object shouldn’t be reused because it
will be deleted from the database as soon as the unit of work completes. You
should also discard any references you may hold to it in the application (of
course, after you finish working with it—for example, after you’ve rendered the
removal-confirmation screen your users see).
Detached objects
To understand detached objects, you need to consider a typical transition of an
instance: First it’s transient, because it just has been created in the application.
Now you make it persistent by calling an operation on the persistence manager.
All of this happens in a single unit of work, and the persistence context for this
unit of work is synchronized with the database at some point (when an SQL
INSERT occurs).
The unit of work is now completed, and the persistence context is closed. But
the application still has a handle: a reference to the instance that was saved. As
long as the persistence context is active, the state of this instance is persistent. At
Working with objects
the end of a unit of work, the persistence context closes. What is the state of the
object you’re holding a reference to now, and what can you do with it?
We refer to these objects as detached, indicating that their state is no longer
guaranteed to be synchronized with database state; they’re no longer attached to
a persistence context. They still contain persistent data (which may soon be stale).
You can continue working with a detached object and modify it. However, at some
point you probably want to make those changes persistent—in other words, bring
the detached instance back into persistent state.
Hibernate offers two operations, reattachment and merging, to deal with this situation. Java Persistence only standardizes merging. These features have a deep
impact on how multitiered applications may be designed. The ability to return
objects from one persistence context to the presentation layer and later reuse
them in a new persistence context is a main selling point of Hibernate and Java
Persistence. It enables you to create long units of work that span user think-time.
We call this kind of long-running unit of work a conversation. We’ll get back to
detached objects and conversations soon.
You should now have a basic understanding of object states and how transitions occur. Our next topic is the persistence context and the management of
objects it provides.
The persistence context
You may consider the persistence context to be a cache of managed entity
instances. The persistence context isn’t something you see in your application; it
isn’t an API you can call. In a Hibernate application, we say that one Session has
one internal persistence context. In a Java Persistence application, an EntityManager has a persistence context. All entities in persistent state and managed in a
unit of work are cached in this context. We walk through the Session and
EntityManager APIs later in this chapter. Now you need to know what this (internal) persistence context is buying you.
The persistence context is useful for several reasons:
Hibernate can do automatic dirty checking and transactional write-behind.
Hibernate can use the persistence context as a first-level cache.
Hibernate can guarantee a scope of Java object identity.
Hibernate can extend the persistence context to span a whole conversation.
All these points are also valid for Java Persistence providers. Let’s look at each
The persistence lifecycle
Automatic dirty checking
Persistent instances are managed in a persistence context—their state is synchronized with the database at the end of the unit of work. When a unit of work completes, state held in memory is propagated to the database by the execution of
SQL INSERT, UPDATE, and DELETE statements (DML). This procedure may also
occur at other times. For example, Hibernate may synchronize with the database
before execution of a query. This ensures that queries are aware of changes made
earlier during the unit of work.
Hibernate doesn’t update the database row of every single persistent object in
memory at the end of the unit of work. ORM software must have a strategy for
detecting which persistent objects have been modified by the application. We call
this automatic dirty checking. An object with modifications that have not yet been
propagated to the database is considered dirty. Again, this state isn’t visible to the
application. With transparent transaction-level write-behind, Hibernate propagates
state changes to the database as late as possible but hides this detail from the
application. By executing DML as late as possible (toward the end of the database
transaction), Hibernate tries to keep lock-times in the database as short as possible. (DML usually creates locks in the database that are held until the transaction completes.)
Hibernate is able to detect exactly which properties have been modified so that
it’s possible to include only the columns that need updating in the SQL UPDATE
statement. This may bring some performance gains. However, it’s usually not a significant difference and, in theory, could harm performance in some environments. By default, Hibernate includes all columns of a mapped table in the SQL
UPDATE statement (hence, Hibernate can generate this basic SQL at startup, not at
runtime). If you want to update only modified columns, you can enable dynamic
SQL generation by setting dynamic-update="true" in a class mapping. The same
mechanism is implemented for insertion of new records, and you can enable
runtime generation of INSERT statements with dynamic-insert="true". We recommend you consider this setting when you have an extraordinarily large number of columns in a table (say, more than 50); at some point, the overhead
network traffic for unchanged fields will be noticeable.
In rare cases, you may also want to supply your own dirty checking algorithm to
Hibernate. By default, Hibernate compares an old snapshot of an object with the
snapshot at synchronization time, and it detects any modifications that require an
update of the database state. You can implement your own routine by supplying a
custom findDirty() method with an org.hibernate.Interceptor for a Session. We’ll show you an implementation of an interceptor later in the book.
Working with objects
We’ll also get back to the synchronization process (known as flushing) and
when it occurs later in this chapter.
The persistence context cache
A persistence context is a cache of persistent entity instances. This means it
remembers all persistent entity instances you’ve handled in a particular unit of
work. Automatic dirty checking is one of the benefits of this caching. Another
benefit is repeatable read for entities and the performance advantage of a unit of
work-scoped cache.
For example, if Hibernate is told to load an object by primary key (a lookup by
identifier), it can first check the persistence context for the current unit of work.
If the entity is found there, no database hit occurs—this is a repeatable read for
an application. The same is true if a query is executed through one of the Hibernate (or Java Persistence) interfaces. Hibernate reads the result set of the query
and marshals entity objects that are then returned to the application. During this
process, Hibernate interacts with the current persistence context. It tries to
resolve every entity instance in this cache (by identifier); only if the instance can’t
be found in the current persistence context does Hibernate read the rest of the
data from the result set.
The persistence context cache offers significant performance benefits and
improves the isolation guarantees in a unit of work (you get repeatable read of
entity instances for free). Because this cache only has the scope of a unit of work,
it has no real disadvantages, such as lock management for concurrent access—a
unit of work is processed in a single thread at a time.
The persistence context cache sometimes helps avoid unnecessary database
traffic; but, more important, it ensures that:
The persistence layer isn’t vulnerable to stack overflows in the case of circular references in a graph of objects.
There can never be conflicting representations of the same database row at
the end of a unit of work. In the persistence context, at most a single object
represents any database row. All changes made to that object may be safely
written to the database.
Likewise, changes made in a particular persistence context are always immediately visible to all other code executed inside that persistence context and
its unit of work (the repeatable read for entities guarantee).
You don’t have to do anything special to enable the persistence context cache. It’s
always on and, for the reasons shown, can’t be turned off.
Object identity and equality
Later in this chapter, we’ll show you how objects are added to this cache (basically, whenever they become persistent) and how you can manage this cache (by
detaching objects manually from the persistence context, or by clearing the persistence context).
The last two items on our list of benefits of a persistence context, the guaranteed scope of identity and the possibility to extend the persistence context to span
a conversation, are closely related concepts. To understand them, you need to take
a step back and consider objects in detached state from a different perspective.
Object identity and equality
A basic Hibernate client/server application may be designed with server-side units
of work that span a single client request. When a request from the application
user requires data access, a new unit of work is started. The unit of work ends
when processing is complete and the response for the user is ready. This is also
called the session-per-request strategy (you can replace the word session with persistence context whenever you read something like this, but it doesn’t roll off the
tongue as well).
We already mentioned that Hibernate can support an implementation of a
possibly long-running unit of work, called a conversation. We introduce the concept of conversations in the following sections as well as the fundamentals of
object identity and when objects are considered equal—which can impact how
you think about and design conversations.
Why is the concept of a conversation useful?
Introducing conversations
For example, in web applications, you don’t usually maintain a database transaction across a user interaction. Users take a long time to think about modifications, but, for scalability reasons, you must keep database transactions short and
release database resources as soon as possible. You’ll likely face this issue whenever you need to guide the user through several screens to complete a unit of
work (from the user’s perspective)—for example, to fill an online form. In this
common scenario, it’s extremely useful to have the support of the persistence service, so you can implement such a conversation with a minimum of coding and
best scalability.
Two strategies are available to implement a conversation in a Hibernate or Java
Persistence application: with detached objects or by extending a persistence context. Both have strength and weaknesses.
Working with objects
Figure 9.2
Conversation implementation with detached object state
The detached object state and the already mentioned features of reattachment or
merging are ways to implement a conversation. Objects are held in detached state
during user think-time, and any modification of these objects is made persistent
manually through reattachment or merging. This strategy is also called session-perrequest-with-detached-objects. You can see a graphical illustration of this conversation
pattern in figure 9.2.
A persistence context only spans the processing of a particular request, and
the application manually reattaches and merges (and sometimes detaches) entity
instances during the conversation.
The alternative approach doesn’t require manual reattachment or merging:
With the session-per-conversation pattern, you extend a persistence context to span
the whole unit of work (see figure 9.3).
First we have a closer look at detached objects and the problem of identity
you’ll face when you implement a conversation with this strategy.
Figure 9.3
Conversation implementation with an extended persistence context
Object identity and equality
The scope of object identity
As application developers, we identify an object using Java object identity (a==b).
If an object changes state, is the Java identity guaranteed to be the same in the
new state? In a layered application, that may not be the case.
In order to explore this, it’s extremely important to understand the relationship between Java identity, a==b, and database identity, x.getId().equals(
y.getId() ). Sometimes they’re equivalent; sometimes they aren’t. We refer to
the conditions under which Java identity is equivalent to database identity as the
scope of object identity.
For this scope, there are three common choices:
A primitive persistence layer with no identity scope makes no guarantees
that if a row is accessed twice the same Java object instance will be
returned to the application. This becomes problematic if the application
modifies two different instances that both represent the same row in a single unit of work. (How should we decide which state should be propagated to the database?)
A persistence layer using persistence context-scoped identity guarantees that, in
the scope of a single persistence context, only one object instance represents a particular database row. This avoids the previous problem and also
allows for some caching at the context level.
Process-scoped identity goes one step further and guarantees that only one
object instance represents the row in the whole process (JVM).
For a typical web or enterprise application, persistence context-scoped identity is
preferred. Process-scoped identity does offer some potential advantages in terms
of cache utilization and the programming model for reuse of instances across
multiple units of work. However, in a pervasively multithreaded application, the
cost of always synchronizing shared access to persistent objects in the global identity map is too high a price to pay. It’s simpler, and more scalable, to have each
thread work with a distinct set of persistent instances in each persistence context.
We would say that Hibernate implements persistence context-scoped identity. So, by nature, Hibernate is best suited for highly concurrent data access in
multiuser applications. However, we already mentioned some issues you’ll face
when objects aren’t associated with a persistence context. Let’s discuss this with
an example.
The Hibernate identity scope is the scope of a persistence context. Let’s see
how this works in code with Hibernate APIs—the Java Persistence code is the
Working with objects
equivalent with EntityManager instead of Session. Even though we haven’t
shown you much about these interfaces, the following examples are simple, and
you should have no problems understanding the methods we call on the Session.
If you request two objects using the same database identifier value in the same
Session, the result is two references to the same in-memory instance. Listing 9.1
demonstrates this with several get() operations in two Sessions.
Listing 9.1
The guaranteed scope of object identity in Hibernate
Session session1 = sessionFactory.openSession();
Transaction tx1 = session1.beginTransaction();
// Load Item with identifier value "1234"
Object a = session1.get(Item.class, new Long(1234) );
Object b = session1.get(Item.class, new Long(1234) );
( a==b ) // True, persistent a and b are identical
// References a and b are now to an object in detached state
Session session2 = sessionFactory.openSession();
Transaction tx2 = session2.beginTransaction();
Object c = session2.get(Item.class, new Long(1234) );
( a==c ) // False, detached a and persistent c are not identical
Object references a and b have not only the same database identity, but also the
same Java identity, because they’re obtained in the same Session. They reference
the same persistent instance known to the persistence context for that unit of
work. Once you’re outside this boundary, however, Hibernate doesn’t guarantee
Java identity, so a and c aren’t identical. Of course, a test for database identity,
a.getId().equals( c.getId() ), will still return true.
If you work with objects in detached state, you’re dealing with objects that are
living outside of a guaranteed scope of object identity.
The identity of detached objects
If an object reference leaves the scope of guaranteed identity, we call it a reference
to a detached object. In listing 9.1, all three object references, a, b, and c, are equal if
we only consider database identity—their primary key value. However, they aren’t
Object identity and equality
identical in-memory object instances. This can lead to problems if you treat them
as equal in detached state. For example, consider the following extension of the
code, after session2 has ended:
Set allObjects = new HashSet();
All three references have been added to a Set. All are references to detached
objects. Now, if you check the size of the collection, the number of elements, what
result do you expect?
First you have to realize the contract of a Java Set: No duplicate elements are
allowed in such a collection. Duplicates are detected by the Set; whenever you
add an object, its equals() method is called automatically. The added object is
checked against all other elements already in the collection. If equals() returns
true for any object already in the collection, the addition doesn’t occur.
If you know the implementation of equals() for the objects, you can find out
the number of elements you can expect in the Set. By default, all Java classes
inherit the equals() method of java.lang.Object. This implementation uses a
double-equals (==) comparison; it checks whether two references refer to the
same in-memory instance on the Java heap.
You may guess that the number of elements in the collection is two. After all, a
and b are references to the same in-memory instance; they have been loaded in
the same persistence context. Reference c is obtained in a second Session; it
refers to a different instance on the heap. You have three references to two
instances. However, you know this only because you’ve seen the code that loaded
the objects. In a real application, you may not know that a and b are loaded in the
same Session and c in another.
Furthermore, you obviously expect that the collection has exactly one element, because a, b, and c represent the same database row.
Whenever you work with objects in detached state, and especially if you test
them for equality (usually in hash-based collections), you need to supply your
own implementation of the equals() and hashCode() methods for your persistent classes.
Working with objects
Understanding equals() and hashCode()
Before we show you how to implement your own equality routine. we have to
bring two important points to your attention. First, in our experience, many Java
developers never had to override the equals() and hashCode() methods before
using Hibernate (or Java Persistence). Traditionally, Java developers seem to be
unaware of the intricate details of such an implementation. The longest discussion threads on the public Hibernate forum are about this equality problem, and
the “blame” is often put on Hibernate. You should be aware of the fundamental
issue: Every object-oriented programming language with hash-based collections
requires a custom equality routine if the default contract doesn’t offer the desired
semantics. The detached object state in a Hibernate application exposes you to
this problem, maybe for the first time.
On the other hand, you may not have to override equals() and hashCode().
The identity scope guarantee provided by Hibernate is sufficient if you never compare detached instances—that is, if you never put detached instances into the
same Set. You may decide to design an application that doesn’t use detached
objects. You can apply an extended persistence context strategy for your conversation implementation and eliminate the detached state from your application completely. This strategy also extends the scope of guaranteed object identity to span
the whole conversation. (Note that you still need the discipline to not compare
detached instances obtained in two conversations!)
Let’s assume that you want to use detached objects and that you have to test
them for equality with your own routine. You can implement equals() and hashCode() several ways. Keep in mind that when you override equals(), you always
need to also override hashCode() so the two methods are consistent. If two objects
are equal, they must have the same hashcode.
A clever approach is to implement equals() to compare just the database
identifier property (often a surrogate primary key) value:
public class User {
public boolean equals(Object other) {
if (this==other) return true;
if (id==null) return false;
if ( !(other instanceof User) ) return false;
final User that = (User) other;
return that.getId() );
public int hashCode() {
return id==null ?
Object identity and equality
System.identityHashCode(this) :
Notice how this equals() method falls back to Java identity for transient instances
(if id==null) that don’t have a database identifier value assigned yet. This is reasonable, because they can’t possibly be equal to a detached instance, which has an
identifier value.
Unfortunately, this solution has one huge problem: Identifier values aren’t
assigned by Hibernate until an object becomes persistent. If a transient object is
added to a Set before being saved, its hash value may change while it’s contained
by the Set, contrary to the contract of java.util.Set. In particular, this problem
makes cascade save (discussed later in the book) useless for sets. We strongly discourage this solution (database identifier equality).
A better way is to include all persistent properties of the persistent class, apart
from any database identifier property, in the equals() comparison. This is how
most people perceive the meaning of equals(); we call it by value equality.
When we say all properties, we don’t mean to include collections. Collection state
is associated with a different table, so it seems wrong to include it. More important,
you don’t want to force the entire object graph to be retrieved just to perform
equals(). In the case of User, this means you shouldn’t include the boughtItems
collection in the comparison. This is the implementation you can write:
public class User {
public boolean equals(Object other) {
if (this==other) return true;
if ( !(other instanceof User) ) return false;
final User that = (User) other;
if ( !this.getUsername().equals( that.getUsername() ) )
return false;
if ( !this.getPassword().equals( that.getPassword() ) )
return false;
return true;
public int hashCode() {
int result = 14;
result = 29 * result + getUsername().hashCode();
result = 29 * result + getPassword().hashCode();
return result;
Working with objects
However, there are again two problems with this approach. First, instances from
different Sessions are no longer equal if one is modified (for example, if the user
changes the password). Second, instances with different database identity
(instances that represent different rows of the database table) can be considered
equal unless some combination of properties is guaranteed to be unique (the
database columns have a unique constraint). In the case of user, there is a unique
property: username.
This leads us to the preferred (and semantically correct) implementation of an
equality check. You need a business key.
Implementing equality with a business key
To get to the solution that we recommend, you need to understand the notion of
a business key. A business key is a property, or some combination of properties, that
is unique for each instance with the same database identity. Essentially, it’s the natural key that you would use if you weren’t using a surrogate primary key instead.
Unlike a natural primary key, it isn’t an absolute requirement that the business
key never changes—as long as it changes rarely, that’s enough.
We argue that essentially every entity class should have some business key, even
if it includes all properties of the class (this would be appropriate for some
immutable classes). The business key is what the user thinks of as uniquely identifying a particular record, whereas the surrogate key is what the application and
database use.
