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1 Lecture 10: Transactions. 2 The Setting uDatabase systems are normally being accessed by many users or processes at the same time. wBoth queries and.

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Presentation on theme: "1 Lecture 10: Transactions. 2 The Setting uDatabase systems are normally being accessed by many users or processes at the same time. wBoth queries and."— Presentation transcript:

1 1 Lecture 10: Transactions

2 2 The Setting uDatabase systems are normally being accessed by many users or processes at the same time. wBoth queries and modifications. uUnlike operating systems, which support interaction of processes, a DMBS needs to keep processes from troublesome interactions.

3 3 Example: Bad Interaction uYou and your domestic partner each take $100 from different ATM’s at about the same time. wThe DBMS better make sure one account deduction doesn’t get lost. uCompare: An OS allows two people to edit a document at the same time. If both write, one’s changes get lost.

4 4 ACID Transactions uA DBMS is expected to support “ACID transactions,” processes that are: wAtomic : Either the whole process is done or none is. wConsistent : Database constraints are preserved. wIsolated : It appears to the user as if only one process executes at a time. wDurable : Effects of a process do not get lost if the system crashes.

5 5 Transactions in SQL uSQL supports transactions, often behind the scenes. wEach statement issued at the generic query interface is a transaction by itself. wIn programming interfaces like Embedded SQL or PSM, a transaction begins the first time a SQL statement is executed and ends with the program or an explicit transaction- end.

6 6 COMMIT uThe SQL statement COMMIT causes a transaction to complete. wIt’s database modifications are now permanent in the database.

7 7 ROLLBACK uThe SQL statement ROLLBACK also causes the transaction to end, but by aborting. wNo effects on the database. uFailures like division by 0 or a constraint violation can also cause rollback, even if the programmer does not request it.

8 8 An Example: Interacting Processes uAssume the usual Sells(bar,beer,price) relation, and suppose that Joe’s Bar sells only Bud for $2.50 and Miller for $3.00. uSally is querying Sells for the highest and lowest price Joe charges. uJoe decides to stop selling Bud and Miller, but to sell only Heineken at $3.50.

9 9 Sally’s Program uSally executes the following two SQL statements, which we call (min) and (max), to help remember what they do. (max)SELECT MAX(price) FROM Sells WHERE bar = ’Joe’’s Bar’; (min)SELECT MIN(price) FROM Sells WHERE bar = ’Joe’’s Bar’;

10 10 Joe’s Program uAt about the same time, Joe executes the following steps, which have the mnemonic names (del) and (ins). (del) DELETE FROM Sells WHERE bar = ’Joe’’s Bar’; (ins) INSERT INTO Sells VALUES(’Joe’’s Bar’, ’Heineken’, 3.50);

11 11 Interleaving of Statements uAlthough (max) must come before (min), and (del) must come before (ins), there are no other constraints on the order of these statements, unless we group Sally’s and/or Joe’s statements into transactions.

12 12 Example: Strange Interleaving uSuppose the steps execute in the order (max)(del)(ins)(min). Joe’s Prices: Statement: Result: uSally sees MAX < MIN! 2.50, 3.00 (del) (ins) 3.50 (min) , 3.00 (max) 3.00

13 13 Fixing the Problem by Using Transactions uIf we group Sally’s statements (max)(min) into one transaction, then she cannot see this inconsistency. uShe sees Joe’s prices at some fixed time. wEither before or after he changes prices, or in the middle, but the MAX and MIN are computed from the same prices.

14 14 Another Problem: Rollback uSuppose Joe executes (del)(ins), not as a transaction, but after executing these statements, thinks better of it and issues a ROLLBACK statement. uIf Sally executes her statements after (ins) but before the rollback, she sees a value, 3.50, that never existed in the database.

15 15 Solution uIf Joe executes (del)(ins) as a transaction, its effect cannot be seen by others until the transaction executes COMMIT. wIf the transaction executes ROLLBACK instead, then its effects can never be seen.

16 16 Isolation Levels uSQL defines four isolation levels = choices about what interactions are allowed by transactions that execute at about the same time. uHow a DBMS implements these isolation levels is highly complex, and a typical DBMS provides its own options.

