Physical Database Design University of California Berkeley School
Physical Database Design University of California, Berkeley School of Information I 257: Database Management IS 257 – Fall 2011 -09 -15 SLIDE 1
Lecture Outline • Review – Relational Algebra and Calculus – Introduction to SQL • Physical Database Design • Access Methods IS 257 – Fall 2011 -09 -15 SLIDE 2
Lecture Outline • Review – Relational Algebra and Calculus – Introduction to SQL • Physical Database Design • Access Methods IS 257 – Fall 2011 -09 -15 SLIDE 3
Relational Algebra Operations • • Select Project Product Union Intersect Difference Join Divide IS 257 – Fall 2011 -09 -15 SLIDE 4
Restrict (Select) • Extracts specified tuples (rows) from a specified relation (table). IS 257 – Fall 2011 -09 -15 SLIDE 5
Project • Extracts specified attributes(columns) from a specified relation. IS 257 – Fall 2011 -09 -15 SLIDE 6
Join • Builds a relation from two specified relations consisting of all possible concatenated pairs, one from each of the two relations, such that in each pair the two tuples satisfy some condition. (E. g. , equal values in a given col. ) A 1 B 1 A 2 B 1 A 3 B 2 IS 257 – Fall 2011 B 1 C 1 B 2 C 2 B 3 C 3 (Natural or Inner) Join A 1 B 1 C 1 A 2 B 1 C 1 A 3 B 2 C 2 2011 -09 -15 SLIDE 7
Outer Join • Outer Joins are similar to PRODUCT -- but will leave NULLs for any row in the first table with no corresponding rows in the second. A 1 A 2 A 3 A 4 IS 257 – Fall 2011 B 1 B 2 B 7 B 1 C 1 B 2 C 2 B 3 C 3 Outer Join A 1 B 1 C 1 A 2 B 1 C 1 A 3 B 2 C 2 A 4 * * 2011 -09 -15 SLIDE 8
Join Items IS 257 – Fall 2011 -09 -15 SLIDE 9
Relational Algebra • What is the name of the customer who ordered Large Red Widgets? – Select “large Red Widgets” from Part as temp 1 – Join temp 1 with Line-item on Part # as temp 2 – Join temp 2 with Invoice on Invoice # as temp 3 – Join temp 3 with customer on cust # as temp 4 – Project Name from temp 4 IS 257 – Fall 2011 -09 -15 SLIDE 10
Relational Calculus • Relational Algebra provides a set of explicit operations (select, project, join, etc) that can be used to build some desired relation from the database. • Relational Calculus provides a notation formulating the definition of that desired relation in terms of the relations in the database without explicitly stating the operations to be performed • SQL is based on the relational calculus. IS 257 – Fall 2011 -09 -15 SLIDE 11
SQL - History • Structured Query Language • SEQUEL from IBM San Jose • ANSI 1992 Standard is the version used by most DBMS today (SQL 92) • Basic language is standardized across relational DBMSs. Each system may have proprietary extensions to standard. IS 257 – Fall 2011 -09 -15 SLIDE 12
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 IS 257 – Fall 2011 -09 -15 SLIDE 13
SELECT • Syntax: – SELECT [DISTINCT] attr 1, attr 2, …, attr 3 FROM rel 1 r 1, rel 2 r 2, … rel 3 r 3 WHERE condition 1 {AND | OR} condition 2 ORDER BY attr 1 [DESC], attr 3 [DESC] IS 257 – Fall 2011 -09 -15 SLIDE 14
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. . . IS 257 – Fall 2011 -09 -15 SLIDE 15
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 (“opt 1”, “opt 2”, …, ”optn”) • BETWEEN val 1 AND val 2 • IS NULL IS 257 – Fall 2011 -09 -15 SLIDE 16
Relational Algebra Selection using SELECT • Syntax: – SELECT * FROM rel 1 WHERE condition 1 {AND | OR} condition 2; IS 257 – Fall 2011 -09 -15 SLIDE 17
Relational Algebra Projection using SELECT • Syntax: – SELECT [DISTINCT] attr 1, attr 2, …, attr 3 FROM rel 1 r 1, rel 2 r 2, … rel 3 r 3; IS 257 – Fall 2011 -09 -15 SLIDE 18
Relational Algebra Join using SELECT • Syntax: – SELECT * FROM rel 1 r 1, rel 2 r 2 WHERE r 1. linkattr = r 2. linkattr ; IS 257 – Fall 2011 -09 -15 SLIDE 19
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 IS 257 – Fall 2011 -09 -15 SLIDE 20
