1. 2009-09-15 SLIDE 1IS 257 – Fall 2009 Physical Database Design University of California, Berkeley School of Information I 257: Database Management

2. 2009-09-15 SLIDE 2IS 257 – Fall 2009 Lecture Outline Review –Normalization Physical Database Design Access Methods

3. 2009-09-15 SLIDE 3IS 257 – Fall 2009 Lecture Outline Review –Normalization Physical Database Design Access Methods

4. 2009-09-15 SLIDE 4IS 257 – Fall 2009 Database Design Process Conceptual Model Logical Model External Model Conceptual requirements Conceptual requirements Conceptual requirements Conceptual requirements Application 1 Application 2Application 3Application 4 Application 2 Application 3 Application 4 External Model External Model External Model Internal Model

5. 2009-09-15 SLIDE 5IS 257 – Fall 2009 Normalization Normalization theory is based on the observation that relations with certain properties are more effective in inserting, updating and deleting data than other sets of relations containing the same data Normalization is a multi-step process beginning with an “unnormalized” relation –Hospital example from Atre, S. Data Base: Structured Techniques for Design, Performance, and Management.

6. 2009-09-15 SLIDE 6IS 257 – Fall 2009 Normal Forms First Normal Form (1NF) Second Normal Form (2NF) Third Normal Form (3NF) Boyce-Codd Normal Form (BCNF) Fourth Normal Form (4NF) Fifth Normal Form (5NF)

7. 2009-09-15 SLIDE 7IS 257 – Fall 2009 Normalization Boyce- Codd and Higher Functional dependency of nonkey attributes on the primary key - Atomic values only Full Functional dependency of nonkey attributes on the primary key No transitive dependency between nonkey attributes All determinants are candidate keys - Single multivalued dependency

8. 2009-09-15 SLIDE 8IS 257 – Fall 2009 Unnormalized Relations First step in normalization is to convert the data into a two-dimensional table In unnormalized relations data can repeat within a column

9. 2009-09-15 SLIDE 9IS 257 – Fall 2009 Unnormalized Relation

10. 2009-09-15 SLIDE 10IS 257 – Fall 2009 First Normal Form To move to First Normal Form a relation must contain only atomic values at each row and column. –No repeating groups –A column or set of columns is called a Candidate Key when its values can uniquely identify the row in the relation.

11. 2009-09-15 SLIDE 11IS 257 – Fall 2009 First Normal Form

12. 2009-09-15 SLIDE 12IS 257 – Fall 2009 Second Normal Form A relation is said to be in Second Normal Form when every nonkey attribute is fully functionally dependent on the primary key. –That is, every nonkey attribute needs the full primary key for unique identification

13. 2009-09-15 SLIDE 13IS 257 – Fall 2009 Second Normal Form

14. 2009-09-15 SLIDE 14IS 257 – Fall 2009 Second Normal Form

15. 2009-09-15 SLIDE 15IS 257 – Fall 2009 Second Normal Form

16. 2009-09-15 SLIDE 16IS 257 – Fall 2009 Third Normal Form A relation is said to be in Third Normal Form if there is no transitive functional dependency between nonkey attributes –When one nonkey attribute can be determined with one or more nonkey attributes there is said to be a transitive functional dependency. The side effect column in the Surgery table is determined by the drug administered –Side effect is transitively functionally dependent on drug so Surgery is not 3NF

17. 2009-09-15 SLIDE 17IS 257 – Fall 2009 Third Normal Form

18. 2009-09-15 SLIDE 18IS 257 – Fall 2009 Third Normal Form

19. 2009-09-15 SLIDE 19IS 257 – Fall 2009 Boyce-Codd Normal Form Most 3NF relations are also BCNF relations. A 3NF relation is NOT in BCNF if: –Candidate keys in the relation are composite keys (they are not single attributes) –There is more than one candidate key in the relation, and –The keys are not disjoint, that is, some attributes in the keys are common

20. 2009-09-15 SLIDE 20IS 257 – Fall 2009 BCNF Relations

21. 2009-09-15 SLIDE 21IS 257 – Fall 2009 Fourth Normal Form Any relation is in Fourth Normal Form if it is BCNF and any multivalued dependencies are trivial Eliminate non-trivial multivalued dependencies by projecting into simpler tables

22. 2009-09-15 SLIDE 22IS 257 – Fall 2009 Fifth Normal Form A relation is in 5NF if every join dependency in the relation is implied by the keys of the relation Implies that relations that have been decomposed in previous NF can be recombined via natural joins to recreate the original relation.

