1. The Database Language SQL Prof. Yin-Fu Huang CSIE, NYUST Chapter 6

2. Database Systems Yin-Fu Huang 6.1Simple Queries in SQL The portion of SQL that supports queries has capabilities very close to that of relational algebra. ANSI SQL, SQL2, SQL-99, SQL:2003 SQL-99 extended with object-relational features and a number of other new capabilities SQL:2003 a collection of extensions to SQL-99 SELECT, FROM, WHERE Example (See Fig. 6.1)

3. Database Systems Yin-Fu Huang 6.1Simple Queries in SQL

4. Database Systems Yin-Fu Huang “All movies produced by Disney Studios in 1990” SELECT* FROMMovies WHEREstudioName=’Disney’ AND year=1990; 6.1.1 Projection in SQL Example SELECT title, length Alias SELECT title AS name, length AS duration An expression in place of an attribute SELECT title AS name, length*0.016667 AS lengthInHours 6.1Simple Queries in SQL

5. Database Systems Yin-Fu Huang A constant as an expression SELECT title AS name, length*0.016667 AS length, ‘hrs.’ AS inHours 6.1.2 Selection in SQL Example SELECT title FROM Movies WHERE (year>1970 OR length<90) AND studioName=’MGM’; 6.1Simple Queries in SQL

6. Database Systems Yin-Fu Huang 6.1.3 Comparison of Strings Lexicographic order 6.1.4 Pattern Matching in SQL s LIKE p where s is a string and p is a pattern % and _ Example SELECT title FROM Movies WHERE title LIKE ’Star ____’; 6.1Simple Queries in SQL

7. Database Systems Yin-Fu Huang Example SELECT title FROM Movies WHERE title LIKE ’%’’s%’; 6.1.5 Dates and Times Example DATE ’1948-05-14’ TIME ’15:00:02.5’ TIMESTAMP ’1948-05-14 12:00:00’ 6.1Simple Queries in SQL

8. Database Systems Yin-Fu Huang 6.1.6 Null Values and Comparisons Involving NULL Different interpretations: 1) Value unknown 2) Value inapplicable 3) Value withheld Two important rules: 1) When we operate on a NULL and any value, including another NULL, using an arithmetic operator like * or +, the result is NULL. 2) When we compare a NULL value and any value, including another NULL, using a comparison operator like = or >, the result is UNKNOWN. 6.1Simple Queries in SQL

9. Database Systems Yin-Fu Huang We cannot use NULL explicitly as an operand. Example NULL+3, NULL=3: not legal The correct way to ask if x has the value NULL is with the expression x IS NULL. 6.1.7 The Truth-Value UNKNOWN The rule: TRUE as 1, FALSE as 0, UNKNOWN as 1/2 1) The AND of two truth-values is the minimum of those values. 2) The OR of two truth-values is the maximum of those values. 3) The negation of true-value v is 1-v. (See Fig. 6.2) 6.1Simple Queries in SQL

10. Database Systems Yin-Fu Huang 6.1Simple Queries in SQL

11. Database Systems Yin-Fu Huang Example SELECT * FROM Movies WHERE length 120 The true meaning of the query is “find all the Movies tuples with non-NULL lengths.” 6.1.8 Ordering the Output ORDER BY DESC and ASC 6.1Simple Queries in SQL

12. Database Systems Yin-Fu Huang Example SELECT * FROM Movies WHERE studioName=’Disney’ AND year=1990 ORDER BY length, title; 6.1Simple Queries in SQL

13. Database Systems Yin-Fu Huang Joins, products, unions, intersections, and differences 6.2.1 Products and Joins in SQL Example “The name of the producer of Star Wars” SELECT name FROM Movies, MovieExec WHERE title=’Star Wars’ AND producerC#=cert#; (See Fig. 6.3) 6.2Queries Involving More than One Relation

14. Database Systems Yin-Fu Huang 6.2Queries Involving More than One Relation

15. Database Systems Yin-Fu Huang 6.2.2 Disambiguating Attributes R.A refers to the attribute A of relation R. Example SELECT MovieStar.name, MovieExec.name FROM MovieStar, MovieExec WHERE MovieStar.address=MovieExec.address; SELECT MovieExec.name FROM Movies, MovieExec WHERE Movies.title=’Star Wars’ AND Movies.producerC#=MovieExec.cert#; 6.2Queries Involving More than One Relation

