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Transaction Management
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10.1 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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10.2 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
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10.3 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.
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10.3 Concurrency in a DBMS
• 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).
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Issues: Effect of interleaving transactions, and crashes.
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10.4 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.
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10.4 Atomicity of Transactions
• 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.
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Example
• Consider two transactions (Transactions):
T1: BEGIN A=A+100, B=B100 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
• Consider a possible interleaving
B=B100
(schedule): T1: A=A+100, T2:
A=1.06*A,
B=1.06*B
B=B100
T1: A=A+100, – This is OK. But what about: A=1.06*A, B=1.06*B T2:
R(B), W(B)
R(A), W(A), R(B), W(B)
T1: R(A), W(A), T2: – The DBMS’s view of the second schedule:
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10.5 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.
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10.5 Scheduling Transactions
• Classification:
the actions of different transactions.
– Serial schedule: Schedule that does not interleave
effect (on the set of objects in the database) of executing the first schedule is identical to the effect of executing the second schedule.
– Equivalent schedules: For any database state, the
• (Note: If each transaction preserves consistency,
every serializable schedule preserves consistency. )
– Serializable schedule: A schedule that is equivalent to some serial execution of the transactions on any consistent database instance.
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10.6 Concurrent Execution of Transaction
schedule T1;T2.
R(B), W(B),
C
T1: R(A), W(A), T2:
R(A), W(A),
R(B), W(B),C
– E.g. Serializable schedule, Equal to the serial
W(A),R(B), W(B), C
schedule T2;T1. T1: R(A), T2: R(A), W(A), R(B), W(B),
C
– E.g. Serializable schedule, Equal to the serial
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10.6 Concurrent Execution of Transaction • 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.
R(B), W(B), Abort
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10.7 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), T2:
R(A), W(A), C
• Unrepeatable Reads (RW Conflicts): E.g., T1: R(A), R(A), W(A), C
R(A), W(A), C
T1: R(A), check if A >0, decrement, T2: R(A), decrement T2:
W(B), C
T1: W(A), T2:
W(A), W(B), C
• Overwriting Uncommitted Data (WW Conflicts):
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10.8 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
Abort
T1: R(A),W(A), completely– we have to do cascading abort. T2:
R(A),W(A),R(B),W(B), Commit
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10.8 Schedules involving Aborted Transactions • 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
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10.9 Recoverable Schedules
• Recoverable schedule: transactions
commit only after all transactions whose changes they read commit.
– In such a case, we can do cascading abort
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), T2:
R(A),W(A),R(B),W(B),
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10.9 Recoverable Schedules
• 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.
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10.10 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.
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10.10 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.
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10.10 Lock-based Concurrency Control – 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.
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10.10 Lock-based Concurrency Control
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In effect, only 'safe' interleaving 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
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10.10 Lock-based Concurrency Control
• Strict 2PL
T1: T2:
X(A),R(A),W(A),X(B),R(B),W(B),Commit X(A),R(A),W(A),X(B),R(B),W(B), Commit
S(A),R(A),
T1: X(C),R(C),W(C),Commit T2:
S(A),R(A),X(B),R(B),W(B), Commit
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10.11 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…
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10.12 Aborting a Transaction
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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.
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10.12 Aborting a Transaction
•
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 X acts at the time of the crash are aborted when the system comes back up.
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10.13 Performance of locking
• Lock-based schemes
– Resolve conflicts between transactions.
– 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.
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10.14 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.
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10.15 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.
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10.15 The Log
– 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.
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10.16 Recovering From a Crash
• Check pointing: 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.
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10.16 Recovering From a 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
is in the log record for the update.
• By restoring the before value of the update, which
• working backwards in the log.
crash occurring during the recovery process!
• Some care must be taken to handle the case of a
