logical database design

© 2005 by Prentice Hall© 2005 by Prentice Hall 11

Chapter 6:Chapter 6:Physical Database Design Physical Database Design

and Performanceand PerformanceModern Database Modern Database

ManagementManagement77thth Edition Edition

Jeffrey A. Hoffer, Mary B. Prescott, Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFaddenFred R. McFadden

22Chapter 6 © 2005 by Prentice Hall© 2005 by Prentice Hall

ObjectivesObjectives Definition of termsDefinition of terms Describe the physical database design Describe the physical database design

processprocess Choose storage formats for attributesChoose storage formats for attributes Select appropriate file organizationsSelect appropriate file organizations Describe three types of file organizationDescribe three types of file organization Describe indexes and their appropriate useDescribe indexes and their appropriate use Translate a database model into efficient Translate a database model into efficient

structuresstructures Know when to use denormalizationKnow when to use denormalization


The Physical Design Stage of SDLC The Physical Design Stage of SDLC (Figures 2-4, 2-5 revisited)(Figures 2-4, 2-5 revisited)

Purpose –develop technology specsDeliverable – program/data structures, technology purchases, organization redesigns

Database activity – physical database design

Project Identification and Selection

Project Initiation and Planning

Analysis

Physical Design

Implementation

Maintenance

Logical Design

Physical Design


Physical Database DesignPhysical Database Design PurposePurpose - translate the logical - translate the logical

description of data into the description of data into the technical technical specificationsspecifications for storing and for storing and retrieving dataretrieving data

Goal - create a design for storing Goal - create a design for storing data that will provide data that will provide adequate adequate performanceperformance and insure and insure database database integrityintegrity, , securitysecurity and and recoverabilityrecoverability


Physical Design ProcessPhysical Design Process

Normalized relations

Volume estimates

Attribute definitions

Response time expectations

Data security needs

Backup/recovery needs

Integrity expectations

DBMS technology used

Inputs

Attribute data types

Physical record descriptions (doesn’t always match logical design)

File organizations

Indexes and database architectures

Query optimization

Leads to

Decisions


Figure 6-1 - Composite usage map (Pine Valley Furniture Company)


Figure 6-1 - Composite usage map (Pine Valley Furniture Company) (Cont.)

Data volumes



Access Frequencies (per hour)



Usage analysis:140 purchased parts accessed per hour 80 quotations accessed from these 140 purchased part accesses 70 suppliers accessed from these 80 quotation accesses



Usage analysis:75 suppliers accessed per hour 40 quotations accessed from these 75 supplier accesses 40 purchased parts accessed from these 40 quotation accesses


Designing FieldsDesigning Fields Field: smallest unit of data in Field: smallest unit of data in

databasedatabase Field design Field design

Choosing data typeChoosing data type Coding, compression, encryptionCoding, compression, encryption Controlling data integrityControlling data integrity


Choosing Data TypesChoosing Data Types CHAR – fixed-length characterCHAR – fixed-length character VARCHAR2 – variable-length VARCHAR2 – variable-length

character (memo)character (memo) LONG – large numberLONG – large number NUMBER – positive/negative numberNUMBER – positive/negative number DATE – actual dateDATE – actual date BLOB – binary large object (good for BLOB – binary large object (good for

graphics, sound clips, etc.)graphics, sound clips, etc.)


Figure 6-2Example code look-up table (Pine Valley Furniture Company)

Code saves space, but costs an additional lookup to obtain actual value.


Field Data IntegrityField Data Integrity Default value – assumed value if no Default value – assumed value if no

explicit valueexplicit value Range control – allowable value Range control – allowable value

limitations (constraints or validation rules)limitations (constraints or validation rules) Null value control – allowing or prohibiting Null value control – allowing or prohibiting

empty fieldsempty fields Referential integrity – range control (and Referential integrity – range control (and

null value allowances) for foreign-key to null value allowances) for foreign-key to primary-key match-upsprimary-key match-ups


Handling Missing DataHandling Missing Data Substitute an estimate of the missing Substitute an estimate of the missing

value (e.g. using a formula)value (e.g. using a formula) Construct a report listing missing valuesConstruct a report listing missing values In programs, ignore missing data unless In programs, ignore missing data unless

the value is significant (sensitivity the value is significant (sensitivity testing)testing)

Triggers can be used to perform these operations


Physical RecordsPhysical Records Physical Record: A group of fields Physical Record: A group of fields

stored in adjacent memory locations stored in adjacent memory locations and retrieved together as a unitand retrieved together as a unit

Page: The amount of data read or Page: The amount of data read or written in one I/O operationwritten in one I/O operation

Blocking Factor: The number of Blocking Factor: The number of physical records per pagephysical records per page


