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Transaction Management Overview

Transaction Management Overview. Chapter 18. Objects, transections. Database ` objects ' are the units in which programs read or write information Pages, records …

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Transaction Management Overview

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  1. Transaction Management Overview Chapter 18

  2. Objects, transections • Database `objects' are the units in which programs read or write information • Pages, records … • A transaction is seen by the DBMS as a series, or listof actions. The actions thatcan be executed by a transaction include reads and writes of database objects

  3. Transactions • Concurrent execution of user programs is essential for good DBMS performance. • Because disk accesses are frequent, and relatively slow, it is important to keep the cpu humming by working on several user programs concurrently. • A user’s program may carry out many operations on the data retrieved from the database, but the DBMS is only concerned about what data is read/written from/to the database. • A transactionis the DBMS’s abstract view of a user program: a sequence of reads and writes.

  4. Concurrency in a DBMS • Users submit transactions, and can think of each transaction as executing by itself. • Concurrency is achieved by the DBMS, which interleaves actions (reads/writes of DB objects) of various transactions. • Each transaction must leave the database in a consistent state if the DB is consistent when the transaction begins. • DBMS will enforce some ICs, depending on the ICs declared in CREATE TABLE statements. • Beyond this, the DBMS does not really understand the semantics of the data. (e.g., it does not understand how the interest on a bank account is computed). • Issues:Effect of interleaving transactions, and crashes.

  5. ACID four important properties of transactions • Users should be able to regard the execution of each transaction as Atomic • must preserve the consistency of the database • even if the DBMS interleaves the actions of several transactions for performance reasonwithout considering the effect ofother concurrently executing transactions: isolation: • Once the DBMS informs the user that a transaction has been successfully completed, its effects should persist even if the system crashes before all its changesare reflected on disk.durability.

  6. Consistency and Isolation • Users are responsible for ensuring transaction consistency • Transection will leave DB in Consistent state • 100 depit acount A • 99 credit acount B, 1 difference ins users logic problem • isolation ensured by guaranteeing that even though actions of severaltransactions might be interleaved, the net effect is identical to executing all transactionsone after the other in some serial order.

  7. Atomicity of Transactions • A transeciton can be incomplete for • Being ABORTED, or terminated due to some kind of anomaly DBMS • İf terminated by DBMS it is restarted • System crash • Unexpected stuation (access some disk ) • a transaction that isinterrupted in the middle may leave the database in an inconsistent state . • either all of a transaction's actions arecarried out, or none are.DMBS undo actions from logs

  8. Atomicity of Transactions • A transaction mightcommitafter completing all its actions, or it could abort(or be aborted by the DBMS) after executing some actions. • A very important property guaranteed by the DBMS for all transactions is that they are atomic.That is, a user can think of a Xact as always executing all its actions in one step, or not executing any actions at all. • DBMS logs all actions so that it can undothe actions of aborted transactions.

  9. http://www.vbdotnetheaven.com/blogs/4995/transaction-processing-concept-in-ado-nethttp://www.vbdotnetheaven.com/blogs/4995/transaction-processing-concept-in-ado-net

  10. Example • Consider two transactions (Xacts): T1: BEGIN A=A+100, B=B-100 END T2: BEGIN A=1.06*A, B=1.06*B END • Intuitively, the first transaction is transferring $100 from B’s account to A’s account. The second is crediting both accounts with a 6% interest payment. • There is no guarantee that T1 will execute before T2 or vice-versa, if both are submitted together. However, the net effect must be equivalent to these two transactions running serially in some order.

