Automatic Database Configuration for DB2 Universal Database

Automatic Database Configuration for DB2 Universal Database
Automatic Database Configuration for DB2 Universal
Database: Compressing Years of Performance Expertise into
Seconds of Execution
Eva Kwan
Sam Lightstone
Adam Storm
Berni Schiefer
Leanne Wu
IBM Canada, Toronto Laboratory
This paper describes the DB2® Configuration Advisor, an expert tool for the
configuration of DB2 databases. This advisor has shown dramatic results for
tuning and configuring DB2 servers on UNIX® and Windows® platforms. The
recognition of the essential need for administration and design tools has
spurred renewed interest among leading relational database management
system (RDBMS) vendors. The DB2 Configuration Advisor is a key feature in
DB2’s Autonomic Computing self-managing technology portfolio. This paper
discusses the purpose and features of this expert advisor. Experimental
results are presented with a description of the advisor’s interfaces.
1. Introduction and motivation
In this paper we present an overview and experimental results for a database
configuration tool for DB2 Universal Database™ for UNIX and Windows (DB2),
known as the DB2 Configuration Advisor. This advisor makes recommendations on
initial settings for configuration parameters and memory allotment within the database
management system, which can be adopted by inexperienced administrators or finetuned by more experienced personnel. The performance of a tuned configuration versus
an untuned configuration may be dramatic, with a measurable and significant
performance improvement. The DB2 Configuration Advisor is one of a growing set of
features for DB2 that reduces the human expertise required in database tuning and
administration by combining automation, artificial intelligence, and expert advice. The
experimental results shown illustrate how this technology can greatly simplify expert
tuning work.
Understanding the priorities of administrators is critical in reducing manual
administration. The increased complexity and volume of data seen in modern database
systems has increased the burden of database administrators. The database administrator
now faces a diverse combination of tasks, as shown in Figure 1.
planning, &
schema & data
creation &
Figure 1. Tasks of a database administrator over the life cycle of a database
Often the design choices for databases are complex and nontrivial. Once a database has a
physical and logical design, the operation of the database requires substantial human
attention for numerous tasks including, but not limited to: table reorganization, data
statistics collection, backup control, disaster recovery planning, performance tuning, and
problem analysis.
As database management systems have grown in size and complexity, recognition of the
importance of ease of administration and of better design tools has also increased, and
this recognition has spurred renewed interest in research and development of software to
reduce administration requirements [Be98] [Br94] [Ch99] [FST88]. IBM ® has recently
increased its focus on autonomic computing, which aims to produce self-managing
systems that run with a minimum of human intervention. This research has resulted in
IBM’s Autonomic Computing initiative, which aims to enhance the IBM portfolio of
clients, middleware, servers, and storage products through self-managing features, and
which has the long term objective of producing true autonomic computing [IBM02a].
This initiative demonstrates IBM’s commitment to providing solutions for increasingly
complex systems. Within DB2, the Autonomic Computing initiative has as its objective
the development of databases that will require minimal expertise and maintenance. As an
automatic configuration feature, the Configuration Advisor falls under the aegis of
IBM’s corporate wide Autonomic Computing research and development effort, of which
the DB2 Autonomic Computing effort is a part.
The goal is to create intelligent software tools that will reduce the burden on database
administrators by providing expert design systems, performance tuning and
configuration technology, with easier to use interfaces for administration and tools for
automation. One area in which research has been focused has been in the development of
internal tuning and configuring technology within the product deliverable. The DB2
UDB Configuration Advisor is one such product of this research. It allows large database
servers to be configured for performance in seconds. While the utility has been available
with DB2 for a few years, significant improvements have been performed for DB2
Universal Database version 8.1. IBM announced IBM DB2 Version 8 for Linux, UNIX
and Windows on September 17, 2002, and the product was made generally available on
Nov. 21, 2002.
2. Objectives and features
The Configuration Advisor is part of DB2 Universal Database for UNIX and Windows.
