1. Transaction Management Overview
Chapter 16
The slides for this text are organized into chapters. This lecture covers Chapter 16, providing an overview of transactions, concurrency control, and recovery.
This chapter is the first of a sequence (Chapters16, 17, and 18) on transaction management that might be covered in full in a course with a systems emphasis. It can also be used stand-alone, as a self-contained overview of these issues, in a course with an application emphasis. It covers the essential concepts in sufficient detail to support a discussion of physical database design and tuning in Chapter 20.

2. 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.

3. Transaction ACID Properties
Atomic
Either all actions are carried out or none are.
Not worry about incomplete transaction.
Consistency
DBMS assumes that the consistency holds for each transaction.
Isolation
Transactions are isolated, or protected, from the effects of concurrently scheduling other transactions.
Durability
The effects of transaction is persist if DBMS informs the user successful execution.

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. 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.

6. Example Consider two transactions (Transactions):
T1: BEGIN A=A+100, B=B 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.

7. 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)

8. 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. )

9. 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

10. 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
T1: R(A), W(A), R(B), W(B), Abort
T2: R(A), W(A), C
Unrepeatable Reads (RW Conflicts): E.g., T1: R(A), check if A >0, decrement, T2: R(A), decrement
T1: R(A), R(A), W(A), C
T2: R(A), W(A), C
Overwriting Uncommitted Data (WW Conflicts):
T1: W(A), W(B), C
T2: W(A), W(B), C

11. 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.
T1: R(A),W(A), Abort
T2: R(A),W(A),R(B),W(B), Commit
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.

12. 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.

13. 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.

14. 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.

15. 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

16. 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

17. 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…

18. 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.

19. 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.

20. 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.

21. 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?

22. 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.

23. 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.

24. 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!

25. 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.