Business key equality means that the equals() method compares only the properties that form the business key. This is a perfect solution that avoids all the problems described earlier. The only downside is that it requires extra thought to
identify the correct business key in the first place. This effort is required anyway;
it’s important to identify any unique keys if your database must ensure data integrity via constraint checking.
For the User class, username is a great candidate business key. It’s never null,
it’s unique with a database constraint, and it changes rarely, if ever:
public class User {
public boolean equals(Object other) {
if (this==other) return true;
if ( !(other instanceof User) ) return false;
final User that = (User) other;
return this.username.equals( that.getUsername() );
public int hashCode() {
Object identity and equality
return username.hashCode();
For some other classes, the business key may be more complex, consisting of a
combination of properties. Here are some hints that should help you identify a
business key in your classes:
Consider what attributes users of your application will refer to when they
have to identify an object (in the real world). How do users tell the difference between one object and another if they’re displayed on the screen?
This is probably the business key you’re looking for.
Every attribute that is immutable is probably a good candidate for the business key. Mutable attributes may be good candidates, if they’re updated
rarely or if you can control the situation when they’re updated.
Every attribute that has a UNIQUE database constraint is a good candidate for
the business key. Remember that the precision of the business key has to be
good enough to avoid overlaps.
Any date or time-based attribute, such as the creation time of the record, is
usually a good component of a business key. However, the accuracy of System.currentTimeMillis() depends on the virtual machine and operating
system. Our recommended safety buffer is 50 milliseconds, which may not
be accurate enough if the time-based property is the single attribute of a
business key.
You can use database identifiers as part of the business key. This seems to
contradict our previous statements, but we aren’t talking about the database
identifier of the given class. You may be able to use the database identifier
of an associated object. For example, a candidate business key for the Bid
class is the identifier of the Item it was made for together with the bid
amount. You may even have a unique constraint that represents this composite business key in the database schema. You can use the identifier value
of the associated Item because it never changes during the lifecycle of a
Bid—setting an already persistent Item is required by the Bid constructor.
If you follow our advice, you shouldn’t have much difficulty finding a good business key for all your business classes. If you have a difficult case, try to solve it without considering Hibernate—after all, it’s purely an object-oriented problem.
Notice that it’s almost never correct to override equals() on a subclass and
include another property in the comparison. It’s a little tricky to satisfy the
Working with objects
requirements that equality be both symmetric and transitive in this case; and,
more important, the business key may not correspond to any well-defined candidate natural key in the database (subclass properties may be mapped to a different table).
You may have also noticed that the equals() and hashCode() methods always
access the properties of the “other” object via the getter methods. This is
extremely important, because the object instance passed as other may be a proxy
object, not the actual instance that holds the persistent state. To initialize this
proxy to get the property value, you need to access it with a getter method. This is
one point where Hibernate isn’t completely transparent. However, it’s a good practice to use getter methods instead of direct instance variable access anyway.
Let’s switch perspective now and consider an implementation strategy for conversations that doesn’t require detached objects and doesn’t expose you to any of
the problems of detached object equality. If the identity scope issues you’ll possibly be exposed to when you work with detached objects seem too much of a burden, the second conversation-implementation strategy may be what you’re
looking for. Hibernate and Java Persistence support the implementation of conversations with an extended persistence context: the session-per-conversation strategy.
Extending a persistence context
A particular conversation reuses the same persistence context for all interactions.
All request processing during a conversation is managed by the same persistence
context. The persistence context isn’t closed after a request from the user has
been processed. It’s disconnected from the database and held in this state during
user think-time. When the user continues in the conversation, the persistence
context is reconnected to the database, and the next request can be processed.
At the end of the conversation, the persistence context is synchronized with the
database and closed. The next conversation starts with a fresh persistence context
and doesn’t reuse any entity instances from the previous conversation; the pattern is repeated.
Note that this eliminates the detached object state! All instances are either transient (not known to a persistence context) or persistent (attached to a particular
persistence context). This also eliminates the need for manual reattachment or
merging of object state between contexts, which is one of the advantages of this
strategy. (You still may have detached objects between conversations, but we consider this a special case that you should try to avoid.)
In Hibernate terms, this strategy uses a single Session for the duration of the
conversation. Java Persistence has built-in support for extended persistence
The Hibernate interfaces
contexts and can even automatically store the disconnected context for you (in a
stateful EJB session bean) between requests.
We’ll get back to conversations later in the book and show you all the details
about the two implementation strategies. You don’t have to choose one right
now, but you should be aware of the consequences these strategies have on object
state and object identity, and you should understand the necessary transitions in
each case.
We now explore the persistence manager APIs and how you make the theory
behind object states work in practice.
The Hibernate interfaces
Any transparent persistence tool includes a persistence manager API. This persistence manager usually provides services for the following:
Basic CRUD (create, retrieve, update, delete) operations
Query execution
Control of transactions
Management of the persistence context
The persistence manager may be exposed by several different interfaces. In the
case of Hibernate, these are Session, Query, Criteria, and Transaction. Under
the covers, the implementations of these interfaces are coupled tightly together.
In Java Persistence, the main interface you interact with is the EntityManager;
it has the same role as the Hibernate Session. Other Java Persistence interfaces
are Query and EntityTransaction (you can probably guess what their counterpart in native Hibernate is).
We’ll now show you how to load and store objects with Hibernate and Java Persistence. Sometimes both have exactly the same semantics and API, and even the
method names are the same. It’s therefore much more important to keep your
eyes open for little differences. To make this part of the book easier to understand, we decided to use a different strategy than usual and explain Hibernate
first and then Java Persistence.
Let’s start with Hibernate, assuming that you write an application that relies on
the native API.
Working with objects
Storing and loading objects
In a Hibernate application, you store and load objects by essentially changing
their state. You do this in units of work. A single unit of work is a set of operations
considered an atomic group. If you’re guessing now that this is closely related to
transactions, you’re right. But, it isn’t necessarily the same thing. We have to
approach this step by step; for now, consider a unit of work a particular sequence
of state changes to your objects that you’d group together.
First you have to begin a unit of work.
Beginning a unit of work
At the beginning of a unit of work, an application obtains an instance of Session
from the application’s SessionFactory:
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
At this point, a new persistence context is also initialized for you, and it will manage all the objects you work with in that Session. The application may have multiple SessionFactorys if it accesses several databases. How the SessionFactory is
created and how you get access to it in your application code depends on your
deployment environment and configuration—you should have the simple HibernateUtil startup helper class ready if you followed the setup in “Handling the
SessionFactory” in chapter 2, section 2.1.3.
You should never create a new SessionFactory just to service a particular
request. Creation of a SessionFactory is extremely expensive. On the other
hand, Session creation is extremely inexpensive. The Session doesn’t even obtain
a JDBC Connection until a connection is required.
The second line in the previous code begins a Transaction on another
Hibernate interface. All operations you execute inside a unit of work occur
inside a transaction, no matter if you read or write data. However, the Hibernate
API is optional, and you may begin a transaction in any way you like—we’ll
explore these options in the next chapter. If you use the Hibernate Transaction
API, your code works in all environments, so you’ll do this for all examples in the
following sections.
After opening a new Session and persistence context, you use it to load and
save objects.
Making an object persistent
The first thing you want to do with a Session is make a new transient object persistent with the save() method (listing 9.2).
The Hibernate interfaces
Listing 9.2
Making a transient instance persistent
Item item = new Item();
item.setName("Playstation3 incl. all accessories");
item.setEndDate( ... );
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Serializable itemId =;
A new transient object item is instantiated as usual B. Of course, you may also
instantiate it after opening a Session; they aren’t related yet. A new Session is
opened using the SessionFactory C. You start a new transaction.
A call to save() D makes the transient instance of Item persistent. It’s now
associated with the current Session and its persistence context.
The changes made to persistent objects have to be synchronized with the database at some point. This happens when you commit() the Hibernate Transaction
E. We say a flush occurs (you can also call flush() manually; more about this
later). To synchronize the persistence context, Hibernate obtains a JDBC connection and issues a single SQL INSERT statement. Note that this isn’t always true for
insertion: Hibernate guarantees that the item object has an assigned database
identifier after it has been saved, so an earlier INSERT may be necessary, depending on the identifier generator you have enabled in your mapping. The save()
operation also returns the database identifier of the persistent instance.
The Session can finally be closed F, and the persistence context ends. The
reference item is now a reference to an object in detached state.
You can see the same unit of work and how the object changes state in
figure 9.4.
It’s better (but not required) to fully initialize the Item instance before managing it with a Session. The SQL INSERT statement contains the values that were
Figure 9.4
Making an object persistent in
a unit of work
Working with objects
held by the object at the point when save() was called. You can modify the object
after calling save(), and your changes will be propagated to the database as an
(additional) SQL UPDATE.
Everything between session.beginTransaction() and tx.commit() occurs
in one transaction. For now, keep in mind that all database operations in transaction scope either completely succeed or completely fail. If one of the UPDATE or
INSERT statements made during flushing on tx.commit() fails, all changes made
to persistent objects in this transaction are rolled back at the database level. However, Hibernate doesn’t roll back in-memory changes to persistent objects. This is
reasonable because a failure of a transaction is normally nonrecoverable, and you
have to discard the failed Session immediately. We’ll discuss exception handling
later in the next chapter.
Retrieving a persistent object
The Session is also used to query the database and retrieve existing persistent
objects. Hibernate is especially powerful in this area, as you’ll see later in the
book. Two special methods are provided for the simplest kind of query: retrieval
by identifier. The get() and load() methods are demonstrated in listing 9.3.
Listing 9.3
Retrieval of a Item by identifier
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item item = (Item) session.load(Item.class, new Long(1234));
// Item item = (Item) session.get(Item.class, new Long(1234));
You can see the same unit of work in figure 9.5.
The retrieved object item is in persistent state and as soon as the persistence
context is closed, in detached state.
Figure 9.5
Retrieving a persistent object by identifier
The Hibernate interfaces
The one difference between get() and load() is how they indicate that the
instance could not be found. If no row with the given identifier value exists in the
database, get() returns null. The load() method throws an ObjectNotFoundException. It’s your choice what error-handling you prefer.
More important, the load() method may return a proxy, a placeholder, without
hitting the database. A consequence of this is that you may get an ObjectNotFoundException later, as soon as you try to access the returned placeholder and force
its initialization (this is also called lazy loading; we discuss load optimization in later
chapters.) The load() method always tries to return a proxy, and only returns an
initialized object instance if it’s already managed by the current persistence context. In the example shown earlier, no database hit occurs at all! The get()
method on the other hand never returns a proxy, it always hits the database.
You may ask why this option is useful—after all, you retrieve an object to
access it. It’s common to obtain a persistent instance to assign it as a reference to
another instance. For example, imagine that you need the item only for a single
purpose: to set an association with a Comment: aComment.setForAuction(item).
If this is all you plan to do with the item, a proxy will do fine; there is no need to
hit the database. In other words, when the Comment is saved, you need the foreign
key value of an item inserted into the COMMENT table. The proxy of an Item provides just that: an identifier value wrapped in a placeholder that looks like the
real thing.
Modifying a persistent object
Any persistent object returned by get(), load(), or any entity queried is already
associated with the current Session and persistence context. It can be modified,
and its state is synchronized with the database (see listing 9.4).
Figure 9.6 shows this unit of work and the object transitions.
Listing 9.4
Modifying a persistent instance
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item item = (Item) session.get(Item.class, new Long(1234));
item.setDescription("This Playstation is as good as new!");
Working with objects
Figure 9.6
Modifying a persistent instance
First, you retrieve the object from the database with the given identifier. You modify the object, and these modifications are propagated to the database during
flush when tx.commit() is called. This mechanism is called automatic dirty
checking—that means Hibernate tracks and saves the changes you make to an
object in persistent state. As soon as you close the Session, the instance is considered detached.
Making a persistent object transient
You can easily make a persistent object transient, removing its persistent state
from the database, with the delete() method (see listing 9.5).
Listing 9.5
Making a persistent object transient using delete()
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item item = (Item) session.load(Item.class, new Long(1234));
Look at figure 9.7.
The item object is in removed state after you call delete(); you shouldn’t continue working with it, and, in most cases, you should make sure any reference to it
Figure 9.7
Making a persistent
object transient
The Hibernate interfaces
in your application is removed. The SQL DELETE is executed only when the Session’s persistence context is synchronized with the database at the end of the unit
of work. After the Session is closed, the item object is considered an ordinary
transient instance. The transient instance is destroyed by the garbage collector if
it’s no longer referenced by any other object. Both the in-memory object instance
and the persistent database row will have been removed.
Do I have to load an object to delete it? Yes, an object has to be loaded into
the persistence context; an instance has to be in persistent state to be
removed (note that a proxy is good enough). The reason is simple: You
may have Hibernate interceptors enabled, and the object must be passed
through these interceptors to complete its lifecycle. If you delete rows in
the database directly, the interceptor won’t run. Having said that, Hibernate (and Java Persistence) offer bulk operations that translate into
direct SQL DELETE statements; we’ll discuss these operations in chapter
12, section 12.2, “Bulk and batch operations.”
Hibernate can also roll back the identifier of any entity that has been deleted, if
you enable the hibernate.use_identifier_rollback configuration option. In
the previous example, Hibernate sets the database identifier property of the
deleted item to null after deletion and flushing, if the option is enabled. It’s then
a clean transient instance that you can reuse in a future unit of work.
Replicating objects
The operations on the Session we have shown you so far are all common; you
need them in every Hibernate application. But Hibernate can help you with some
special use cases—for example, when you need to retrieve objects from one database and store them in another. This is called replication of objects.
Replication takes detached objects loaded in one Session and makes them
persistent in another Session. These Sessions are usually opened from two different SessionFactorys that have been configured with a mapping for the same
persistent class. Here is an example:
Session session = sessionFactory1.openSession();
Transaction tx = session.beginTransaction();
Item item = (Item) session.get(Item.class, new Long(1234));
Session session2 = sessionFactory2.openSession();
Transaction tx2 = session2.beginTransaction();
session2.replicate(item, ReplicationMode.LATEST_VERSION);
Working with objects
The ReplicationMode controls the details of the replication procedure:
ReplicationMode.IGNORE—Ignores the object when there is an existing
database row with the same identifier in the target database.
ReplicationMode.OVERWRITE—Overwrites any existing database row with
the same identifier in the target database.
ReplicationMode.EXCEPTION—Throws an exception if there is an existing
database row with the same identifier in the target database.
ReplicationMode.LATEST_VERSION—Overwrites the row in the target
database if its version is earlier than the version of the object, or ignores
the object otherwise. Requires enabled Hibernate optimistic concurrency
You may need replication when you reconcile data entered into different databases, when you’re upgrading system configuration information during product
upgrades (which often involves a migration to a new database instance), or when
you need to roll back changes made during non-ACID transactions.
You now know the persistence lifecycle and the basic operations of the persistence manager. Using these together with the persistent class mappings we discussed in earlier chapters, you may now create your own small Hibernate
application. Map some simple entity classes and components, and then store and
load objects in a stand-alone application. You don’t need a web container or application server: Write a main() method, and call the Session as we discussed in the
previous section.
In the next sections, we cover the detached object state and the methods to reattach and merge detached objects between persistence contexts. This is the foundation knowledge you need to implement long units of work—conversations. We
assume that you’re familiar with the scope of object identity as explained earlier
in this chapter.
Working with detached objects
Modifying the item after the Session is closed has no effect on its persistent representation in the database. As soon as the persistence context is closed, item
becomes a detached instance.
If you want to save modifications you made to a detached object, you have to
either reattach or merge it.
The Hibernate interfaces
Reattaching a modified detached instance
A detached instance may be reattached to a new Session (and managed by this
new persistence context) by calling update() on the detached object. In our
experience, it may be easier for you to understand the following code if you
rename the update() method in your mind to reattach()—however, there is a
good reason it’s called updating.
The update() method forces an update to the persistent state of the object in
the database, always scheduling an SQL UPDATE. See listing 9.6 for an example of
detached object handling.
Listing 9.6
Updating a detached instance
item.setDescription(...); // Loaded in previous Session
Session sessionTwo = sessionFactory.openSession();
Transaction tx = sessionTwo.beginTransaction();
It doesn’t matter if the item object is modified before or after it’s passed to
update(). The important thing here is that the call to update() is reattaching the
detached instance to the new Session (and persistence context). Hibernate
always treats the object as dirty and schedules an SQL UPDATE., which will be executed during flush. You can see the same unit of work in figure 9.8.
You may be surprised and probably hoped that Hibernate could know that you
modified the detached item’s description (or that Hibernate should know you did
not modify anything). However, the new Session and its fresh persistence context
don’t have this information. Neither does the detached object contain some internal list of all the modifications you’ve made. Hibernate has to assume that an
Figure 9.8
Reattaching a detached object
Working with objects
UDPATE in the database is needed. One way to avoid this UDPATE statement is to
configure the class mapping of Item with the select-before-update="true"
attribute. Hibernate then determines whether the object is dirty by executing a
SELECT statement and comparing the object’s current state to the current database state.
If you’re sure you haven’t modified the detached instance, you may prefer
another method of reattachment that doesn’t always schedule an update of the
Reattaching an unmodified detached instance
A call to lock() associates the object with the Session and its persistence context
without forcing an update, as shown in listing 9.7.
Listing 9.7
Reattaching a detached instance with lock()
Session sessionTwo = sessionFactory.openSession();
Transaction tx = sessionTwo.beginTransaction();
sessionTwo.lock(item, LockMode.NONE);
In this case, it does matter whether changes are made before or after the object has
been reattached. Changes made before the call to lock() aren’t propagated to
the database, you use it only if you’re sure the detached instance hasn’t been
modified. This method only guarantees that the object’s state changes from
detached to persistent and that Hibernate will manage the persistent object again.
