The Relational Model and SQL

Transcription

1 Databases 2009 Michael I. Schwartzbach Computer Science, University of Aarhus 1

2 What is a Database? Main Entry: da ta base Pronunciation: \dā-tə-bās, da- also dä-\ Function: noun Date: circa 1962 : a usually large collection of data organized especially for rapid search and retrieval (as by a computer) database transitive verb Queries are much more general than searching Efficient, convenient, and safe storage of and multi-user access to massive amounts of persistent data 2 2

3 What is a Database? Main Entry: da ta base Pronunciation: \dā-tə-bās, da- also dä-\ Function: noun Date: circa 1962 : a usually large collection of data organized especially for rapid search and retrieval (as by a computer) database transitive verb Bank accounts Queries are much more general than searching Blog archives Efficient, convenient, and safe storage of and Human multi-user access to massive amounts of genome persistent data Google.com Amazon.com Student records 3 3

4 Data Models A (mathematical) representation of data: tables trees graphs Operations on data: insert, update, delete, query Constraints on data: datatypes uniqueness dependencies 4 4

5 The Relational Data Model Data are stored in tables (relations): name Joe Jacques Jose age city London Paris Madrid Simple but still covers many real scenarios 5 5

6 The Relational Data Model Data are stored in tables (relations) name age city row Joe 22 London Jacques 27 Paris Jose 34 Madrid 6 6

7 The Relational Data Model Data are stored in tables (relations): schema name age city Joe 22 London Jacques 27 Paris Jose 34 Madrid 7 7

8 The Relational Data Model Data are stored in tables (relations): name age city Joe Jacques London Paris column Jose 34 Madrid 8 8

9 The Relational Data Model Data are stored in tables (relations): name age city attribute Joe 22 London Jacques 27 Paris Jose 34 Madrid 9 9

10 The Relational Data Model Data are stored in tables (relations): name age city Joe 22 London Jacques 27 Paris attribute value Jose 34 Madrid 10 10

11 The Relational Data Model Data are stored in tables (relations): name Joe Jacques Jose age city London Paris Madrid Abstract tables: invariant under permutation of rows and columns no information is stored in the order May or may not allow duplicate rows 11 11

12 The Relational Data Model Data are stored in tables (relations): city Madrid London Paris name Jose Joe Jacques age Abstract tables: invariant under permutation of rows and columns no information is stored in the order May or may not allow duplicate rows 12 12

13 NULL Values An attribute value may be NULL: it is currently unknown it is not relevant in this row animal lion crocodile Tyrannosaurus Rex polar bear color yellow green NULL white zoo Copenhagen London NULL Berlin NULL values are often treated specially 13 13

14 Advantages of The Relational Model A simple, intuitive model Often convenient for real-life data but richer models are also needed, e.g. XML An elegant mathematical foundation the relational algebra Allows efficient algorithms Industrial strength implementations are available 14 14

15 Schemas Relation schema: name of the relation names of the attributes types of the attributes constraints Database schema: collection of all relation schemas 15 15

16 A Running Example The database behind a tiny calendar system: Rooms People Meetings Participants Equipment 16 16

17 Rooms room Turing-222 Ada-333 Aud-E capacity room: the name of a room capacity: the number of people that it will hold 17 17

18 People userid name group office mis Michael I. Schwartzbach vip Turing-222 doina Doina Bucur phd NULL bnielsen Kai Birger Nielsen tap Hopper-017 userid: unique user name name: ordinary name group: vip, tap, phd office: a room or NULL 18 18

19 Meetings meetid date slot owner what mis ddb mis ddb amoeller TA-meeting meetid: a unique id date: the date of the meeting slot: 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 owner: the userid of the owner what: a textual description 19 19

20 Participants meetid pid Store-Aud mis sigurd status a a d meetid: the id of the meeting pid: a userid or a room status: u(nknown), a(ccept), d(ecline) 20 20

21 Equipment room Store-Aud Store-Aud Hopper-017 type projector whiteboard mini-fridge room: the name of a room type: the type of equipment 21 21