16. Database Systems Yin-Fu Huang 6.2.3 Tuple Variables Tuple variable Example SELECT Star1.name, Star2.name FROM MovieStar Star1, MovieStar Star2 WHERE Star1.address= Star2.address AND Star1.name<Star2.name; 6.2.4 Interpreting Multirelation Queries Several ways to define the meaning of the select-from-where expressions 6.2Queries Involving More than One Relation

17. Database Systems Yin-Fu Huang 1) Nested Loops (See Fig. 6.4) 2) Parallel Assignment 3) Conversion to Relational Algebra Example π A1, A5 (σ A2=A6 AND A1<A5 (ρ M(A1, A2, A3, A4) (MovieStar)× ρ N(A5, A6, A7, A8) (MovieStar))) 6.2Queries Involving More than One Relation

18. Database Systems Yin-Fu Huang 6.2Queries Involving More than One Relation

19. Database Systems Yin-Fu Huang 6.2.5 Union, Intersection, and Difference of Queries Example “The names and addresses of all female movie stars who are also movie executives with a net worth over $10,000,000.” (See Fig. 6.5) Example (SELECT name, address FROM MovieStar) EXCEPT (SELECT name, address FROM MovieExec) If necessary to get a common set of attributes, we can rename attributes. 6.2Queries Involving More than One Relation

20. Database Systems Yin-Fu Huang 6.2Queries Involving More than One Relation

21. Database Systems Yin-Fu Huang Example (SELECT title, year FROM Movies) UNION (SELECT MovieTitle AS title, movieYear AS year FROM StarsIn) 6.2Queries Involving More than One Relation

22. Database Systems Yin-Fu Huang A query that is part of another is called a subquery. A number of ways that subqueries can be used: 1) Subqueries can return a single constant, and this constant can be compared with another value in a WHERE clause. 2) Subqueries can return relations that can be used in various way in WHERE clauses. 3) Subqueries can appear in FROM clauses, followed by a tuple variable that represents the tuples in the result of the subquery. 6.3.1 Subqueries that Produce Scalar Values We may compare the result of a subquery to a constant or attribute. 6.3Subqueries

23. Database Systems Yin-Fu Huang Example (See Fig. 6.6) SELECT name FROM MovieExec WHERE cert#=12345 If zero tuples or more than one tuple is produced by the subquery, it is a run-time error. 6.3.2 Conditions Involving Relations A number of SQL operations that we can apply to a relation R and produce a boolean result. 6.3Subqueries

24. Database Systems Yin-Fu Huang 6.3Subqueries

25. Database Systems Yin-Fu Huang s: a scalar value, R: a one-column relation 1) EXISTS R 2) s IN R, s NOT IN R 3) s > ALL R, s <> ALL R 4) s > ANY R, s = ANY R 6.3.3 Conditions Involving Tuples If a tuple t has the same number of components as a relation R, then it makes sense to compare t and R in expressions. Example “all the producers of movies in which Harrison Ford stars” (See Fig. 6.7 and Fig. 6.8) 6.3Subqueries

26. Database Systems Yin-Fu Huang 6.3Subqueries

27. Database Systems Yin-Fu Huang 6.3Subqueries

28. Database Systems Yin-Fu Huang A single select-from-where expression: duplicates (See Fig. 6.9) 6.3.4 Correlated Subqueries Correlated query: to be evaluated many times, once for each assignment of a value to some term in the subquery that come from a tuple variable outside the subquery. Example “the titles that have been used for two or more movies” (See Fig. 6.10) When writing a correlated query, it is important that we be aware of the scoping rules for names. 6.3Subqueries

29. Database Systems Yin-Fu Huang 6.3Subqueries

30. Database Systems Yin-Fu Huang 6.3Subqueries

31. Database Systems Yin-Fu Huang 6.3.5 Subqueries in FROM Clauses Example “the producers of Harrison Ford's movies” (See Fig. 6.11) 6.3.6 SQL Join Expressions Cross join (Cartesian product) e.g., Movies CROSS JOIN StarsIn; Join Example Movie JOIN StarsIn ON title=movieTitle AND year=movieYear; 6.3Subqueries

32. Database Systems Yin-Fu Huang 6.3Subqueries

33. Database Systems Yin-Fu Huang Using the whole expression as a subquery in a FROM clause and using a SELECT clause to remove the undesired attributes. SELECT title, year, length, genre, studioName, producerC#, starName FROM Movies JOIN StarsIn ON title=movieTitle AND year=movieYear; 6.3.7 Natural Joins Differing from a theta-join 1) The join condition is that all pairs of attributes from the two relations having a common name are equated. 2) One of each pair of equated attributes is projected out. 6.3Subqueries