IBM’s DB2 Universal Database product provides a set of configuration parameters
settings that can be used to configure and tune the DB2 environment. The objective of
the Advisor is to define a set of initial database configuration parameters and memory
assignments to optimize system performance without extensive monitoring of the
system. These configuration parameters include ones that control memory distribution
(sorting, locking, caching database pages and working heaps), parallelism, I/O
optimization (asynchronous page readers and writers), many aspects of logging (file size,
buffer size), and recovery. While there are default settings provided with each
installation of DB2, it would be impossible to provide a single configuration that would
perform equally well for the diverse workloads and systems that use DB2. The
complexity of the memory topologies for enterprise database management systems
makes these assignments a difficult task, even for skilled administrators deeply aware of
their workload characteristics. The Configuration Advisor can be used both to
recommend configuration settings, as well as to apply the settings.
The Configuration Advisor has been developed with three execution interfaces. The first
is a graphical interface that is part of the DB2 Control Center, the graphical interface for
DB2’s administration. The second is through a DB2 system command, whereby the
Configuration Advisor can be invoked through a text command [IBM02c ]. The
command level interface is a popular interface for DB2 administration, and also lends
itself towards scripting. The third interface is through programmable APIs (DB2
provides both a C level API and a stored procedure interface to the Configuration
Advisor) which allows database applications, including independent software vendors
(ISVs), to programmatically invoke the Configuration advisor for seamless configuration
of DB2 databases [IBM02b ].
3. Design
The DB2 Configuration Advisor is designed on the principle that configuration choices
can be made by modeling each database configuration setting as a mathematical
expression which combines three sets of information regarding the system environment,
the database characteristics and user priorities. The three sets of information are:
User specification of the database environment (designed as a small set of basic
input, generally requiring minimal skill).
Autonomically sensed system characteristics (such as number of CPUs, disks,
amount of RAM, number and size of relational tables, etc).
Expert heuristics for database configuration, as reported by experienced
database administrators and performance tuning experts.
The first two of these information classes are essentially parametric, and can typically be
represented as scalar values which are input by the user. The advisor is designed so that
the responses collected from the user make up the minimal set of characteristics that are
required to effectively configure a database. Furthermore, the advisor is designed so that
the questions require neither lengthy nor detailed analysis of the database in order to be
answered. The second set of input information is obtained through the automatic
detection of system resources, which includes the amount of physical memory, number
of disks, storage available, and the number of CPUs. Included in this second set of
information is information about the existing database’s current characteristics. This
information includes number of tables defined, the number and size of defined buffer
pools, and the number of database containers. The first two classes of parametric
information are then combined with a set of expert heuristics to define mathematical
expressions of the estimated ideal settings, which define the configuration model. The
expert heuristics were collected through an ad hoc process of surveying over a dozen of
DB2’s leading performance experts and architects, as well as through review of
published information on DB2 performance tuning [SV99] [IBM02d ].
The configuration model expresses the configuration settings as a mathematical
The model for the configuration settings is further divided into three
distinct classes:
Independently modeled configuration settings, which can be modeled
independent of other configuration settings.
Dependant configuration setting, where the value of one setting affects the
model of another (or perhaps co-dependency), and
Zero sum game relationships (such as memory for sort, caching, locking etc) in
which a fixed resource must be divided among a set of configuration
Figure 2 illustrates the design model for the Configuration Advisor. With one of the
design goals of the advisor being to reduce human skill and involvement, the user
specification is kept intentionally trivial, with a long term aim of reducing it further.
Autonomically sensed
system characteristics
User specification of the
database environment
Figure 2: Design model for the DB2 Configuration Advisor combining user specified basic
description, autonomically sensed system characteristics and expert heuristics.
The configuration model includes algorithms that are based on documented guidelines,
expert advice, and system behaviors generalized from analysis of workloads. For most
configuration parameters, the advisor automates the calculation of settings based on
known guidelines. Other configuration parameters are more complex, and this is where
the advisor incorporates the expert heuristics of performance experts and architects at
IBM. The developers have an in-depth insight into DB2’s implementation, and
performance experts have valuable experience in performance tuning.
The goal of the advisor is to be useful across a wide range of systems so its internal
algorithms also include information gleaned from analysis of a wide range of workloads.