22 SQL Structured Query Language Invented by IBM in the 1970s (many versions) Declarative, no low-level manipulations Algebraic foundations Representations, operations, constraints Query optimization DB2, Oracle, SQL Server, MySQL 22 22

23 Declaring Tables (1/3) CREATE TABLE Rooms ( room VARCHAR(15), capacity INT ); CREATE TABLE People ( name VARCHAR(40), office VARCHAR(15), userid VARCHAR(15), group CHAR(3) ); 23 23

24 Declaring Tables (2/3) CREATE TABLE Meetings ( meetid INT, date DATE, slot INT, owner VARCHAR(15), what VARCHAR(40) ); 24 24

25 Declaring Tables (3/3) CREATE TABLE Participants ( meetid INT, pid VARCHAR(15), status CHAR(1) ); CREATE TABLE Equipment ( room VARCHAR(15), type VARCHAR(20) ); 25 25

26 SQL Types INT 217 CHAR(2) 'aa', 'ab', '12', '++' VARCHAR(5) '', '12345', 'foo', 'x''y' FLOAT 3.14, 42, DATE ' ' TIME '14:15:00' CLOB a text file BLOB a movie XML an XML document 26 26

27 Refinements NOT NULL the value cannot be NULL DEFAULT value a default value is specified UNIQUE the value is unique in the table unless it is NULL PRIMARY KEY the value is unique in the table the value is never NULL 27 27

28 Refined Tables (1/3) CREATE TABLE Rooms ( room VARCHAR(15) PRIMARY KEY NOT NULL, capacity INT NOT NULL ); CREATE TABLE People ( name VARCHAR(40) NOT NULL, office VARCHAR(15), userid VARCHAR(15) PRIMARY KEY NOT NULL, group CHAR(3) ); 28 28

29 Declaring Tables (2/3) CREATE TABLE Meetings ( meetid INT PRIMARY KEY NOT NULL, date DATE, slot INT, owner VARCHAR(15) NOT NULL, what VARCHAR(40) ); 29 29

30 Declaring Tables (3/3) CREATE TABLE Participants ( meetid INT NOT NULL, pid VARCHAR(15) NOT NULL, status CHAR(1) DEFAULT 'u' ); CREATE TABLE Equipment ( room VARCHAR(15) PRIMARY KEY NOT NULL, type VARCHAR(20) NOT NULL ); 30 30

31 SELECT-FROM FROM-WHERE The basic form of an SQL query SELECT desired attributes FROM one or more tables WHERE condition about the involved rows 31 31

32 Simple Example Which kind of meetings have Michael arranged? SELECT what FROM Meetings WHERE owner = 'mis'; what ddb ddb 32 32

33 Loop Semantics for Single Table Loop through all rows in the table Check if the condition is true Project the rows onto the desired attributes Note that duplicates still are kept

34 Renamings in SELECT The selected attributes can be given new names SELECT name, group AS category FROM People WHERE office = 'Hopper-223'; name Kari Rye Schougaard Nørgaard Mikkel Baun Kjærgaard category phd phd 34 34

35 Expressions in SELECT The attributes may have computed values SELECT owner, date, slot*60 AS minute FROM Meetings WHERE owner = 'mis'; owner mis mis date minute

36 Conditions in WHERE AND, OR, NOT, =, <>, <, >, <=, >=, LIKE,... SELECT owner, what FROM Meetings WHERE slot >= 12 AND slot < 16 AND what LIKE '%beer%'; owner amoeller amoeller amoeller what Afternoon beer Belgian beer testing Return empty beer bottles 36 36

37 3-Valued Logic Any comparison with NULL yields unknown This gives 3 truth values: true, false, unknown Boolean connectives are defined appropriately: AND tt ff u OR tt ff u NOT tt tt ff u tt tt tt tt tt ff ff ff ff ff ff tt ff u ff tt u u ff u u tt u u u u The WHERE clause accepts if the result is true 37 37