17 17 Choosing the Isolation Level uWithin a transaction, we can say: SET TRANSACTION ISOLATION LEVEL X where X = 1.SERIALIZABLE 2.REPEATABLE READ 3.READ COMMITTED 4.READ UNCOMMITTED

18 18 Serializable Transactions uIf Sally = (max)(min) and Joe = (del)(ins) are each transactions, and Sally runs with isolation level SERIALIZABLE, then she will see the database either before or after Joe runs, but not in the middle. uIt’s up to the DBMS vendor to figure out how to do that, e.g.: wTrue isolation in time. wKeep Joe’s old prices around to answer Sally’s queries.

19 19 Isolation Level Is Personal Choice uYour choice, e.g., run serializable, affects only how you see the database, not how others see it. uExample: If Joe Runs serializable, but Sally doesn’t, then Sally might see no prices for Joe’s Bar. wi.e., it looks to Sally as if she ran in the middle of Joe’s transaction.

20 20 Read-Commited Transactions uIf Sally runs with isolation level READ COMMITTED, then she can see only committed data, but not necessarily the same data each time. uExample: Under READ COMMITTED, the interleaving (max)(del)(ins)(min) is allowed, as long as Joe commits. wSally sees MAX < MIN.

21 21 Repeatable-Read Transactions uRequirement is like read-committed, plus: if data is read again, then everything seen the first time will be seen the second time. wBut the second and subsequent reads may see more tuples as well.

22 22 Example: Repeatable Read uSuppose Sally runs under REPEATABLE READ, and the order of execution is (max)(del)(ins)(min). w(max) sees prices 2.50 and w(min) can see 3.50, but must also see 2.50 and 3.00, because they were seen on the earlier read by (max).

23 23 Read Uncommitted uA transaction running under READ UNCOMMITTED can see data in the database, even if it was written by a transaction that has not committed (and may never). uExample: If Sally runs under READ UNCOMMITTED, she could see a price 3.50 even if Joe later aborts.

24 24 Concurrency Control T1T2…Tn DB (consistency constraints)

25 25 Review uWhy do we need transaction? uWhat’s ACID? uWhat’s SQL support for transaction? uWhat’s the four isolation level wSERIALIZABLE wREPEATABLE READ wREAD COMMITTED wREAD UNCOMMITTED

26 26 Example: T1:Read(A)T2:Read(A) A  A+100A  A  2Write(A)Read(B) B  B+100B  B  2Write(B) Constraint: A=B

27 27 Schedule A T1T2 Read(A); A  A+100 Write(A); Read(B); B  B+100; Write(B); Read(A);A  A  2; Write(A); Read(B);B  B  2; Write(B); AB

28 28 Schedule B T1T2 Read(A);A  A  2; Write(A); Read(B);B  B  2; Write(B); Read(A); A  A+100 Write(A); Read(B); B  B+100; Write(B); AB

29 29 Schedule C T1T2 Read(A); A  A+100 Write(A); Read(A);A  A  2; Write(A); Read(B); B  B+100; Write(B); Read(B);B  B  2; Write(B); AB

30 30 Schedule D T1T2 Read(A); A  A+100 Write(A); Read(A);A  A  2; Write(A); Read(B);B  B  2; Write(B); Read(B); B  B+100; Write(B); AB

31 31 Schedule E T1T2’ Read(A); A  A+100 Write(A); Read(A);A  A  1; Write(A); Read(B);B  B  1; Write(B); Read(B); B  B+100; Write(B); AB Same as Schedule D but with new T2’

32 32 uWant schedules that are “good”, regardless of winitial state and wtransaction semantics uOnly look at order of read and writes Example: Sc=r 1 (A)w 1 (A)r 2 (A)w 2 (A)r 1 (B)w 1 (B)r 2 (B)w 2 (B)

33 33 Sc’=r 1 (A)w 1 (A) r 1 (B)w 1 (B)r 2 (A)w 2 (A)r 2 (B)w 2 (B) T 1 T 2 Example: Sc=r 1 (A)w 1 (A)r 2 (A)w 2 (A)r 1 (B)w 1 (B)r 2 (B)w 2 (B)

