LIS569 – Database Systems Dr. Jianqiang Wang Physical Database Design

1. LIS569 – Database SystemsDr. Jianqiang WangPhysical Database Design Week 7, Feb. 25, 2013

2. Objectives • 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

3. Physical Database Design • Purpose is to translate the logical design into the technical specifications for storing and retrieving data • Goal is to create a design for storing data that will provide adequate performance and insure database integrity, security, and recoverability

4. Inputs Decisions • Normalized relations • Attribute definitions • Volume estimates • Response time expectations • Data security needs • Backup/recovery needs • Integrity expectations • DBMS technology used • Attribute data types • Physical record descriptions (doesn’t always match logical design) • File organizations • Indexes and database architectures • Query optimization Leads to Physical Design Process

5. Example of Composite Usage Map

6. Example of Composite Usage Map Data volumes

7. Example of Composite Usage Map Access Frequencies (per hour)

8. Example of Composite Usage Map Usage analysis: 14,000 purchased parts accessed per hour  8,000 quotations accessed from these 14,000 purchased part accesses  7,000 suppliers accessed from these 8,000 quotation accesses

9. Example of Composite Usage Map Usage analysis: 7,500 suppliers accessed per hour  4,000 quotations accessed from these 7,500 supplier accesses  4,000 purchased parts accessed from these 4000 quotation accesses

10. Designing Fields • Field: smallest unit of data in database • Field design • Choosing data type • Coding, compression, encryption • Controlling data integrity

11. MySQL Text Types • CHAR(size): a fixed length string, up to 255 characters. • VARCHAR(size): a variable length string, up to 255 characters. • TINYTEXT: a string with a maximum length of 255 characters. • TEXT: a string with a maximum length of 65,535 characters. • BLOB: Binary Large Objects, up to 65,535 bytes. • MEDIUMTEXT: a string with a maximum length of 16,777,215 characters. • MEDIUMBLOB: up to 16,777,215 bytes of data. • LONGTEXT: a string with a maximum length of 4,294,967,295 characters. • LONGBLOB: up to 4,294,967,295 bytes of data. • ENUM(x,y,z,etc.): let you enter a list of possible values, up to 65535 values. • SET: similar to ENUM except that SET may contain up to 64 list items and can store more than one choice

12. MySQL Number Types • TINYINT(size): -128 to 127 normal. 0 to 255 UNSIGNED. The maximum number of digits may be specified in parenthesis (same below). • SMALLINT(size): -32768 to 32767 normal. 0 to 65535 UNSIGNED. • MEDIUMINT(size): -8388608 to 8388607 normal. 0 to 16777215 UNSIGNED. • INT(size): -2147483648 to 2147483647 normal. 0 to 4294967295 UNSIGNED. • BIGINT(size): -9223372036854775808 to 9223372036854775807 normal. 0 to 18446744073709551615 UNSIGNED. • FLOAT(size,d) : a small number with a floating decimal point, the maximum number of digits to the right of the decimal point is specified in the d parameter. • DOUBLE(size,d) : a large number with a floating decimal point. • DECIMAL(size,d) : a DOUBLE stored as a string , allowing for a fixed decimal point.

13. MySQL Date/Time Types DATE(): a date in the format of: YYYY-MM-DD. The supported range is from '1000-01-01' to '9999-12-31’. DATETIME(): a date and time combination in the format of YYYY-MM-DD HH:MM:SS. The supported range is from '1000-01-01 00:00:00' to '9999-12-31 23:59:59' TIMESTAMP(): a timestamp whose values are stored as the number of seconds since the Unix epoch ('1970-01-01 00:00:00' UTC). Format: YYYY-MM-DD HH:MM:SS. The supported range is from '1970-01-01 00:00:01' UTC to '2038-01-09 03:14:07' UTC. TIME() : a time in the format of HH:MM:SS, range from '-838:59:59' to '838:59:59‘. YEAR(): a year in two-digit or four-digit format, values in four-digit format: 1901 to 2155 or values in two-digit format: 70 to 69 (representing years from 1970 to 2069).

14. Example of a code look-up table Code saves space, but costs an additional lookup to obtain actual value

15. 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 Sarbanes-Oxley Act (SOX) legislates importance of financial data integrity

16. 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

17. 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 • Many-to-many relationship with non-key attributes (associative entity) • Reference data (1:N relationship where 1-side has data not used in any other relationship)

18. Denormalization: two entities with one-to-one relationship

19. Denormalization: a M:N relationship with nonkey attributes Extra table access required Null description possible

20. Denormalization: reference data Extra table access required Data duplication

21. 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 relations • 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)

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

23. Designing Physical Files • Physical File: • A named portion of secondary memory allocated for the purpose of storing physical records • Tablespace–named set of disk storage elements in which physical files for database tables can be stored • Extent–contiguous section of disk space • Constructs to link two pieces of data: • Sequential storage • Pointers–field of data that can be used to locate related fields or records

24. File Organizations • Technique for physically arranging records of a file on secondary storage • Factors for selecting file organization: • 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 • Sequential • Indexed • Hashed

25. 1 2 If sorted – every insert or delete requires resort Records of the file are stored in sequence by the primary key field values If not sorted Average time to find desired record = n/2 n

26. Indexed File Organizations • Indexed File Organization: the storage of records either 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)

27. uses a tree search Average time to find desired record = depth of the tree

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

29. 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

30. Rules for Using Indexes • Use on larger tables • Index the primary key of each table • Index search fields (fields frequently in WHERE clause) • Fields in SQL ORDER BY and GROUP BY commands • When there are >100 values but not when there are <30 values

31. Rules for Using Indexes (cont.) • Avoid use of indexes for fields with long values; perhaps compress values first • If key to index is used to determine location of record, use surrogate (like sequence nbr) to allow even spread in storage area • DBMS may have limit on number of indexes per table and number of bytes per indexed field(s) • Be careful of indexing attributes with null values; many DBMSs will not recognize null values in an index search