1. 2013-10-01 SLIDE 1IS 257 – Fall 2013 Physical Database Design University of California, Berkeley School of Information I 257: Database Management

2. 2013-10-01 SLIDE 2IS 257 – Fall 2013 Lecture Outline Review –Introduction to SQL –SQLite Physical Database Design Access Methods

3. 2013-10-01 SLIDE 3IS 257 – Fall 2013 Lecture Outline Review –Introduction to SQL –SQLite Physical Database Design Access Methods

4. 2013-10-01 SLIDE 4IS 257 – Fall 2013 SQL - History Structured Query Language SEQUEL from IBM San Jose ANSI 1992 Standard is the version used by most DBMS today (SQL92) Basic language is standardized across relational DBMSs. Each system may have proprietary extensions to standard.

5. 2013-10-01 SLIDE 5IS 257 – Fall 2013 SQL Uses Database Definition and Querying –Can be used as an interactive query language –Can be imbedded in programs Relational Calculus combines Select, Project and Join operations in a single command: SELECT

6. 2013-10-01 SLIDE 6IS 257 – Fall 2013 SELECT Syntax: –SELECT [DISTINCT] attr1, attr2,…, attr3 FROM rel1 r1, rel2 r2,… rel3 r3 WHERE condition1 {AND | OR} condition2 ORDER BY attr1 [DESC], attr3 [DESC]

7. 2013-10-01 SLIDE 7IS 257 – Fall 2013 SELECT Syntax: –SELECT a.author, b.title FROM authors a, bibfile b, au_bib c WHERE a.AU_ID = c.AU_ID and c.accno = b.accno ORDER BY a.author ; Examples in Access...

8. 2013-10-01 SLIDE 8IS 257 – Fall 2013 SELECT Conditions = equal to a particular value >= greater than or equal to a particular value > greater than a particular value <= less than or equal to a particular value <> not equal to a particular value LIKE “*term*” (may be other wild cards in other systems) IN (“opt1”, “opt2”,…,”optn”) BETWEEN val1 AND val2 IS NULL

9. 2013-10-01 SLIDE 9IS 257 – Fall 2013 Using an Aggregate Function SELECT DIVECUST.Name, Sum([Price]*[qty]) AS Total FROM (DIVECUST INNER JOIN DIVEORDS ON DIVECUST.[Customer No] = DIVEORDS.[Customer No]) INNER JOIN DIVEITEM ON DIVEORDS.[Order No] = DIVEITEM.[Order No] GROUP BY DIVECUST.Name HAVING (((DIVECUST.Name) Like "*Jazdzewski"));

10. 2013-10-01 SLIDE 10IS 257 – Fall 2013 Sorting SELECT BIOLIFE.[Common Name], BIOLIFE.[Length (cm)] FROM BIOLIFE ORDER BY BIOLIFE.[Length (cm)] DESC; Note: the square brackets are not part of the standard, But are used in Access for names with embedded blanks

11. 2013-10-01 SLIDE 11IS 257 – Fall 2013 Subqueries SELECT SITES.[Site Name], SITES.[Destination no] FROM SITES WHERE sites.[Destination no] IN (SELECT [Destination no] from DEST where [avg temp (f)] >= 78); Can be used as a form of JOIN.

12. 2013-10-01 SLIDE 12IS 257 – Fall 2013 Aggregate Functions Count Avg SUM MAX MIN Others may be available in different systems

13. 2013-10-01 SLIDE 13IS 257 – Fall 2013 Using Aggregate functions SELECT attr1, Sum(attr2) AS name FROM tab1, tab2... GROUP BY attr1, attr3 HAVING condition;

14. 2013-10-01 SLIDE 14IS 257 – Fall 2013 GROUP BY SELECT DEST.[Destination Name], Count(*) AS Expr1 FROM DEST INNER JOIN DIVEORDS ON DEST.[Destination Name] = DIVEORDS.Destination GROUP BY DEST.[Destination Name] HAVING ((Count(*))>1); Provides a list of Destinations with the number of orders going to that destination

15. 2013-10-01 SLIDE 15IS 257 – Fall 2013 SQL Commands Data Definition Statements –For creation of relations/tables…

16. 2013-10-01 SLIDE 16IS 257 – Fall 2013 Create Table CREATE TABLE table-name (attr1 attr- type PRIMARY KEY, attr2 attr- type,…,attrN attr-type); Adds a new table with the specified attributes (and types) to the database.