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. IS 257 – Fall 2011 -09 -15 SLIDE 21
Aggregate Functions • • • Count Avg SUM MAX MIN Others may be available in different systems IS 257 – Fall 2011 -09 -15 SLIDE 22
Using Aggregate functions • SELECT attr 1, Sum(attr 2) AS name FROM tab 1, tab 2. . . GROUP BY attr 1, attr 3 HAVING condition; IS 257 – Fall 2011 -09 -15 SLIDE 23
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")); IS 257 – Fall 2011 -09 -15 SLIDE 24
GROUP BY • SELECT DEST. [Destination Name], Count(*) AS Expr 1 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 IS 257 – Fall 2011 -09 -15 SLIDE 25
SQL Commands • Data Definition Statements – For creation of relations/tables… IS 257 – Fall 2011 -09 -15 SLIDE 26
Create Table • CREATE TABLE table-name (attr 1 attrtype PRIMARY KEY, attr 2 attrtype, …, attr. N attr-type); • Adds a new table with the specified attributes (and types) to the database. IS 257 – Fall 2011 -09 -15 SLIDE 27
Access Data Types (Not My. SQL) • • • 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) IS 257 – Fall 2011 -09 -15 SLIDE 28
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. 402823 E 38 to – 1. 401298 E– 45 for negative values and from 1. 401298 E– 45 to 3. 402823 E 38 for positive values. 4 bytes • Double – Stores numbers from – 1. 79769313486231 E 308 to – 4. 94065645841247 E– 324 for negative values and from 1. 79769313486231 E 308 to 4. 94065645841247 E– 324 for positive values. 15 8 bytes • Replication ID – Globally unique identifier (GUID) IS 257 – Fall 2011 N/A 16 bytes 2011 -09 -15 SLIDE 29
Oracle Data Types • • CHAR (size) -- max 2000 VARCHAR 2(size) -- up to 4000 DATE DECIMAL, FLOAT, INTEGER(s), SMALLINT, 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 IS 257 – Fall 2011 -09 -15 SLIDE 30
Creating a new table from existing tables • Access and Postgre. SQL Syntax: SELECT [DISTINCT] attr 1, attr 2, …, attr 3 INTO newtablename FROM rel 1 r 1, rel 2 r 2, … rel 3 r 3 WHERE condition 1 {AND | OR} condition 2 ORDER BY attr 1 [DESC], attr 3 [DESC] IS 257 – Fall 2011 -09 -15 SLIDE 31
How to do it in My. SQL 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 | +------+ IS 257 – Fall 2011 -09 -15 SLIDE 32
Database Design Process Application 1 External Model Application 2 Application 3 Application 4 External Model Application 1 Conceptual requirements Application 2 Conceptual requirements Application 3 Conceptual requirements Conceptual Model Logical Model Internal Model Application 4 Conceptual requirements IS 257 – Fall 2011 Physical Design 2011 -09 -15 SLIDE 33
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 2011 -09 -15 SLIDE 34
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 2011 -09 -15 SLIDE 35
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 2011 -09 -15 SLIDE 36
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 2011 -09 -15 SLIDE 37
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 2011 -09 -15 SLIDE 38
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 2011 -09 -15 SLIDE 39
My. SQL Data Types • My. SQL 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) IS 257 – Fall 2011 -09 -15 SLIDE 40
My. SQL 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 My. SQL cannot represent – – – DATETIME '0000 -00 -00 00: 00' DATE '0000 -00 -00' TIMESTAMP (4. 1 and up) '0000 -00 -00 00: 00' TIMESTAMP (before 4. 1) 0000000 TIME '00: 00' YEAR 0000 IS 257 – Fall 2011 -09 -15 SLIDE 41
My. SQL 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 IS 257 – Fall 2011 -09 -15 SLIDE 42
My. SQL Data Types • A BLOB is a binary large object that can hold a variable amount of data. • The four BLOB types are TINYBLOB, MEDIUMBLOB, and LONGBLOB. These differ only in the maximum length of the values they can hold • The four TEXT types are TINYTEXT, MEDIUMTEXT, and LONGTEXT. These correspond to the four BLOB types and have the same maximum lengths and storage requirements • TINY=1 byte, BLOB and TEXT=2 bytes, MEDIUM=3 bytes, LONG=4 bytes IS 257 – Fall 2011 -09 -15 SLIDE 43
