Post on 20-Jan-2016
Transaction Management, Concurrency Control and Recovery
Chapter 20
1
Overview
2
What are transactions? What is a schedule? What is concurrency control? Why we need concurrency control:
Three problems. Serializabiltiy and Concurrency control:
Theory: Conflict Serializability View Serializability
Practice: Locking Time-stamping Optimistic techniques
Recovery facilities
What is a Transaction?
3
Transaction
Action, or series of actions, carried out by user or application, which accesses or changes contents of database.
Logical unit of work on the database.
Transforms database from one consistent state to another, although consistency may be violated during transaction.
Example:
Read(staffNo, salary)
salary=salary * 1.1
write(staffNo , salary)
What is a Transaction?
4
Can have one of two outcomes: Success - transaction commits and database reaches a new
consistent state. Failure - transaction aborts, and database must be restored to
consistent state before it started (rolled back or undone).
Committed transaction cannot be aborted.
Aborted transactions that are rolled back can be restarted later.
Properties of Transactions
5
Four basic (ACID) properties of a transaction are:
Atomicity ‘All or nothing’ property.
Consistency Must transform database from one consistent state to another.
Isolation Partial effects of incomplete transactions should not be visible to other transactions.
Durability Effects of a committed transaction are permanent and must not be lost because of later failure.
We deal with transactions in a schedule.
Schedule
6
Time T1 T2 T3 Balance1
Balance2
t0Begin
Transaction100 200
t1Read(Balance
1)Begin
Transaction100 200
t2Read(Balance
1)Begin
Transaction100 200
t3Balance1 +=
500Read(Balance
2)100 200
t4Write(Balance
1)600 200
t5 CommitRead(Balance
1)600 200
: : : : : :
Ord
er o
f ex
ecu
tion
Running TransactionsData Items affected
by transactions (optional)
Start with t0 or t1
Schedule Rules
7
Never start two transactions at the same time.
Never perform Reads and Writes of different transactions at the same time.
Each transaction should end with a commit or abort (rollback).
Schedule Definitions
8
ScheduleSequence of reads/writes by set of concurrent transactions.
Serial ScheduleSchedule where operations of each transaction are executed consecutively without any interleaved operations from other transactions.
No guarantee that results of all serial executions of a given set of transactions will be identical. (Think of an example)
Non-Serial ScheduleSchedule where operations from set of concurrent transactions are interleaved
Example of a Serial Schedule
9
Time T1 T2
t0 Begin Transaction
t1 Read(Balance1)
t2 Balance1 += 500
t3 Commit
t4Begin
Transaction
t5 Read(Balance1)
t6 Commit
Example of a non-Serial Schedule
10
Time T1 T2
t0 Begin Transaction
t1Begin
Transaction
t2 Read(Balance1)
t3 Read(Balance1)
t4 Commit
t5 Balance1 += 500
t6 Commit
What is Concurrency Control?
11
Concurrency: transactions running simultaneously.
Concurrency Control: Process of managing simultaneous
operations (transactions) on the database without having them
interfere with one another.
Prevents interference when two or more users are accessing
database simultaneously and at least one is updating data.
Although two transactions may be correct in themselves,
interleaving of operations may produce an incorrect result.
Why we Need Concurrency Control?
12
Three examples of potential problems caused by
concurrency:
Lost update problem.
Uncommitted dependency problem.
Inconsistent analysis problem.
Lost Update Problem
13
Successfully completed update is overridden by another user. T1 withdrawing £10 from an account with balx, initially £100. T2 depositing £100 into same account. Serially, final balance would be £190. Loss of T2’s update avoided by preventing T1 from reading balx
until after update
Time T1 T2
balx
t1 begin-transaction
100
t2 begin-transaction read(balx)
100
t3 read(balx) balx = balx +100
100
t4 balx = balx -10 write(balx)
200
t5 write(balx) commit
90
t6 commit
90
Uncommitted Dependency Problem
14
Occurs when one transaction can see intermediate results of another transaction before it has committed.
