Massively Distributed Database Systems - Transaction management

1. Massively Distributed Database Systems- Transaction management Spring 2014 Ki-Joune Li http://isel.cs.pusan.ac.kr/~lik Pusan National University

2. Basic Concepts of Transaction • Transaction • A set of operations • Atomic : • All or Nothing : Consistent State of Database • Example : Flight Reservation • Cf. Partially Done : Inconsistent State

3. Transaction States the initial state; the transaction stays in this state while it is executing PartiallyCommitted Committed ALL Active NOTHING Failed Aborted after the discovery that normal execution can no longer proceed. after the transaction has been rolled back and the database restored to its state prior to the start of the transaction. - restart the transaction or - kill the transaction

4. Transition between consistent states • Transaction • Example : Flight Reservation Consistent State Consistent State Set of operations

5. All State 2 Consistent State 2’ PartiallyDone Consistent Nothing ACID Properties • Atomicity. • All or Nothing • Not Partially Done • Example : Failure in Flight Reservation • Consistency. • Execution of a transaction preserves the consistency of the database. State 1

6. No Effect ACID Properties • Isolation. • Although multiple transactions may execute concurrently, each transaction must be unaware of other concurrently executing transactions. • Intermediate transaction results must be hidden from other concurrently executed transactions. • Durability. • After a transaction completes successfully, the changes it has made to the database persist, even if there are system failures. DB Transation 1 Transation 2

7. Example • Transaction : Transfer $50 from account A to account B: 1. read(A) 2. A := A –50 3. write(A) 4. read(B) 5. B := B + 50 6. write(B) • Consistency requirement • the sum of A and B is unchanged after the transaction. • Atomicity requirement • Durability • Isolation

8. Example : Concurrent Execution • Two Transactions • T1 : transfer $50 from A to B, • T2 transfer 10% of the balance from A to B Serial Schedule Concurrent Schedule

9. Serializability • What happens after these transactions ? • Serial Schedule : • Always Correct • T1 T2 and T2 T1 • Concurrent Schedule • Serializable if Result (T1 || T2) = Result(T1 T2) or Result(T2 T1)

10. Transaction Management • Transaction Management • Guarantee ACID Properties of Transaction by • Concurrency Control : Isolation and Consistency • Recovery : Atomicity and Durability

11. Transaction management: Concurrency Control

12. For given transactions T1, T2,.., Tn, Schedule (History) S is serializable if Result(S)  Result(Sk) where Sk is a serial excution schedule. Note that Result(Si ) may be different from Result(Sj) (i j ) How to detect whether S is serializable Conflict Graph Serializability

13. S2 T1 r(a) r(b) affects T2 w(a) w(b) Conflict Graph S1 r(b) T1 r(a) affects Res(S1)  Res( (T1, T2) ) T2 w(a) w(b) Res(S1)  Res( (T1, T2) ) Res(S1)  Res( (T2, T1) )

14. T1 T2 Detect Cycle in Conflict Graph • If  Cycle in Conflict Graph • Then Not Serializable • Otherwise Serializable T1 r(a) r(b) affects T2 w(a) w(b)

15. How to make it serializable Control the order of execution of operations in concurrent transactions. Two Approaches Two Phase Locking Protocol Locking on each operation Timestamping : Ordering by timestamp on each transaction and each operation How to make it serializable

16. A lock mechanism to control concurrent access to a data item Data items can be locked in two modes : Exclusive (X) mode :Data item can be both read as well as written. X-lock is requested using lock-X instruction. Shared (S) mode : Data item can only be read. S-lock is requested using lock-S instruction. Lock requests are made to concurrency-control manager. Transaction can proceed only after request is granted. Lock-Based Protocols

17. Lock-compatibility matrix A transaction may be granted a lock on an item if the requested lock is compatible with locks already held Any number of transactions can hold shared locks If a lock cannot be granted, the requesting transaction is made to wait till all incompatible locks held have been released. the lock is then granted. Lock-Based Protocol

18. Lock-Based Protocol in Distributed DBS • Majority Protocol • Local lock manager at each site administers lock and unlock requests for data items stored at that site. • When a transaction wishes to lock an un replicated data item Q residing at site Si, a message is sent to Si ‘s lock manager. • If Q is locked in an incompatible mode, then the request is delayed until it can be granted. • When the lock request can be granted, the lock manager sends a message back to the initiator indicating that the lock request has been granted. • If Q is replicated at n sites, then a lock request message must be sent to more than half of the n sites in which Q is stored. • The transaction does not operate on Q until it has obtained a lock on a majority of the replicas of Q. • When writing the data item, transaction performs writes on all replicas.