My. SQL 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. IS 257 – Fall 2011 -09 -15 SLIDE 44
My. SQL 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 8 byte number IS 257 – Fall 2011 -09 -15 SLIDE 45
Controlling Data Integrity • • • Default values Range control Null value control Referential integrity (next time) Handling missing data IS 257 – Fall 2011 -09 -15 SLIDE 46
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 2011 -09 -15 SLIDE 47
Designing Physical/Internal Model • Overview • terminology • Access methods IS 257 – Fall 2011 -09 -15 SLIDE 48
Physical Design • Internal Model/Physical Model User request Interface 1 External Model DBMS Internal Model Access Methods Interface 2 Operating System Access Methods Interface 3 Data Base IS 257 – Fall 2011 -09 -15 SLIDE 49
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 2011 -09 -15 SLIDE 50
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 2011 -09 -15 SLIDE 51
Lecture Outline • Review – Relational Algebra and Calculus – Introduction to SQL • Physical Database Design • Access Methods IS 257 – Fall 2011 -09 -15 SLIDE 52
Internal Model Access Methods • Many types of access methods: – Physical Sequential – Indexed Random – Inverted – Direct – Hashed • Differences in – Access Efficiency – Storage Efficiency IS 257 – Fall 2011 -09 -15 SLIDE 53
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 2011 -09 -15 SLIDE 54
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 2011 -09 -15 SLIDE 55
Index Sequential Data File Actual Value IS 257 – Fall 2011 Address Block Number Dumpling 1 Harty 2 Texaci 3 . . . … Adams Becker Dumpling Block 1 Getta Harty Block 2 Mobile Sunoci Texaci Block 3 2011 -09 -15 SLIDE 56
Indexed Sequential: Two Levels Key Value Address 150 1 385 2 001 003. . 150 Address 385 7 678 8 805 9 … Key Value Address 536 3 678 4 Key Value 251. . 385 455 480. . 536 605 610. . 678 Address 785 5 805 6 791. . 805 IS 257 – Fall 2011 705 710. . 785 2011 -09 -15 SLIDE 57
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 2011 -09 -15 SLIDE 58
Indexed Random Becker Harty Actual Value Address Block Number Adams 2 Becker 1 Dumpling 3 Getta 2 Harty 1 Adams Getta Dumpling IS 257 – Fall 2011 -09 -15 SLIDE 59
Btree F B || D || F| || P || Z| H || L || P| R || S || Z| Devils Aces Boilers Cars IS 257 – Fall 2011 Flyers Hawkeyes Hoosiers Minors Panthers Seminoles 2011 -09 -15 SLIDE 60
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 2011 -09 -15 SLIDE 61
Inverted CH 145 101, 103, 104 Actual Value Address Block Number CH 145 1 CS 201 2 CS 623 3 PH 345 … CS 201 102 Student name Course Number Adams CH 145 Becker cs 201 Dumpling ch 145 Getta ch 145 Harty cs 623 Mobile cs 623 CS 623 105, 106 IS 257 – Fall 2011 -09 -15 SLIDE 62
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 2011 -09 -15 SLIDE 63
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 2011 -09 -15 SLIDE 64
Comparative Access Methods Factor Storage space Sequential retrieval on primary key Random Retr. Multiple Key Retr. Deleting records Sequential No wasted space Indexed Hashed No wasted space for data but extra space for index more space needed for addition and deletion of records after initial load Very fast Moderately Fast Impractical Moderately Fast Very fast with multiple indexes OK if dynamic Very fast OK if dynamic very easy Easy but requires Maintenance of indexes very easy Impractical Possible but needs a full scan create wasted space Adding records requires rewriting file Updating records usually requires rewriting file IS 257 – Fall 2011 Not possible very easy 2011 -09 -15 SLIDE 65
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