17. 2013-10-01 SLIDE 17 INSERT INSERT INTO table-name (col1, col2, col3, …, colN) VALUES (val1, val2, val3,…, valN); INSERT INTO table-name (col1, col2, col3, …, colN) SELECT… Column list is optional, if omitted assumes all columns in table definition and order IS 257 – Fall 2013

18. 2013-10-01 SLIDE 18IS 257 – Fall 2013 Creating a new table from existing tables Access and PostgreSQL Syntax: SELECT [DISTINCT] attr1, attr2,…, attr3 INTO newtablename FROM rel1 r1, rel2 r2,… rel3 r3 WHERE condition1 {AND | OR} condition2 ORDER BY attr1 [DESC], attr3 [DESC]

19. 2013-10-01 SLIDE 19IS 257 – Fall 2013 How to do it in MySQL mysql> SELECT * FROM foo; +---+ | n | +---+ | 1 | +---+ mysql> CREATE TABLE bar (m INT) SELECT n FROM foo; Query OK, 1 row affected (0.02 sec) Records: 1 Duplicates: 0 Warnings: 0 mysql> SELECT * FROM bar; +------+---+ | m | n | +------+---+ | NULL | 1 | +------+---+

20. 2013-10-01 SLIDE 20 SQLite3 Light-weight implementation of a relational DBMS (~340Kb) –Includes most of the features of full DBMS –Intended to be imbedded in programs Available on iSchool servers and for other machines as open source Used as the data manager in iPhone apps and Firefox (among many others) Databases are stored as files in the OS IS 257 – Fall 2013

21. 2013-10-01 SLIDE 21 SQLite3 Data types SQLite uses a more general dynamic type system. In SQLite, the datatype of a value is associated with the value itself, not with its container Types are: –NULL: The value is a NULL value. –INTEGER: The value is a signed integer, stored in 1, 2, 3, 4, 6, or 8 bytes depending on the magnitude of the value –REAL: The value is a floating point value, stored as an 8-byte IEEE floating point number. –TEXT. The value is a text string, stored using the database encoding (UTF-8, UTF-16BE or UTF-16LE). (default max 1,000,000,000 chars) –BLOB. The value is a blob of data, stored exactly as it was input. IS 257 – Fall 2013

22. 2013-10-01 SLIDE 22 SQLite3 Command line [dhcp137:~] ray% sqlite3 test.db SQLite version 3.6.22 Enter ".help" for instructions Enter SQL statements terminated with a ";" sqlite>.tables sqlite> create table stuff (id int, name varchar(30),address varchar(50)); sqlite>.tables stuff sqlite> insert into stuff values (1,'Jane Smith',"123 east st."); sqlite> select * from stuff; 1|Jane Smith|123 east st. sqlite> insert into stuff values (2, 'Bob Jones', '234 west st.'); sqlite> insert into stuff values (3, 'John Smith', '567 North st.'); sqlite> update stuff set address = "546 North st." where id = 1; sqlite> select * from stuff; 1|Jane Smith|546 North st. 2|Bob Jones|234 west st. 3|John Smith|567 North st. IS 257 – Fall 2013

23. 2013-10-01 SLIDE 23 Wildcard searching sqlite> select * from stuff where name like '%Smith%'; 1|Jane Smith|546 North st. 3|John Smith|567 North st. sqlite> select * from stuff where name like 'J%Smith%'; 1|Jane Smith|546 North st. 3|John Smith|567 North st. sqlite> select * from stuff where name like 'Ja%Smith%'; 1|Jane Smith|546 North st. sqlite> select * from stuff where name like 'Jones'; sqlite> select * from stuff where name like '%Jones'; 2|Bob Jones|234 west st. sqlite> select name from stuff...> ; Jane Smith Bob Jones John Smith sqlite> IS 257 – Fall 2013

24. 2013-10-01 SLIDE 24 Create backups sqlite>.dump PRAGMA foreign_keys=OFF; BEGIN TRANSACTION; CREATE TABLE stuff (id int, name varchar(30),address varchar(50)); INSERT INTO "stuff" VALUES(1,'Jane Smith','546 North st.'); INSERT INTO "stuff" VALUES(2,'Bob Jones','234 west st.'); INSERT INTO "stuff" VALUES(3,'John Smith','567 North st.'); COMMIT; sqlite>.schema CREATE TABLE stuff (id int, name varchar(30),address varchar(50)); IS 257 – Fall 2013