Of course, any modifications you make to the object once it’s in managed persistent state require updating of the database.
We discuss Hibernate lock modes in the next chapter. By specifying LockMode.NONE here, you tell Hibernate not to perform a version check or obtain any
database-level locks when reassociating the object with the Session. If you specified LockMode.READ, or LockMode.UPGRADE, Hibernate would execute a SELECT
statement in order to perform a version check (and to lock the row(s) in the database for updating).
The Hibernate interfaces
Making a detached object transient
Finally, you can make a detached instance transient, deleting its persistent state
from the database, as in listing 9.8.
Listing 9.8
Making a detached object transient using delete()
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
This means you don’t have to reattach (with update() or lock()) a detached
instance to delete it from the database. In this case, the call to delete() does two
things: It reattaches the object to the Session and then schedules the object for
deletion, executed on tx.commit(). The state of the object after the delete()
call is removed.
Reattachment of detached objects is only one possible way to transport data
between several Sessions. You can use another option to synchronize modifications to a detached instance with the database, through merging of its state.
Merging the state of a detached object
Merging of a detached object is an alternative approach. It can be complementary
to or can replace reattachment. Merging was first introduced in Hibernate to deal
with a particular case where reattachment was no longer sufficient (the old name
for the merge() method in Hibernate 2.x was saveOrUpdateCopy()). Look at the
following code, which tries to reattach a detached object:
item.getId(); // The database identity is "1234"
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item item2 = (Item) session.get(Item.class, new Long(1234));
session.update(item); // Throws exception!
Given is a detached item object with the database identity 1234. After modifying
it, you try to reattach it to a new Session. However, before reattachment, another
instance that represents the same database row has already been loaded into the
Working with objects
persistence context of that Session. Obviously, the reattachment through
update() clashes with this already persistent instance, and a NonUniqueObjectException is thrown. The error message of the exception is A persistent instance with
the same database identifier is already associated with the Session! Hibernate can’t decide
which object represents the current state.
You can resolve this situation by reattaching the item first; then, because the
object is in persistent state, the retrieval of item2 is unnecessary. This is straightforward in a simple piece of code such as the example, but it may be impossible to
refactor in a more sophisticated application. After all, a client sent the detached
object to the persistence layer to have it managed, and the client may not (and
shouldn’t) be aware of the managed instances already in the persistence context.
You can let Hibernate merge item and item2 automatically:
item.getId() // The database identity is "1234"
Session session= sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item item2 = (Item) session.get(Item.class, new Long(1234));
Item item3 = (Item) session.merge(item);
(item == item2) // False
(item == item3) // False
(item2 == item3) // True
return item3;
Look at this unit of work in figure 9.9.
Figure 9.9
Merging a detached instance
into a persistent instance
The Hibernate interfaces
The merge(item) call D results in several actions. First, Hibernate checks
whether a persistent instance in the persistence context has the same database
identifier as the detached instance you’re merging. In this case, this is true: item
and item2, which were loaded with get() C, have the same primary key value.
If there is an equal persistent instance in the persistence context, Hibernate
copies the state of the detached instance onto the persistent instance E. In other
words, the new description that has been set on the detached item is also set on
the persistent item2.
If there is no equal persistent instance in the persistence context, Hibernate
loads it from the database (effectively executing the same retrieval by identifier as
you did with get()) and then merges the detached state with the retrieved
object’s state. This is shown in figure 9.10.
Figure 9.10
Merging a detached instance
into an implicitly loaded
persistent instance
If there is no equal persistent instance in the persistence context, and a lookup in
the database yields no result, a new persistent instance is created, and the state of
the merged instance is copied onto the new instance. This new object is then
scheduled for insertion into the database and returned by the merge() operation.
An insertion also occurs if the instance you passed into merge() was a transient
instance, not a detached object.
The following questions are likely on your mind:
What exactly is copied from item to item2? Merging includes all value-typed
properties and all additions and removals of elements to any collection.
What state is item in? Any detached object you merge with a persistent
instance stays detached. It doesn’t change state; it’s unaffected by the merge
operation. Therefore, item and the other two references aren’t the same in
Hibernate’s identity scope. (The first two identity checks in the last
Working with objects
example.) However, item2 and item3 are identical references to the same
persistent in-memory instance.
Why is item3 returned from the merge() operation? The merge() operation always returns a handle to the persistent instance it has merged the
state into. This is convenient for the client that called merge(), because it
can now either continue working with the detached item object and merge
it again when needed, or discard this reference and continue working with
item3. The difference is significant: If, before the Session completes, subsequent modifications are made to item2 or item3 after merging, the client
is completely unaware of these modifications. The client has a handle only
to the detached item object, which is now getting stale. However, if the client decides to throw away item after merging and continue with the
returned item3, it has a new handle on up-to-date state. Both item and
item2 should be considered obsolete after merging.
Merging of state is slightly more complex than reattachment. We consider it an
essential operation you’ll likely have to use at some point if you design your application logic around detached objects. You can use this strategy as an alternative
for reattachment and merge every time instead of reattaching. You can also use it
to make any transient instance persistent. As you’ll see later in this chapter, this is
the standardized model of Java Persistence; reattachment isn’t supported.
We haven’t paid much attention so far to the persistence context and how it
manages persistent objects.
Managing the persistence context
The persistence context does many things for you: automatic dirty checking, guaranteed scope of object identity, and so on. It’s equally important that you know
some of the details of its management, and that you sometimes influence what
goes on behind the scenes.
Controlling the persistence context cache
The persistence context is a cache of persistent objects. Every object in persistent
state is known to the persistence context, and a duplicate, a snapshot of each persistent instance, is held in the cache. This snapshot is used internally for dirty
checking, to detect any modifications you made to your persistent objects.
Many Hibernate users who ignore this simple fact run into an OutOfMemoryException. This is typically the case when you load thousands of objects in a Session but never intend to modify them. Hibernate still has to create a snapshot of
The Hibernate interfaces
each object in the persistence context cache and keep a reference to the managed
object, which can lead to memory exhaustion. (Obviously, you should execute a
bulk data operation if you modify thousands of objects—we’ll get back to this kind
of unit of work in chapter 12, section 12.2, “Bulk and batch operations.”)
The persistence context cache never shrinks automatically. To reduce or
regain the memory consumed by the persistence context in a particular unit of
work, you have to do the following:
Keep the size of your persistence context to the necessary minimum.
Often, many persistent instances in your Session are there by accident—
for example, because you needed only a few but queried for many. Make
objects persistent only if you absolutely need them in this state; extremely
large graphs can have a serious performance impact and require significant memory for state snapshots. Check that your queries return only
objects you need. As you’ll see later in the book, you can also execute a
query in Hibernate that returns objects in read-only state, without creating
a persistence context snapshot.
You can call session.evict(object) to detach a persistent instance manually from the persistence context cache. You can call session.clear() to
detach all persistent instances from the persistence context. Detached
objects aren’t checked for dirty state; they aren’t managed.
With session.setReadOnly(object, true), you can disable dirty checking
for a particular instance. The persistence context will no longer maintain
the snapshot if it’s read-only. With session.setReadOnly(object, false),
you can re-enable dirty checking for an instance and force the recreation of
a snapshot. Note that these operations don’t change the object’s state.
At the end of a unit of work, all the modifications you made have to be synchronized with the database through SQL DML statements. This process is called flushing of the persistence context.
Flushing the persistence context
The Hibernate Session implements write-behind. Changes to persistent objects
made in the scope of a persistence context aren’t immediately propagated to the
database. This allows Hibernate to coalesce many changes into a minimal number
of database requests, helping minimize the impact of network latency. Another
excellent side-effect of executing DML as late as possible, toward the end of the
transaction, is shorter lock durations inside the database.
Working with objects
For example, if a single property of an object is changed twice in the same
persistence context, Hibernate needs to execute only one SQL UPDATE. Another
example of the usefulness of write-behind is that Hibernate is able to take
advantage of the JDBC batch API when executing multiple UPDATE, INSERT, or
DELETE statements.
The synchronization of a persistence context with the database is called flushing. Hibernate flushes occur at the following times:
When a Transaction on the Hibernate API is committed
Before a query is executed
When the application calls session.flush() explicitly
Flushing the Session state to the database at the end of a unit of work is required
in order to make the changes durable and is the common case. Note that automatic flushing when a transaction is committed is a feature of the Hibernate API!
Committing a transaction with the JDBC API doesn’t trigger a flush. Hibernate
doesn’t flush before every query. If changes are held in memory that would affect
the results of the query, Hibernate synchronizes first by default.
You can control this behavior by explicitly setting the Hibernate FlushMode via
a call to session.setFlushMode(). The default flush mode is FlushMode.AUTO
and enables the behavior described previously. If you chose FlushMode.COMMIT,
the persistence context isn’t flushed before query execution (it’s flushed only
when you call Transaction.commit() or Session.flush() manually). This setting may expose you to stale data: Modifications you make to managed objects
only in memory may conflict with the results of the query. By selecting FlushMode.MANUAL, you may specify that only explicit calls to flush() result in synchronization of managed state with the database.
Controlling the FlushMode of a persistence context will be necessary later in
the book, when we extend the context to span a conversation.
Repeated flushing of the persistence context is often a source for performance
issues, because all dirty objects in the persistence context have to be detected at
flush-time. A common cause is a particular unit-of-work pattern that repeats a
query-modify-query-modify sequence many times. Every modification leads to a
flush and a dirty check of all persistent objects, before each query. A FlushMode.COMMIT may be appropriate in this situation.
Always remember that the performance of the flush process depends in part
on the size of the persistence context—the number of persistent objects it manages. Hence, the advice we gave for managing the persistence context, in the previous section, also applies here.
The Java Persistence API
You’ve now seen the most important strategies and some optional ones for
interacting with objects in a Hibernate application and what methods and operations are available on a Hibernate Session. If you plan to work only with Hibernate APIs, you can skip the next section and go directly to the next chapter and
read about transactions. If you want to work on your objects with Java Persistence
and/or EJB 3.0 components, read on.
The Java Persistence API
We now store and load objects with the Java Persistence API. This is the API you
use either in a Java SE application or with EJB 3.0 components, as a vendor-independent alternative to the Hibernate native interfaces.
You’ve read the first sections of this chapter and know the object states defined
by JPA and how they’re related to Hibernate’s. Because the two are similar, the
first part of this chapter applies no matter what API you’ll choose. It follows that
the way you interact with your objects, and how you manipulate the database, are
also similar. So, we also assume that you have learned the Hibernate interfaces in
the previous section (you also miss all the illustrations if you skip the previous section; we won’t repeat them here). This is important for another reason: JPA provides a subset of functionality of the superset of Hibernate native APIs. In other
words, there are good reasons to fall back to native Hibernate interfaces whenever
you need to. You can expect that the majority of the functionality you’ll need in
an application is covered by the standard, and that this is rarely necessary.
As in Hibernate, you store and load objects with JPA by manipulating the current state of an object. And, just as in Hibernate, you do this in a unit of work, a
set of operations considered to be atomic. (We still haven’t covered enough
ground to explain all about transactions, but we will soon.)
To begin a unit of work in a Java Persistence application, you need to get an
EntityManager (the equivalent to the Hibernate Session). However, where you
open a Session from a SessionFactory in a Hibernate application, a Java Persistence application can be written with managed and unmanaged units of work. Let’s
keep this simple, and assume that you first want to write a JPA application that
doesn’t benefit from EJB 3.0 components in a managed environment.
Storing and loading objects
The term unmanaged refers to the possibility to create a persistence layer with Java
Persistence that runs and works without any special runtime environment. You
can use JPA without an application server, outside of any runtime container, in a
Working with objects
plain Java SE application. This can be a servlet application (the web container
doesn’t provide anything you’d need for persistence) or a simple main() method.
Another common case is local persistence for desktop applications, or persistence
for two-tiered systems, where a desktop application accesses a remote database tier
(although there is no good reason why you can’t use a lightweight modular application server with EJB 3.0 support in such a scenario).
Beginning a unit of work in Java SE
In any case, because you don’t have a container that could provide an EntityManager for you, you need to create one manually. The equivalent of the Hibernate
SessionFactory is the JPA EntityManagerFactory:
EntityManagerFactory emf =
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
The first line of code is part of your system configuration. You should create one
EntityManagerFactory for each persistence unit you deploy in a Java Persistence
application. We covered this already in chapter 2, section 2.2.2, “Using Hibernate
EntityManager,” so we won’t repeat it here. The next three lines are equivalent to
how you’d begin a unit of work in a stand-alone Hibernate application: First, an
EntityManager is created, and then a transaction is started. To familiarize yourself
with EJB 3.0 jargon, you can call this EntityManager application-managed. The transaction you started here also has a special description: It’s a resource-local transaction.
You’re controlling the resources involved (the database in this case) directly in
your application code; no runtime container takes care of this for you.
The EntityManager has a fresh persistence context assigned when it’s created.
In this context, you store and load objects.
Making an entity instance persistent
An entity class is the same as one of your Hibernate persistent classes. Of course,
you’d usually prefer annotations to map your entity classes, as a replacement of
Hibernate XML mapping files. After all, the (primary) reason you’re using Java
Persistence is the benefit of standardized interfaces and mappings.
Let’s create a new instance of an entity and bring it from transient into persistent state:
Item item = new Item();
item.setName("Playstation3 incl. all accessories");
item.setEndDate( ... );
The Java Persistence API
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
This code should look familiar if you’ve followed the earlier sections of this chapter. The transient item entity instance becomes persistent as soon as you call persist() on it; it’s now managed in the persistence context. Note that persist()
doesn’t return the database identifier value of the entity instance (this little difference, compared to Hibernate’s save() method, will be important again when you
implement conversations in “Delaying insertion until flush-time” in chapter 11,
section 11.2.3.
Should I use persist() on the Session? The Hibernate Session interface also
features a persist() method. It has the same semantics as the persist()
operation of JPA. However, there’s an important difference between the
two operations with regard to flushing. During synchronization, a Hibernate Session doesn’t cascade the persist() operation to associated entities and collections, even if you mapped an association with this option.
It’s only cascaded to entities that are reachable when you call persist()!
Only save() (and update()) are cascaded at flush-time if you use the
Session API. In a JPA application, however, it’s the other way round:
Only persist() is cascaded at flush-time.
Managed entity instances are monitored. Every modification you make to an
instance in persistent state is at some point synchronized with the database
(unless you abort the unit of work). Because the EntityTransaction is managed
by the application, you need to do the commit() manually. The same rule applies
to the application-controlled EntityManager: You need to release all resources by
closing it.
Retrieving an entity instance
The EntityManager is also used to query the database and retrieve persistent
entity instances. Java Persistence supports sophisticated query features (which
we’ll cover later in the book). The most basic is as always the retrieval by identifier:
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
Working with objects
You don’t need to cast the returned value of the find() operation; it’s a generic
method. and its return type is set as a side effect of the first parameter. This is a
minor but convenient benefit of the Java Persistence API—Hibernate native methods have to work with older JDKs that don’t support generics.
The retrieved entity instance is in a persistent state and can now be modified
inside the unit of work or be detached for use outside of the persistence context.
If no persistent instance with the given identifier can be found, find() returns
null. The find() operation always hits the database (or a vendor-specific transparent cache), so the entity instance is always initialized during loading. You can
expect to have all of its values available later in detached state.
If you don’t want to hit the database, because you aren’t sure you’ll need a fully
initialized instance, you can tell the EntityManager to attempt the retrieval of a
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.getReference(Item.class, new Long(1234));
This operation returns either the fully initialized item (for example, if the
instance was already available in the current persistence context) or a proxy (a hollow placeholder).
As soon as you try to access any property of the item that isn’t the database
identifier property, an additional SELECT is executed to fully initialize the placeholder. This also means you should expect an EntityNotFoundException at this
point (or even earlier, when getReference() is executed). A logical conclusion is
that if you decide to detach the item reference, no guarantees are made that it
will be fully initialized (unless, of course, you access one of its nonidentifier properties before detachment).
Modifying a persistent entity instance
An entity instance in persistent state is managed by the current persistence context. You can modify it and expect that the persistence context flushes the necessary SQL DML at synchronization time. This is the same automatic dirty checking
feature provided by the Hibernate Session:
The Java Persistence API
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
A persistent entity instance is retrieved by its identifier value. Then, you modify
one of its mapped properties (a property that hasn’t been annotated with @Transient or the transient Java keyword). In this code example, the next synchronization with the database occurs when the resource-local transaction is committed.
The Java Persistence engine executes the necessary DML, in this case an UDPATE.
Making a persistent entity instance transient
If you want to remove the state of an entity instance from the database, you have
to make it transient. Use the remove() method on your EntityManager:
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
The semantics of the remove() Java Persistence method are the same as the
delete() method’s on the Hibernate Session. The previously persistent object is
now in removed state, and you should discard any reference you’re holding to it in
the application. An SQL DELETE is executed during the next synchronization of
the persistence context. The JVM garbage collector detects that the item is no
longer referenced by anyone and finally deletes the last trace of the object. However, note that you can’t call remove() on an entity instance in detached state, or
an exception will be thrown. You have to merge the detached instance first and
then remove the merged object (or, alternatively, get a reference with the same
identifier, and remove that).
Flushing the persistence context
All modifications made to persistent entity instances are synchronized with the
database at some point, a process called flushing. This write-behind behavior is the
Working with objects
same as Hibernate’s and guarantees the best scalability by executing SQL DML as
late as possible.
The persistence context of an EntityManager is flushed whenever commit()
on an EntityTransaction is called. All the previous code examples in this section
of the chapter have been using that strategy. However, JPA implementations are
allowed to synchronize the persistence context at other times, if they wish.