The analysis allows the advisor to better classify systems and choose the key
characteristics on which to base recommendations, a difficult task due to the complex
interactions and uniqueness of each database. The result, as our results indicate, is a
good set of initial settings, which can later be fine-tuned as administrators monitor
system performance during run time.
To the authors’ knowledge this approach is unique in the industry. Several other vendors
provide database configuration tools; however these tools typically focus on a distinct
subset of configuration settings (such as buffer pool configuration), are not generally
based on parametric models of database configuration, and typically lack the extensive
autonomic sensing capability that the DB2 Configuration Advisor embodies.
4. Experimental results
To examine the effectiveness of the Configuration Advisor, we performed four
experiments on systems running distinct workloads and environments, comparing the
performance of the database after tuning by the Configuration Advisor to the system
performance achieved through tuning by an expert database administrator. These
experiments include a degree of inaccuracy, since the tuning performed by human
administrators has variable quality, and it is impossible to assess how close this tuning is
to optimal. However, what these experiments do illustrate is the degree to which the
Configuration Advisor is able to tune the database system relative to the human expert.
While the human administrator typically spends a number of days tuning these
configuration parameters, the advisor provides recommendations for the same set of
parameters within seconds.
The four experiments included:
An Online Transaction Processing (OLTP) industry standard workload, run
once on a 32-bit implementation, and again on a 64-bit implementation. This
benchmark simulates a population of terminal operators executing transactions
against a relational database. The benchmark models the transaction
environment of an order-entry environment.
Two operational workloads tested on-site at two of the world’s leading global
investment banks.
While all tests in this set of experiments were run on AIX, this was more due to general
availability of test systems rather than any platform-specific constraints on the advisor.
4.1 64-bit OLTP
The 64-bit OLTP experiment was run on an RS/6000® 44P Model 270 4-way 375 MHz
Power3-II server, configured for 2-way processing. The system had 8 GB memory, and
was running AIX 4.3.3 with DB2 UDB EE V7.2 (64-bit). The storage system on this
server included 3 ServeRAID 4H (SCSI) adapters as follows: fifty-six 9 GB SCSI Disks
(data), fifty-six 9 GB SCSI Disks (data), fourteen 9 GB SCSI Disks (log, backup, temp
tablespaces). The hand-tuned system was configured by DB2 performance specialists at
IBM. In this experiment the database performance after tuning with the Configuration
Advisor was observed to be 93.58% of the hand-tuned system.
4.2 32-bit OLTP
The 32-bit OLTP experiment was run on an RS/6000 44P Model 270 4-way 375 MHz
Power3-II server, with 8 GB of memory, running AIX 4.3.3 with DB2 UDB EE V7.2
(32-bit). The storage system on this server included 3 ServeRAID 4H (SCSI) adapters as
follows: twenty-eight 9 GB SCSI Disks (data), forty-two 9 GB SCSI Disks (data),
fourteen 9 GB SCSI Disks (log, backup, temp tablespaces). As in the 64-bit experiment,
the hand-tuned system was configured by DB2 performance specialists. In this
experiment the database performance after tuning with the Configuration Advisor was
observed to be 91.52% of the hand-tuned system.
4.3 Global investment bank A
Two sets of tests were conducted on production workloads. The first of these tests was
conducted on a high volume OLTP test system that stress tested the database using an
authentication application. The application simulated authentication operations that
would result from customer accesses of the bank’s products.
The test environment consisted of a 6-way RS/6000 Model F80 server with each
processor running at 500 MHz. The server had 4 GB of memory and was running AIX
4.3.3. Its data was spread over 14 of its 16 drives (two 9 GB drives and twelve 18 GB
drives). In this experiment the hand-tuned system was configured by the bank’s
database administrator. The results of the experiment show that after the Configuration
Advisor was run, the workload performed 5.62% better than the hand-tuned system.
4.4 Global investment bank B
The second of the two production system tests was conducted on a system that allows
customers to view their web pages and change their preferences. These preferences are
sequenced into records in a DB2 UDB database. The transaction types are mixed
between selects and inserts or deletes, with 60% of the workload being selects. To
provide for load balancing and for hot backup solutions, the system exploits two servers
with peer-to-peer replication.