19. This is a protocol which ensures conflict-serializable schedules. Phase 1: Growing Phase transaction may obtain locks transaction may not release locks Phase 2: Shrinking Phase transaction may release locks transaction may not obtain locks The protocol assures serializability The Two-Phase Locking Protocol

20. Strict 2PL Two Phase Locking 2PL

21. Problem of Two Phase Locking Protocol • Deadlock • Growing Phase and Shrinking Phase • Prevention and Avoidance : Impossible • Only Detection may be possible • When a deadlock occurs • Detection of Deadlock : Wait-For-Graph • Abort a transaction • How to choose a transaction to kill ?

22. Each transaction is issued a timestamp when TS(Ti) <TS(Tj) : old transaction Ti and new transaction Tj Each data Q, two timestamp : W-timestamp(Q) : largest time-stamp for successful write(Q) R-timestamp(Q) : largest time-stamp for successful read(Q) Timestamp-Based Protocols

23. Transaction Ti issues a read(Q) If TS(Ti) W-timestamp(Q), then Ti needs to read a value of Q that was already overwritten. Hence, the read operation is rejected, and Ti is rolled back. If TS(Ti) W-timestamp(Q), then the read operation is executed, and R-timestamp(Q) is set set Timestamp-Based Protocols : Read

24. Transaction Ti issues write(Q). If TS(Ti) < R-timestamp(Q), then the value of Q that Ti is producing was needed previously, and the system assumed that that value would never be produced. Hence, the write operation is rejected, and Ti is rolled back. If TS(Ti) < W-timestamp(Q), then Ti is attempting to write an obsolete value of Q. Hence, this write operation is rejected, and Ti is rolled back. Otherwise, the write operation is executed, and W-timestamp(Q) is reset Timestamp-Based Protocols : Write

25. How to manage global TS – Clock • In distributed systems • Multiple Physical Clocks : No Centralized Clock • Logical Clock rather than a Global Physical Clock

26. Global Clock and Logical Clock • Global Clock • TAI (Temps Atomique International) at Paris • Time Server • Broadcasting from Satellite • Granularity • Logical Clock • Not absolute time but for ordering • Lamport Algorithm • Correction of Clock • T(A, tx) < T(B, rx)

27. 1 1 2 2 Logical Clock • Logical Clock • Not absolute time but for ordering • Lamport Algorithm • Correction of Clock – Never run backward • C(A, tx) < C(B, rx) 5 10 5 10 6 11 6 11 3 3 7 12 7 12 4 4 8 13 8 13 5 5 9 14 9 14 6 10 15 6 10 15 7 11 16 7 16 16 8 12 17 8 17 17 9 13 18 18 18 18 10 14 19 19 19 19

28. Implementation of Concurrency Control in Distributed Systems • Three Managers • TM (Transaction Manager) : Ensure the Atomicity • Scheduler : Main Responsibility for Concurrency Control • DM (Data Manager) : Simple Read/Write In distributed systems In a single machine

29. Transaction Management: Recovery

30. Transaction failure : Logical errors: internal error condition System errors: system error condition (e.g., deadlock) System crash: a power failure or other hardware or software failure Fail-stop assumption: non-volatile storage contents are assumed to not be corrupted by system crash Database systems have numerous integrity checks to prevent corruption of disk data Disk failure Failure Classification

31. Recovery Algorithms • Recovery algorithms : should ensure • database consistency • transaction atomicity and • durability despite failures • Recovery algorithms have two parts • Preparing Information for Recovery : During normal transaction • Actions taken after a failure to recover the database

32. Volatile storage: does not survive system crashes examples: main memory, cache memory Nonvolatile storage: survives system crashes examples: disk, tape, flash memory, non-volatile (battery backed up) RAM Stable storage: a mythical form of storage that survives all failures approximated by maintaining multiple copies on distinct nonvolatile media Storage Structure