Hibernate, as a JPA implementation, synchronizes at the following times:
When an EntityTransaction is committed
Before a query is executed
When the application calls em.flush() explicitly
These are the same rules we explained for native Hibernate in the previous section. And as in native Hibernate, you can control this behavior with a JPA interface, the FlushModeType:
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
List result = em.createQuery(...).getResultList();
Switching the FlushModeType to COMMIT for an EntityManager disables automatic
synchronization before queries; it occurs only when the transaction is committed
or when you flush manually. The default FlushModeType is AUTO.
Just as with native Hibernate, controlling the synchronization behavior of a
persistence context will be important functionality for the implementation of conversations, which we’ll attack later.
You now know the basic operations of Java Persistence, and you can go ahead
and store and load some entity instances in your own application. Set up your system as described in chapter 2, section 2.2, “Starting a Java Persistence project,”
and map some classes to your database schema with annotations. Write a main()
method that uses an EntityManager and an EntityTransaction; we think you’ll
soon see how easy it is to use Java Persistence even without EJB 3.0 managed components or an application server.
Let’s discuss how you work with detached entity instances.
The Java Persistence API
Working with detached entity instances
We assume you already know how a detached object is defined (if you don’t, read
the first section of this chapter again). You don’t necessarily have to know how
you’d work with detached objects in Hibernate, but we’ll refer you to earlier sections if a strategy with Java Persistence is the same as in native Hibernate.
First, let’s see again how entity instances become detached in a Java Persistence
JPA persistence context scope
You’ve used Java Persistence in a Java SE environment, with application-managed
persistence contexts and transactions. Every persistent and managed entity
instance becomes detached when the persistence context is closed. But wait—we
didn’t tell you when the persistence context is closed.
If you’re familiar with native Hibernate, you already know the answer: The persistence context ends when the Session is closed. The EntityManager is the
equivalent in JPA; and by default, if you created the EntityManager yourself, the
persistence context is scoped to the lifecycle of that EntityManager instance.
Look at the following code:
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
User user = em.find(User.class, new Long(3456));
In the first transaction, you retrieve an Item object. The transaction then completes, but the item is still in persistent state. Hence, in the second transaction,
you not only load a User object, but also update the modified persistent item
when the second transaction is committed (in addition to an update for the dirty
user instance).
Just like in native Hibernate code with a Session, the persistence context
begins with createEntityManager() and ends with close().
Closing the persistence context isn’t the only way to detach an entity instance.
Working with objects
Manual detachment of entity instances
An entity instance becomes detached when it leaves the persistence context. A
method on the EntityManager allows you to clear the persistence context and
detach all persistent instances:
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
item.setDescription(...); // Detached entity instance!
After the item is retrieved, you clear the persistence context of the EntityManager. All entity instances that have been managed by that persistence context are
now detached. The modification of the detached instance isn’t synchronized with
the database during commit.
Where is eviction of individual instances? The Hibernate Session API features the evict(object) method. Java Persistence doesn’t have this capability. The reason is probably only known by some expert group
members—we can’t explain it. (Note that this is a nice way of saying that
experts couldn’t agree on the semantics of the operation.) You can only
clear the persistence context completely and detach all persistent objects.
You have to fall back to the Session API as described in chapter 2, section 2.2.4, “Switching to Hibernate interfaces,” if you want to evict individual instances from the persistence context.
Obviously you also want to save any modifications you made to a detached entity
instance at some point.
Merging detached entity instances
Whereas Hibernate offers two strategies, reattachment and merging, to synchronize
any changes of detached objects with the database, Java Persistence only offers the
latter. Let’s assume you’ve retrieved an item entity instance in a previous persistence context, and now you want to modify it and save these modifications.
EntityManager em = emf.createEntityManager();
EntityTransaction tx = em.getTransaction();
Item item = em.find(Item.class, new Long(1234));
The Java Persistence API
item.setDescription(...); // Detached entity instance!
EntityManager em2 = emf.createEntityManager();
EntityTransaction tx2 = em2.getTransaction();
Item mergedItem = (Item) em2.merge(item);
The item is retrieved in a first persistence context and merged, after modification
in detached state, into a new persistence context. The merge() operation does
several things:
First, the Java Persistence engine checks whether a persistent instance in the
persistence context has the same database identifier as the detached instance
you’re merging. Because, in our code examples, there is no equal persistent
instance in the second persistence context, one is retrieved from the database
through lookup by identifier. Then, the detached entity instance is copied onto the
persistent instance. In other words, the new description that has been set on the
detached item is also set on the persistent mergedItem, which is returned from
the merge() operation.
If there is no equal persistent instance in the persistence context, and a lookup
by identifier in the database is negative, the merged instance is copied onto a
fresh persistent instance, which is then inserted into the database when the second persistence context is synchronized with the database.
Merging of state is an alternative to reattachment (as provided by native Hibernate). Refer to our earlier discussion of merging with Hibernate in section 9.3.2,
“Merging the state of a detached object”; both APIs offer the same semantics, and
the notes there apply for JPA mutatis mutandis.
You’re now ready to expand your Java SE application and experiment with the
persistence context and detached objects in Java Persistence. Instead of only storing and loading entity instances in a single unit of work, try to use several and try
to merge modifications of detached objects. Don’t forget to watch your SQL log to
see what’s going on behind the scenes.
Once you’ve mastered basic Java Persistence operations with Java SE, you’ll
probably want to do the same in a managed environment. The benefits you get
from JPA in a full EJB 3.0 container are substantial. No longer do you have to manage EntityManager and EntityTransaction yourself. You can focus on what
you’re supposed to do: load and store objects.
Working with objects
Using Java Persistence in EJB components
A managed runtime environment implies some sort of container. Your application
components live inside this container. Most containers these days are implemented using an interception technique, method calls on objects are intercepted
and any code that needs to be executed before (or after) the method is applied.
This is perfect for any cross-cutting concerns: Opening and closing an EntityManager, because you need it inside the method that is called, is certainly one. Your
business logic doesn’t need to be concerned with this aspect. Transaction demarcation is another concern a container can take care of for you. (You’ll likely find
other aspects in any application.)
Unlike older application servers from the EJB 2.x era, containers that support
EJB 3.0 and other Java EE 5.0 services are easy to install and use—refer to our discussion in chapter 2, section 2.2.3, “Introducing EJB components,” to prepare
your system for the following section. Furthermore, the EJB 3.0 programming
model is based on plain Java classes. You shouldn’t be surprised if you see us writing many EJBs in this book; most of the time, the only difference from a plain JavaBean is a simple annotation, a declaration that you wish to use a service provided
by the environment the component will run in. If you can’t modify the source
code and add an annotation, you can turn a class into an EJB with an XML deployment descriptor. Hence, (almost) every class can be a managed component in EJB
3.0, which makes it much easier for you to benefit from Java EE 5.0 services.
The entity classes you’ve created so far aren’t enough to write an application.
You also want stateless or stateful session beans, components that you can use to
encapsulate your application logic. Inside these components, you need the services of the container: for example, you usually want the container to inject an
EntityManager, so that you can load and store entity instances.
Injecting an EntityManager
Remember how you create an instance of an EntityManager in Java SE? You have
to open it from an EntityManagerFactory and close it manually. You also have to
begin and end a resource-local transaction with the EntityTransaction interface.
In an EJB 3.0 server, a container-managed EntityManager is available through
dependency injection. Consider the following EJB session bean that implements a
particular action in the CaveatEmptor application:
public class ManageAuctionBean implements ManageAuction {
// Use field injection:
Using Java Persistence in EJB components
private EntityManager em;
or setter injection:
public void setEntityManager(EntityManager em) {
this.em = em;
public Item findAuctionByName(String name) {
return (Item) em.createQuery()...
It’s a stateless action, and it implements the ManageAuction interface. These
details of stateless EJBs aren’t our concern at this time, what is interesting is that
you can access the EntityManager in the findAuctionByName() method of the
action. The container automatically injects an instance of an EntityManager into
the em field of the bean, before the action method executes. The visibility of the
field isn’t important for the container, but you need to apply the @PersistenceContext annotation to indicate that you want the container’s service. You could
also create a public setter method for this field and apply the annotation on this
method. This is the recommended approach if you also plan to set the EntityManager manually—for example, during integration or functional testing.
The injected EntityManager is maintained by the container. You don’t have to
flush or close it, nor do you have to start and end a transaction—in the previous
example you tell the container that the findAuctionByName() method of the session bean requires a transaction. (This is the default for all EJB session bean methods.) A transaction must be active when the method is called by a client (or a
new transaction is started automatically). When the method returns, the transaction either continues or is committed, depending on whether it was started for
this method.
The persistence context of the injected container-managed EntityManager is
bound to the scope of the transaction, Hence, it’s flushed automatically and
closed when the transaction ends. This is an important difference, if you compare
it with earlier examples that showed JPA in Java SE! The persistence context there
wasn’t scoped to the transaction but to the EntityManager instance you closed
explicitly. The transaction-scoped persistence context is the natural default for a
stateless bean, as you’ll see when you focus on conversation implementation and
transactions later, in the following chapters.
Working with objects
A nice trick that obviously works only with JBoss EJB 3.0 is the automatic injection of a Session object, instead of an EntityManager:
public class ManageAuctionBean implements ManageAuction {
private Session session;
This is mostly useful if you have a managed component that would rely on the
Hibernate API.
Here is a variation that works with two databases—that is, two persistence units:
public class ManageAuctionBean implements ManageAuction {
@PersistenceContext(unitName = "auctionDB")
private EntityManager auctionEM;
@PersistenceContext(unitName = "auditDB")
private EntityManager auditEM;
public void createAuction(String name, BigDecimal price) {
Item newItem = new Item(name, price);
auditEM.persist( new CreateAuctionEvent(newItem) );
The unitName refers to the configured and deployed persistence unit. If you work
with one database (one EntityManagerFactory or one SessionFactory), you
don’t need to declare the name of the persistence unit for injection. Note that
EntityManager instances from two different persistence units aren’t sharing the
same persistence context. Naturally, both are independent caches of managed
entity objects, but that doesn’t mean they can’t participate in the same system
If you write EJBs with Java Persistence, the choice is clear: You want the
EntityManager with the right persistence context injected into your managed
components by the container. An alternative you’ll rarely use is the lookup of a
container-managed EntityManager.
Using Java Persistence in EJB components
Looking up an EntityManager
Instead of letting the container inject an EntityManager on your field or setter
method, you can look it up from JNDI when you need it:
@PersistenceContext(name = "em/auction", unitName = "auctionDB")
public class ManageAuctionBean implements ManageAuction {
SessionContext ctx;
public Item findAuctionByName(String name) {
EntityManager em = (EntityManager) ctx.lookup("em/auction");
return (Item) em.createQuery()...
Several things are happening in this code snippet: First, you declare that you want
the component environment of the bean populated with an EntityManager and
that the name of the bound reference is supposed to be em/auction. The full
name in JNDI is java:comp/env/em/auction—the java:comp/env/ part is the so
called bean-naming context. Everything in that subcontext of JNDI is bean-dependent. In other words, the EJB container reads this annotation and knows that it
has to bind an EntityManager for this bean only, at runtime when the bean executes, under the namespace in JNDI that is reserved for this bean.
You look up the EntityManager in your bean implementation with the help of
the SessionContext. The benefit of this context is that it automatically prefixes
the name you’re looking for with java:comp/env/; hence, it tries to find the reference in the bean’s naming context, and not the global JNDI namespace. The
@Resource annotation instructs the EJB container to inject the SessionContext
for you.
A persistence context is created by the container when the first method on the
EntityManager is called, and it’s flushed and closed when the transaction ends—
when the method returns.
Injection and lookup are also available if you need an EntityManagerFactory.
Accessing an EntityManagerFactory
An EJB container also allows you to access an EntityManagerFactory for a persistence unit directly. Without a managed environment, you have to create the
EntityManagerFactory with the help of the Persistence bootstrap class. In a
Working with objects
container, you can again utilize automatic dependency injection to get an
public class ManageAuctionBean implements ManageAuction {
@PersistenceUnit(unitName = "auctionDB")
EntityManagerFactory auctionDB;
public Item findAuctionByName(String name) {
EntityManager em = auctionDB.createEntityManager();
Item item = (Item) em.createQuery()...
return item;
The unitName attribute is optional and only required if you have more than one
configured persistence unit (several databases). The EntityManager you created
from the injected factory is again application-managed—the container won’t
flush this persistence context, nor close it. It’s rare that you mix container-managed factories with application-managed EntityManager instances, but doing so
is useful if you need more control over the lifecycle of an EntityManager in an
EJB component.
You may create an EntityManager outside of any JTA transaction boundaries;
for example, in an EJB method that doesn’t require a transaction context. It’s
then your responsibility to notify the EntityManager that a JTA transaction is
active, when needed, with the joinTransaction() method. Note that this operation doesn’t bind or scope the persistence context to the JTA transaction; it’s only
a hint that switches the EntityManager to transactional behavior internally.
The previous statements aren’t complete: If you close() the EntityManager, it
doesn’t immediately close its persistence context, if this persistence context has
been associated with a transaction. The persistence context is closed when the
transaction completes. However, any call of the closed EntityManager throws an
exception (except for the getTransaction() method in Java SE and the
isOpen() method). You can switch this behavior with the hibernate.
ejb.discard_ pc_on_close configuration setting. You don’t have to worry about
this if you never call the EntityManager outside of transaction boundaries.
Another reason for accessing your EntityManagerFactory may be that you
want to access a particular vendor extension on this interface, like we discussed in
chapter 2, section 2.2.4, “Switching to Hibernate interfaces.”
You can also look up an EntityManagerFactory if you bind it to the EJB’s naming context first:
@PersistenceUnit(name= "emf/auction", unitName = "auctionDB")
public class ManageAuctionBean implements ManageAuction {
SessionContext ctx;
public Item findAuctionByName(String name) {
EntityManagerFactory auctionDB =
(EntityManagerFactory) ctx.lookup("emf/auction");
EntityManager em = auctionDB.createEntityManager();
Item item = (Item) em.createQuery()...
return item;
Again, there is no particular advantage if you compare the lookup technique with
automatic injection.
We’ve covered a lot of ground in this chapter. You now know that the basic
interfaces in Java Persistence aren’t much different from those provided by Hibernate. Loading and storing objects is almost the same. The scope of the persistence
context is slightly different, though; in Hibernate, it’s by default the same as the
Session scope. In Java Persistence, the scope of the persistence context varies,
depending on whether you create an EntityManager yourself, or you let the container manage and bind it to the current transaction scope in an EJB component.
Table 9.1 shows a summary you can use to compare native Hibernate features
and Java Persistence.
We’ve already talked about conversations in an application and how you can
design them with detached objects or with an extended persistence context.
Although we haven’t had time to discuss every detail, you can probably already see
Working with objects
Table 9.1
Hibernate and JPA comparison chart for chapter 9
Hibernate Core
Java Persistence and EJB 3.0
Hibernate defines and relies on four object
states: transient, persistent, removed, and
Equivalent object states are standardized and
defined in EJB 3.0.
Detached objects can be reattached to a new
persistence context or merged onto persistent
Only merging is supported with the Java Persistence
management interfaces.
At flush-time, the save() and update()
operations can be cascaded to all associated
and reachable instances. The persist()
operation can only be cascaded to reachable
instances at call-time.
At flush-time, the persist()operation can be cascaded to all associated and reachable instances. If
you fall back to the Session API, save() and
update() are only cascaded to reachable
instances at call-time.
A get() hits the database; a load() may
return a proxy.
A find() hits the database; a getReference()
may return a proxy.
Dependency injection of a Session in an EJB
works only in JBoss Application Server.
Dependency injection of an EntityManager
works in all EJB 3.0 components.
that working with detached objects requires discipline (outside of the guaranteed
scope of object identity) and manual reattachment or merging. In practice, and
from our experience over the years, we recommend that you consider detached
objects a secondary option and that you first look at an implementation of conversations with an extended persistence context.
Unfortunately, we still don’t have all the pieces to write a really sophisticated
application with conversations. You may especially miss more information about
transactions. The next chapter covers transaction concepts and interfaces.
and concurrency
This chapter covers
Database transactions
Transactions with Hibernate and Java
Nontransactional data access
Transactions and concurrency
In this chapter, we finally talk about transactions and how you create and control
units of work in a application. We’ll show you how transactions work at the lowest level (the database) and how you work with transactions in an application
that is based on native Hibernate, on Java Persistence, and with or without
Enterprise JavaBeans.
Transactions allow you to set the boundaries of a unit of work: an atomic
group of operations. They also help you isolate one unit of work from another
unit of work in a multiuser application. We talk about concurrency and how you
can control concurrent data access in your application with pessimistic and optimistic strategies.
Finally, we look at nontransactional data access and when you should work
with your database in autocommit mode.
10.1 Transaction essentials
Let’s start with some background information. Application functionality requires
that several things be done at the same time. For example, when an auction finishes, three different tasks have to be performed by the CaveatEmptor application:
Mark the winning (highest amount) bid.
Charge the seller the cost of the auction.
Notify the seller and successful bidder.
What happens if you can’t bill the auction costs because of a failure in the external credit-card system? The business requirements may state that either all listed
actions must succeed or none must succeed. If so, you call these steps collectively
a transaction or unit of work. If only one step fails, the whole unit of work must fail.
This is known as atomicity, the notion that all operations are executed as an
atomic unit.
Furthermore, transactions allow multiple users to work concurrently with the
same data without compromising the integrity and correctness of the data; a particular transaction should not be visible to other concurrently running transactions. Several strategies are important to fully understand this isolation behavior,
and we’ll explore them in this chapter.
Transactions have other important attributes, such as consistency and durability.