  11. Example (Contd.) • Consider a possible interleaving (schedule): T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B • This is OK. But what about: T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B • The DBMS’s view of the second schedule: T1: R(A), W(A), R(B), W(B) T2: R(A), W(A), R(B), W(B)

  12. Scheduling Transactions • a schedule represents an actual or potential execution sequence. • DBMS interleaves the actions ofdifferent transactions to improve performance

  13. Scheduling Transactions • Ensuring transaction isolation while permitting such concurrent execution isdifficult, but is necessary for performance reasons • I/O activity can be done in parallel with CPU activity in a computer. • Over-lapping I/O and CPU activity, • Reduce • i/o , cpuidle time • Increase • system throughput

  14. Scheduling Transactions • Serial schedule: Schedule that does not interleave the actions of different transactions. • Equivalent schedules:For any database state, the effect (on the set of objects in the database) of executing the first schedule is identical to the effect of executing the second schedule. • Serializable schedule: A schedule that is equivalent to some serial execution of the transactions. (Note: If each transaction preserves consistency, every serializable schedule preserves consistency. )

  15. Anomalies with Interleaved Execution • Two actions on the same data object conflict if at leastone of them is a write • Write-Read WR conflict T1 T2 «dirty read» • Read-Write RW conflict • Write-Write WW conflict

  16. Anomalies with Interleaved Execution • Reading Uncommitted Data (WR Conflicts, “dirty reads”): • Unrepeatable Reads (RW Conflicts): T1: R(A), W(A), R(B), W(B), Abort T2: R(A), W(A), C T1: R(A), R(A), W(A), C T2: R(A), W(A), C

  17. Anomalies (Continued) • Overwriting Uncommitted Data (WW Conflicts): • if transaction does not reads object beforewriting itsuch a write is called a blind write T1: W(A), W(B), C T2: W(A), W(B), C

  18. Schedules Involving Aborted Transactions • Intuitively, all actions of aborted transactions are to be undone. • if T2 had not been committed, cascading abort of T1 and T2, also releated transection • But T2 is committed, thus it is Unrecoverable Schedule • recoverable schedule is one in which transactions commit only after (and if !) alltransactions whose changes they read commit.

  19. avoid cascading aborts schedule • If transactions read only the changesof committed transactions, not only is the schedule recoverable, but also aborting atransaction can be accomplished without cascading the abort to other transactions.Such a schedule is said to avoid cascading aborts

  20. Aborting a Transaction • If a transaction Ti is aborted, all its actions have to be undone. Not only that, if Tj reads an object last written by Ti, Tj must be aborted as well! • Most systems avoid such cascading abortsby releasing a transaction’s locks only at commit time. • If Ti writes an object, Tj can read this only after Ti commits. • In order to undo the actions of an aborted transaction, the DBMS maintains a log in which every write is recorded. This mechanism is also used to recover from system crashes: all active Xacts at the time of the crash are aborted when the system comes back up.

  21. Lock-Based Concurrency Control • Strict Two-phase Locking (Strict 2PL) Protocol: • Each Xact must obtain a S (shared) lock on object before reading, and an X (exclusive) lock on object before writing. • All locks held by a transaction are released when the transaction completes • (Non-strict) 2PL Variant: Release locks anytime, but cannot acquire locks after releasing any lock. • If an Xact holds an X lock on an object, no other Xact can get a lock (S or X) on that object. • Strict 2PL allows only serializable schedules. • Additionally, it simplifies transaction aborts • (Non-strict) 2PL also allows only serializable schedules, but involves more complex abort processing

  22. Deadlock • Consider the following example: • transaction T1 gets an exclusive lock on object A,T2 gets an exclusive lock on B, T1 requests an exclusive lock on B and is queued,and T2 requests an exclusive lock on A and is queued. Now, T1 is waiting for T2torelease its lock and T2 is waiting for T1 to release its lock! Such a cycle of transactionswaiting for locks to be released is called a deadlock

  23. Deadlock Prevention • giving each transaction a priority and ensuring that lowerpriority transactions are not allowed to wait for higher priority transactions (or viceversa). • Timestamppriority. • Ti requests a lock and transaction Tj holds a conflicting lock • Wait –die • lower priority transactions can never wait for higher prioritytransactions. • Wound-wait • higher priority transactions never wait forlower priority transactions

