Physical Database Design and Performance - CCSA211-1

CHAPTER 5:
PHYSICAL DATABASE DESIGN AND
PERFORMANCE
Modern Database Management
11th Edition
Jeffrey A. Hoffer, V. Ramesh,
Heikki Topi
© 2013 Pearson Education, Inc. Publishing as Prentice Hall
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OBJECTIVES
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Define terms
Describe the physical database design process
Choose storage formats for attributes
Select appropriate file organizations
Describe three types of file organization
Describe indexes and their appropriate use
Translate a database model into efficient
structures
Know when and how to use denormalization
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PHYSICAL DATABASE DESIGN
 Purpose–translate
the logical description
of data into the technical specifications
for storing and retrieving data
 Goal–create a design for storing data that
will provide adequate performance and
ensure database integrity, security, and
recoverability
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PHYSICAL DESIGN PROCESS
Inputs
Normalized
Volume
Decisions
relations
Attribute data
estimates
Physical record descriptions
(doesn’t always match
logical design)
Attribute definitions
Response time
expectations
Data
Leads to
security needs
File
organizations
Indexes
and database
architectures
Backup/recovery needs
Integrity
DBMS
expectations
types
Query
optimization
technology used
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PHYSICAL DESIGN FOR
REGULATORY COMPLIANCE
Sarbanes- Oxley Act (SOX) – protect investors by
improving accuracy and reliability
 Committee of Sponsoring Organizations (COSO)
of the Treadway Commission
 IT Infrastructure Library (ITIL)
 Control Objectives for Information and Related
Technology (COBIT)

Regulations and standards that impact physical design decisions
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Figure 5-1 Composite usage map
(Pine Valley Furniture Company)
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Figure 5-1 Composite usage map
(Pine Valley Furniture Company) (cont.)
Data volumes
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Figure 5-1 Composite usage map
(Pine Valley Furniture Company) (cont.)
Access Frequencies
(per hour)
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8
Figure 5-1 Composite usage map
(Pine Valley Furniture Company) (cont.)
Usage analysis:
14,000 purchased parts
accessed per hour 
8000 quotations accessed
from these 140 purchased part
accesses 
7000 suppliers accessed from
these 8000 quotation accesses
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Figure 5-1 Composite usage map
(Pine Valley Furniture Company) (cont.)
Usage analysis:
7500 suppliers accessed per
hour 
4000 quotations accessed
from these 7500 supplier
accesses 
4000 purchased parts
accessed from these 4000
quotation accesses
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DESIGNING FIELDS
 Field:
smallest unit of application data
recognized by system software
 Field design
Choosing
data type
Coding, compression, encryption
Controlling data integrity
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CHOOSING DATA TYPES
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Figure 5-2 Example of a code look-up table
(Pine Valley Furniture Company)
Code saves space, but costs
an additional lookup to
obtain actual value
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FIELD DATA INTEGRITY

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Default value–assumed value if no explicit
value
Range control–allowable value limitations
(constraints or validation rules)
Null value control–allowing or prohibiting
empty fields
Referential integrity–range control (and
null value allowances) for foreign-key to
primary-key match-ups
Sarbanes-Oxley Act (SOX) legislates importance of financial data integrity
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HANDLING MISSING DATA
Substitute an estimate of the missing
value (e.g., using a formula)
 Construct a report listing missing values
 In programs, ignore missing data unless
the value is significant (sensitivity testing)

Triggers can be used to perform these operations.
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DENORMALIZATION
 Transforming normalized relations into non-normalized
physical record specifications
 Benefits:
 Can improve performance (speed) by reducing number of table
lookups (i.e. reduce number of necessary join queries)
 Costs (due to data duplication)
 Wasted storage space
 Data integrity/consistency threats
 Common denormalization opportunities
 One-to-one relationship (Fig. 5-3)
 Many-to-many relationship with non-key attributes (associative entity)
(Fig. 5-4)
 Reference data (1:N relationship where 1-side has data not used in
any other relationship) (Fig. 5-5)
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Figure 5-3 A possible denormalization situation: two entities with oneto-one relationship
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Figure 5-4 A possible denormalization situation: a many-to-many
relationship with nonkey attributes
Extra table
access
required
Null description possible
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Figure 5-5
A possible
denormalization
situation:
reference data
Extra table
access
required
Data duplication
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DENORMALIZE WITH CAUTION

Denormalization can
 Increase
chance of errors and inconsistencies
 Reintroduce anomalies
 Force reprogramming when business rules
change