Consistency means that a transaction works on a consistent set of data: a set of
data that is hidden from other concurrently running transactions and that is left
in a clean and consistent state after the transactions completes. Your database
integrity rules guarantee consistency. You also want correctness of a transaction. For
Transaction essentials
example, the business rules dictate that the seller is charged once, not twice. This
is a reasonable assumption, but you may not be able to express it with database
constraints. Hence, the correctness of a transaction is the responsibility of the
application, whereas consistency is the responsibility of the database. Durability
means that once a transaction completes, all changes made during that transaction become persistent and aren’t lost even if the system subsequently fails.
Together, these transaction attributes are known as the ACID criteria.
Database transactions have to be short. A single transaction usually involves
only a single batch of database operations. In practice, you also need a concept
that allows you to have long-running conversations, where an atomic group of database operations occur in not one but several batches. Conversations allow the user
of your application to have think-time, while still guaranteeing atomic, isolated,
and consistent behavior.
Now that we’ve defined our terms, we can talk about transaction demarcation
and how you can define the boundaries of a unit of work.
10.1.1 Database and system transactions
Databases implement the notion of a unit of work as a database transaction. A database transaction groups data-access operations—that is, SQL operations. All SQL
statements execute inside a transaction; there is no way to send an SQL statement
to a database outside of a database transaction. A transaction is guaranteed to end
in one of two ways: It’s either completely committed or completely rolled back. Hence,
we say database transactions are atomic. In figure 10.1, you can see this graphically.
To execute all your database operations inside a transaction, you have to mark
the boundaries of that unit of work. You must start the transaction and at some
point, commit the changes. If an error occurs (either while executing operations
or when committing the transaction), you have to roll back the transaction to
leave the data in a consistent state. This is known as transaction demarcation and,
depending on the technique you use, involves more or less manual intervention.
Figure 10.1
Lifecycle of an atomic unit of
work—a transaction
Transactions and concurrency
In general, transaction boundaries that begin and end a transaction can be set
either programmatically in application code or declaratively.
Programmatic transaction demarcation
In a nonmanaged environment, the JDBC API is used to mark transaction boundaries. You begin a transaction by calling setAutoCommit(false) on a JDBC Connection and end it by calling commit(). You may, at any time, force an immediate
rollback by calling rollback().
In a system that manipulates data in several databases, a particular unit of
work involves access to more than one resource. In this case, you can’t achieve
atomicity with JDBC alone. You need a transaction manager that can handle several
resources in one system transaction. Such transaction-processing systems expose
the Java Transaction API (JTA) for interaction with the developer. The main API in
JTA is the UserTransaction interface with methods to begin() and commit() a
system transaction.
Furthermore, programmatic transaction management in a Hibernate application is exposed to the application developer via the Hibernate Transaction interface. You aren’t forced to use this API—Hibernate also lets you begin and end
JDBC transactions directly, but this usage is discouraged because it binds your
code to direct JDBC. In a Java EE environment (or if you installed it along with
your Java SE application), a JTA-compatible transaction manager is available, so
you should call the JTA UserTransaction interface to begin and end a transaction
programmatically. However, the Hibernate Transaction interface, as you may
have guessed, also works on top of JTA. We’ll show you all these options and discuss portability concerns in more detail.
Programmatic transaction demarcation with Java Persistence also has to work
inside and outside of a Java EE application server. Outside of an application
server, with plain Java SE, you’re dealing with resource-local transactions; this is
what the EntityTransaction interface is good for—you’ve seen it in previous
chapters. Inside an application server, you call the JTA UserTransaction interface
to begin and end a transaction.
Let’s summarize these interfaces and when they’re used:
java.sql.Connection—Plain JDBC transaction demarcation with setAutoCommit(false), commit(), and rollback(). It can but shouldn’t be
used in a Hibernate application, because it binds your application to a plain
JDBC environment.
Transaction essentials
org.hibernate.Transaction—Unified transaction demarcation in Hibernate applications. It works in a nonmanaged plain JDBC environment and
also in an application server with JTA as the underlying system transaction
service. The main benefit, however, is tight integration with persistence context management—for example, a Session is flushed automatically when
you commit. A persistence context can also have the scope of this transaction (useful for conversations; see the next chapter). Use this API in Java SE
if you can’t have a JTA-compatible transaction service.
javax.transaction.UserTransaction—Standardized interface for programmatic transaction control in Java; part of JTA. This should be your primary choice whenever you have a JTA-compatible transaction service and
want to control transactions programmatically.
javax.persistence.EntityTransaction—Standardized interface for pro-
grammatic transaction control in Java SE applications that use Java Persistence.
Declarative transaction demarcation, on the other hand, doesn’t require extra
coding; and by definition, it solves the problem of portability.
Declarative transaction demarcation
In your application, you declare (for example, with annotations on methods)
when you wish to work inside a transaction. It’s then the responsibility of the
application deployer and the runtime environment to handle this concern. The
standard container that provides declarative transaction services in Java is an EJB
container, and the service is also called container-managed transactions (CMT). We’ll
again write EJB session beans to show how both Hibernate and Java Persistence
can benefit from this service.
Before you decide on a particular API, or for declarative transaction demarcation, let’s explore these options step by step. First, we assume you’re going to use
native Hibernate in a plain Java SE application (a client/server web application,
desktop application, or any two-tier system). After that, you’ll refactor the code to
run in a managed Java EE environment (and see how to avoid that refactoring in
the first place). We also discuss Java Persistence along the way.
10.1.2 Transactions in a Hibernate application
Imagine that you’re writing a Hibernate application that has to run in plain Java;
no container and no managed database resources are available.
Transactions and concurrency
Programmatic transactions in Java SE
You configure Hibernate to create a JDBC connection pool for you, as you did in
“The database connection pool” in chapter 2, section 2.1.3. In addition to the
connection pool, no additional configuration settings are necessary if you’re writing a Java SE Hibernate application with the Transaction API:
The hibernate.transaction.factory_class option defaults to org.hibernate.transaction.JDBCTransactionFactory, which is the correct factory
for the Transaction API in Java SE and for direct JDBC.
You can extend and customize the Transaction interface with your own
implementation of a TransactionFactory. This is rarely necessary but has
some interesting use cases. For example, if you have to write an audit log
whenever a transaction is started, you can add this logging to a custom
Transaction implementation.
Hibernate obtains a JDBC connection for each Session you’re going to work with:
Session session = null;
Transaction tx = null;
try {
session = sessionFactory.openSession();
tx = session.beginTransaction();
} catch (RuntimeException ex) {
} finally {
A Hibernate Session is lazy. This is a good thing—it means it doesn’t consume
any resources unless they’re absolutely needed. A JDBC Connection from the connection pool is obtained only when the database transaction begins. The call to
beginTransaction() translates into setAutoCommit(false) on the fresh JDBC
Connection. The Session is now bound to this database connection, and all SQL
statements (in this case, all SQL required to conclude the auction) are sent on this
connection. All database statements execute inside the same database transaction.
(We assume that the concludeAuction() method calls the given Session to
access the database.)
We already talked about write-behind behavior, so you know that the bulk of SQL
statements are executed as late as possible, when the persistence context of the
Transaction essentials
Session is flushed. This happens when you call commit() on the Transaction, by
default. After you commit the transaction (or roll it back), the database connection is released and unbound from the Session. Beginning a new transaction with
the same Session obtains another connection from the pool.
Closing the Session releases all other resources (for example, the persistence
context); all managed persistent instances are now considered detached.
Is it faster to roll back read-only transactions? If code in a transaction reads
data but doesn’t modify it, should you roll back the transaction instead of
committing it? Would this be faster? Apparently, some developers found
this to be faster in some special circumstances, and this belief has spread
through the community. We tested this with the more popular database
systems and found no difference. We also failed to discover any source of
real numbers showing a performance difference. There is also no reason
why a database system should have a suboptimal implementation—why it
should not use the fastest transaction cleanup algorithm internally.
Always commit your transaction and roll back if the commit fails. Having
said that, the SQL standard includes a SET TRANSACTION READ ONLY statement. Hibernate doesn’t support an API that enables this setting,
although you could implement your own custom Transaction and
TransactionFactory to add this operation. We recommend you first
investigate if this is supported by your database and what the possible performance benefits, if any, will be.
We need to discuss exception handling at this point.
Handling exceptions
If concludeAuction() as shown in the last example (or flushing of the persistence
context during commit) throws an exception, you must force the transaction to
roll back by calling tx.rollback(). This rolls back the transaction immediately,
so no SQL operation you sent to the database has any permanent effect.
This seems straightforward, although you can probably already see that catching RuntimeException whenever you want to access the database won’t result in
nice code.
A history of exceptions—Exceptions and how they should be handled always
end in heated debates between Java developers. It isn’t surprising that
Hibernate has some noteworthy history as well. Until Hibernate 3.x, all
exceptions thrown by Hibernate were checked exceptions, so every Hibernate API forced the developer to catch and handle exceptions. This strategy was influenced by JDBC, which also throws only checked exceptions.
However, it soon became clear that this doesn’t make sense, because all
Transactions and concurrency
exceptions thrown by Hibernate are fatal. In many cases, the best a
developer can do in this situation is to clean up, display an error message,
and exit the application. Therefore, starting with Hibernate 3.x, all
exceptions thrown by Hibernate are subtypes of the unchecked RuntimeException, which is usually handled in a single location in an application.
This also makes any Hibernate template or wrapper API obsolete.
First, even though we admit that you wouldn’t write your application code with
dozens (or hundreds) of try/catch blocks, the example we showed isn’t complete. This is an example of the standard idiom for a Hibernate unit of work with
a database transaction that contains real exception handling:
Session session = null;
Transaction tx = null;
try {
session = sessionFactory.openSession();
tx = session.beginTransaction();
} catch (RuntimeException ex) {
try {
} catch (RuntimeException rbEx) {
log.error("Couldn’t roll back transaction", rbEx);
throw ex;
} finally {
Any Hibernate operation, including flushing the persistence context, can throw a
RuntimeException. Even rolling back a transaction can throw an exception! You
want to catch this exception and log it; otherwise, the original exception that led
to the rollback is swallowed.
An optional method call in the example is setTimeout(), which takes the
number of seconds a transaction is allowed to run. However, real monitored transactions aren’t available in a Java SE environment. The best Hibernate can do if
you run this code outside of an application server (that is, without a transaction
manager) is to set the number of seconds the driver will wait for a PreparedStatement to execute (Hibernate exclusively uses prepared statements). If the
limit is exceeded, an SQLException is thrown.
Transaction essentials
You don’t want to use this example as a template in your own application,
because you should hide the exception handling with generic infrastructure code.
You can, for example, write a single error handler for RuntimeException that
knows when and how to roll back a transaction. The same can be said about opening and closing a Session. We discuss this with more realistic examples later in
the next chapter and again in chapter 16, section 16.1.3, “The Open Session in
View pattern.”
Hibernate throws typed exceptions, all subtypes of RuntimeException that help
you identify errors:
The most common HibernateException is a generic error. You have to
either check the exception message or find out more about the cause by
calling getCause() on the exception.
A JDBCException is any exception thrown by Hibernate’s internal JDBC
layer. This kind of exception is always caused by a particular SQL statement,
and you can get the offending statement with getSQL(). The internal
exception thrown by the JDBC connection (the JDBC driver, actually) is
available with getSQLException() or getCause(), and the database- and
vendor-specific error code is available with getErrorCode().
Hibernate includes subtypes of JDBCException and an internal converter
that tries to translate the vendor-specific error code thrown by the database
driver into something more meaningful. The built-in converter can produce JDBCConnectionException, SQLGrammarException, LockAquisitionException, DataException, and ConstraintViolationException for the
most important database dialects supported by Hibernate. You can either
manipulate or enhance the dialect for your database, or plug in a SQLExceptionConverterFactory to customize this conversion.
Other RuntimeExceptions thrown by Hibernate should also abort a transaction. You should always make sure you catch RuntimeException, no matter
what you plan to do with any fine-grained exception-handling strategy.
You now know what exceptions you should catch and when to expect them. However, one question is probably on your mind: What should you do after you’ve
caught an exception?
All exceptions thrown by Hibernate are fatal. This means you have to roll back
the database transaction and close the current Session. You aren’t allowed to continue working with a Session that threw an exception.
Transactions and concurrency
Usually, you also have to exit the application after you close the Session following an exception, although there are some exceptions (for example, StaleObjectStateException) that naturally lead to a new attempt (possibly after
interacting with the application user again) in a new Session. Because these are
closely related to conversations and concurrency control, we’ll cover them later.
Can I use exceptions for validation? Some developers get excited once they
see how many fine-grained exception types Hibernate can throw. This
can lead you down the wrong path. For example, you may be tempted to
catch the ConstraintViolationException for validation purposes. If a
particular operation throws this exception, why not display a (customized
depending on the error code and text) failure message to application
users and let them correct the mistake? This strategy has two significant
disadvantages. First, throwing unchecked values against the database to
see what sticks isn’t the right strategy for a scalable application. You want
to implement at least some data-integrity validation in the application
layer. Second, all exceptions are fatal for your current unit of work. However, this isn’t how application users will interpret a validation error—
they expect to still be inside a unit of work. Coding around this mismatch
is awkward and difficult. Our recommendation is that you use the
fine-grained exception types to display better looking (fatal) error messages. Doing so helps you during development (no fatal exceptions
should occur in production, ideally) and also helps any customer-support
engineer who has to decide quickly if it’s an application error (constraint
violated, wrong SQL executed) or if the database system is under load
(locks couldn’t be acquired).
Programmatic transaction demarcation in Java SE with the Hibernate Transaction interface keeps your code portable. It can also run inside a managed environment, when a transaction manager handles the database resources.
Programmatic transactions with JTA
A managed runtime environment compatible with Java EE can manage resources
for you. In most cases, the resources that are managed are database connections,
but any resource that has an adaptor can integrate with a Java EE system (messaging or legacy systems, for example). Programmatic transaction demarcation on
those resources, if they’re transactional, is unified and exposed to the developer
with JTA; javax.transaction.UserTransaction is the primary interface to begin
and end transactions.
The common managed runtime environment is a Java EE application server.
Of course, application servers provide many more services, not only management
of resources. Many Java EE services are modular—installing an application server
Transaction essentials
isn’t the only way to get them. You can obtain a stand-alone JTA provider if managed resources are all you need. Open source stand-alone JTA providers include
JBoss Transactions (, ObjectWeb JOTM
(, and others. You can install such a JTA service along
with your Hibernate application (in Tomcat, for example). It will manage a pool
of database connections for you, provide JTA interfaces for transaction demarcation, and provide managed database connections through a JNDI registry.
The following are benefits of managed resources with JTA and reasons to use
this Java EE service:
A transaction-management service can unify all resources, no matter of
what type, and expose transaction control to you with a single standardized
API. This means that you can replace the Hibernate Transaction API and use
JTA directly everywhere. It’s then the responsibility of the application
deployer to install the application on (or with) a JTA-compatible runtime
environment. This strategy moves portability concerns where they belong;
the application relies on standardized Java EE interfaces, and the runtime
environment has to provide an implementation.
A Java EE transaction manager can enlist multiple resources in a single
transaction. If you work with several databases (or more than one resource),
you probably want a two-phase commit protocol to guarantee atomicity of a
transaction across resource boundaries. In such a scenario, Hibernate is
configured with several SessionFactorys, one for each database, and their
Sessions obtain managed database connections that all participate in the
same system transaction.
The quality of JTA implementations is usually higher compared to simple
JDBC connection pools. Application servers and stand-alone JTA providers
that are modules of application servers usually have had more testing in
high-end systems with a large transaction volume.
JTA providers don’t add unnecessary overhead at runtime (a common misconception). The simple case (a single JDBC database) is handled as efficiently as with plain JDBC transactions. The connection pool managed
behind a JTA service is probably much better software than a random connection pooling library you’d use with plain JDBC.
Let’s assume that you aren’t sold on JTA and that you want to continue using the
Hibernate Transaction API to keep your code runnable in Java SE and with managed Java EE services, without any code changes. To deploy the previous code
Transactions and concurrency
examples, which all call the Hibernate Transaction API, on a Java EE application
server, you need to switch the Hibernate configuration to JTA:
The hibernate.transaction.factory_class option must be set to org.
Hibernate needs to know the JTA implementation on which you’re deploying, for two reasons: First, different implementations may expose the JTA
UserTransaction, which Hibernate has to call internally now, under different names. Second, Hibernate has to hook into the synchronization process
of the JTA transaction manager to handle its caches. You have to set the
hibernate.transaction.manager_lookup_class option to configure
both: for example, to org.hibernate.transaction.JBossTransactionManagerLookup. Lookup classes for the most common JTA implementations
and application servers are packaged with Hibernate (and can be customized if needed). Check the Javadoc for the package.
Hibernate is no longer responsible for managing a JDBC connection pool;
it obtains managed database connections from the runtime container.
These connections are exposed by the JTA provider through JNDI, a global
registry. You must configure Hibernate with the right name for your database resources on JNDI, as you did in chapter 2, section 2.4.1, “Integration
with JTA.”
Now the same piece of code you wrote earlier for Java SE directly on top of JDBC
will work in a JTA environment with managed datasources:
Session session = null;
Transaction tx = null;
try {
session = sessionFactory.openSession();
tx = session.beginTransaction();
} catch (RuntimeException ex) {
try {
} catch (RuntimeException rbEx) {
log.error("Couldn't roll back transaction", rbEx);
throw ex;
Transaction essentials
} finally {
However, the database connection-handling is slightly different. Hibernate
obtains a managed database connection for each Session you’re using and,
again, tries to be as lazy as possible. Without JTA, Hibernate would hold on to a
particular database connection from the beginning until the end of the transaction. With a JTA configuration, Hibernate is even more aggressive: A connection is
obtained and used for only a single SQL statement and then is immediately
returned to the managed connection pool. The application server guarantees
that it will hand out the same connection during the same transaction, when it’s
needed again for another SQL statement. This aggressive connection-release
mode is Hibernate’s internal behavior, and it doesn’t make any difference for
your application and how you write code. (Hence, the code example is line-by-line
the same as the last one.)