T4 updates balx to £200 but it aborts, so balx should be back at original value of £100.
T3 has read new value of balx (£200) and uses value as basis of £10 reduction, giving a new balance of £190, instead of £90.
Problem avoided by preventing T3 from reading balx until after T4 commits or aborts.
Time T3 T4
balx
t1 begin-transaction
100
t2 read(balx)
100
t3 balx = balx +100
100
t4 begin_transaction write(balx)
200
t5 read(balx) :
200
t6 balx = balx -10 rollback
100
t7 write(balx)
190
t8 commit
190
Inconsistent Analysis Problem
15
Occurs when transaction reads several values but
second transaction updates some of them during
execution of first.
Sometimes referred to as dirty read or unrepeatable
read.
T6 is totaling balances of account x (£100), account y
(£50), and account z (£25).
Meantime, T5 has transferred £10 from balx to balz, so T6
now has wrong result (£10 too high).
Inconsistent Analysis Problem
16
Problem avoided by preventing T6 from reading balx and balz until after T5 completed updates.
Serializability
17
Serializability is a property of a schedule: We say serializable schedule and non-serializable
schedule.
But what makes a schedule serializable?
A serializable schedule is a non-serial schedule that
allows transactions to execute concurrently without
interfering with one another.
In other words, a non-serial schedule that is equivalent
to some serial schedule.
Main goal is to prevent transactions interfering with
each other (3 problems discussed earlier).
Serializability
18
Two types of seriailizability: Conflict.
View. Serializability
Theory Practice
Conflict Serializabili
ty Test
View Serializabili
ty TestLocking
Time-stamping
Optimistic
Conflict Serializability
19
In serializability, ordering of read/writes is important:
(a) If two transactions only read a data item, they do not
conflict and order is not important.
(b) If two transactions either read or write completely
separate data items, they do not conflict and order is
not important.
(c) If one transaction writes a data item and another reads
or writes same data item, order of execution is
important. They conflict.
Conflict Serializability
20
Schedule S1 is conflict serializable if it is conflict equivalent to a serial schedule.
Two ways of testing a schedule for conflict serialiazibility:
1. A schedule is conflict serializable if you can switch order of 2 non-conflicting operations until you reach a serial schedule.
2. Precedence graph.
Testing for Conflict Serializability
21
Time T7 T8
t1 begin-transaction
t2 read(balx)
t3 write(balx)
t4 begin_transaction
t5 read(balx)
t6 write(balx)
t7 read(baly)
t8 write(baly)
t9 commit
t10 read(baly)
t11 write(baly)
t12 commit
T7 T8
begin-transaction
read(balx)
write(balx)
begin_transaction
read(balx)
read(baly)
write(balx)
write(baly)
commit
read(baly)
write(baly)
commit
Testing for Conflict Serializability
22
Time T7 T8
t1 begin-transaction
t2 read(balx)
t3 write(balx)
t4 begin_transaction
t5 read(baly)
t6 read(balx)
t7 write(balx)
t8 write(baly)
t9 commit
t10 read(baly)
t11 write(baly)
t12 commit
T7 T8
begin-transaction
read(balx)
write(balx)
read(baly)
write(baly)
commit
begin_transaction
read(balx)
write(balx)
read(baly)
write(baly)
commit
Non-conflict Serializable Schedule
23
Time T7 T8
t1 begin-transaction
t2 read(balx)
t3 begin_transaction
t4 write(balx)
t5 write(balx)
t6 commit
t7 commit
T7 T8
begin-transaction
read(balx)
write(balx)
commit
begin_transaction
write(balx)
commit
Testing for Conflict Serializability – Precedence Graph
24
Create:
node for each transaction;
a directed edge Ti Tj, if Tj reads the value of an item written by Ti;
a directed edge Ti Tj, if Tj writes a value into an item after it has
been read by Ti.
a directed edge Ti Tj, if Tj writes a value into an item after it has
been written by Ti.