DenormalizationDenormalization Transforming Transforming normalizednormalized relations into relations into

unnormalizedunnormalized physical record specifications physical record specifications Benefits:Benefits:

Can improve performance (speed) be reducing number of Can improve performance (speed) be reducing number of table lookups (i.e table lookups (i.e reduce number of necessary join queriesreduce number of necessary join queries))

Costs (due to data duplication)Costs (due to data duplication) Wasted storage spaceWasted storage space Data integrity/consistency threatsData integrity/consistency threats

Common denormalization opportunitiesCommon denormalization opportunities One-to-one relationship (Fig 6-3)One-to-one relationship (Fig 6-3) Many-to-many relationship with attributes (Fig. 6-4)Many-to-many relationship with attributes (Fig. 6-4) Reference data (1:N relationship where 1-side has data not Reference data (1:N relationship where 1-side has data not

used in any other relationship) (Fig. 6-5)used in any other relationship) (Fig. 6-5)


Fig 6-5 A possible denormalization situation: reference data

Extra table access required

Data duplication


PartitioningPartitioning Horizontal Partitioning: Distributing the rows of Horizontal Partitioning: Distributing the rows of

a table into several separate filesa table into several separate files Useful for situations where different users need Useful for situations where different users need

access to different rowsaccess to different rows Three types: Key Range Partitioning, Hash Three types: Key Range Partitioning, Hash

Partitioning, or Composite PartitioningPartitioning, or Composite Partitioning Vertical Partitioning: Distributing the columns Vertical Partitioning: Distributing the columns

of a table into several separate filesof a table into several separate files Useful for situations where different users need Useful for situations where different users need

access to different columnsaccess to different columns The primary key must be repeated in each fileThe primary key must be repeated in each file

Combinations of Horizontal and VerticalCombinations of Horizontal and Vertical

Partitions often correspond with User Schemas (user views)


Partitioning (cont.)Partitioning (cont.) Advantages of Partitioning:Advantages of Partitioning:

Efficiency: Records used together are grouped togetherEfficiency: Records used together are grouped together Local optimization: Each partition can be optimized for Local optimization: Each partition can be optimized for

performanceperformance Security, recoverySecurity, recovery Load balancing: Partitions stored on different disks, Load balancing: Partitions stored on different disks,

reduces contentionreduces contention Take advantage of parallel processing capabilityTake advantage of parallel processing capability

Disadvantages of Partitioning:Disadvantages of Partitioning: Inconsistent access speed: Slow retrievals across partitionsInconsistent access speed: Slow retrievals across partitions Complexity: non-transparent partitioningComplexity: non-transparent partitioning Extra space or update time: duplicate data; access from Extra space or update time: duplicate data; access from

multiple partitionsmultiple partitions


Partitioning in Oracle 9iPartitioning in Oracle 9i Key-range partitioning:Key-range partitioning:

Partition defined by a range of values for Partition defined by a range of values for column(s) in a tablecolumn(s) in a table

May result in uneven distributionMay result in uneven distribution Hash partitioning:Hash partitioning:

Data spread evenly across partitions Data spread evenly across partitions independent of key valueindependent of key value

Composite partitioning:Composite partitioning: Combination of key and hash partitioningCombination of key and hash partitioning


Data ReplicationData Replication Purposely storing the same data in Purposely storing the same data in

multiple locations of the databasemultiple locations of the database Improves performance by allowing Improves performance by allowing

multiple users to access the same data multiple users to access the same data at the same time with minimum at the same time with minimum contentioncontention

Sacrifices data integrity due to data Sacrifices data integrity due to data duplicationduplication

Best for data that is not updated oftenBest for data that is not updated often


Designing Physical FilesDesigning Physical Files Physical File: Physical File:

A named portion of secondary memory allocated A named portion of secondary memory allocated for the purpose of storing physical recordsfor the purpose of storing physical records

Tablespace – named set of disk storage elements Tablespace – named set of disk storage elements in which physical files for database tables can be in which physical files for database tables can be storedstored

Extent – contiguous section of disk spaceExtent – contiguous section of disk space Constructs to link two pieces of data:Constructs to link two pieces of data:

Sequential storageSequential storage Pointers – field of data that can be used to locate Pointers – field of data that can be used to locate

related fields or recordsrelated fields or records


File OrganizationsFile Organizations Technique for physically arranging records of a file Technique for physically arranging records of a file

on secondary storageon secondary storage Factors for selecting file organization:Factors for selecting file organization:

Fast data retrieval and throughputFast data retrieval and throughput Efficient storage space utilizationEfficient storage space utilization Protection from failure and data lossProtection from failure and data loss Minimizing need for reorganizationMinimizing need for reorganization Accommodating growthAccommodating growth Security from unauthorized useSecurity from unauthorized use

