Database Management systems Subject Code: 10CS54 Prepared By: Subject Name: Database Management systems Subject Code: 10CS54 Prepared By: Hymavathi M Department: CSE 4/11/2019

Unit –VIII 4/11/2019

TRANSACTION MANAGEMENT A transaction is a unit of a program execution that accesses and possibly modifies various data objects (tuples, relations). A transaction is a Logical unit of database processing that includes one or more access operations (read -retrieval, write - insert or update, delete). A transaction (set of operations) may be stand-alone specified in a high level language like SQL submitted interactively, or may be embedded within a program. A transaction (collection of actions) makes transformations of system states while preserving the database consistency. 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/writ ten from/to the database. A transaction is the DBMS’s abstract view of a user program: a sequence of reads and writes. 4/11/2019

PROPERTIES OF TRANSACTION The DBMS need to ensure the following properties of transactions: 1.Atomicity – Transactions are either done or not done – They are never left partially executed An executing transaction completes in its entirety or it is aborted altogether. –e.g., Transfer Money (Amount, X, Y) means i ) DEBIT (Amount, X); ii) CREDIT (Amount, Y). Either both take place or none 2. Consistency – Transactions should leave the database in a consistent state –If a transaction violates the database’s consistency rules, the entire transaction will be rolled back and the database will be restored to a state consistent with those rules. 4/11/2019

3. Isolation – Transactions must behave as if they were executed in isolation. An executing transaction cannot reveal its (incomplete) results before it commits. –Consequently, the net effect is identical to executing all transactions, the one after the other in some serial order. 4. Durability The effects of transaction is persist if DBMS informs the user successful execution. 4/11/2019

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. 4/11/2019

Atomicity of Transactions A transaction is seen by DBMS as a series or list of actions. read/write database object. A transaction might commit after completing all its actions. or it could abort (or be aborted by the DBMS) after executing some actions. Transactions are atomic: a user can think of a transaction 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. 4/11/2019

Example T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B Consider two transactions (Transactions): 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. Consider a possible interleaving (schedule): This is OK. But what about: The DBMS’s view of the second schedule: T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B T1: R(A), W(A), R(B), W(B) T2: R(A), W(A), R(B), W(B) 4/11/2019

Scheduling Transactions Schedule: an actual or potential execution sequence. A list of actions from a set of transactions as seen by DBMS. The order in which two actions of a transaction T appear in a schedule must be the same as the order in which they appear in T. Classification : 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 on any consistent database instance. (Note: If each transaction preserves consistency, every serializable schedule preserves consistency. ) 4/11/2019

Concurrent Execution of Transaction E.g. Serializable schedule, Equal to the serial schedule T1;T2. E.g. Serializable schedule, Equal to the serial schedule T2;T1. Why concurrent execution? CPU and I/O can work in parallel to increase system throughput. Interleaved execution of a short transaction with a long transaction allows the short transaction to complete quickly, thus prevent stuck transaction or unpredicatable delay in response time. T1: R(A), W(A), R(B), W(B), C T2: R(A), W(A), R(B), W(B),C T1: R(A), W(A),R(B), W(B), C T2: R(A), W(A), R(B), W(B), C 4/11/2019

Anomalies with Interleaved Execution Reading Uncommitted Data (WR Conflicts, “dirty reads”): e.g. T1: A+100, B+100, T2: A*1.06, B*1.06 Unrepeatable Reads (RW Conflicts): E.g., T1: R(A), check if A >0, decrement, T2: R(A), decrement 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 4/11/2019

T1: W(A), W(B), C T2: W(A), W(B), C Overwriting Uncommitted Data (WW Conflicts): T1: W(A), W(B), C T2: W(A), W(B), C 4/11/2019

Schedules involving Aborted Transactions Serializable schedule: A schedule whose effect on any consistent database instance is guaranteed to be identical to that of some complete serial schedule over the set of committed transactions. Aborted transactions being undone completely – we have to do cascading abort. Eg: Can we do cascading abort above? We have to abort changes made by T2, but T2 is already committed – we say the above schedule is an Unrecoverable schedule. What we need is Recoverable schedule. T1: R(A),W(A), Abort T2: R(A),W(A),R(B),W(B), Commit 4/11/2019