Perhaps other methods could be used to
improve performance of joins
 Organization
of tables in the database (file
organization and clustering)
 Proper query design and optimization
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PARTITIONING

Horizontal Partitioning: Distributing the rows of a
logical relation into several separate tables


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Vertical Partitioning: Distributing the columns of a
logical relation into several separate physical tables


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Useful for situations where different users need access to
different rows
Three types: Key Range Partitioning, Hash Partitioning, or
Composite Partitioning
Useful for situations where different users need access to
different columns
The primary key must be repeated in each file
Combinations of Horizontal and Vertical
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PARTITIONING PROS AND CONS
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Advantages of Partitioning:
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Efficiency: Records used together are grouped together
Local optimization: Each partition can be optimized for
performance
Security: data not relevant to users are segregated
Recovery and uptime: smaller files take less time to back up
Load balancing: Partitions stored on different disks, reduces
contention
Disadvantages of Partitioning:



Inconsistent access speed: Slow retrievals across partitions
Complexity: Non-transparent partitioning
Extra space or update time: Duplicate data; access from
multiple partitions
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ORACLE’S HORIZONTAL PARTITIONING

Range partitioning

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Hash partitioning

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Partitions defined via hash functions
Will guarantee balanced distribution of rows
Partition could contain widely varying valued fields
List partitioning

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Partitions defined by range of field values
Could result in unbalanced distribution of rows
Like-valued fields share partitions
Based on predefined lists of values for the partitioning
key
Composite partitioning

Combination of the other approaches
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DESIGNING PHYSICAL DATABASE FILES

Physical File:
A
named portion of secondary memory allocated
for the purpose of storing physical records
 Tablespace–named logical storage unit in which
data from multiple tables/views/objects can be
stored

Tablespace components
 Segment
– a table, index, or partition
 Extent–contiguous section of disk space
 Data block – smallest unit of storage
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Figure 5-6 DBMS terminology in an Oracle 11g environment
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FILE ORGANIZATIONS
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Technique for physically arranging records of a file
on secondary storage
Factors for selecting file organization:

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Fast data retrieval and throughput
Efficient storage space utilization
Protection from failure and data loss
Minimizing need for reorganization
Accommodating growth
Security from unauthorized use
Types of file organizations
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
Sequential
Indexed
Hashed
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Figure 5-7a
Sequential file
organization
Records of the
file are stored in
sequence by the
primary key
field values.
If sorted –
every insert or
delete requires
resort
If not sorted
Average time to
find desired record
= n/2
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INDEXED FILE ORGANIZATIONS
Storage of records sequentially or
nonsequentially with an index that allows
software to locate individual records
 Index: a table or other data structure used to
determine in a file the location of records that
satisfy some condition
 Primary keys are automatically indexed
 Other fields or combinations of fields can also
be indexed; these are called secondary keys
(or nonunique keys)

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Figure 5-7b Indexed file organization
uses a tree search
Average time to find desired
record = depth of the tree
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Figure 5-7c
Hashed file
organization
Hash algorithm
Usually uses divisionremainder to determine
record position. Records
with same position are
grouped in lists.
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Figure 6-8 Join Indexes–speeds up join operations
b) Join index for matching foreign
key (FK) and primary key (PK)
a) Join index
for common
non-key
columns
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CLUSTERING FILES
In some relational DBMSs, related records from
different tables can be stored together in the
same disk area
 Useful for improving performance of join
operations
 Primary key records of the main table are
stored adjacent to associated foreign key
records of the dependent table
 e.g. Oracle has a CREATE CLUSTER command

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RULES FOR USING INDEXES
1. Use on larger tables
2. Index the primary key of each table
3. Index search fields (fields frequently in
WHERE clause)
4. Fields in SQL ORDER BY and GROUP BY
commands
5. When there are >100 values but not when
there are <30 values
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RULES FOR USING INDEXES (CONT.)
6. Avoid use of indexes for fields with long
values; perhaps compress values first
7. If key to index is used to determine location of
record, use surrogate (like sequence number)
to allow even spread in storage area
8. DBMS may have limit on number of indexes
per table and number of bytes per indexed
field(s)
9. Be careful of indexing attributes with null
values; many DBMSs will not recognize null
values in an index search
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QUERY OPTIMIZATION
Parallel query processing–possible when
working in multiprocessor systems
 Overriding automatic query optimization–
allows for query writers to preempt the
automated optimization
 Oracle example:

/* */ clause is a hint to override Oracle’s default
query plan
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