  24. Deadlock Detection • Deadlocks tend to be rare and typically involve very few transactions • The lock manager maintains a structure called a waits-for graph to detect deadlockcycle • The nodes correspond to active transactions, and there is an arc from Ti toTj if (and only if) Ti is waiting for Tj to release a lock. The lock manager addsedges to this graph when it queues lock requests and removes edges when it grantslock requests

  25. CRASH RECOVERY • recovery manager of a DBMS is responsible for ensuring transaction atomicityand durability • atomicity by undoing the actions of transactions that do not commit • durability by making sure that all actions of committed transactionssurvive system crashes and media failures

  26. after crashes • recovery manager is given control • responsible • for bringing the database to a consistent state • for undoing the actions of an aborted transaction.

  27. The Log • The following actions are recorded in the log: • Ti writes an object: the old value and the new value. • Log record must go to diskbeforethe changed page! • Ti commits/aborts: a log record indicating this action. • Log records are chained together by Xact id, so it’s easy to undo a specific Xact. • Log is often duplexed and archived on stable storage. • All log related activities (and in fact, all CC related activities such as lock/unlock, dealing with deadlocks etc.) are handled transparently by the DBMS.

  28. Recovering From a Crash • There are 3 phases in the Aries recovery algorithm: • Analysis: Scan the log forward (from the most recent checkpoint) to identify all Xacts that were active, and all dirty pages in the buffer pool at the time of the crash. • Redo: Redoes all updates to dirty pages in the buffer pool, as needed, to ensure that all logged updates are in fact carried out and written to disk. • Undo: The writes of all Xacts that were active at the crash are undone (by restoring the before value of the update, which is in the log record for the update), working backwards in the log. (Some care must be taken to handle the case of a crash occurring during the recovery process!)

  29. Summary • Concurrency control and recovery are among the most important functions provided by a DBMS. • Users need not worry about concurrency. • System automatically inserts lock/unlock requests and schedules actions of different Xacts in such a way as to ensure that the resulting execution is equivalent to executing the Xacts one after the other in some order. • Write-ahead logging (WAL) is used to undo the actions of aborted transactions and to restore the system to a consistent state after a crash. • Consistent state: Only the effects of commited Xacts seen.

  30. Sql server Transection isolation • Dirty Reads occur when one transaction reads data written by another, uncommitted, transaction. The danger with dirty reads is that the other transaction might never commit, leaving the original transaction with "dirty" data. • Non-repeatable Reads occur when one transaction attempts to access the same data twice and a second transaction modifies the data between the first transaction's read attempts. This may cause the first transaction to read two different values for the same data, causing the original read to be non-repeatable • Phantom Reads occur when one transaction accesses a range of data more than once and a second transaction inserts or deletes rows that fall within that range between the first transaction's read attempts. This can cause "phantom" rows to appear or disappear from the first transaction's perspective.

  31. SQL Server's isolation models each attempt to conquer a subset of these problems, providing database administrators with a way to balance transaction isolation and business requirements. The five SQL Server isolation models are: • The Read Committed Isolation Model is SQL Server’s default behavior. In this model, the database does not allow transactions to read data written to a table by an uncommitted transaction. This model protects against dirty reads, but provides no protection against phantom reads or non-repeatable reads.

  32. The Read Uncommitted Isolation Model offers essentially no isolation between transactions. Any transaction can read data written by an uncommitted transaction. This leaves the transactions vulnerable to dirty reads, phantom reads and non-repeatable reads. • The Repeatable Read Isolation Model goes a step further than the Read Committed model by preventing transactions from writing data that was read by another transaction until the reading transaction completes. This isolation model protect against both dirty reads and non-repeatable reads.