38 A Surprise? People: userid name group office mis Michael Schwartzbach vip Turing-222 doina Doina Bucur phd NULL bnielsen Kai Birger Nielsen tap Hopper-017 SELECT userid FROM People WHERE office='turing-222' OR office<>'turing-222'; userid mis bnielsen 38 38

39 Testing for NULL People: userid name group office mis Michael Schwartzbach vip Turing-222 doina Doina Bucur phd NULL bnielsen Kai Birger Nielsen tap Hopper-017 SELECT userid FROM People WHERE office IS NULL; userid doina 39 39

40 Multiple Relations Which people have booked meetings this day? SELECT name FROM People, Meetings WHERE date = ' ' AND owner = userid; The relations are joined 40 40

41 General Loop Semantics Loop through all rows in all tables For each combination: check if the condition is true project the rows onto the desired attributes Note that duplicates still are kept

42 Prefixing Attribute Variables Avoid possible name clashes: SELECT People.name FROM People, Meetings WHERE Meetings.date = ' ' AND Meetings.owner = People.userid; 42 42

43 Naming Row Variables Enables self-joins: SELECT p1.name AS roomie1, p2.name AS roomie2 FROM People p1, People p2 WHERE p1.office = p2.office AND p1.userid <> p2.userid; A table of all roommates

44 Avoiding Symmetric Pairs SELECT p1.name AS roomie1, p2.name AS roomie2 FROM People p1, People p2 WHERE p1.office = p2.office AND p1.userid < p2.userid; 44 44

45 Aggregation The SELECT clause may involve aggregations: SUM AVG COUNT MIN MAX NULLs are ignored in these computations Except that count(*) counts all rows 45 45

46 Requirements Aggregation of a column computes: a 1 a 2 a 3... a n for some operator x a 1 a 2 This is only well-formed if is: commutative: a b = b a associative: (a b) c = a (b c) since the rows may be permuted a 3... a n 46 46

47 Simple Example What is the average capacity of a room? SELECT AVG(capacity) AS average FROM Rooms; average

48 Avoiding Duplicates SELECT DISTINCT removes duplicates This is expensive! But sometime necessary... What kinds of equipment do we have? SELECT DISTINCT type FROM Equipment; 48 48

49 Avoiding Duplicates in Aggregation How many kinds of equipment do we have? SELECT COUNT(DISTINCT type) as number FROM Equipment; number

50 Scalar Functions Lots of useful functions are available: integer and float functions string functions calendar functions... SELECT CHARACTER_LENGTH(name,CODEUNITS16), UPPER(group) FROM People; 50 50

51 Subqueries Any query in parentheses can be used in: FROM clauses WHERE clauses A query may be used as a value: if it returns only one row and one column otherwise, a run-time error occurs 51 51

52 Simple Example Who shares an office with Brian? SELECT name FROM People WHERE office = (SELECT office FROM People WHERE userid='bbc'); 52 52

53 Membership Tests IN and NOT IN test membership in tables Who has someone arranged to meet? SELECT pid FROM Participants WHERE meetid IN (SELECT meetid FROM Meetings WHERE owner='mis') AND pid NOT IN (SELECT room FROM Rooms); 53 53

54 Correlated Subqueries Which meetings exceed the capacity of a room? SELECT meetid FROM Meetings WHERE (SELECT COUNT(DISTINCT pid) FROM Participants WHERE meetid=meetings.meetid AND status<>'d' AND pid NOT IN (SELECT room FROM Rooms) ) > (SELECT capacity FROM Rooms, Participants WHERE room=pid AND meetid=meetings.meetid) ; 54 54

55 Correlated Subqueries Which meetings exceed the capacity of a room? SELECT meetid FROM Meetings static nested scope rules WHERE (SELECT COUNT(DISTINCT pid) FROM Participants WHERE meetid=meetings.meetid AND status<>'d' AND pid NOT IN (SELECT room FROM Rooms) ) > (SELECT capacity FROM Rooms, Participants WHERE room=pid AND meetid=meetings.meetid) ; 55 55