Types of file organizationsTypes of file organizations SequentialSequential IndexedIndexed HashedHashed


Figure 6-7a Sequential file organization

If not sortedAverage time to find desired record = n/2

1

2

n

Records of the file are stored in sequence by the primary key field values

If sorted – every insert or delete requires resort


Indexed File OrganizationsIndexed File Organizations Index – a separate table that contains Index – a separate table that contains

organization of records for quick retrievalorganization of records for quick retrieval Primary keys are automatically indexedPrimary keys are automatically indexed Oracle has a CREATE INDEX operation, and Oracle has a CREATE INDEX operation, and

MS ACCESS allows indexes to be created for MS ACCESS allows indexes to be created for most field typesmost field types

Indexing approaches:Indexing approaches: B-tree index, Fig. 6-7bB-tree index, Fig. 6-7b Bitmap index, Fig. 6-8Bitmap index, Fig. 6-8 Hash Index, Fig. 6-7cHash Index, Fig. 6-7c Join Index, Fig 6-9Join Index, Fig 6-9


Fig. 6-7b – B-tree index

uses a tree searchAverage time to find desired record = depth of the tree

Leaves of the tree are all at same level

consistent access time


Fig 6-7cHashed file or index organization

Hash algorithmUsually uses division-remainder to determine record position. Records with same position are grouped in lists


Fig 6-8Bitmap index index organization

Bitmap saves on space requirementsRows - possible values of the attributeColumns - table rowsBit indicates whether the attribute of a row has the values


Fig 6-9 Join Index – speeds up join operations


Clustering FilesClustering Files In some relational DBMSs, related records In some relational DBMSs, related records

from different tables can be stored together from different tables can be stored together in the same disk areain the same disk area

Useful for improving performance of join Useful for improving performance of join operationsoperations

Primary key records of the main table are Primary key records of the main table are stored adjacent to associated foreign key stored adjacent to associated foreign key records of the dependent tablerecords of the dependent table

e.g. Oracle has a CREATE CLUSTER e.g. Oracle has a CREATE CLUSTER commandcommand


Rules for Using IndexesRules for Using Indexes1. Use on larger tables1. Use on larger tables2. Index the primary key of each table2. Index the primary key of each table3. Index search fields (fields frequently 3. Index search fields (fields frequently

in WHERE clause)in WHERE clause)4. Fields in SQL ORDER BY and GROUP 4. Fields in SQL ORDER BY and GROUP

BY commandsBY commands5. When there are >100 values but not 5. When there are >100 values but not

when there are <30 valueswhen there are <30 values


Rules for Using Indexes Rules for Using Indexes (cont.)(cont.)

6. DBMS may have limit on number of 6. DBMS may have limit on number of indexes per table and number of bytes indexes per table and number of bytes per indexed field(s)per indexed field(s)

7. Null values will not be referenced from 7. Null values will not be referenced from an indexan index

8. Use indexes heavily for non-volatile 8. Use indexes heavily for non-volatile databases; limit the use of indexes for databases; limit the use of indexes for volatile databasesvolatile databases

Why? Because modifications (e.g. inserts, Why? Because modifications (e.g. inserts, deletes) require updates to occur in index filesdeletes) require updates to occur in index files


RAIDRAID Redundant Array of Inexpensive Redundant Array of Inexpensive

DisksDisks A set of disk drives that appear to A set of disk drives that appear to

the user to be a single disk drivethe user to be a single disk drive Allows parallel access to data Allows parallel access to data

(improves access speed)(improves access speed) Pages are arranged in Pages are arranged in stripesstripes


Figure 6-10RAID with four

disks and striping

Here, pages 1-4 can be read/written simultaneously


Raid Types (Figure 6-11)Raid Types (Figure 6-11) Raid 0Raid 0

Maximized parallelismMaximized parallelism No redundancyNo redundancy No error correctionNo error correction no fault-toleranceno fault-tolerance

Raid 1Raid 1 Redundant data – fault tolerantRedundant data – fault tolerant Most common formMost common form

Raid 2Raid 2 No redundancyNo redundancy One record spans across data One record spans across data

disksdisks Error correction in multiple Error correction in multiple

disks– reconstruct damaged disks– reconstruct damaged datadata

Raid 3 Error correction in one disk Record spans multiple data disks (more

than RAID2) Not good for multi-user environments,

Raid 4 Error correction in one disk Multiple records per stripe Parallelism, but slow updates due to

error correction contention Raid 5

Rotating parity array Error correction takes place in same disks as

data storage Parallelism, better performance than Raid4


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logical database design

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