25. 2013-10-01 SLIDE 25 Creating Tables from Tables sqlite> create table names as select name, id from stuff; sqlite>.schema CREATE TABLE names(name TEXT,id INT); CREATE TABLE stuff (id int, name varchar(30),address varchar(50)); sqlite> select * from names; Jane Smith|1 Bob Jones|2 John Smith|3 sqlite> create table names2 as select name as xx, id as key from stuff; sqlite>.schema CREATE TABLE names(name TEXT,id INT); CREATE TABLE names2(xx TEXT,"key" INT); CREATE TABLE stuff (id int, name varchar(30),address varchar(50)); sqlite> drop table names2; sqlite>.schema CREATE TABLE names(name TEXT,id INT); CREATE TABLE stuff (id int, name varchar(30),address varchar(50)); IS 257 – Fall 2013

26. 2013-10-01 SLIDE 26 Using SQLite3 from Python SQLite is available as a loadable python library –You can use any SQL commands to create, add data, search, update and delete IS 257 – Fall 2013

27. 2013-10-01 SLIDE 27 SQLite3 from Python [dhcp137:~] ray% python Python 2.5.1 (r251:54869, Apr 18 2007, 22:08:04) [GCC 4.0.1 (Apple Computer, Inc. build 5367)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import sqlite3 >>> sqlite3.version '2.3.2’ >>> sqlite3.sqlite_version '3.3.14' >>> IS 257 – Fall 2013

28. 2013-10-01 SLIDE 28 SQLite3 from Python [dhcp137:~] ray% python Python 2.5.1 (r251:54869, Apr 18 2007, 22:08:04) [GCC 4.0.1 (Apple Computer, Inc. build 5367)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import sqlite3 as lite >>> import sys >>> con = None >>> try:... con = lite.connect('newtest.db')... cur = con.cursor()... cur.execute('SELECT SQLITE_VERSION()')... data = cur.fetchone()... print "SQLite version: %s" % data... except lite.Error, e:... print "Error %s:" % e.args[0]... sys.exit(1)... finally:... if con:... con.close()... SQLite version: 3.3.14 >>> IS 257 – Fall 2013

29. 2013-10-01 SLIDE 29 SQLite3 from Python #!/usr/bin/python2.7 # -*- coding: utf-8 -*- import sqlite3 as lite import sys # our data is defined as a tuple of tuples… cars = ( (1, 'Audi', 52642), (2, 'Mercedes', 57127), (3, 'Skoda', 9000), (4, 'Volvo', 29000), (5, 'Bentley', 350000), (6, 'Hummer', 41400), (7, 'Volkswagen', 21600) ) con = lite.connect(’newtest.db') with con: cur = con.cursor() cur.execute("DROP TABLE IF EXISTS Cars") cur.execute("CREATE TABLE Cars(Id INT, Name TEXT, Price INT)") cur.executemany("INSERT INTO Cars VALUES(?, ?, ?)", cars) IS 257 – Fall 2013

30. 2013-10-01 SLIDE 30 Another Example #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect(':memory:') with con: cur = con.cursor() cur.execute("CREATE TABLE Friends(Id INTEGER PRIMARY KEY, Name TEXT);") cur.execute("INSERT INTO Friends(Name) VALUES ('Tom');") cur.execute("INSERT INTO Friends(Name) VALUES ('Rebecca');") cur.execute("INSERT INTO Friends(Name) VALUES ('Jim');") cur.execute("INSERT INTO Friends(Name) VALUES ('Robert');") lid = cur.lastrowid print "The last Id of the inserted row is %d" % lid IS 257 – Fall 2013

31. 2013-10-01 SLIDE 31 Retrieving Data #!/usr/bin/python # -*- coding: utf-8 -*- import sqlite3 as lite import sys #connect to the cars database… con = lite.connect(’newtest.db') with con: cur = con.cursor() cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print row ray% python2.7 retrnewtest.py (1, u'Audi', 52642) (2, u'Mercedes', 57127) (3, u'Skoda', 9000) (4, u'Volvo', 29000) (5, u'Bentley', 350000) (6, u'Hummer', 41400) (7, u'Volkswagen', 21600) (8, u'Citroen', 21000) ray% IS 257 – Fall 2013

32. 2013-10-01 SLIDE 32 Updating data cur.execute("UPDATE Cars set Price = 450000 where Name = 'Bentley'") cur.execute("SELECT * FROM Cars") rows = cur.fetchall() for row in rows: print row (1, u'Audi', 52642) (2, u'Mercedes', 57127) (3, u'Skoda', 9000) (4, u'Volvo', 29000) (5, u'Bentley', 450000) (6, u'Hummer', 41400) (7, u'Volkswagen', 21600) (8, u'Citroen', 21000) ray% IS 257 – Fall 2013