34 34 Review uWhy do we need transaction? uWhat’s ACID? uWhat’s SQL support for transaction? uWhat’s the four isolation level wSERIALIZABLE wREPEATABLE READ wREAD COMMITTED wREAD UNCOMMITTED

35 35 Example: T1:Read(A)T2:Read(A) A  A+100A  A  2Write(A)Read(B) B  B+100B  B  2Write(B) Constraint: A=B

36 36 Schedule D T1T2 Read(A); A  A+100 Write(A); Read(A);A  A  2; Write(A); Read(B);B  B  2; Write(B); Read(B); B  B+100; Write(B); AB

37 37 However, for Sd: Sd=r 1 (A)w 1 (A)r 2 (A)w 2 (A) r 2 (B)w 2 (B)r 1 (B)w 1 (B) uas a matter of fact, T 2 must precede T 1 in any equivalent schedule, i.e., T 2  T 1

38 38 T 1 T 2 Sd cannot be rearranged into a serial schedule Sd is not “equivalent” to any serial schedule Sd is “bad” u T 2  T 1 u Also, T 1  T 2

39 39 Schedule C T1T2 Read(A); A  A+100 Write(A); Read(A);A  A  2; Write(A); Read(B); B  B+100; Write(B); Read(B);B  B  2; Write(B); AB

40 40 Returning to Sc Sc=r 1 (A)w 1 (A)r 2 (A)w 2 (A)r 1 (B)w 1 (B)r 2 (B)w 2 (B) T 1  T 2 T 1  T 2  no cycles  Sc is “equivalent” to a serial schedule (in this case T 1,T 2 )

41 41 Concepts Transaction: sequence of r i (x), w i (x) actions Conflicting actions: r 1(A) w 2(A) w 1(A) w 2(A) r 1(A) w 2(A) Schedule: represents chronological order in which actions are executed Serial schedule: no interleaving of actions or transactions

42 42 Definition S 1, S 2 are conflict equivalent schedules if S 1 can be transformed into S 2 by a series of swaps on non-conflicting actions.

43 43 Definition A schedule is conflict serializable if it is conflict equivalent to some serial schedule.

44 44 Nodes: transactions in S Arcs: Ti  Tj whenever - p i (A), q j (A) are actions in S - p i (A) < S q j (A) - at least one of p i, q j is a write Precedence graph P(S) (S is schedule )

45 45 Exercise: uWhat is P(S) for S = w 3 (A) w 2 (C) r 1 (A) w 1 (B) r 1 (C) w 2 (A) r 4 (A) w 4 (D) uIs S serializable?

46 46 Another Exercise: uWhat is P(S) for S = w 1 (A) r 2 (A) r 3 (A) w 4 (A) ?

47 47 Lemma S 1, S 2 conflict equivalent  P(S 1 )=P(S 2 ) Proof: Assume P(S 1 )  P(S 2 )   T i : T i  T j in S 1 and not in S 2  S 1 = …p i (A)... q j (A)… p i, q j S 2 = …q j (A)…p i (A)... conflict  S 1, S 2 not conflict equivalent

48 48 Note: P(S 1 )=P(S 2 )  S 1, S 2 conflict equivalent Counter example: S 1 =w 1 (A) r 2 (A) w 2 (B) r 1 (B) S 2 =r 2 (A) w 1 (A) r 1 (B) w 2 (B)

49 49 Theorem P(S 1 ) acyclic  S 1 conflict serializable (  ) Assume S 1 is conflict serializable   S s : S s, S 1 conflict equivalent  P(S s ) = P(S 1 )  P(S 1 ) acyclic since P(S s ) is acyclic

50 50 (  ) Assume P(S 1 ) is acyclic Transform S 1 as follows: (1) Take T 1 to be transaction with no incident arcs (2) Move all T 1 actions to the front S 1 = ……. q j (A)……. p 1 (A)….. (3) we now have S 1 = (4) repeat above steps to serialize rest! T 1 T 2 T 3 T 4 Theorem P(S 1 ) acyclic  S 1 conflict serializable


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