Recoverable Schedules Recoverable schedule: transactions commit only after all transactions whose changes they read commit. In such a case, we can do cascading abort Eg below: Note that T2 cannot commit before T1, therefore when T1 aborts, we can abort T2 as well. T1: R(A),W(A), Abort T2: R(A),W(A),R(B),W(B), Another technique: A transaction reads changes only of committed transactions. Advantage of this approach is: the schedule is recoverable, and we will never have to cascade aborts. 4/11/2019

Lock-based Concurrency Control Only serializable, recoverable schedules are allowed. No actions of committed transactions are lost while undoing aborted transactions. Lock protocol: a set of rules to be followed by each transaction ( and enforced by DBMS) to ensure that, even though actions of several transactions is interleaved, the net effect is identical to executing all transactions in some serial order. 4/11/2019

Lock-Based Concurrency Control Strict Two-phase Locking (Strict 2PL) Protocol: Rule 1: Each Transaction must obtain a S (shared) lock on object before reading, and an X (exclusive) lock on object before writing. Rule 2: 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. A transaction that has an exclusive lock can also read the object. A transaction that requests a lock is suspended until the DBMS is able to grant it the requested lock. 4/11/2019

Lock-Based Concurrency Control In effect, only 'safe' interleavings of transactions are allowed. Two transactions access completely independent parts of database. If accessing same objects, all actions of one of transactions (has the lock) are completed before the other transaction can proceed. 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 4/11/2019

Lock-based Concurrency Control Strict 2PL T1: X(A),R(A),W(A),X(B),R(B),W(B),Commit T2: X(A),R(A),W(A),X(B),R(B),W(B), Commit T1: S(A),R(A), X(C),R(C),W(C),Commit T2: S(A),R(A),X(B),R(B),W(B), Commit 4/11/2019

Deadlocks Deadlock: Two transactions are waiting for locks from each other. e.g., T1 holds exclusive lock on A, requests an exclusive lock on B and is queued. T2 holds an exclusive lock on B, and request lock on A and queued. Deadlock detecting Timeout mechanism… 4/11/2019

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. 4/11/2019

Performance of locking Lock-based schemes Resolve conflicts between transactins. Two basic mechanisms: blocking and aborting. Blocked transactions hold lock, and force others to wait. Aborting wastes the work done thus far. Deadlock is an extreme instance of blocking. A set of transactions is forever blocked unless one of the deadlocked transactions is aborted. Overhead of locking is primarily from delays due to blocking. 4/11/2019

Crash Recovery Recovery Manager is responsible for ensuring transaction atomicity and durability. Ensure atomicity by undoing the actions of transactions that do not commit. Ensure durability by making sure that all actions of committed transactions survive system crashes and media failure. Transaction Manager controls execution of transactions. Acquire lock before reading and writing. 4/11/2019

Stealing Frames and Forcing Pages Steal approach: Can the changes made to an object O in the buffer pool by a transaction R be written to disk before T commits? Happen in Page replacement. Force approach: When a transaction commits, must we ensure that all the changes it has made to objects in the buffer pool are immediately forced to disk? 4/11/2019

Stealing Frames and Forcing Pages Simplest: No-steal, force. Why? No-steal --- No undo. Force --- No redo. Most system use steal, no-force approach. Why? Assumption in No-steal: all pages modified by ongoing transactions can be accommodated in the buffer pool. Unrealistic. Force results in excessive page I/O. Steal: replaced page is written to disk even if its transaction is still alive. No-force: pages in the buffer pool that are modified by a transaction are not forced to disk when the transaction commits. 4/11/2019

The Log Log: information maintained during normal execution of transactions to enable it to perform its task in the event of a failure. 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 transaction id, so it’s easy to undo a specific transaction. All log related activities are handled transparently by DBMS. In fact, all CC related activities such as lock/unlock, dealing with deadlocks etc. 4/11/2019

Recovering From a Crash Checkpointing: saves information about active transactions and dirty buffer pool pages, also helps reduce the time taken to recover 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 transactions that were active, and all dirty pages in the buffer pool at the time of the crash. Redo: Redo 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 transactions 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! 4/11/2019

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 4/11/2019