23. 2009-09-15 SLIDE 23IS 257 – Fall 2009 Normalization Normalization is performed to reduce or eliminate Insertion, Deletion or Update anomalies. However, a completely normalized database may not be the most efficient or effective implementation. “Denormalization” is sometimes used to improve efficiency.

24. 2009-09-15 SLIDE 24IS 257 – Fall 2009 Denormalization Usually driven by the need to improve query speed Query speed is improved at the expense of more complex or problematic DML (Data manipulation language) for updates, deletions and insertions.

25. 2009-09-15 SLIDE 25IS 257 – Fall 2009 Downward Denormalization Customer ID Address Name Telephone Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Before: Customer ID Address Name Telephone Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Cust Name After:

26. 2009-09-15 SLIDE 26IS 257 – Fall 2009 Upward Denormalization Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Cust Name Order Price Order Item Order No Item No Item Price Num Ordered Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Cust Name Order Item Order No Item No Item Price Num Ordered

27. 2009-09-15 SLIDE 27IS 257 – Fall 2009 Using RDBMS to help normalize Example database: Cookie Database of books, libraries, publisher and holding information for a shared (union) catalog

28. 2009-09-15 SLIDE 28IS 257 – Fall 2009 Cookie relationships

29. 2009-09-15 SLIDE 29IS 257 – Fall 2009 Cookie BIBFILE relation

30. 2009-09-15 SLIDE 30IS 257 – Fall 2009 How to Normalize? Currently no way to have multiple authors for a given book, and there is duplicate data spread over the BIBFILE table Can we use the DBMS to help us normalize? Access example…

31. 2009-09-15 SLIDE 31IS 257 – Fall 2009 DBMS Assisted Normalization SELECT DISTINCT items to be factored out into a new table Add new ID (autonumber) to table create primary key SELECT DISTINCT ID from new table and original table ID to new linking table Remove column(s) factored out from original table

32. 2009-09-15 SLIDE 32IS 257 – Fall 2009 Lecture Outline Review –Normalization –Using Relational DBs in normalization Physical Database Design Access Methods

33. 2009-09-15 SLIDE 33IS 257 – Fall 2009 Database Design Process Conceptual Model Logical Model External Model Conceptual requirements Conceptual requirements Conceptual requirements Conceptual requirements Application 1 Application 2Application 3Application 4 Application 2 Application 3 Application 4 External Model External Model External Model Internal Model Physical Design

34. 2009-09-15 SLIDE 34IS 257 – Fall 2009 Physical Database Design Many physical database design decisions are implicit in the technology adopted –Also, organizations may have standards or an “information architecture” that specifies operating systems, DBMS, and data access languages -- thus constraining the range of possible physical implementations. We will be concerned with some of the possible physical implementation issues

35. 2009-09-15 SLIDE 35IS 257 – Fall 2009 Physical Database Design The primary goal of physical database design is data processing efficiency We will concentrate on choices often available to optimize performance of database services Physical Database Design requires information gathered during earlier stages of the design process

36. 2009-09-15 SLIDE 36IS 257 – Fall 2009 Physical Design Information Information needed for physical file and database design includes: –Normalized relations plus size estimates for them –Definitions of each attribute –Descriptions of where and when data are used entered, retrieved, deleted, updated, and how often –Expectations and requirements for response time, and data security, backup, recovery, retention and integrity –Descriptions of the technologies used to implement the database

37. 2009-09-15 SLIDE 37IS 257 – Fall 2009 Physical Design Decisions There are several critical decisions that will affect the integrity and performance of the system –Storage Format –Physical record composition –Data arrangement –Indexes –Query optimization and performance tuning

