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Physical Database Design

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1 Physical Database Design
University of California, Berkeley School of Information IS 257: Database Management IS 257 – Fall 2006

2 Lecture Outline Review Physical Database Design Normalization
Access Methods IS 257 – Fall 2006

3 Lecture Outline Review Physical Database Design Normalization
Access Methods IS 257 – Fall 2006

4 Database Design Process
Application 1 Application 2 Application 3 Application 4 External Model External Model External Model External Model Application 1 Conceptual requirements Application 2 Conceptual requirements Conceptual Model Logical Model Internal Model Application 3 Conceptual requirements Application 4 Conceptual requirements IS 257 – Fall 2006

5 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. IS 257 – Fall 2006

6 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) IS 257 – Fall 2006

7 Unnormalized Relations
Normalization Unnormalized Relations First normal form Second normal form Boyce- Codd and Higher Third normal form Functional dependencyof nonkey attributes on the primary key - Atomic values only Full Functional dependencyof nonkey attributes on the primary key No transitive dependency between nonkey attributes All determinants are candidate keys - Single multivalued dependency IS 257 – Fall 2006

8 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 IS 257 – Fall 2006

9 Unnormalized Relation
IS 257 – Fall 2006

10 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. IS 257 – Fall 2006

11 First Normal Form IS 257 – Fall 2006

12 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 IS 257 – Fall 2006

13 Second Normal Form IS 257 – Fall 2006

14 Second Normal Form IS 257 – Fall 2006

15 Second Normal Form IS 257 – Fall 2006

16 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 IS 257 – Fall 2006

17 Third Normal Form IS 257 – Fall 2006

18 Third Normal Form IS 257 – Fall 2006

19 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 IS 257 – Fall 2006

20 BCNF Relations IS 257 – Fall 2006

21 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 IS 257 – Fall 2006

22 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. IS 257 – Fall 2006

23 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. IS 257 – Fall 2006

24 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. IS 257 – Fall 2006

25 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: IS 257 – Fall 2006

26 Upward Denormalization
Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Cust Name Order Item Item No Item Price Num Ordered Order Order No Date Taken Date Dispatched Date Invoiced Cust ID Cust Name Order Price Order Item Item No Item Price Num Ordered IS 257 – Fall 2006

27 Lecture Outline Review Physical Database Design Normalization
Access Methods IS 257 – Fall 2006

28 Database Design Process
Application 1 Application 2 Application 3 Application 4 External Model External Model External Model External Model Application 1 Conceptual requirements Application 2 Conceptual requirements Conceptual Model Logical Model Internal Model Application 3 Conceptual requirements Application 4 Conceptual requirements Physical Design IS 257 – Fall 2006

29 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 IS 257 – Fall 2006

30 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 IS 257 – Fall 2006

31 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 IS 257 – Fall 2006

32 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 IS 257 – Fall 2006

33 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 IS 257 – Fall 2006

34 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) IS 257 – Fall 2006

35 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 chars) IS 257 – Fall 2006

36 Access Numeric types Byte Integer Long Integer (Default) Single Double
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 E38 to – E–45 for negative values and from E–45 to E38 for positive values. 4 bytes Double Stores numbers from – E308 to – E–324 for negative values and from E308 to E–324 for positive values bytes Replication ID Globally unique identifier (GUID) N/A 16 bytes IS 257 – Fall 2006

37 Controlling Data Integrity
Default values Range control Null value control Referential integrity (next time) Handling missing data IS 257 – Fall 2006

38 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 IS 257 – Fall 2006

39 Designing Physical/Internal Model
Overview terminology Access methods IS 257 – Fall 2006

40 Physical Design Internal Model/Physical Model DBMS User request
Interface 1 DBMS Internal Model Access Methods External Model Interface 2 Operating System Access Methods Interface 3 Data Base IS 257 – Fall 2006

41 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. IS 257 – Fall 2006

42 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 IS 257 – Fall 2006

43 Internal Model Access Methods
Many types of access methods: Physical Sequential Indexed Sequential Indexed Random Inverted Direct Hashed Differences in Access Efficiency Storage Efficiency IS 257 – Fall 2006

44 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) IS 257 – Fall 2006

45 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 IS 257 – Fall 2006

46 Index Sequential Data File Block 1 Adams Becker Block 2 Block 3 Getta
Address Block Number 1 2 3 … Actual Value Dumpling Harty Texaci ... Adams Becker Getta Mobile Sunoci IS 257 – Fall 2006

47 Indexed Sequential: Two Levels
Address 7 8 9 … Key Value 385 678 805 001 003 . 150 705 710 785 251 455 480 536 605 610 791 1 2 3 4 5 6 IS 257 – Fall 2006

48 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 IS 257 – Fall 2006

49 Indexed Random Address Block Number 2 1 3 Actual Value Adams Becker
Dumpling Getta Harty IS 257 – Fall 2006

50 Btree F | | P | | Z | R | | S | | Z | H | | L | | P | B | | D | | F |
Devils Aces Boilers Cars Minors Panthers Seminoles Flyers Hawkeyes Hoosiers IS 257 – Fall 2006

51 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 IS 257 – Fall 2006

52 Inverted 101, 103,104 102 105, 106 Adams Becker Dumpling Getta Harty
Address Block Number 1 2 3 … Actual Value CH 145 CS 201 CS 623 PH 345 101, 103,104 102 105, 106 Adams Becker Dumpling Getta Harty Mobile Student name Course CH145 cs201 ch145 cs623 IS 257 – Fall 2006

53 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 IS 257 – Fall 2006

54 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 IS 257 – Fall 2006

55 Comparative Access Methods
Factor Storage space Sequential retrieval on primary key Random Retr. Multiple Key Retr. Deleting records Adding records Updating records 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 Not possible very easy Indexed No wasted space for data but extra space for index Moderately Fast Very fast with multiple indexes OK if dynamic Easy but requires Maintenance of indexes IS 257 – Fall 2006

56 Next Time Indexes and when to index Integrity Constraints
Referential Integrity IS 257 – Fall 2006

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