If precedence graph contains cycle, schedule is not conflict serializable.
Test Schedule: Is it conflict serializable?
25
Time T7 T8
t1 begin-transaction
t2 read(balx)
t3 balx = balx +100
t 4 write(balx)
t5 begin_transaction
t6 read(balx)
t7 balx = balx * 1.1
t8 write(balx)
t9 read(baly)
t10 baly = baly * 1.1
t11 write(baly)
t12 commit
t13 read(baly)
t14 write(baly)
t15 commit
View Serializability
26
Offers less stringent definition of schedule equivalence than conflict
serializability.
Two schedules S1 and S2 are view equivalent if:
For each data item x, if Ti reads initial value of x in S1, Ti must also read
initial value of x in S2.
For each read on x by Ti in S1, if value read by x is written by Tj, Ti must
also read value of x produced by Tj in S2.
For each data item x, if last write on x performed by Ti in S1, same
transaction must perform final write on x in S2.
View Serializability
27
Schedule is view serializable if it is view equivalent to a serial
schedule.
Every conflict serializable schedule is view serializable, although
converse is not true.
It can be shown that any view serializable schedule that is not
conflict serializable contains one or more blind writes.
All Schedules
View Serializable Schedules
Conflict Serializable Schedules
View Serializable Schedule
28
Time T7 T8
t1 begin-transaction
t2 read(balx)
t3 write(balx)
t4 read(baly)
t5 write(baly)
t6 commit
t7 begin-transaction
t8 read(balx)
t9 write(balx)
t10 read(baly)
t11 write(baly)
t12 commit
T7 T8
begin-transaction
read(balx)
write(balx)
begin_transaction
read(balx)
write(balx)
read(baly)
write(baly)
commit
read(baly)
write(baly)
commit
View Serializable Schedule
29
Time T11 T12 T13
t1 begin-transaction
t2 read(balx)
t3 begin_transaction
t4 write(balx)
t5 commit
t6 write(balx)
t7 commit
t8
begin_transaction
t9
write(balx)
t10 commitIs this schedule conflict serializable?
Recoverable Schedule
30
A schedule where, for each pair of transactions Ti and Tj, if Tj reads a data item previously written by Ti, then the commit operation of Ti precedes the commit operation of Tj.
Concurrency Control Techniques
31
Serializability
Theory Practice
Conflict Serializabili
ty Test
View Serializabili
ty TestLocking
Time-stamping
Optimistic
Concurrency Control Techniques
32
Two basic concurrency control techniques:
Locking,
Timestamping.
Both are conservative approaches: delay transactions in case they
conflict with other transactions.
Optimistic methods assume conflict is rare and only check for
conflicts at commit.
Concurrency Control Techniques Overview
33
Locking Time-stamping
Optimistic
Basic Rule
s2PL
Deadlock Prevention
Basic Time-stamp
Ordering
Multi-version
Time-stamp Ordering
Deadlock Detection
Wait-Die
Wound-
Wait
Wait-for
Graph
Thomas’s Write Rule
Regular
Strict
Rigorous Time
outs
Locking
34
Main Idea: Transaction uses locks to deny access to other
transactions and so prevent incorrect updates.
Most widely used approach to ensure serializability.
A transaction must claim:
a shared (read) on x before it can read it.
or an exclusive (write) lock on x before it can write it.
Lock prevents other transactions from reading or writing the
locked data item.
Locking – Basic Rules
35
Shared Lock: If transaction has shared lock on item, it can read but not update item. More than one transaction can hold a shared lock on an item.
Exclusive Lock: If transaction has exclusive lock on item, can both read and update item. Only one transaction can hold an exclusive lock on an item.
Some systems allow transaction to: upgrade read lock to an exclusive lock. downgrade exclusive lock to a shared lock.