38. 2009-09-15 SLIDE 38IS 257 – Fall 2009 Storage Format Choosing the storage format of each field (attribute). The DBMS provides some set of data types that can be used for the physical storage of fields in the database Data Type (format) is chosen to minimize storage space and maximize data integrity

39. 2009-09-15 SLIDE 39IS 257 – Fall 2009 Objectives of data type selection Minimize storage space Represent all possible values Improve data integrity Support all data manipulations The correct data type should, in minimal space, represent every possible value (but eliminate illegal values) for the associated attribute and can support the required data manipulations (e.g. numerical or string operations)

40. 2009-09-15 SLIDE 40IS 257 – Fall 2009 Access Data Types Numeric (1, 2, 4, 8 bytes, fixed or float) Text (255 max) Memo (64000 max) Date/Time (8 bytes) Currency (8 bytes, 15 digits + 4 digits decimal) Autonumber (4 bytes) Yes/No (1 bit) OLE (limited only by disk space) Hyperlinks (up to 64000 chars)

41. 2009-09-15 SLIDE 41IS 257 – Fall 2009 Access Numeric types Byte –Stores numbers from 0 to 255 (no fractions). 1 byte Integer – Stores numbers from –32,768 to 32,767 (no fractions) 2 bytes Long Integer(Default) –Stores numbers from –2,147,483,648 to 2,147,483,647 (no fractions). 4 bytes Single –Stores numbers from -3.402823E38 to –1.401298E–45 for negative values and from 1.401298E–45 to 3.402823E38 for positive values. 4 bytes Double –Stores numbers from –1.79769313486231E308 to – 4.94065645841247E–324 for negative values and from 1.79769313486231E308 to 4.94065645841247E–324 for positive values.158 bytes Replication ID –Globally unique identifier (GUID)N/A16 bytes

42. 2009-09-15 SLIDE 42IS 257 – Fall 2009 Controlling Data Integrity Default values Range control Null value control Referential integrity (next time) Handling missing data

43. 2009-09-15 SLIDE 43IS 257 – Fall 2009 Designing Physical Records A physical record is a group of fields stored in adjacent memory locations and retrieved together as a unit Fixed Length and variable fields

44. 2009-09-15 SLIDE 44IS 257 – Fall 2009 Designing Physical/Internal Model Overview terminology Access methods

45. 2009-09-15 SLIDE 45IS 257 – Fall 2009 Physical Design Internal Model/Physical Model Operating System Access Methods Data Base User request DBMS Internal Model Access Methods External Model Interface 1 Interface 3 Interface 2

46. 2009-09-15 SLIDE 46IS 257 – Fall 2009 Physical Design Interface 1: User request to the DBMS. The user presents a query, the DBMS determines which physical DBs are needed to resolve the query Interface 2: The DBMS uses an internal model access method to access the data stored in a logical database. Interface 3: The internal model access methods and OS access methods access the physical records of the database.

47. 2009-09-15 SLIDE 47IS 257 – Fall 2009 Physical File Design A Physical file is a portion of secondary storage (disk space) allocated for the purpose of storing physical records Pointers - a field of data that can be used to locate a related field or record of data Access Methods - An operating system algorithm for storing and locating data in secondary storage Pages - The amount of data read or written in one disk input or output operation

48. 2009-09-15 SLIDE 48IS 257 – Fall 2009 Internal Model Access Methods Many types of access methods: –Physical Sequential –Indexed Sequential –Indexed Random –Inverted –Direct –Hashed Differences in –Access Efficiency –Storage Efficiency

49. 2009-09-15 SLIDE 49IS 257 – Fall 2009 Physical Sequential Key values of the physical records are in logical sequence Main use is for “dump” and “restore” Access method may be used for storage as well as retrieval Storage Efficiency is near 100% Access Efficiency is poor (unless fixed size physical records)

50. 2009-09-15 SLIDE 50IS 257 – Fall 2009 Indexed Sequential Key values of the physical records are in logical sequence Access method may be used for storage and retrieval Index of key values is maintained with entries for the highest key values per block(s) Access Efficiency depends on the levels of index, storage allocated for index, number of database records, and amount of overflow Storage Efficiency depends on size of index and volatility of database