56 EXISTS and NOT EXISTS Check for emptiness or non-emptiness of a table Who is alone in an office? SELECT name FROM People p1 WHERE NOT EXISTS ( SELECT * FROM People WHERE office = p1.office AND userid <> p1.userid ); 56 56

57 ANY and ALL Allow comparisons against: any row in a subquery all rows in a subquery Which are the latest meetings that are planned? SELECT what FROM Meetings WHERE date >= ALL( SELECT date FROM Meetings ); 57 57

58 UNION, INTERSECT, and EXCEPT Treats tables with the same schema as sets: removes duplicates (unless ALL is added) computes,, and \ Who do not participate in a meeting they have themselves arranged? (SELECT owner AS userid, meetid FROM Meetings) EXCEPT (SELECT pid AS userid, meetid FROM Participants); 58 58

59 The JOIN Operator T1 JOIN T2 ON condition is syntactic sugar for: SELECT * FROM T1,T2 WHERE condition 59 59

60 Dangling Rows and FULL JOIN T1 JOIN T2 ON condition A row in T1 or T2 that does not match a row in the other table is dangling An ordinary JOIN throws away dangling rows A FULL JOIN preserves dangling rows by padding them with NULL values A LEFT or RIGHT JOIN preserves dangling rows from one argument only 60 60

61 Simple Example In which offices are meetings planned? All offices with meetings or NULL: SELECT office,meetid FROM People LEFT JOIN Participants ON pid=office; Only those offices with meetings: SELECT office,meetid FROM People JOIN Participants ON pid=office; 61 61

62 Grouping SELECT-FROM-WHERE-GROUP BY Rows are grouped by a list of attributes Aggregations in SELECT are done for each group The attributes in SELECT must be either: aggregated mentioned in the GROUP BY list 62 62

63 Simple Example How many meetings have each person arranged? SELECT owner, COUNT(meetid) as number FROM Meetings GROUP BY owner; owner amoeller kjensen mis number

64 Advanced Example What is the average number of invitations for the meetings that each person has arranged? SELECT owner, AVG(pidno) AS average FROM (SELECT owner, m.meetid, COUNT(pid) as pidno FROM Meetings m, Participants p WHERE m.meetid = p.meetid GROUP BY owner, m.meetid) GROUP BY owner; 64 64

65 HAVING A HAVING clause may eliminate some groups Which offices have more than one occupant? SELECT office FROM People GROUP BY office HAVING COUNT(*) > 1; Attributes in HAVING must be aggregated or mentioned in GROUP BY 65 65

66 Modifications SQL commands may change the database Three kinds of modifications: insert one or more rows delete one or more rows update existing rows or columns Modifications do not return a result 66 66

67 Inserting a Single Row INSERT INTO table VALUES ( list of values ); INSERT INTO Participants VALUES (42432, 'mis', 'a'); Optionally specify attribute names: INSERT INTO Participants(pid, status, meetid) VALUES ('mis', 'a', 42432); Missing values are NULL or defaults 67 67

68 Inserting a Subquery Invite everyone Anders meets with to his Belgian beer tasting: INSERT INTO Participants ( SELECT AS meetid, pid, 'u' AS status FROM Meetings, Participants WHERE Meetings.meetid=Participants.meetid AND owner = 'amoeller' AND pid <> 'amoeller' AND pid NOT IN (SELECT room FROM Rooms)); 68 68

69 Deleting Some Rows DELETE FROM table WHERE condition; Delete Michael's office: DELETE FROM Rooms WHERE room='turing-222'; Delete all offices: DELETE FROM Rooms; 69 69

70 Deleting a Subquery Delete all people with a roommate: DELETE FROM People p WHERE EXISTS( SELECT * FROM People WHERE office = p.office AND userid <> p.userid ); 70 70

71 Meaning of Deletion First the condition is computed for all rows Then the deletions are performed Otherwise the last person in a multi-person office would not be deleted! 71 71

72 Update UPDATE table SET attribute assignments WHERE condition; Move Anders to a smaller office: UPDATE People SET office = 'Turing-213' WHERE userid = 'amoeller'; 72 72

73 SQL is Everywhere 73 73