Locking -- Commands
36
To acquire a shared (read) lock on X: Read_Lock(x) RLock(X) Shared_Lock(X) SLock(X)
To acquire an exclusive (write) lock on X: Write_Lock(X) WLock(X) Exclusive_Lock(X) XLock(X)
To release a lock on X: Unlock(X)
Time T9 T10
t1 begin-transaction
t2 write_lock(balx)
t3 read(balx)
t4 balx = balx + 100
t5 write(balx)
t6 unlock(balx)
t7 begin_transaction
t8 write_lock(balx)
t9 read(balx)
t10 balx = balx * 1.1
t11 write(balx)
t12 unlock(balx)
t13 write_lock(baly)
t14 read(baly)
t15 baly = baly * 1.1
t16 write(baly)
t17 commit/unlock(baly)
t18 write_lock(baly)
t19 read(baly)
t20 baly = baly - 100
t21 write(baly)
t22 commit/unlock(baly)
Correct use of locks. But is the execution correct?
Two-Phase Locking (2PL)
38
We just saw that locking alone doesn’t always work.
Solution: 2PL.
Transaction follows 2PL protocol if all locking operations precede first unlock operation in the transaction.
Two phases for transaction: Growing phase - acquires all locks but cannot release any
locks. Shrinking phase - releases locks but cannot acquire any new
locks.
With 2PL, we can prevent the three problems.
Original Lost Update Problem
39
Time T1 T2
balx
t1 begin-transaction
100
t2 begin-transaction read(balx)
100
t3 read(balx) balx = balx +100
100
t4 balx = balx -10 write(balx)
200
t5 write(balx) commit
90
t6 commit
90
Preventing Lost Update Problem
40
Time T1 T2
balx
t1 begin-transaction
100
t2 begin_transaction write_lock(balx)
100
t3 write_lock(balx) read(balx)100
t4 WAIT balx = balx +100
100
t5 WAIT write(balx)
200
t6 WAIT commit/unlock(balx)
200
t7 read(balx)
200
t8 balx = balx -10
200
t9 write(balx)
190
t10 commit/unlock(balx)
190
Original Uncommitted Dependency Problem
41
Time T3 T4
balx
t1 begin-transaction
100
t2 read(balx)
100
t3 balx = balx +100
100
t4 begin_transaction write(balx)
200
t5 read(balx) :
200
t6 balx = balx -10 rollback
100
t7 write(balx)
190
t8 commit
190
Preventing Uncommitted Dependency Problem
42
Time T3 T4
balx
t1 begin-transaction
100
t2 write_lock(balx)
100
t3 read(balx)100
t4 begin_transaction balx = balx +100
100
t5 write_lock(balx) write(balx)
200
t6 WAIT commit/unlock(balx)
200
t7 read(balx)
200
t8 balx = balx -10
200
t9 write(balx)
190
t10 commit/unlock(balx)
190
Original Inconsistent Analysis Problem
43
Preventing Inconsistent Analysis Problem
44
A Potential Problem with 2PL
45
Cascading Rollbacks
46
If every transaction in a schedule follows 2PL, schedule is serializable.
However, problems can occur with interpretation of when locks can be
released.
Cascading rollback is undesirable since they potentially lead to the undoing
of a significant amount of work
To prevent this with 2PL, 2 solutions: 1. Rigorous 2PL: Leave release of all locks until end of transaction.
2. Strict 2PL: Holds only exclusive locks until the end of the transaction.
BOTH are still 2PL. So both still have growing and shrinking phases.
2PL still may cause deadlock.
Problems with 2PL
47
1. Cascading Rollbacks: Solved with strict or rigorous 2PL.
2. Dead Locks: Happen in regular 2PL, and also in strict and
rigorous 2PL. Handled using deadlock detection and
prevention techniques.
Deadlocks
48
Deadlock: An impasse that may result when two (or more)
transactions are each waiting for locks held by the other to be
released.
Once a deadlock happens, only one way to break deadlock: abort
one or more of the transactions.
Deadlock should be transparent to user, so DBMS should restart
aborted transaction(s).