A JTA system supports global transaction timeouts; it can monitor transactions.
So, setTimeout() now controls the global JTA timeout setting—equivalent to calling UserTransaction.setTransactionTimeout().
The Hibernate Transaction API guarantees portability with a simple change
of Hibernate configuration. If you want to move this responsibility to the application deployer, you should write your code against the standardized JTA interface,
instead. To make the following example a little more interesting, you’ll also work
with two databases (two SessionFactorys) inside the same system transaction:
UserTransaction utx = (UserTransaction) new InitialContext()
Session session1 = null;
Session session2 = null;
try {
session1 = auctionDatabase.openSession();
session2 = billingDatabase.openSession();
} catch (RuntimeException ex) {
try {
Transactions and concurrency
} catch (RuntimeException rbEx) {
log.error("Couldn't roll back transaction", rbEx);
throw ex;
} finally {
(Note that this code snippet can throw some other, checked exceptions, like a
NamingException from the JNDI lookup. You need to handle these accordingly.)
First, a handle on a JTA UserTransaction must be obtained from the JNDI registry. Then, you begin and end a transaction, and the (container-provided) database connections used by all Hibernate Sessions are enlisted in that transaction
automatically. Even if you aren’t using the Transaction API, you should still configure hibernate.transaction.factory_class and hibernate.transaction.
manager_lookup_class for JTA and your environment, so that Hibernate can
interact with the transaction system internally.
With default settings, it’s also your responsibility to flush() each Session
manually to synchronize it with the database (to execute all SQL DML). The
Hibernate Transaction API did this automatically for you. You also have to close
all Sessions manually. On the other hand, you can enable the hibernate.transaction.flush_before_completion and/or the hibernate.transaction.auto_
close_session configuration options and let Hibernate take care of this for you
again—flushing and closing is then part of the internal synchronization procedure of the transaction manager and occurs before (and after, respectively) the
JTA transaction ends. With these two settings enabled the code can be simplified
to the following:
UserTransaction utx = (UserTransaction) new InitialContext()
Session session1 = null;
Session session2 = null;
try {
session1 = auctionDatabase.openSession();
session2 = billingDatabase.openSession();
} catch (RuntimeException ex) {
Transaction essentials
try {
} catch (RuntimeException rbEx) {
log.error("Couldn't roll back transaction", rbEx);
throw ex;
The session1 and session2 persistence context is now flushed automatically during commit of the UserTransaction, and both are closed after the transaction
Our advice is to use JTA directly whenever possible. You should always try to
move the responsibility for portability outside of the application and, if you can,
require deployment in an environment that provides JTA.
Programmatic transaction demarcation requires application code written
against a transaction demarcation interface. A much nicer approach that avoids
any nonportable code spread throughout your application is declarative transaction demarcation.
Container-managed transactions
Declarative transaction demarcation implies that a container takes care of this
concern for you. You declare if and how you want your code to participate in a
transaction. The responsibility to provide a container that supports declarative
transaction demarcation is again where it belongs, with the application deployer.
CMT is a standard feature of Java EE and, in particular, EJB. The code we’ll
show you next is based on EJB 3.0 session beans ( Java EE only); you define transaction boundaries with annotations. Note that the actual data-access code doesn’t
change if you have to use the older EJB 2.1 session beans; however, you have to
write an EJB deployment descriptor in XML to create your transaction assembly—
this is optional in EJB 3.0.
(A stand-alone JTA implementation doesn’t provide container-managed and
declarative transactions. However, JBoss Application Server is available as a modular server with a minimal footprint, and it can provide only JTA and an EJB 3.0
container, if needed.)
Suppose that an EJB 3.0 session bean implements an action that ends an auction. The code you previously wrote with programmatic JTA transaction demarcation is moved into a stateless session bean:
public class ManageAuctionBean implements ManageAuction {
Transactions and concurrency
public void endAuction(Item item) {
Session session1 = auctionDatabase.openSession();
Session session2 = billingDatabase.openSession();
concludeAuction(session1, item);
billAuction(session2, item);
The container notices your declaration of a TransactionAttribute and applies it
to the endAuction() method. If no system transaction is running when the
method is called, a new transaction is started (it’s REQUIRED). Once the method
returns, and if the transaction was started when the method was called (and not
by anyone else), the transaction commits. The system transaction is automatically
rolled back if the code inside the method throws a RuntimeException.
We again show two SessionFactorys for two databases, for the sake of the
example. They could be assigned with a JNDI lookup (Hibernate can bind them
there at startup) or from an enhanced version of HibernateUtil. Both obtain
database connections that are enlisted with the same container-managed transaction. And, if the container’s transaction system and the resources support it, you
again get a two-phase commit protocol that ensures atomicity of the transaction
across databases.
You have to set some configuration options to enable CMT with Hibernate:
The hibernate.transaction.factory_class option must be set to org.
You need to set hibernate.transaction.manager_lookup_class to the
right lookup class for your application server.
Also note that all EJB session beans default to CMT, so if you want to disable CMT
and call the JTA UserTransaction directly in any session bean method, annotate
the EJB class with @TransactionManagement(TransactionManagementType.
BEAN). You’re then working with bean-managed transactions (BMT). Even if it may
work in most application servers, mixing CMT and BMT in a single bean isn’t
allowed by the Java EE specification.
The CMT code already looks much nicer than the programmatic transaction
demarcation. If you configure Hibernate to use CMT, it knows that it should flush
and close a Session that participates in a system transaction automatically. Furthermore, you’ll soon improve this code and even remove the two lines that open
a Hibernate Session.
Let’s look at transaction handling in a Java Persistence application.
Transaction essentials
10.1.3 Transactions with Java Persistence
With Java Persistence, you also have the design choice to make between programmatic transaction demarcation in application code or declarative transaction
demarcation handled automatically by the runtime container. Let’s investigate
the first option with plain Java SE and then repeat the examples with JTA and EJB
The description resource-local transaction applies to all transactions that are controlled by the application (programmatic) and that aren’t participating in a global
system transaction. They translate directly into the native transaction system of the
resource you’re dealing with. Because you’re working with JDBC databases, this
means a resource-local transaction translates into a JDBC database transaction.
Resource-local transactions in JPA are controlled with the EntityTransaction
API. This interface exists not for portability reasons, but to enable particular features of Java Persistence—for instance, flushing of the underlying persistence context when you commit a transaction.
You’ve seen the standard idiom of Java Persistence in Java SE many times. Here
it is again with exception handling:
EntityManager em = null;
EntityTransaction tx = null;
try {
em = emf.createEntityManager();
tx = em.getTransaction();
} catch (RuntimeException ex) {
try {
} catch (RuntimeException rbEx) {
log.error("Couldn't roll back transaction", rbEx);
throw ex;
} finally {
This pattern is close to its Hibernate equivalent, with the same implications: You
have to manually begin and end a database transaction, and you must guarantee
that the application-managed EntityManager is closed in a finally block.
(Although we often show code examples that don’t handle exceptions or are
wrapped in a try/catch block, this isn’t optional.)
Transactions and concurrency
Exceptions thrown by JPA are subtypes of RuntimeException. Any exception
invalidates the current persistence context, and you aren’t allowed to continue
working with the EntityManager once an exception has been thrown. Therefore,
all the strategies we discussed for Hibernate exception handling also apply to Java
Persistence exception handling. In addition, the following rules apply:
Any exception thrown by any method of the EntityManager interfaces triggers an automatic rollback of the current transaction.
Any exception thrown by any method of the javax.persistence.Query
interface triggers an automatic rollback of the current transaction, except
for NoResultException and NonUniqueResultException. So, the previous
code example that catches all exceptions also executes a rollback for these
Note that JPA doesn’t offer fine-grained SQL exception types. The most common
exception is javax.persistence.PersistenceException. All other exceptions
thrown are subtypes of PersistenceException, and you should consider them all
fatal except NoResultException and NonUniqueResultException. However, you
may call getCause() on any exception thrown by JPA and find a wrapped native
Hibernate exception, including the fine-grained SQL exception types.
If you use Java Persistence inside an application server or in an environment
that at least provides JTA (see our earlier discussions for Hibernate), you call the
JTA interfaces for programmatic transaction demarcation. The EntityTransaction interface is available only for resource-local transactions.
JTA transactions with Java Persistence
If your Java Persistence code is deployed in an environment where JTA is available,
and you want to use JTA system transactions, you need to call the JTA UserTransaction interface to control transaction boundaries programmatically:
UserTransaction utx = (UserTransaction) new InitialContext()
EntityManager em = null;
try {
em = emf.createEntityManager();
} catch (RuntimeException ex) {
try {
Transaction essentials
} catch (RuntimeException rbEx) {
log.error("Couldn't roll back transaction", rbEx);
throw ex;
} finally {
The persistence context of the EntityManager is scoped to the JTA transaction.
All SQL statements flushed by this EntityManager are executed inside the JTA
transaction on a database connection that is enlisted with the transaction. The
persistence context is flushed and closed automatically when the JTA transaction commits. You could use several EntityManagers to access several databases
in the same system transaction, just as you’d use several Sessions in a native
Hibernate application.
Note that the scope of the persistence context changed! It’s now scoped to the
JTA transaction, and any object that was in persistent state during the transaction
is considered detached once the transaction is committed.
The rules for exception handling are equivalent to those for resource-local
transactions. If you use JTA in EJBs, don’t forget to set @TransactionManagement(TransactionManagementType.BEAN) on the class to enable BMT.
You won’t often use Java Persistence with JTA and not also have an EJB container available. If you don’t deploy a stand-alone JTA implementation, a Java EE
5.0 application server will provide both. Instead of programmatic transaction
demarcation, you’ll probably utilize the declarative features of EJBs.
Java Persistence and CMT
Let’s refactor the ManageAuction EJB session bean from the earlier Hibernate-only examples to Java Persistence interfaces. You also let the container inject
an EntityManager:
public class ManageAuctionBean implements ManageAuction {
@PersistenceContext(unitName = "auctionDB")
private EntityManager auctionEM;
@PersistenceContext(unitName = "billingDB")
private EntityManager billingEM;
public void endAuction(Item item)
throws AuctionNotValidException {
concludeAuction(auctionEM, item);
Transactions and concurrency
billAuction(billingEM, item);
Again, what happens inside the concludeAuction() and billAuction() methods
isn’t relevant for this example; assume that they need the EntityManagers to
access the database. The TransactionAttribute for the endAuction() method
requires that all database access occurs inside a transaction. If no system transaction is active when endAuction() is called, a new transaction is started for this
method. If the method returns, and if the transaction was started for this method,
it’s committed. Each EntityManager has a persistence context that spans the
scope of the transaction and is flushed automatically when the transaction commits. The persistence context has the same scope as the endAuction() method, if
no transaction is active when the method is called.
Both persistence units are configured to deploy on JTA, so two managed database connections, one for each database, are enlisted inside the same transaction,
and atomicity is guaranteed by the transaction manager of the application server.
You declare that the endAuction() method may throw an AuctionNotValidException. This is a custom exception you write; before ending the auction, you
check if everything is correct (the end time of the auction has been reached, there
is a bid, and so on). This is a checked exception that is a subtype of java.lang.
Exception. An EJB container treats this as an application exception and doesn’t trigger any action if the EJB method throws this exception. However, the container
recognizes system exceptions, which by default are all unchecked RuntimeExceptions
that may be thrown by an EJB method. A system exception thrown by an EJB
method enforces an automatic rollback of the system transaction.
In other words, you don’t need to catch and rethrow any system exception
from your Java Persistence operations—let the container handle them. You have
two choices how you can roll back a transaction if an application exception is
thrown: First, you can catch it and call the JTA UserTransaction manually, and set
it to roll back. Or you can add an @ApplicationException(rollback = true)
annotation to the class of AuctionNotValidException—the container will then
recognize that you wish an automatic rollback whenever an EJB method throws
this application exception.
You’re now ready to use Java Persistence and Hibernate inside and outside of
an application server, with or without JTA, and in combination with EJBs and container-managed transactions. We’ve discussed (almost) all aspects of transaction
Controlling concurrent access
atomicity. Naturally, you probably still have questions about the isolation between
concurrently running transactions.
10.2 Controlling concurrent access
Databases (and other transactional systems) attempt to ensure transaction isolation,
meaning that, from the point of view of each concurrent transaction, it appears
that no other transactions are in progress. Traditionally, this has been implemented with locking. A transaction may place a lock on a particular item of data in
the database, temporarily preventing access to that item by other transactions.
Some modern databases such as Oracle and PostgreSQL implement transaction
isolation with multiversion concurrency control (MVCC) which is generally considered
more scalable. We’ll discuss isolation assuming a locking model; most of our
observations are also applicable to multiversion concurrency, however.
How databases implement concurrency control is of the utmost importance in
your Hibernate or Java Persistence application. Applications inherit the isolation
guarantees provided by the database management system. For example, Hibernate never locks anything in memory. If you consider the many years of experience that database vendors have with implementing concurrency control, you’ll
see the advantage of this approach. On the other hand, some features in Hibernate and Java Persistence (either because you use them or by design) can improve
the isolation guarantee beyond what is provided by the database.
We discuss concurrency control in several steps. We explore the lowest layer
and investigate the transaction isolation guarantees provided by the database.
Then, we look at Hibernate and Java Persistence features for pessimistic and optimistic concurrency control at the application level, and what other isolation guarantees Hibernate can provide.
10.2.1 Understanding database-level concurrency
Your job as a Hibernate application developer is to understand the capabilities of
your database and how to change the database isolation behavior if needed in
your particular scenario (and by your data integrity requirements). Let’s take a
step back. If we’re talking about isolation, you may assume that two things are
either isolated or not isolated; there is no grey area in the real world. When we
talk about database transactions, complete isolation comes at a high price. Several
isolation levels are available, which, naturally, weaken full isolation but increase performance and scalability of the system.
Transactions and concurrency
Transaction isolation issues
First, let’s look at several phenomena that may occur when you weaken full transaction isolation. The ANSI SQL standard defines the standard transaction isolation
levels in terms of which of these phenomena are permissible in a database management system:
A lost update occurs if two transactions both update a row and then the second
transaction aborts, causing both changes to be lost. This occurs in systems that
don’t implement locking. The concurrent transactions aren’t isolated. This is
shown in figure 10.2.
Figure 10.2
Lost update: two transactions update the
same data without locking.
A dirty read occurs if a one transaction reads changes made by another transaction
that has not yet been committed. This is dangerous, because the changes made by
the other transaction may later be rolled back, and invalid data may be written by
the first transaction, see figure 10.3.
An unrepeatable read occurs if a transaction reads a row twice and reads different
state each time. For example, another transaction may have written to the row
and committed between the two reads, as shown in figure 10.4.
A special case of unrepeatable read is the second lost updates problem. Imagine
that two concurrent transactions both read a row: One writes to it and commits,
and then the second writes to it and commits. The changes made by the first
writer are lost. This issue is especially relevant if you think about application
Figure 10.3
Dirty read: transaction A reads
uncommitted data.
Controlling concurrent access
Figure 10.4
Unrepeatable read: transaction A executes two
nonrepeatable reads
conversations that need several database transactions to complete. We’ll explore
this case later in more detail.
A phantom read is said to occur when a transaction executes a query twice, and
the second result set includes rows that weren’t visible in the first result set or rows
that have been deleted. (It need not necessarily be exactly the same query.) This
situation is caused by another transaction inserting or deleting rows between the
execution of the two queries, as shown in figure 10.5.
Figure 10.5
Phantom read: transaction A reads new
data in the second select.
Now that you understand all the bad things that can occur, we can define the
transaction isolation levels and see what problems they prevent.
ANSI transaction isolation levels
The standard isolation levels are defined by the ANSI SQL standard, but they
aren’t peculiar to SQL databases. JTA defines exactly the same isolation levels, and
you’ll use these levels to declare your desired transaction isolation later. With
increased levels of isolation comes higher cost and serious degradation of performance and scalability:
A system that permits dirty reads but not lost updates is said to operate in
read uncommitted isolation. One transaction may not write to a row if another
uncommitted transaction has already written to it. Any transaction may read
any row, however. This isolation level may be implemented in the database-management system with exclusive write locks.
Transactions and concurrency
A system that permits unrepeatable reads but not dirty reads is said to implement read committed transaction isolation. This may be achieved by using
shared read locks and exclusive write locks. Reading transactions don’t
block other transactions from accessing a row. However, an uncommitted
writing transaction blocks all other transactions from accessing the row.
A system operating in repeatable read isolation mode permits neither unrepeatable reads nor dirty reads. Phantom reads may occur. Reading transactions block writing transactions (but not other reading transactions), and
writing transactions block all other transactions.
Serializable provides the strictest transaction isolation. This isolation level
emulates serial transaction execution, as if transactions were executed one
after another, serially, rather than concurrently. Serializability may not be
implemented using only row-level locks. There must instead be some other
mechanism that prevents a newly inserted row from becoming visible to a
transaction that has already executed a query that would return the row.
How exactly the locking system is implemented in a DBMS varies significantly;
each vendor has a different strategy. You should study the documentation of your
DBMS to find out more about the locking system, how locks are escalated (from
row-level, to pages, to whole tables, for example), and what impact each isolation
level has on the performance and scalability of your system.
It’s nice to know how all these technical terms are defined, but how does that
help you choose an isolation level for your application?
Choosing an isolation level
Developers (ourselves included) are often unsure what transaction isolation level
to use in a production application. Too great a degree of isolation harms scalability of a highly concurrent application. Insufficient isolation may cause subtle,
unreproduceable bugs in an application that you’ll never discover until the system
is working under heavy load.