  33. The Serializable Isolation Model uses range locks to prevent transactions from inserting or deleting rows in a range being read by another transaction. The Serializable model protects against all three concurrency problems. • The Snapshot Isolation Model also protects against all three concurrency problems, but does so in a different manner. It provides each transaction with a "snapshot" of the data it requests. The transaction may then access that snapshot for all future references, eliminating the need to return to the source table for potentially dirty data.

  34. If you need to change the isolation model in use by SQL Server, simply issue the command: • SET TRANSACTION ISOLATION LEVEL <level> • where <level> is replaced with any of the following keywords: • READ COMMITTED • READ UNCOMMITTED • REPEATABLE READ • SERIALIZABLE • SNAPSHOT

  35. BEGIN TRAN UPDATE authors SET au_fname = 'John' WHERE au_id = '172-32-1176' UPDATE authors SET au_fname = 'Marg' WHERE au_id = '213-46-8915' COMMIT TRAN

  36. BEGIN TRAN UPDATE authors SET au_fname = 'John' WHERE au_id = '172-32-1176' UPDATE authors SET au_fname = 'JohnY' WHERE city = 'Lawrence' IF @@ROWCOUNT = 5 COMMIT TRAN ELSE ROLLBACK TRAN

  37. Create Proc TranTest1 AS BEGIN TRAN INSERT INTO [authors]([au_id], [au_lname], [au_fname], [phone], [contract]) VALUES ('172-32-1176', 'Gates', 'Bill', ' 800-BUY-MSFT', 1) UPDATE authors SET au_fname = 'Johnzzz' WHERE au_id = '172-32-1176' COMMIT TRAN GO

  38. CreateProc TranTest2 AS BEGIN TRAN INSERT INTO [authors]([au_id], [au_lname], [au_fname], [phone], [contract]) VALUES ('172-32-1176', 'Gates', 'Bill', ' 800-BUY-MSFT', 1) IF @@ERROR <> 0 BEGIN ROLLBACK TRAN return 10 END UPDATE authors SET au_fname = 'Johnzzz' WHERE au_id = '172-32-1176' IF @@ERROR <> 0 BEGIN ROLLBACK TRAN return 11 END COMMIT TRAN GO

  39. USE pubs DECLARE @intErrorCode INT BEGIN TRAN UPDATE Authors SET Phone = '415 354-9866' WHERE au_id = '724-80-9391' SELECT @intErrorCode = @@ERROR IF (@intErrorCode <> 0) GOTO PROBLEM UPDATE Publishers SET city = 'Calcutta', country = 'India' WHERE pub_id = '9999' SELECT @intErrorCode = @@ERROR IF (@intErrorCode <> 0) GOTO PROBLEM COMMIT TRAN PROBLEM: IF (@intErrorCode <> 0) BEGIN PRINT 'Unexpected error occurred!' ROLLBACK TRAN END

  40. Nested trasnection Figure 1: A COMMIT always balances a BEGIN TRANSACTION by reducing the transaction count by one. Figure 2: A single ROLLBACK always rolls back the entire transaction

  41. USE pubs SELECT 'Before BEGIN TRAN', @@TRANCOUNT -- The value of @@TRANCOUNT is 0 BEGIN TRAN SELECT 'After BEGIN TRAN', @@TRANCOUNT -- The value of @@TRANCOUNT is 1 DELETE sales BEGIN TRAN nested SELECT 'After BEGIN TRAN nested', @@TRANCOUNT -- The value of @@TRANCOUNT is 2 DELETE titleauthor COMMIT TRAN nested -- Does nothing except decrement the value of @@TRANCOUNT SELECT 'After COMMIT TRAN nested', @@TRANCOUNT -- The value of @@TRANCOUNT is 1 ROLLBACK TRAN SELECT 'After ROLLBACK TRAN', @@TRANCOUNT -- The value of @@TRANCOUNT is 0 -- because ROLLBACK TRAN always rolls back all transactions and sets -- @@TRANCOUNT to 0. SELECT TOP 5 au_id FROM titleauthor

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