Ch 7 Physical D B Design

Physical Database Design and Performance Modern Database Management Jeffrey A. Hoffer, Mary B. Prescott, Fred R. McFadden

The Physical Design Stage of SDLC (figures 2.4, 2.5 revisited) Project Identification and Selection Project Initiation and Planning Analysis Physical Design Implementation Maintenance Logical Design Purpose –develop technology specs Deliverable – pgm/data structures, technology purchases, organization redesigns Database activity – physical database design

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 insure database integrity , security and recoverability

Physical 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) Data volumes

Figure 6.1 - Composite usage map (Pine Valley Furniture Company) Access Frequencies (per hour)

Figure 6.1 - Composite usage map (Pine Valley Furniture Company) Usage analysis: 200 purchased parts accessed per hour  80 quotations accessed from these 200 purchased part accesses  70 suppliers accessed from these 80 quotation accesses

Figure 6.1 - Composite usage map (Pine Valley Furniture Company) 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 Fields Field: smallest unit of data in database Field design Choosing data type Coding, compression, encryption Controlling data integrity

Choosing Data Types CHAR – fixed-length character VARCHAR2 – variable-length character (memo) LONG – large number NUMBER – positive/negative number DATE – actual date BLOB – binary large object (good for graphics, sound clips, etc.)

Figure 6.2 Example code-look-up table (Pine Valley Furniture Company) Code saves space, but costs an additional lookup to obtain actual value.

Field Data Integrity 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

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 Triggers can be used to perform these operations

Physical Records Physical Record: A group of fields stored in adjacent memory locations and retrieved together as a unit Page: The amount of data read or written in one I/O operation Blocking Factor: The number of physical records per page

Denormalization Transforming normalized relations into unnormalized physical record specifications Benefits: Can improve performance (speed) be 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 6.3) Many-to-many relationship with attributes (Fig. 6.4) Reference data (1:N relationship where 1-side has data not used in any other relationship) (Fig. 6.5)

Fig 6.5 – A possible denormalization situation: reference data Extra table access required Data duplication

Partitioning Horizontal Partitioning: Distributing the rows of a table into several separate files Useful for situations where different users need access to different rows Three types: Key Range Partitioning, Hash Partitioning, or Composite Partitioning Vertical Partitioning: Distributing the columns of a table into several separate files 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 Partitions often correspond with User Schemas (user views)

Partitioning Advantages of Partitioning: Records used together are grouped together Each partition can be optimized for performance Security, recovery Partitions stored on different disks: contention Take advantage of parallel processing capability Disadvantages of Partitioning: Slow retrievals across partitions Complexity

Data Replication Purposely storing the same data in multiple locations of the database Improves performance by allowing multiple users to access the same data at the same time with minimum contention Sacrifices data integrity due to data duplication Best for data that is not updated often

Designing Physical Files Physical File: A named portion of secondary memory allocated for the purpose of storing physical records Constructs to link two pieces of data: Sequential storage. Pointers. File Organization: How the files are arranged on the disk. Access Method: How the data can be retrieved based on the file organization.

Figure 6-7 (a) Sequential file organization If not sorted Average 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 Organizations Index – a separate table that contains organization of records for quick retrieval Primary keys are automatically indexed Oracle has a CREATE INDEX operation, and MS ACCESS allows indexes to be created for most field types Indexing approaches: B-tree index, Fig. 6-7b Bitmap index, Fig. 6-8 Hash Index, Fig. 6-7c Join Index, Fig 6-9

Fig. 6-7b – B-tree index uses a tree search Average time to find desired record = depth of the tree Leaves of the tree are all at same level  consistent access time

Fig 6-7c Hashed file or index organization Hash algorithm Usually uses division-remainder to determine record position. Records with same position are grouped in lists.

Fig 6-8 Bitmap index index organization Bitmap saves on space requirements Rows - possible values of the attribute Columns - table rows Bit indicates whether the attribute of a row has the values

Fig 6-9 Join Index – speeds up join operations

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

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

Rules for Using Indexes 6. DBMS may have limit on number of indexes per table and number of bytes per indexed field(s) 7. Null values will not be referenced from an index 8. Use indexes heavily for non-volatile databases; limit the use of indexes for volatile databases Why? Because modifications (e.g. inserts, deletes) require updates to occur in index files

RAID Redundant Array of Inexpensive Disks A set of disk drives that appear to the user to be a single disk drive Allows parallel access to data (improves access speed) Pages are arranged in stripes

Figure 6-10 – RAID with four disks and striping Here, pages 1-4 can be read/written simultaneously

Raid Types (Figure 6-11) 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

Database Architectures (figure 6-12 Legacy Systems Current Technology Data Warehouses

Query Optimization Parallel Query Processing Override Automatic Query Optimization Data Block Size -- Performance tradeoffs: Block contention Random vs. sequential row access speed Row size Overhead Balancing I/O Across Disk Controllers

Query Optimization Wise use of indexes Compatible data types Simple queries Avoid query nesting Temporary tables for query groups Select only needed columns No sort without index

Ch 7 Physical D B Design

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Ch 7 Physical D B Design