Note that we refer to optimistic locking (with versioning) in the following explanation, a concept explained later in this chapter. You may want to skip this section
and come back when it’s time to make the decision for an isolation level in your
application. Picking the correct isolation level is, after all, highly dependent on
your particular scenario. Read the following discussion as recommendations, not
carved in stone.
Hibernate tries hard to be as transparent as possible regarding transactional
semantics of the database. Nevertheless, caching and optimistic locking affect
Controlling concurrent access
these semantics. What is a sensible database isolation level to choose in a Hibernate application?
First, eliminate the read uncommitted isolation level. It’s extremely dangerous to
use one transaction’s uncommitted changes in a different transaction. The rollback or failure of one transaction will affect other concurrent transactions. Rollback of the first transaction could bring other transactions down with it, or
perhaps even cause them to leave the database in an incorrect state. It’s even possible that changes made by a transaction that ends up being rolled back could be
committed anyway, because they could be read and then propagated by another
transaction that is successful!
Secondly, most applications don’t need serializable isolation (phantom reads
aren’t usually problematic), and this isolation level tends to scale poorly. Few
existing applications use serializable isolation in production, but rather rely on
pessimistic locks (see next sections) that effectively force a serialized execution of
operations in certain situations.
This leaves you a choice between read committed and repeatable read. Let’s first
consider repeatable read. This isolation level eliminates the possibility that one
transaction can overwrite changes made by another concurrent transaction (the
second lost updates problem) if all data access is performed in a single atomic
database transaction. A read lock held by a transaction prevents any write lock a
concurrent transaction may wish to obtain. This is an important issue, but
enabling repeatable read isn’t the only way to resolve it.
Let’s assume you’re using versioned data, something that Hibernate can do for
you automatically. The combination of the (mandatory) persistence context
cache and versioning already gives you most of the nice features of repeatable
read isolation. In particular, versioning prevents the second lost updates problem,
and the persistence context cache also ensures that the state of the persistent
instances loaded by one transaction is isolated from changes made by other transactions. So, read-committed isolation for all database transactions is acceptable if
you use versioned data.
Repeatable read provides more reproducibility for query result sets (only for
the duration of the database transaction); but because phantom reads are still
possible, that doesn’t appear to have much value. You can obtain a repeatable-read guarantee explicitly in Hibernate for a particular transaction and piece
of data (with a pessimistic lock).
Setting the transaction isolation level allows you to choose a good default locking strategy for all your database transactions. How do you set the isolation level?
Transactions and concurrency
Setting an isolation level
Every JDBC connection to a database is in the default isolation level of the DBMS—
usually read committed or repeatable read. You can change this default in the
DBMS configuration. You may also set the transaction isolation for JDBC connections on the application side, with a Hibernate configuration option:
hibernate.connection.isolation = 4
Hibernate sets this isolation level on every JDBC connection obtained from a
connection pool before starting a transaction. The sensible values for this option
are as follows (you may also find them as constants in java.sql.Connection):
1—Read uncommitted isolation
2—Read committed isolation
4—Repeatable read isolation
8—Serializable isolation
Note that Hibernate never changes the isolation level of connections obtained
from an application server-provided database connection in a managed environment! You can change the default isolation using the configuration of your application server. (The same is true if you use a stand-alone JTA implementation.)
As you can see, setting the isolation level is a global option that affects all connections and transactions. From time to time, it’s useful to specify a more restrictive lock for a particular transaction. Hibernate and Java Persistence rely on
optimistic concurrency control, and both allow you to obtain additional locking
guarantees with version checking and pessimistic locking.
10.2.2 Optimistic concurrency control
An optimistic approach always assumes that everything will be OK and that conflicting data modifications are rare. Optimistic concurrency control raises an
error only at the end of a unit of work, when data is written. Multiuser applications usually default to optimistic concurrency control and database connections
with a read-committed isolation level. Additional isolation guarantees are
obtained only when appropriate; for example, when a repeatable read is required.
This approach guarantees the best performance and scalability.
Understanding the optimistic strategy
To understand optimistic concurrency control, imagine that two transactions read
a particular object from the database, and both modify it. Thanks to the read-committed isolation level of the database connection, neither transaction will run into
Controlling concurrent access
Figure 10.6
Conversation B overwrites
changes made by conversation A.
any dirty reads. However, reads are still nonrepeatable, and updates may also be
lost. This is a problem you’ll face when you think about conversations, which are
atomic transactions from the point of view of your users. Look at figure 10.6.
Let’s assume that two users select the same piece of data at the same time. The
user in conversation A submits changes first, and the conversation ends with a successful commit of the second transaction. Some time later (maybe only a second),
the user in conversation B submits changes. This second transaction also commits
successfully. The changes made in conversation A have been lost, and (potentially
worse) modifications of data committed in conversation B may have been based
on stale information.
You have three choices for how to deal with lost updates in these second transactions in the conversations:
Last commit wins—Both transactions commit successfully, and the second
commit overwrites the changes of the first. No error message is shown.
First commit wins—The transaction of conversation A is committed, and the
user committing the transaction in conversation B gets an error message.
The user must restart the conversation by retrieving fresh data and go
through all steps of the conversation again with nonstale data.
Merge conflicting updates—The first modification is committed, and the transaction in conversation B aborts with an error message when it’s committed.
The user of the failed conversation B may however apply changes selectively, instead of going through all the work in the conversation again.
If you don’t enable optimistic concurrency control, and by default it isn’t enabled,
your application runs with a last commit wins strategy. In practice, this issue of lost
updates is frustrating for application users, because they may see all their work
lost without an error message.
Transactions and concurrency
Obviously, first commit wins is much more attractive. If the application user of
conversation B commits, he gets an error message that reads, Somebody already committed modifications to the data you’re about to commit. You’ve been working with stale
data. Please restart the conversation with fresh data. It’s your responsibility to design
and write the application to produce this error message and to direct the user to
the beginning of the conversation. Hibernate and Java Persistence help you with
automatic optimistic locking, so that you get an exception whenever a transaction
tries to commit an object that has a conflicting updated state in the database.
Merge conflicting changes, is a variation of first commit wins. Instead of displaying
an error message that forces the user to go back all the way, you offer a dialog that
allows the user to merge conflicting changes manually. This is the best strategy
because no work is lost and application users are less frustrated by optimistic concurrency failures. However, providing a dialog to merge changes is much more
time-consuming for you as a developer than showing an error message and forcing the user to repeat all the work. We’ll leave it up to you whether you want to use
this strategy.
Optimistic concurrency control can be implemented many ways. Hibernate
works with automatic versioning.
Enabling versioning in Hibernate
Hibernate provides automatic versioning. Each entity instance has a version,
which can be a number or a timestamp. Hibernate increments an object’s version
when it’s modified, compares versions automatically, and throws an exception if a
conflict is detected. Consequently, you add this version property to all your persistent entity classes to enable optimistic locking:
public class Item {
private int version;
You can also add a getter method; however, version numbers must not be modified by the application. The <version> property mapping in XML must be placed
immediately after the identifier property mapping:
<class name="Item" table="ITEM">
<id .../>
<version name="version" access="field" column="OBJ_VERSION"/>
Controlling concurrent access
The version number is just a counter value—it doesn’t have any useful semantic
value. The additional column on the entity table is used by your Hibernate application. Keep in mind that all other applications that access the same database can
(and probably should) also implement optimistic versioning and utilize the same
version column. Sometimes a timestamp is preferred (or exists):
public class Item {
private Date lastUpdated;
<class name="Item" table="ITEM">
<id .../>
<timestamp name="lastUpdated"
In theory, a timestamp is slightly less safe, because two concurrent transactions
may both load and update the same item in the same millisecond; in practice,
this won’t occur because a JVM usually doesn’t have millisecond accuracy (you
should check your JVM and operating system documentation for the guaranteed precision).
Furthermore, retrieving the current time from the JVM isn’t necessarily safe in
a clustered environment, where nodes may not be time synchronized. You can
switch to retrieval of the current time from the database machine with the
source="db" attribute on the <timestamp> mapping. Not all Hibernate SQL dialects support this (check the source of your configured dialect), and there is
always the overhead of hitting the database for every increment.
We recommend that new projects rely on versioning with version numbers, not
Optimistic locking with versioning is enabled as soon as you add a <version>
or a <timestamp> property to a persistent class mapping. There is no other switch.
How does Hibernate use the version to detect a conflict?
Automatic management of versions
Every DML operation that involves the now versioned Item objects includes a version check. For example, assume that in a unit of work you load an Item from the
database with version 1. You then modify one of its value-typed properties, such as
the price of the Item. When the persistence context is flushed, Hibernate detects
Transactions and concurrency
that modification and increments the version of the Item to 2. It then executes
the SQL UPDATE to make this modification permanent in the database:
update ITEM set INITIAL_PRICE='12.99', OBJ_VERSION=2
where ITEM_ID=123 and OBJ_VERSION=1
If another concurrent unit of work updated and committed the same row, the
OBJ_VERSION column no longer contains the value 1, and the row isn’t updated.
Hibernate checks the row count for this statement as returned by the JDBC
driver—which in this case is the number of rows updated, zero—and throws a
StaleObjectStateException. The state that was present when you loaded the
Item is no longer present in the database at flush-time; hence, you’re working
with stale data and have to notify the application user. You can catch this exception and display an error message or a dialog that helps the user restart a conversation with the application.
What modifications trigger the increment of an entity’s version? Hibernate
increments the version number (or the timestamp) whenever an entity instance is
dirty. This includes all dirty value-typed properties of the entity, no matter if
they’re single-valued, components, or collections. Think about the relationship
between User and BillingDetails, a one-to-many entity association: If a CreditCard is modified, the version of the related User isn’t incremented. If you add or
remove a CreditCard (or BankAccount) from the collection of billing details, the
version of the User is incremented.
If you want to disable automatic increment for a particular value-typed property or collection, map it with the optimistic-lock="false" attribute. The
inverse attribute makes no difference here. Even the version of an owner of an
inverse collection is updated if an element is added or removed from the
inverse collection.
As you can see, Hibernate makes it incredibly easy to manage versions for optimistic concurrency control. If you’re working with a legacy database schema or
existing Java classes, it may be impossible to introduce a version or timestamp
property and column. Hibernate has an alternative strategy for you.
Versioning without version numbers or timestamps
If you don’t have version or timestamp columns, Hibernate can still perform automatic versioning, but only for objects that are retrieved and modified in the same
persistence context (that is, the same Session). If you need optimistic locking for
conversations implemented with detached objects, you must use a version number
or timestamp that is transported with the detached object.
Controlling concurrent access
This alternative implementation of versioning checks the current database
state against the unmodified values of persistent properties at the time the object
was retrieved (or the last time the persistence context was flushed). You may
enable this functionality by setting the optimistic-lock attribute on the class
<class name="Item" table="ITEM" optimistic-lock="all">
<id .../>
The following SQL is now executed to flush a modification of an Item instance:
update ITEM set ITEM_PRICE='12.99'
where ITEM_ID=123
and ITEM_PRICE='9.99'
and ...
and SELLER_ID=45
Hibernate lists all columns and their last known nonstale values in the WHERE
clause of the SQL statement. If any concurrent transaction has modified any of
these values, or even deleted the row, this statement again returns with zero
updated rows. Hibernate then throws a StaleObjectStateException.
Alternatively, Hibernate includes only the modified properties in the restriction (only ITEM_PRICE, in this example) if you set optimistic-lock="dirty".
This means two units of work may modify the same object concurrently, and a
conflict is detected only if they both modify the same value-typed property (or a
foreign key value). In most cases, this isn’t a good strategy for business entities.
Imagine that two people modify an auction item concurrently: One changes the
price, the other the description. Even if these modifications don’t conflict at the
lowest level (the database row), they may conflict from a business logic perspective. Is it OK to change the price of an item if the description changed completely?
You also need to enable dynamic-update="true" on the class mapping of the
entity if you want to use this strategy, Hibernate can’t generate the SQL for these
dynamic UPDATE statements at startup.
We don’t recommend versioning without a version or timestamp column in a
new application; it’s a little slower, it’s more complex, and it doesn’t work if you’re
using detached objects.
Optimistic concurrency control in a Java Persistence application is pretty much
the same as in Hibernate.
Transactions and concurrency
Versioning with Java Persistence
The Java Persistence specification assumes that concurrent data access is handled
optimistically, with versioning. To enable automatic versioning for a particular
entity, you need to add a version property or field:
public class Item {
@Column(name = "OBJ_VERSION")
private int version;
Again, you can expose a getter method but can’t allow modification of a version
value by the application. In Hibernate, a version property of an entity can be of
any numeric type, including primitives, or a Date or Calendar type. The JPA specification considers only int, Integer, short, Short, long, Long, and java.sql.
Timestamp as portable version types.
Because the JPA standard doesn’t cover optimistic versioning without a version
attribute, a Hibernate extension is needed to enable versioning by comparing the
old and new state:
optimisticLock = org.hibernate.annotations.OptimisticLockType.ALL
public class Item {
You can also switch to OptimisticLockType.DIRTY if you only wish to compare
modified properties during version checking. You then also need to set the
dynamicUpdate attribute to true.
Java Persistence doesn’t standardize which entity instance modifications
should trigger an increment of the version. If you use Hibernate as a JPA provider,
the defaults are the same—every value-typed property modification, including
additions and removals of collection elements, triggers a version increment. At
the time of writing, no Hibernate annotation for disabling of version increments
on particular properties and collections is available, but a feature request for
@OptimisticLock(excluded=true) exists. Your version of Hibernate Annotations
probably includes this option.
Hibernate EntityManager, like any other Java Persistence provider, throws a
javax.persistence.OptimisticLockException when a conflicting version is
Controlling concurrent access
detected. This is the equivalent of the native StaleObjectStateException in
Hibernate and should be treated accordingly.
We’ve now covered the basic isolation levels of a database connection, with the
conclusion that you should almost always rely on read-committed guarantees from
your database. Automatic versioning in Hibernate and Java Persistence prevents
lost updates when two concurrent transactions try to commit modifications on the
same piece of data. To deal with nonrepeatable reads, you need additional isolation guarantees.
10.2.3 Obtaining additional isolation guarantees
There are several ways to prevent nonrepeatable reads and upgrade to a higher
isolation level.
Explicit pessimistic locking
We already discussed switching all database connections to a higher isolation level
than read committed, but our conclusion was that this is a bad default when scalability of the application is a concern. You need better isolation guarantees only
for a particular unit of work. Also remember that the persistence context cache
provides repeatable reads for entity instances in persistent state. However, this
isn’t always sufficient.
For example, you may need repeatable read for scalar queries:
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item i = (Item) session.get(Item.class, 123);
String description = (String)
session.createQuery("select i.description from Item i" +
" where = :itemid")
.setParameter("itemid", i.getId() )
This unit of work executes two reads. The first retrieves an entity instance by
identifier. The second read is a scalar query, loading the description of the
already loaded Item instance again. There is a small window in this unit of work
in which a concurrently running transaction may commit an updated item
description between the two reads. The second read then returns this committed
data, and the variable description has a different value than the property i.
Transactions and concurrency
This example is simplified, but it’s enough to illustrate how a unit of work that
mixes entity and scalar reads is vulnerable to nonrepeatable reads, if the database
transaction isolation level is read committed.
Instead of switching all database transactions into a higher and nonscalable
isolation level, you obtain stronger isolation guarantees when necessary with the
lock() method on the Hibernate Session:
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item i = (Item) session.get(Item.class, 123);
session.lock(i, LockMode.UPGRADE);
String description = (String)
session.createQuery("select i.description from Item i" +
" where = :itemid")
.setParameter("itemid", i.getId() )
Using LockMode.UPGRADE results in a pessimistic lock held on the database for the
row(s) that represent the Item instance. Now no concurrent transaction can
obtain a lock on the same data—that is, no concurrent transaction can modify the
data between your two reads. This can be shortened as follows:
Session session = sessionFactory.openSession();
Transaction tx = session.beginTransaction();
Item i = (Item) session.get(Item.class, 123, LockMode.UPGRADE);
A LockMode.UPGRADE results in an SQL SELECT ... FOR UPDATE or similar,
depending on the database dialect. A variation, LockMode.UPGRADE_NOWAIT, adds
a clause that allows an immediate failure of the query. Without this clause, the
database usually waits when the lock can’t be obtained (perhaps because a concurrent transaction already holds a lock). The duration of the wait is database-dependent, as is the actual SQL clause.
Can I use long pessimistic locks? The duration of a pessimistic lock in
Hibernate is a single database transaction. This means you can’t use an
exclusive lock to block concurrent access for longer than a single database transaction. We consider this a good thing, because the only solution would be an extremely expensive lock held in memory (or a
so-called lock table in the database) for the duration of, for example, a
whole conversation. These kinds of locks are sometimes called offline
Controlling concurrent access
locks. This is almost always a performance bottleneck; every data access
involves additional lock checks to a synchronized lock manager. Optimistic locking, however, is the perfect concurrency control strategy and performs well in long-running conversations. Depending on your
conflict-resolution options (that is, if you had enough time to implement
merge changes), your application users are as happy with it as with blocked
concurrent access. They may also appreciate not being locked out of particular screens while others look at the same data.
Java Persistence defines LockModeType.READ for the same purpose, and the
EntityManager also has a lock() method. The specification doesn’t require that
this lock mode is supported on nonversioned entities; however, Hibernate supports it on all entities, because it defaults to a pessimistic lock in the database.
The Hibernate lock modes
Hibernate supports the following additional LockModes:
LockMode.NONE—Don’t go to the database unless the object isn’t in any
LockMode.READ—Bypass all caches, and perform a version check to verify
that the object in memory is the same version that currently exists in the
LockMode.UPDGRADE—Bypass all caches, do a version check (if applicable),
and obtain a database-level pessimistic upgrade lock, if that is supported.