33. 2013-10-01 SLIDE 33 Add another row… [dhcp137:~] ray% python2.7 Python 2.7.2 (default, Oct 11 2012, 20:14:37) [GCC 4.2.1 Compatible Apple Clang 4.0 … >>> import sqlite3 as lite >>> import sys >>> >>> con = lite.connect(’newtest.db') >>> >>> with con:... cur = con.cursor()... cur.execute("INSERT INTO Cars VALUES(8,'Citroen',21000)")... >>> IS 257 – Fall 2013

34. 2013-10-01 SLIDE 34 From the SQLite3 command line [dhcp137:~] ray% sqlite3 newtest.db SQLite version 3.6.22 Enter ".help" for instructions Enter SQL statements terminated with a ";" sqlite> select * from cars; 1|Audi|52642 2|Mercedes|57127 3|Skoda|9000 4|Volvo|29000 5|Bentley|350000 6|Hummer|41400 7|Volkswagen|21600 8|Citroen|21000 sqlite> INSERT more data… sqlite> select * from cars; 1|Audi|52642 2|Mercedes|57127 3|Skoda|9000 4|Volvo|29000 5|Bentley|450000 6|Hummer|41400 7|Volkswagen|21600 8|Citroen|21000 10|Audi|51000 11|Mercedes|55000 12|Mercedes|56300 13|Volvo|31500 14|Volvo|31000 15|Audi|52000 17|Hummer|42400 16|Hummer|42400 IS 257 – Fall 2013

35. 2013-10-01 SLIDE 35 Use Aggregates to summarize data #!/usr/bin/python2.7 # -*- coding: utf-8 -*- import sqlite3 as lite import sys con = lite.connect('newtest.db') with con: cur = con.cursor() cur.execute("SELECT Name, AVG(Price) FROM Cars GROUP BY Name") rows = cur.fetchall() for row in rows: print row ray% python2.7 aggnewtest.py (u'Audi', 51880.666666666664) (u'Bentley', 450000.0) (u'Citroen', 21000.0) (u'Hummer', 42066.666666666664) (u'Mercedes', 56142.333333333336) (u'Skoda', 9000.0) (u'Volkswagen', 21600.0) (u'Volvo', 30500.0) IS 257 – Fall 2013

36. 2013-10-01 SLIDE 36IS 257 – Fall 2013 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

37. 2013-10-01 SLIDE 37IS 257 – Fall 2013 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

38. 2013-10-01 SLIDE 38IS 257 – Fall 2013 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

39. 2013-10-01 SLIDE 39IS 257 – Fall 2013 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

40. 2013-10-01 SLIDE 40IS 257 – Fall 2013 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

41. 2013-10-01 SLIDE 41IS 257 – Fall 2013 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

42. 2013-10-01 SLIDE 42IS 257 – Fall 2013 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)

43. 2013-10-01 SLIDE 43IS 257 – Fall 2013 Access Data Types (Not MySQL) 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)

44. 2013-10-01 SLIDE 44IS 257 – Fall 2013 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

45. 2013-10-01 SLIDE 45IS 257 – Fall 2013 Oracle Data Types CHAR (size) -- max 2000 VARCHAR2(size) -- up to 4000 DATE DECIMAL, FLOAT, INTEGER, INTEGER(s), SMALLINT, NUMBER, NUMBER(size,d) –All numbers internally in same format… LONG, LONG RAW, LONG VARCHAR –up to 2 Gb -- only one per table BLOB, CLOB, NCLOB -- up to 4 Gb BFILE -- file pointer to binary OS file

46. 2013-10-01 SLIDE 46IS 257 – Fall 2013 MySQL Data Types MySQL supports all of the standard SQL numeric data types. These types include the exact numeric data types (INTEGER, SMALLINT, DECIMAL, and NUMERIC), as well as the approximate numeric data types (FLOAT, REAL, and DOUBLE PRECISION). The keyword INT is a synonym for INTEGER, and the keyword DEC is a synonym for DECIMAL Numeric (can also be declared as UNSIGNED) –TINYINT (1 byte) –SMALLINT (2 bytes) –MEDIUMINT (3 bytes) –INT (4 bytes) –BIGINT (8 bytes) –NUMERIC or DECIMAL –FLOAT –DOUBLE (or DOUBLE PRECISION)

47. 2013-10-01 SLIDE 47IS 257 – Fall 2013 MySQL Data Types The date and time types for representing temporal values are DATETIME, DATE, TIMESTAMP, TIME, and YEAR. Each temporal type has a range of legal values, as well as a “zero” value that is used when you specify an illegal value that MySQL cannot represent –DATETIME '0000-00-00 00:00:00' –DATE '0000-00-00' –TIMESTAMP (4.1 and up) '0000-00-00 00:00:00' –TIMESTAMP (before 4.1) 00000000000000 –TIME '00:00:00' –YEAR 0000