34. Database Systems Yin-Fu Huang Example MovieStar NATURAL JOIN MovieExec; 6.3.8 Outerjoins An outerjoin operator is a way to augment the result of a join by the dangling tuples, padded with null values. Example MovieStar NATURAL FULL OUTER JOIN MovieExec; (See Fig. 6.12) NATURAL LEFT (RIGHT) OUTER JOIN 6.3Subqueries

35. Database Systems Yin-Fu Huang 6.3Subqueries

36. Database Systems Yin-Fu Huang Example Movies FULL OUTER JOIN StarsIn ON title=movieTitle AND year=movieYear; 6.3Subqueries

37. Database Systems Yin-Fu Huang Relations that are bags 6.4.1 Eliminating Duplicates DISTINCT Example (See Fig. 6.9) SELECT DISTINCT name 6.4Full-Relation Operations

38. Database Systems Yin-Fu Huang 6.4Full-Relation Operations

39. Database Systems Yin-Fu Huang 6.4.2 Duplicates in Unions, Intersections, and Differences Example (SELECT title, year FROM Movies) UNION ALL (SELECT MovieTitle AS title, movieYear AS year FROM StarsIn) R INTERSECT ALL S R EXCEPT ALL S 6.4.3 Grouping and Aggregation in SQL SQL provides all the capability of the γ operator through the use of aggregation operators in SELECT clauses and a special GROUP BY clause. 6.4Full-Relation Operations

40. Database Systems Yin-Fu Huang 6.4.4 Aggregation Operators SUM, AVG, MIN, MAX, COUNT Example SELECT AVG(netWorth) FROM MovieExec; SELECT COUNT(*) FROM StarsIn; SELECT COUNT(DISTINCT starName) FROM StarsIn; 6.4Full-Relation Operations

41. Database Systems Yin-Fu Huang 6.4.5 Grouping GROUP BY Example SELECT studioName, SUM(length) FROM Movies GROUP BY studioName; It is also possible to use a GROUP BY clause in a query about several relations. Example (See Fig. 6.13) 6.4Full-Relation Operations

42. Database Systems Yin-Fu Huang 6.4Full-Relation Operations

43. Database Systems Yin-Fu Huang 6.4.6 Grouping, Aggregation, and Nulls A few rules about Nulls: 1) The value NULL is ignored in any aggregation. 2) NULL is treated as an ordinary value when forming groups. 3) When we perform any aggregation except count over an empty bag of values, the result is NULL. The count of an empty bag is 0. Example A BSELECT A, COUNT(B) SELECT A, SUM(B) NULLNULLFROM R FROM R GROUP BY A; (NULL, 0) (NULL, NULL) 6.4Full-Relation Operations

44. Database Systems Yin-Fu Huang 6.4.7 HAVING Clauses We could restrict the tuples prior to grouping in a way that would make undesired groups empty. e.g., WHERE producerC#=cert# AND netWorth>=10000000 However, sometimes we want to choose our groups based on some aggregate property of the group itself. Example (See Fig. 6.14) Several rules about HAVING clauses: 1) An aggregation in a HAVING clause applied only to the tuples of the group being tested. 6.4Full-Relation Operations

45. Database Systems Yin-Fu Huang 6.4Full-Relation Operations

46. Database Systems Yin-Fu Huang 2) Any attribute of relations in the FROM clause may be aggregated in the HAVING clause, but only those attributes that are in the GROUP BY list may appear unaggregated in the HAVING clause. 6.4Full-Relation Operations

47. Database Systems Yin-Fu Huang Insert tuples into a relation Delete certain tuples from a relation Update values of certain components of certain existing tuples 6.5.1 Insertion INSERT INTO R(A 1, …, A n ) VALUES (v 1, …, v n ); The tuple created has default values for all missing attributes. Example INSERT INTO StarsIn(movieTitle, movieYear, starName) VALUES (‘The Maltese Falcon’, 1942, ‘Sydney Greenstreet’); 6.5Database Modifications

48. Database Systems Yin-Fu Huang INSERT INTO StarsIn VALUES (‘The Maltese Falcon’, 1942, ‘Sydney Greenstreet’); The order of the values must be the same as the standard order of attributes for the relation. Subquery Example (See Fig. 6.15) 6.5.2 Deletion DELETE FROM R WHERE ; 6.5Database Modifications