Example Deadlock
49
Time T9
t1 begin-transaction
t2 write_lock(balx)
t3 read(balx)
t4 balx = balx - 10
t5 write(balx)
t6 write_lock(baly)
t7 WAIT
t8 WAIT
t9 WAIT
t10 WAIT
t11 :
T10
begin-transaction
write_lock(baly)
read(baly)
baly = baly + 100
write(baly)
wait_lock(balx)
WAIT
WAIT
WAIT
:
Deadlock Handling
50
Two general techniques for handling deadlock:
Deadlock prevention: DBMS doesn’t allow deadlock to happen. Timeouts. Wait-Die. Wound-wait.
Deadlock detection and recovery: DBMS allows deadlocks to happens but detects and recovers from them. Wait-for Graphs (WFG).
Timeouts
51
Transaction that requests lock will only wait for a system-
defined period of time.
If lock has not been granted within this period, lock
request times out.
DBMS assumes transaction deadlocked, even though it may
not be, and it aborts and automatically restarts the transaction.
Timestamps
52
Before we discuss Wait-die and Wound-wait techniques, introduce timestamps.
A timestamp is a unique number given to each transaction.
Traditionally, it is the time the transaction started.
The smaller the timestamp, the older the transaction.
Timestamps
53
TS(T11) = 1 TS(T12) = 3 TS(T13) = 8
Time T11 T12 T13
t1 begin-transaction
t2 read(balx)
t3 begin_transaction
t4 write(balx)
t5 commit
t6 write(balx)
t7 commit
t8
begin_transaction
t9
write(balx)
t10 commit
Wait-Die Technique
54
Only an older transaction can wait for younger one, otherwise transaction is aborted (dies) and restarted with same timestamp. (Why the same?)
If a transaction Ti requests a lock on an item held by Tj: If Ti > Tj [TS(Ti) < TS(Tj)], Ti waits for Tj to release the lock.
If Ti < Tj [TS(Ti) > TS(Tj)], Ti is aborted and restarted with the same TS.
Wound-Wait Technique
55
only a younger transaction can wait for an older one. If older transaction requests lock held by younger one, younger one is aborted (wounded) and restarted with same timestamp. (Why the same?)
If a transaction Ti requests a lock on an item held by Tj: If Ti > Tj [TS(Ti) < TS(Tj)], Tj is aborted and Ti gets the lock.
If Ti < Tj [TS(Ti) > TS(Tj)], Ti waits for Tj to release the lock.
Deadlock Detection and Recovery
56
Usually handled by construction of wait-for graph (WFG)
showing transaction dependencies:
Create a node for each transaction.
Create edge Ti Tj, if Ti waiting to lock item
locked by Tj.
Deadlock exists if and only if WFG contains cycle.
Example Schedule with WFG
57
Time T9
t1 begin-transaction
t2 write_lock(balx)
t3 read(balx)
t4 balx = balx - 10
t5 write(balx)
t6 write_lock(baly)
t7 WAIT
t8 WAIT
t9 WAIT
t10 WAIT
t11 :
T10
begin-transaction
write_lock(baly)
read(baly)
baly = baly + 100
write(baly)
wait_lock(balx)
WAIT
WAIT
WAIT
:
T9T10
Deadlock Detection and Recovery
58
WFG is created at regular intervals.
Several issues when recovering from a deadlock:
choice of deadlock victim;
avoiding starvation.
Self-read: pages 596-597
Concurrency Control Techniques Overview
59
Locking Time-stamping
Optimistic
Basic Rule
s2PL
Deadlock Prevention
Basic Time-stamp
Ordering
Multi-version
Time-stamp Ordering
Deadlock Detection
Wait-Die
Wound-
Wait
Wait-for
Graph
Thomas’s Write Rule
Regular
Strict
Rigorous Time
outs
Timestamping
60
Main Idea: Transactions ordered globally so that older transactions (smaller
timestamps) get priority in the event of conflict.
Conflict is resolved by rolling back (aborting) and restarting transaction.
