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Chapter 6:Chapter 6:Physical Database Design Physical Database Design
and Performanceand Performance
Modern Database Modern Database ManagementManagement99thth Edition Edition
Jeffrey A. Hoffer, Mary B. Prescott, Jeffrey A. Hoffer, Mary B. Prescott, Heikki TopiHeikki Topi
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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 and how to use denormalizationKnow when and how to use denormalization
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Physical Database DesignPhysical Database Design
Purpose–translate the logical Purpose–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 data Goal–create a design for storing data that will provide that will provide adequate adequate performanceperformance and insure and insure database database integrityintegrity, , securitysecurity, and , and recoverabilityrecoverability
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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
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Figure 6-1 Composite usage map (Pine Valley Furniture Company)
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Figure 6-1 Composite usage map (Pine Valley Furniture Company) (cont.)
Data volumes
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Figure 6-1 Composite usage map (Pine Valley Furniture Company) (cont.)
Access Frequencies (per hour)
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Figure 6-1 Composite usage map (Pine Valley Furniture Company) (cont.)
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
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Figure 6-1 Composite usage map (Pine Valley Furniture Company) (cont.)
Usage analysis:75 suppliers accessed per hour 40 quotations accessed from these 75 supplier accesses 40 purchased parts accessed from these 40 quotation accesses
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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
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Choosing Data TypesChoosing Data Types
CHAR–fixed-length characterCHAR–fixed-length character VARCHAR2–variable-length character VARCHAR2–variable-length character
(memo)(memo) LONG–large numberLONG–large number NUMBER–positive/negative numberNUMBER–positive/negative number INEGER–positive/negative whole numberINEGER–positive/negative whole number DATE–actual dateDATE–actual date BLOB–binary large object (good for graphics, BLOB–binary large object (good for graphics,
sound clips, etc.)sound clips, etc.)
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Figure 6-2 Example 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 IntegrityField Data Integrity Default value–assumed value if no Default value–assumed value if no
explicit valueexplicit value Range control–allowable value limitations Range control–allowable value limitations
(constraints or validation rules)(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
Sarbanes-Oxley Act (SOX) legislates importance of financial data integrity
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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
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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
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DenormalizationDenormalization Transforming Transforming normalizednormalized relations into relations into
unnormalizedunnormalized physical record specifications physical record specifications Benefits:Benefits:
Can improve performance (speed) by reducing number of Can improve performance (speed) by 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)
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Figure 6-3 A possible denormalization situation: two entities with one-to-one relationship
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Figure 6-4 A possible denormalization situation: a many-to-many relationship with nonkey attributes
Extra table access required
Null description possible
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Figure 6-5A possible denormalization situation:reference data
Extra table access required
Data duplication
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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 relationsof a table into several separate relations 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)
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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 Inconsistent access speed: Slow retrievals across
partitionspartitions 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
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Oracle 10i Horizontal Oracle 10i Horizontal Partitioning MethodsPartitioning Methods
Key range partitioningKey range partitioning Partitions defined by range of field valuesPartitions defined by range of field values Could result in unbalanced distribution of rowsCould result in unbalanced distribution of rows Like-valued fields share partitionsLike-valued fields share partitions
Hash partitioningHash partitioning Partitions defined via hash functionsPartitions defined via hash functions Will guarantee balanced distribution of rowsWill guarantee balanced distribution of rows Partition could contain widely varying valued Partition could contain widely varying valued
fieldsfields Composite partitioningComposite partitioning
Combines key range and hashCombines key range and hash
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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
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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
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Figure 6-6 Physical file terminology in an Oracle environment
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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
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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
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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
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Figure 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
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Figure 6-7cHashed file or index organization
Hash algorithmUsually uses division-remainder to determine record position. Records with same position are grouped in lists
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Figure 6-8Bitmap index organization
Bitmap saves on space requirementsRows - possible values of the attribute
Columns - table rows
Bit indicates whether the attribute of a row has the values
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Figure 6-9 Join Indexes–speeds up join operations
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Clustering FilesClustering Files
In some relational DBMSs, related records In some relational DBMSs, related records from different tables can be stored from different tables can be stored together in the same disk areatogether in 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
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Rules for Using IndexesRules for Using Indexes
1.1. Use on larger tablesUse on larger tables2.2. Index the primary key of each tableIndex the primary key of each table3.3. Index search fields (fields frequently Index search fields (fields frequently
in WHERE clause)in WHERE clause)4.4. Fields in SQL ORDER BY and GROUP Fields in SQL ORDER BY and GROUP
BY commandsBY commands5.5. When there are >100 values but When there are >100 values but
not when there are <30 valuesnot when there are <30 values
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Rules for Using Indexes Rules for Using Indexes (cont.)(cont.)
6.6. Avoid use of indexes for fields with long Avoid use of indexes for fields with long values; perhaps compress values firstvalues; perhaps compress values first
7.7. DBMS may have limit on number of indexes DBMS may have limit on number of indexes per table and number of bytes per indexed per table and number of bytes per indexed field(s)field(s)
8.8. Null values will not be referenced from an Null values will not be referenced from an indexindex
9.9. Use indexes heavily for non-volatile 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, deletes) require Why? Because modifications (e.g. inserts, deletes) require updates to occur in index filesupdates to occur in index files
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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
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Figure 6-10RAID with four disks and striping
Here, pages 1-4 can be read/written simultaneously
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Raid Types (Figure 6-10)Raid Types (Figure 6-10) Raid 0
Maximized parallelism No redundancy No error correction no fault-tolerance
Raid 1 Redundant data–fault tolerant Most common form
Raid 2 No redundancy One record spans across data
disks Error correction in multiple
disks–reconstruct damaged data
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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Data
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Legacy
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Current
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Data
Warehouses
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Query OptimizationQuery Optimization Parallel query processing – possible when Parallel query processing – possible when
working in multiprocessor systemsworking in multiprocessor systems Overriding automatic query optimization – Overriding automatic query optimization –
allows for query writers to preempt the allows for query writers to preempt the automated optimizationautomated optimization
Picking data block size – factors to Picking data block size – factors to consider include:consider include: Block contention, random and sequential row Block contention, random and sequential row
access speed, row sizeaccess speed, row size Balancing I/O across disk controllersBalancing I/O across disk controllers
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Query Design GuidelinesQuery Design Guidelines Understand how indexes are usedUnderstand how indexes are used Keep optimization statistics up-to-dateKeep optimization statistics up-to-date Use compatible data types for fields and Use compatible data types for fields and
literalsliterals Write simple queriesWrite simple queries Break complex queries into multiple, Break complex queries into multiple,
simple partssimple parts Don’t use one query inside anotherDon’t use one query inside another Don’t combine a table with itselfDon’t combine a table with itself
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Query Design Guidelines Query Design Guidelines (cont.)(cont.)
Create temporary tables for groups of Create temporary tables for groups of queriesqueries
Combine update operationsCombine update operations Retrieve only the data you needRetrieve only the data you need Don’t have the DBMS sort without an indexDon’t have the DBMS sort without an index Learn!Learn! Consider the total query processing time Consider the total query processing time
for ad hoc queriesfor ad hoc queries
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