Transaction Management Overview

Dr.S.Sivakumar,Principal

C.P.A College, Bodinayakanur

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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 transaction is the DBMS’s abstract view of a user program: a sequence of reads and writes.

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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 failures.

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Atomicity of Transactions

• A transaction might commit after 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 undo the actions of aborted transactions.

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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.

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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)

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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. )

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Anomalies with Interleaved Execution

• Reading Uncommitted Data (WR Conflicts, “dirty reads”):��

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• Unrepeatable Reads (RW Conflicts):

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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

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Anomalies (Continued)

• Overwriting Uncommitted Data (WW Conflicts):

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T1: W(A), W(B), C

T2: W(A), W(B), C

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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

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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 aborts by 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.

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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 disk before the 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 duplicated 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.

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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!)

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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.

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