33. Modifying the database must be committed Otherwise it may leave the database in an inconsistent state. Example Consider transaction Ti that transfers $50 from account A to account B; goal is either to perform all database modifications made by Tior none at all. Several output operations may be required for Ti For example : output(A) and output(B). A failure may occur after one of these modifications have been made but before all of them are made. Recovery and Atomicity

34. Recovery and Atomicity (Cont.) • To ensure atomicity despite failures, • we first output information describing the modifications to stable storage without modifying the database itself. • Log-based recovery

35. A log : must be kept on stable storage. <Ti, Start>, and <Ti, Start> < Ti, X, V1, V2 > Logging Method When transaction Tistarts, <Tistart> log record When Tifinishes, <Ticommit> log record Before Tiexecutes write(X), <Ti, X, Vold, Vnew>log record We assume for now that log records are written directly to stable storage Two approaches using logs Deferred database modification Immediate database modification Log-Based Recovery

36. The deferred database modification scheme records all modifications to the log, writes them after commit. Log Scheme Transaction starts by writing <Ti start> record to log. write(X) : <Ti, X, V> Note: old value is not needed for this scheme The write is not performed on X at this time, but is deferred. When Ticommits, <Ticommit> is written to the log Finally, executes the previously deferred writes. Deferred Database Modification

37. Recovering Method During recovery after a crash, a transaction needs to be redone if and only if both <Tistart> and<Ti commit> are there in the log. Redoing a transaction Ti( redoTi) sets the value of all data items updated by the transaction to the new values. Deletes Tisuch that <Ti ,start> exists but <Ti commit> does not. Deferred Database Modification (Cont.)

38. If log on stable storage at time of crash is as in case: (a) No redo actions need to be taken (b) redo(T0) must be performed since <T0 commit> is present (c) redo(T0) must be performed followed by redo(T1) since <T0 commit> and <Ti commit> are present Deferred Database Modification : Example T0: read (A) T1 : read (C)A: = A - 50C:= C- 100 write (A) write (C) read (B) B:= B + 50write (B)

39. Immediate database modification scheme Database updates of an uncommitted transaction For undoing : both old value and new value Recovery procedure has two operations undo(Ti) : restores the value of all data items updated by Ti redo(Ti) : sets the value of all data items updated by Ti When recovering after failure: Undo if the log <Ti start>, but not <Ticommit>. Redo if the log both the record <Tistart> and <Ti commit>. Immediate Database Modification

40. Recovery actions in each case above are: (a) undo (T0): B is restored to 2000 and A to 1000. (b) undo (T1) and redo (T0): C is restored to 700, and then A and B are set to 950 and 2050 respectively. (c) redo (T0) and redo (T1): A and B are set to 950 and 2050 respectively. Then C is set to 600 Immediate Database Modification : Example

41. Idempotent Operation • Result (Op(x)) = Result (Op(Op(x)) • Example • Increment(x); : Not Idempotent • x=a; write(x); : Idempotent • Operations in Log Record • Must be Idempotent, otherwise • Multiple Executions (for redo) may cause incorrect results

42. Mutual Exclusions and Election algorithms

43. Mutual Exclusion : Monitor (Coordinator) • In a single system : Easy to implement by semaphore • In distributed systems : No shared data for semaphore • A Centralized Algorithm • Simple • But single point of failure

44. Mutual Exclusion : Distributed Algorithm • No central coordinator • Rule : When a system receive a request • Not in CS or Not to enter : OK to requester • In CS : No reply • To enter : compare Timestamp and one with lower TS wins

45. Mutual Exclusion : Token Ring • One with token can enter CS • If a system wants to enter CS : • wait Token and process it • Otherwise, pass token to the next Wait until it gets the token Token

46. Mutual Exclusion : Comparison

47. Election : Bully Algorithm • When it is found that a coordinator is required

48. Election : Ring Algorithm • When it is found that the coordinator has crashed, • Circulate message with priority 8 1 Elected [3] Elect [3,4,5,7,8] Elected [3] Elect [3,4,5,7] 2 7 Elected [3] Elect [3,4,5,7,8,2] Elected [3] Elect [3,4,5] 6 3 Site 3 finds that the coordinator has crashed Elect [3] No response Elect [3,4] Elected [3] 5 4 Elected [3]