51. 2009-09-15 SLIDE 51IS 257 – Fall 2009 Index Sequential Data File Block 1 Block 2 Block 3 Address Block Number 123…123… Actual Value Dumpling Harty Texaci... Adams Becker Dumpling Getta Harty Mobile Sunoci Texaci

52. 2009-09-15 SLIDE 52IS 257 – Fall 2009 Indexed Sequential: Two Levels Address 789…789… Key Value 385 678 805 001 003. 150 705 710. 785 251. 385 455 480. 536 605 610. 678 791. 805 Address 1212 Key Value 150 385 Address 3434 Key Value 536 678 Address 5656 Key Value 785 805

53. 2009-09-15 SLIDE 53IS 257 – Fall 2009 Indexed Random Key values of the physical records are not necessarily in logical sequence Index may be stored and accessed with Indexed Sequential Access Method Index has an entry for every data base record. These are in ascending order. The index keys are in logical sequence. Database records are not necessarily in ascending sequence. Access method may be used for storage and retrieval

54. 2009-09-15 SLIDE 54IS 257 – Fall 2009 Indexed Random Address Block Number 2132121321 Actual Value Adams Becker Dumpling Getta Harty Becker Harty Adams Getta Dumpling

55. 2009-09-15 SLIDE 55IS 257 – Fall 2009 Btree F | | P | | Z | R | | S | | Z |H | | L | | P |B | | D | | F | Devils Aces Boilers Cars Minors Panthers Seminoles Flyers Hawkeyes Hoosiers

56. 2009-09-15 SLIDE 56IS 257 – Fall 2009 Inverted Key values of the physical records are not necessarily in logical sequence Access Method is better used for retrieval An index for every field to be inverted may be built Access efficiency depends on number of database records, levels of index, and storage allocated for index

57. 2009-09-15 SLIDE 57IS 257 – Fall 2009 Inverted Address Block Number 123…123… Actual Value CH 145 CS 201 CS 623 PH 345 CH 145 101, 103,104 CS 201 102 CS 623 105, 106 Adams Becker Dumpling Getta Harty Mobile Student name Course Number CH145 cs201 ch145 cs623

58. 2009-09-15 SLIDE 58IS 257 – Fall 2009 Direct Key values of the physical records are not necessarily in logical sequence There is a one-to-one correspondence between a record key and the physical address of the record May be used for storage and retrieval Access efficiency always 1 Storage efficiency depends on density of keys No duplicate keys permitted

59. 2009-09-15 SLIDE 59IS 257 – Fall 2009 Hashing Key values of the physical records are not necessarily in logical sequence Many key values may share the same physical address (block) May be used for storage and retrieval Access efficiency depends on distribution of keys, algorithm for key transformation and space allocated Storage efficiency depends on distibution of keys and algorithm used for key transformation

60. 2009-09-15 SLIDE 60IS 257 – Fall 2009 Comparative Access Methods Indexed No wasted space for data but extra space for index Moderately Fast Very fast with multiple indexes OK if dynamic OK if dynamic Easy but requires Maintenance of indexes Factor Storage space Sequential retrieval on primary key Random Retr. Multiple Key Retr. Deleting records Adding records Updating records Sequential No wasted space Very fast Impractical Possible but needs a full scan can create wasted space requires rewriting file usually requires rewriting file Hashed more space needed for addition and deletion of records after initial load Impractical Very fast Not possible very easy

61. 2009-09-15 SLIDE 61IS 257 – Fall 2009 Database Creation in Access Simplest to use a design view –wizards are available, but less flexible Need to watch the default values Helps to know what the primary key is, or if one is to be created automatically –Automatic creation is more complex in other RDBMS and ORDBMS Need to make decision about the physical storage of the data

62. 2009-09-15 SLIDE 62IS 257 – Fall 2009 Database Creation in Access Some Simple Examples

63. 2009-09-15 SLIDE 63IS 257 – Fall 2009 Next Time Indexes and when to index Integrity Constraints Referential Integrity