Equivalent to LockModeType.READ in Java Persistence. This mode transparently falls back to LockMode.READ if the database SQL dialect doesn’t support a SELECT ... FOR UPDATE option.
LockMode.UPDGRADE_NOWAIT—The same as UPGRADE, but use a SELECT ...
FOR UPDATE NOWAIT, if supported. This disables waiting for concurrent lock
releases, thus throwing a locking exception immediately if the lock can’t be
obtained. This mode transparently falls back to LockMode.UPGRADE if the
database SQL dialect doesn’t support the NOWAIT option.
LockMode.FORCE—Force an increment of the objects version in the data-
base, to indicate that it has been modified by the current transaction. Equivalent to LockModeType.WRITE in Java Persistence.
LockMode.WRITE—Obtained automatically when Hibernate has written to a
row in the current transaction. (This is an internal mode; you may not specify it in your application.)
Transactions and concurrency
By default, load() and get() use LockMode.NONE. A LockMode.READ is most
useful with session.lock() and a detached object. Here’s an example:
Item item = ... ;
Bid bid = new Bid();
Transaction tx = session.beginTransaction();
session.lock(item, LockMode.READ);
This code performs a version check on the detached Item instance to verify that
the database row wasn’t updated by another transaction since it was retrieved,
before saving the new Bid by cascade (assuming the association from Item to Bid
has cascading enabled).
(Note that EntityManager.lock() doesn’t reattach the given entity instance—
it only works on instances that are already in managed persistent state.)
Hibernate LockMode.FORCE and LockModeType.WRITE in Java Persistence have
a different purpose. You use them to force a version update if by default no version would be incremented.
Forcing a version increment
If optimistic locking is enabled through versioning, Hibernate increments the
version of a modified entity instance automatically. However, sometimes you want
to increment the version of an entity instance manually, because Hibernate
doesn’t consider your changes to be a modification that should trigger a version
Imagine that you modify the owner name of a CreditCard:
Session session = getSessionFactory().openSession();
Transaction tx = session.beginTransaction();
User u = (User) session.get(User.class, 123);
u.getDefaultBillingDetails().setOwner("John Doe");
When this Session is flushed, the version of the BillingDetail’s instance (let’s
assume it’s a credit card) that was modified is incremented automatically by
Hibernate. This may not be what you want—you may want to increment the version of the owner, too (the User instance).
Call lock() with LockMode.FORCE to increment the version of an entity
Nontransactional data access
Session session = getSessionFactory().openSession();
Transaction tx = session.beginTransaction();
User u = (User) session.get(User.class, 123);
session.lock(u, LockMode.FORCE);
u.getDefaultBillingDetails().setOwner("John Doe");
Any concurrent unit of work that works with the same User row now knows that
this data was modified, even if only one of the values that you’d consider part of
the whole aggregate was modified. This technique is useful in many situations
where you modify an object and want the version of a root object of an aggregate
to be incremented. Another example is a modification of a bid amount for an
auction item (if these amounts aren’t immutable): With an explicit version increment, you can indicate that the item has been modified, even if none of its
value-typed properties or collections have changed. The equivalent call with Java
Persistence is em.lock(o, LockModeType.WRITE).
You now have all the pieces to write more sophisticated units of work and create conversations. We need to mention one final aspect of transactions, however,
because it becomes essential in more complex conversations with JPA. You must
understand how autocommit works and what nontransactional data access means
in practice.
10.3 Nontransactional data access
Many DBMSs enable the so called autocommit mode on every new database connection by default. The autocommit mode is useful for ad hoc execution of SQL.
Imagine that you connect to your database with an SQL console and that you
run a few queries, and maybe even update and delete rows. This interactive data
access is ad hoc; most of the time you don’t have a plan or a sequence of statements that you consider a unit of work. The default autocommit mode on the
database connection is perfect for this kind of data access—after all, you don’t
want to type begin a transaction and end a transaction for every SQL statement you write and execute. In autocommit mode, a (short) database transaction
begins and ends for each SQL statement you send to the database. You’re working
effectively nontransactionally, because there are no atomicity or isolation guarantees for your session with the SQL console. (The only guarantee is that a single
SQL statement is atomic.)
Transactions and concurrency
An application, by definition, always executes a planned sequence of statements. It seems reasonable that you therefore always create transaction boundaries to group your statements into units that are atomic. Therefore, the
autocommit mode has no place in an application.
10.3.1 Debunking autocommit myths
Many developers still like to work with an autocommit mode, often for reasons
that are vague and not well defined. Let’s first debunk a few of these reasons
before we show you how to access data nontransactionally if you want (or have) to:
Many application developers think they can talk to a database outside of a
transaction. This obviously isn’t possible; no SQL statement can be send to a
database outside of a database transaction. The term nontransactional data
access means there are no explicit transaction boundaries, no system transaction, and that the behavior of data access is that of the autocommit mode.
It doesn’t mean no physical database transactions are involved.
If your goal is to improve performance of your application by using the
autocommit mode, you should think again about the implications of many
small transactions. Significant overhead is involved in starting and ending a
database transaction for every SQL statement, and it may decrease the performance of your application.
If your goal is to improve the scalability of your application with the autocommit mode, think again: A longer-running database transaction, instead
of many small transactions for every SQL statement, may hold database
locks for a longer time and probably won’t scale as well. However, thanks to
the Hibernate persistence context and write-behind of DML, all write locks
in the database are already held for a short time. Depending on the isolation level you enable, the cost of read locks is likely negligible. Or, you may
use a DBMS with multiversion concurrency that doesn’t require read locks
(Oracle, PostgreSQL, Informix, Firebird), because readers are never
blocked by default.
Because you’re working nontransactionally, not only do you give up any
transactional atomicity of a group of SQL statements, but you also have
weaker isolation guarantees if data is modified concurrently. Repeatable
reads based on read locks are impossible with autocommit mode. (The persistence context cache helps here, naturally.)
Nontransactional data access
Many more issues must be considered when you introduce nontransactional data
access in your application. We’ve already noted that introducing a new type of
transaction, namely read-only transactions, can significantly complicate any future
modification of your application. The same is true if you introduce nontransactional operations.
You would then have three different kinds of data access in your application: in
regular transactions, in read-only transactions, and now also nontransactional,
with no guarantees. Imagine that you have to introduce an operation that writes
data into a unit of work that was supposed to only read data. Imagine that you
have to reorganize operations that were nontransactional to be transactional.
Our recommendation is to not use the autocommit mode in an application,
and to apply read-only transactions only when there is an obvious performance
benefit or when future code changes are highly unlikely. Always prefer regular
ACID transactions to group your data-access operations, regardless of whether you
read or write data.
Having said that, Hibernate and Java Persistence allow nontransactional data
access. In fact, the EJB 3.0 specification forces you to access data nontransactionally if you want to implement atomic long-running conversations. We’ll approach
this subject in the next chapter. Now we want to dig a little deeper into the consequences of the autocommit mode in a plain Hibernate application. (Note that,
despite our negative remarks, there are some good use cases for the autocommit
mode. In our experience autocommit is often enabled for the wrong reasons and
we wanted to wipe the slate clean first.)
10.3.2 Working nontransactionally with Hibernate
Look at the following code, which accesses the database without transaction
Session session = sessionFactory.openSession();
session.get(Item.class, 123l);
By default, in a Java SE environment with a JDBC configuration, this is what happens if you execute this snippet:
A new Session is opened. It doesn’t obtain a database connection at this
The call to get() triggers an SQL SELECT. The Session now obtains a JDBC
Connection from the connection pool. Hibernate, by default, immediately
Transactions and concurrency
turns off the autocommit mode on this connection with setAutoCommit(false). This effectively starts a JDBC transaction!
The SELECT is executed inside this JDBC transaction. The Session is closed,
and the connection is returned to the pool and released by Hibernate—
Hibernate calls close() on the JDBC Connection. What happens to the
uncommitted transaction?
The answer to that question is, “It depends!” The JDBC specification doesn’t say
anything about pending transactions when close() is called on a connection.
What happens depends on how the vendors implement the specification. With
Oracle JDBC drivers, for example, the call to close() commits the transaction!
Most other JDBC vendors take the sane route and roll back any pending transaction when the JDBC Connection object is closed and the resource is returned to
the pool.
Obviously, this won’t be a problem for the SELECT you’ve executed, but look at
this variation:
Session session = getSessionFactory().openSession();
Long generatedId =;
This code results in an INSERT statement, executed inside a transaction that is
never committed or rolled back. On Oracle, this piece of code inserts data permanently; in other databases, it may not. (This situation is slightly more complicated:
The INSERT is executed only if the identifier generator requires it. For example,
an identifier value can be obtained from a sequence without an INSERT. The persistent entity is then queued until flush-time insertion—which never happens in
this code. An identity strategy requires an immediate INSERT for the value to be
We haven’t even touched on autocommit mode yet but have only highlighted a
problem that can appear if you try to work without setting explicit transaction
boundaries. Let’s assume that you still think working without transaction demarcation is a good idea and that you want the regular autocommit behavior. First,
you have to tell Hibernate to allow autocommitted JDBC connections in the
Hibernate configuration:
<property name="connection.autocommit">true</property>
With this setting, Hibernate no longer turns off autocommit when a JDBC connection is obtained from the connection pool—it enables autocommit if the connec-
Nontransactional data access
tion isn’t already in that mode. The previous code examples now work
predictably, and the JDBC driver wraps a short transaction around every SQL statement that is send to the database—with the implications we listed earlier.
In which scenarios would you enable the autocommit mode in Hibernate, so
that you can use a Session without beginning and ending a transaction manually?
Systems that benefit from autocommit mode are systems that require on-demand
(lazy) loading of data, in a particular Session and persistence context, but in
which it is difficult to wrap transaction boundaries around all code that might trigger on-demand data retrieval. This is usually not the case in web applications that
follow the design patterns we discuss in chapter 16. On the other hand, desktop
applications that access the database tier through Hibernate often require
on-demand loading without explicit transaction boundaries. For example, if you
double-click on a node in a Java Swing tree view, all children of that node have to
be loaded from the database. You'd have to wrap a transaction around this event
manually; the autocommit mode is a more convenient solution. (Note that we are
not proposing to open and close Sessions on demand!)
10.3.3 Optional transactions with JTA
The previous discussion focused on autocommit mode and nontransactional data
access in an application that utilizes unmanaged JDBC connections, where Hibernate manages the connection pool. Now imagine that you want to use Hibernate
in a Java EE environment, with JTA and probably also CMT. The connection.
autocommit configuration option has no effect in this environment. Whether
autocommit is used depends on your transaction assembly.
Imagine that you have an EJB session bean that marks a particular method as
public class ItemFinder {
public Item findItemById(Long id) {
Session s = getSessionFactory().openSession();
Item item = (Item) s.get(Item.class, id);
return item;
The findItemById() method produces an immediate SQL SELECT that returns
the Item instance. Because the method is marked as not supporting a transaction
context, no transaction is started for this operation, and any existing transaction
Transactions and concurrency
context is suspended for the duration of this method. The SELECT is effectively
executed in autocommit mode. (Internally, an autocommitted JDBC connection is
assigned to serve this Session.)
Finally, you need to know that the default FlushMode of a Session changes
when no transaction is in progress. The default behavior, FlushMode.AUTO, results
in a synchronization before every HQL, SQL, or Criteria query. This is bad, of
course, because DML UPDATE, INSERT, and DELETE operations execute in addition
to a SELECT for the query. Because you’re working in autocommit mode, these
modifications are permanent. Hibernate prevents this by disabling automatic
flushing when you use a Session outside of transaction boundaries. You then
have to expect that queries may return stale data or data that is conflicting with
the state of data in your current Session—effectively the same issue you have to
deal with when FlushMode.MANUAL is selected.
We’ll get back to nontransactional data access in the next chapter, in our discussion of conversations. You should consider autocommit behavior a feature that
you'd possibly use in conversations with Java Persistence or EJBs, and when wrapping programmatic transaction boundaries around all data access events would be
difficult (in a desktop application, for example). In most other cases, autocommit
results in systems that are difficult to maintain, with now performance or scalability benefit. (In our opinion, RDBMS vendors should not enable autocommit by
default. SQL query consoles and tools should enable autocommit mode on a connection, when necessary.)
10.4 Summary
In this chapter, you learned about transactions, concurrency, isolation, and locking. You now know that Hibernate relies on the database concurrency control
mechanism but provides better isolation guarantees in a transaction, thanks to
automatic versioning and the persistence context cache. You learned how to set
transaction boundaries programmatically with the Hibernate API, JTA UserTransaction, and the JPA EntityTransaction interface. We also looked at transaction
assembly with EJB 3.0 components and how you can work nontransactionally with
autocommit mode.
Table 10.1 shows a summary you can use to compare native Hibernate features
and Java Persistence.
Table 10.1
Hibernate and JPA comparison chart for chapter 10
Hibernate Core
Java Persistence and EJB 3.0
The Transaction API can be configured for
The EntityTransaction API is only useful
with resource-local transactions.
Hibernate can be configured to integrate with
JTA and container-managed transactions in
With Java Persistence, no extra configuration
besides the database connection name changes
between Java SE and Java EE.
Hibernate defaults to optimistic concurrency
control for best scalability, with automatic versioning.
Java Persistence standardizes optimistic concurrency control with automatic versioning.
We’ve now finished our discussion and exploration of the fundamentals involved
in storing and loading objects in a transactional fashion. Next, we’ll bring all the
pieces together by creating more realistic conversations between the user and the
This chapter covers
Implementation of conversations with Hibernate
Implementation of conversations with JPA
Conversations with EJB 3.0 components
Propagating the Hibernate Session
You’ve tried the examples in previous chapters and stored and loaded objects
inside transactions. Very likely you’ve noticed that code examples of five lines are
excellent to help you understand a particular issue and learn an API and how
objects change their state. If you take the next step and try to apply what you’ve
learned in your own application, you’ll probably soon realize that you’re missing
two important concepts.
The first concept we’ll show you in this chapter—persistence context propagation—is useful when you have to call several classes to complete a particular action
in your application and they all need database access. So far, we had only a single
method that opened and closed a persistence context (a Session or an EntityManager) internally. Instead of passing the persistence context between classes
and methods manually, we’ll show you the mechanisms in Hibernate and Java Persistence that can take care of propagation automatically. Hibernate can help you
to create more complex units of work.
The next design problem you’ll run into is that of application flow when your
application user has to be guided through several screens to complete a unit of
work. You must create code that controls the navigation from screen to screen—
however, this is outside of the scope of persistence, and we won’t have much to say
about it in this chapter. What is partly the responsibility of the persistence mechanism is the atomicity and isolation of data access for a unit of work that spans possible user think-time. We call a unit of work that completes in several client/server
request and response cycles a conversation. Hibernate and Java Persistence offer
several strategies for the implementation of conversations, and in this chapter we
show you how the pieces fit together with realistic examples.
We start with Hibernate and then, in the second half of the chapter, discuss
JPA conversations. Let’s create more complex data access examples first, to see
how several classes can reuse the same persistence context through automatic
11.1 Propagating the Hibernate Session
Recall the use case we introduced in the previous chapter: An event that triggers
the end of an auction has to be processed (chapter 10, section 10.1, “Transaction
essentials”). For the following examples, it doesn’t matter who triggered this
event; probably an automatic timer ends auctions when their end date and time is
reached. It could also be a human operator who triggers the event.
Implementing conversations
To process the event, you need to execute a sequence of operations: check the
winning bid for the auction, charge the cost of the auction, notify the seller and
winner, and so on. You could write a single class that has one big procedure. A better design is to move the responsibility for each of these steps into reusable
smaller components and to separate them by concern. We’ll have much more to
say about this in chapter 16. For now, assume that you followed our advice and
that several classes need to be called inside the same unit of work to process the
closing of an auction.
11.1.1 The use case for Session propagation
Look at the code example in Listing 11.1, which controls the processing of the
Listing 11.1 Controller code that closes and ends an auction
public class ManageAuction {
itemDAO = new ItemDAO();
paymentDAO = new PaymentDAO();
public void endAuction(Item item) {
// Reattach item
// Set winning bid
Bid winningBid = itemDAO.getMaxBid( item.getId() );
item.setBuyer( winningBid.getBidder() );
// Charge seller
Payment payment = new Payment(item);
// Notify seller and winner
The ManageAuction class is called a controller. Its responsibility is to coordinate all
the steps necessary to process a particular event. The method endAuction() is
called by the timer (or user interface) when the event is triggered. The controller
doesn’t contain all the code necessary to complete and close the auction; it delegates as much as possible to other classes. First, it needs two stateless service
Propagating the Hibernate Session
objects called data access objects (DAOs) to complete its work—they’re instantiated
directly for each instance of the controller. The endAuction() method uses the
DAOs when it needs to access the database. For example, the ItemDAO is used to
reattach the detached item and to query the database for the highest bid. The
PaymentDAO is used to make a transient new payment persistent. You don’t even
need to see how the seller and winner of the auction are notified—you have
enough code to demonstrate that context propagation is required.
The code in listing 11.1 doesn’t work. First, there is no transaction demarcation. All the code in endAuction() is to be considered an atomic unit of work: It
either all fails or all completes successfully. So, you need to wrap a transaction
around all these operations. You’ll do that with different APIs next.
A more difficult problem is the persistence context. Imagine that ItemDAO and
PaymentDAO use a different persistence context in every method (they’re stateless). In other words, itemDAO.getMaxBid() and paymentDAO.makePersistent()
both open, flush, and close their own persistence context (a Session or an EntityManager). This is an anti-pattern that should be avoided at all times! In