48. 2013-10-01 SLIDE 48IS 257 – Fall 2013 MySQL Data Types The string types are CHAR, VARCHAR, BINARY, VARBINARY, BLOB, TEXT, ENUM, and SET Maximum length for CHAR is 255 and for VARCHAR is 65,535 VARCHAR uses 1 or 2 bytes for the length For longer things there is BLOB and TEXT

49. 2013-10-01 SLIDE 49IS 257 – Fall 2013 MySQL Data Types A BLOB is a binary large object that can hold a variable amount of data. The four BLOB types are TINYBLOB, BLOB, MEDIUMBLOB, and LONGBLOB. These differ only in the maximum length of the values they can hold The four TEXT types are TINYTEXT, TEXT, MEDIUMTEXT, and LONGTEXT. These correspond to the four BLOB types and have the same maximum lengths and storage requirements TINY=1byte, BLOB and TEXT=2bytes, MEDIUM=3bytes, LONG=4bytes

50. 2013-10-01 SLIDE 50IS 257 – Fall 2013 MySQL Data Types BINARY and VARBINARY are like CHAR and VARCHAR but are intended for binary data of 255 bytes or less ENUM is a list of values that are stored as their addresses in the list –For example, a column specified as ENUM('one', 'two', 'three') can have any of the values shown here. The index of each value is also shown: Value = Index NULL = NULL ‘’ = 0 'one’ = 1 ‘two’ = 2 ‘three’ = 3 –An enumeration can have a maximum of 65,535 elements.

51. 2013-10-01 SLIDE 51IS 257 – Fall 2013 MySQL Data Types The final string type (for this version) is a SET A SET is a string object that can have zero or more values, each of which must be chosen from a list of allowed values specified when the table is created. SET column values that consist of multiple set members are specified with members separated by commas (‘,’) For example, a column specified as SET('one', 'two') NOT NULL can have any of these values: –'' –'one' –'two' –'one,two‘ A set can have up to 64 member values and is stored as an 8byte number

52. 2013-10-01 SLIDE 52IS 257 – Fall 2013 Controlling Data Integrity Default values Range control Null value control Referential integrity (next time) Handling missing data

53. 2013-10-01 SLIDE 53IS 257 – Fall 2013 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

54. 2013-10-01 SLIDE 54IS 257 – Fall 2013 Designing Physical/Internal Model Overview terminology Access methods

55. 2013-10-01 SLIDE 55IS 257 – Fall 2013 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

56. 2013-10-01 SLIDE 56IS 257 – Fall 2013 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.

57. 2013-10-01 SLIDE 57IS 257 – Fall 2013 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

58. 2013-10-01 SLIDE 58IS 257 – Fall 2013 Lecture Outline Review –Relational Algebra and Calculus –Introduction to SQL Physical Database Design Access Methods

59. 2013-10-01 SLIDE 59IS 257 – Fall 2013 Internal Model Access Methods Many types of access methods: –Physical Sequential –Indexed Sequential –Indexed Random –Inverted –Direct –Hashed Differences in –Access Efficiency –Storage Efficiency

60. 2013-10-01 SLIDE 60IS 257 – Fall 2013 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)

61. 2013-10-01 SLIDE 61IS 257 – Fall 2013 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

62. 2013-10-01 SLIDE 62IS 257 – Fall 2013 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

63. 2013-10-01 SLIDE 63IS 257 – Fall 2013 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

64. 2013-10-01 SLIDE 64IS 257 – Fall 2013 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

65. 2013-10-01 SLIDE 65IS 257 – Fall 2013 Indexed Random Address Block Number 2132121321 Actual Value Adams Becker Dumpling Getta Harty Becker Harty Adams Getta Dumpling

66. 2013-10-01 SLIDE 66IS 257 – Fall 2013 Btree F | | P | | Z | R | | S | | Z |H | | L | | P |B | | D | | F | Devils Aces Boilers Cars Minors Panthers Seminoles Flyers Hawkeyes Hoosiers

67. 2013-10-01 SLIDE 67IS 257 – Fall 2013 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

68. 2013-10-01 SLIDE 68IS 257 – Fall 2013 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

69. 2013-10-01 SLIDE 69IS 257 – Fall 2013 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

70. 2013-10-01 SLIDE 70IS 257 – Fall 2013 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

71. 2013-10-01 SLIDE 71IS 257 – Fall 2013 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