49. Database Systems Yin-Fu Huang 6.5Database Modifications

50. Database Systems Yin-Fu Huang Example DELETE FROM StarsIn WHERE movieTitle=’The Maltese Falcon’ AND movieYear=1942 AND StarName=’Sydney Greenstreet’; DELETE FROM MovieExec WHERE netWorth<10000000; 6.5.3 Updates UPDATE R SET WHERE ; 6.5Database Modifications

51. Database Systems Yin-Fu Huang Example UPDATE MovieExec SET name=’Pres. ’ || name WHERE cert# IN (SELECT presC# FROM Studio); 6.5Database Modifications

52. Database Systems Yin-Fu Huang Considering what can happen to leave the database in a state that doesn’t reflect the operations performed on it. 6.6.1 Serializability What could go wrong if the DBMS were completely unconstrained as to the order in which it operated upon the database. Example (See Fig. 6.16) The problem is solved in SQL by the notion of a “transaction.” Serializability: These transactions must behave as if they were run serially – one at a time, with no overlap. 6.6Transactions in SQL

53. Database Systems Yin-Fu Huang 6.6Transactions in SQL

54. Database Systems Yin-Fu Huang 6.6.2 Atomicity It is possible for a single operation to put the database in an unacceptable state if there is a hardware or software “crash” while the operation is executing. Example 1. Add $100 to account 456 2. Subtract $100 from account 123 Atomicity: Certain combinations of database operations need to be done atomically; that is, either they are both done or neither is done. 6.6Transactions in SQL

55. Database Systems Yin-Fu Huang 6.6.3 Transactions When using the generic SQL interface, each statement is a transaction by itself. However, SQL allows the programmer to group several statements into a single transaction. START TRANSACTION, COMMIT, ROLLBACK 6.6.4 Read-Only Transactions When a transaction only reads data and does not write data, we have more freedom to let the transaction execute in parallel with other transactions. However, read-only transactions would not be allowed to run in parallel with a transaction that wrote the same data. 6.6Transactions in SQL

56. Database Systems Yin-Fu Huang SET TRANSACTION READ ONLY; SET TRANSACTION READ WRITE; (default) 6.6.5 Dirty Reads Dirty data is a common term for data written by a transaction that has not yet committed. The risk in reading dirty data is that the transaction that wrote it may eventually abort. Sometimes the dirty read matters, and sometime it doesn’t. It may make sense to risk an occasional dirty read and thus avoid 1) the time-consuming work by the DBMS and 2) the loss of parallelism. 6.6Transactions in SQL

57. Database Systems Yin-Fu Huang Example 1. Add money to account 2 2. Test if account 1 has enough money a) No, remove the money from account 2 b) Yes, subtract the money from account 1 A1: $100A2:$200A3:$300 1. T2 executes step 1 and adds $250 to A3 2. T1 executes step 1 and adds $150 to A2 3. T2 executes the test of step 2 and finds A2 has enough funds 4. T1 executes the test of step 2 and finds A1 has not enough funds 5. T2 executes step 2b and subtracts $250 from A2 6. T1 executes step 2a and subtracts $150 from A2 (-$50) 6.6Transactions in SQL

58. Database Systems Yin-Fu Huang Example 1. Find an available seat and reserve it by setting ‘occupied’ 2. Ask the custom for approval of the seat a) Yes, commit b) No, release the seat by setting ‘available’ and repeat step 1 It might well make sense to implement this seat-choosing function in a way that allowed dirty reads, in order to speed up the average processing time for booking requests. SET TRANSACTION READ WRITE ISOLATION LEVEL READ UNCOMMITTED; 6.6Transactions in SQL

59. Database Systems Yin-Fu Huang 6.6.6 Other Isolation Levels SET TRANSACTION ISOLATION LEVEL READ COMMITTED; SET TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION ISOLATION LEVEL SERIALIZABLE; (default) For READ COMMITTED, it allows a transaction to issue the same query several times and get different answers. For REPEATABLE READ, it is possible that a second or subsequent execution of the same query will retrieve phantom tuples that result from insertions into the database while our transaction is executing. Example the seat-choosing program (See Fig. 6.17) 6.6Transactions in SQL

60. Database Systems Yin-Fu Huang 6.6Transactions in SQL

61. Database Systems Yin-Fu Huang The End.