The two servers had identical hardware specifications and topologies. Both were IBM
RS/6000 Model 270 4-way 375 MHz servers with 4 GB of memory running AIX 4.3.3.
The storage system included sixteen 18 GB drives. At the time of the experiment, the
research prototype of the Configuration Advisor did not include a model for systems that
deploy peer-to-peer bulk replication. As a result, the advisor under configured memory
for locking and logging, resulting in sub optimal system performance. The administrator
at the customer site decided to leave the settings for locking and logging at their preadvisor values and adopted all of the remaining Configuration Advisor
recommendations. The result was a best-ever system performance for the database, with
the new configuration performing at 224.72% of the original administrator-tuned
throughput. It is interesting to note that the alteration in buffer pool size (set by the
advisor) was an important change in the configuration. While the advisor was clearly
inadequate without the planned extensions to support bulk replication, the
recommendations of the advisor were effective in combination with two adjustments by
the database administrator (for locking and logging) in achieving a massive
improvement in system performance.
4.5 Experimental Summary
In all of these experiments (the result of which are in Figure 3) the Configuration
Advisor was able to evaluate and compute a revised or proposed configuration in two or
three seconds. The performance of the advisor is significant when compared to the
amount of time typically spent configuring large database management systems, usually
on the order of one to two weeks.
Percentage of hand-tuned
OLTP 32 bit
OLTP 64bit
Investment Bank A
Hand tuned
Advisor as percentage of tuned
Default configuration
Figure 3. Database performance for hand-tuned parameters versus Configuration Advisor tuning
In the two cases in which the advisor was tested against systems configured by IBM
performance experts, the resulting performance was within 10% of the hand-tuned result.
As well, in these two cases when the Configuration Advisor selections were tested
against the default configuration, the Advisor significantly outperformed the default
In the two cases where the Configuration Advisor was tested against databases in the
field, the Configuration Advisor exceeded the hand-tuned result (though in the case of
“Global investment bank B” two parameters were left at the hand-tuned setting to
account for the special needs of peer-to-peer replication).
5. Conclusions
The Configuration Advisor has the ability to reduce and possibly eliminate the tedious
and time-consuming task of configuring a system for desired performance as is shown
with the benchmarking results. In this paper we have studied the quality of the
recommendations provided by the Configuration Advisor by comparing its performance
to both industry standard benchmark systems tuned and configured by database
specialists inside IBM, and systems in use by two major brokerage firms. Our results
indicate that the advisor shows promise for providing quality recommendations on
database configuration, and, in some cases, exceeding the performance quality of
human-configured systems, as observed in the case of the two investment banking
systems studied. If performance on the system is not an absolute priority, the
Configuration Advisor eliminates the need for frequent manual tuning of performance
related configuration parameters. If the system is required to be configured for near
optimal performance, the advisor supplies a configuration that can serve as a springboard
for further fine-tuning based on specific workload characteristics.
The simplicity and speed of the Configuration Advisor provide compelling arguments
for deployment, and as part of both the Autonomic Computing initiative the advisor
demonstrates some of the benefits that autonomic computing promises to bring. All these
factors combine to make the Configuration Advisor a valuable tool for database
6. Future work
In addition to further refinement of the current modeling for database configuration,
there are several avenues for future research. The first is incorporating automatic
workload characterization schemes into the inputs that are used by the Configuration
Advisor. Currently, the Advisor depends on user-specified workload characterizations.
If the advisor could automatically detect these characteristics the result would be less
work on the part of the user and increased Advisor accuracy. Another area in which
further work could be performed is in investigating closed-loop performance feedback
for the advisor. The advisor’s current architecture produces identical results regardless of
the number of times it is run, if the same inputs are used on a given system. By providing
a mechanism by which the advisor could learn about the effectiveness of its past
recommendations, it will be possible for the advisor to provide a better and more
intelligent configuration with subsequent iterations. Additionally, there is ongoing
research to refine the configuration model within the advisor. Improving this model will
allow the advisor to perform well on an even wider range of database systems.
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