No locks so no deadlock.
Timestamp
A unique identifier created by DBMS that indicates relative starting time of
a transaction.
Timestamping
A concurrency control protocol that orders transactions in such a way that
order transactions. Transactions with smaller timestamps, get priority in
the event of conflict
Timestamping
61
2 Techniques:
1.Basic Timestamp Ordering. Thomas’s Write Rule.
2.Multiversion Timestamp Ordering.
Basic Timestamp Ordering
62
Read/write proceeds only if last update on that data item was carried
out by an older transaction.
Otherwise, transaction requesting read/write is restarted and given a
new timestamp.
Main Goal: Ordering writes then reads/writes as they would have
been ordered in a serial schedule.
Timestamps are also set for data items:
read-timestamp - timestamp of last transaction to read item;
write-timestamp - timestamp of last transaction to write item.
Basic Timestamping – Read(x)
63
Consider a read(x) transaction T with timestamp TS(T):
TS(T) < write_timestamp(x)
x already updated by younger (later) transaction.
Transaction T must be aborted and restarted with a new
timestamp.
TS(T) write_timestamp(x)
execute the read(x) operation of T
read_timestamp(x) = TS(T)
Basic Timestamping – Write(x)
64
TS(T) < read_timestamp(x)
x already read by younger transaction.
Transaction T must be aborted and restarted with a new
timestamp.
TS(T) < write_timestamp(x)
x already written by younger transaction.
Transaction T must be aborted and restarted with a new
timestamp.
Otherwise, operation is accepted and executed.
Write_timestamp(x) = TS(T)
Basic Timestamp Ordering
65
Thomas’s Write Rule
66
Provide greater concurrency by rejecting obsolete write operations.
When a read(x) is encountered, behave just like in slide 62.
When a write(x) is encountered, perform the following check:
TS(T) < read_timestamp(x)
x already read by younger transaction.
Transaction T must be aborted and restarted with a new
timestamp.
TS(T) < write_timestamp(x)
x already written by younger transaction.
Ignores the write operation (ignore obsolete write rule)
Otherwise, operation is accepted and executed.
Write_timestamp(x) = TS(T)
Comparison of Methods
67
All Schedules
View Serializable Schedules
Conflict Serializable Schedules
2PL TS
Multiversion Timestamp Ordering
68
Main Idea: Versioning of data can be used to increase concurrency so create multiple versions of each data item. Basic timestamp assumes only one version of data item exists,
and so only one transaction can access data item at a time. Multiversion allows multiple transactions to read and write different
versions of same data item. Multiversion ensures each transaction sees consistent set of
versions for all data items it accesses. In multiversion:
Each write operation creates new version of data item while retaining old version.
When transaction attempts to read data item, system selects one version that ensures serializability NO ABORTS ON READs
Each version has a read and a write timestamp. Versions can be deleted once they are no longer required.
Multiversion Timestamping – Read(x)
69
When a transaction T wishes to read x, we find the correct version and let it read it.
The correct version, xi, is the latest version written by an older transaction: TS(T) write_timestamp(xi)
After xi is found and read by T, we need to record that xi was read by T: read_timestamp(xi) = max(read_timestamp(xi), TS(T))
Absolutely no aborts on read.
Multiversion Timestamping – Write(x)
70
When a transaction T wishes to write x, we need to perform a test first then write a new version.
Test: we need make sure that there is no older version of x that has been read by a transaction younger than T The transaction that is younger than T should read T’s version, not this older version.
Multiversion Timestamping – Write(x)
71
1. Find the correct version, xi: is the latest version written by an older transaction:
TS(T) write_timestamp(xi)
2. Test it: make sure that no younger transaction has already read xi:
TS(T) < read_timestamp(xi)? If yes: Abort T. If no: create a new version xj of x:
read_timestamp(xj) = write_timestamp(xj) = TS(T)
72
Time T1 T2 T3 T4 T5
0 Begin1 Begin2 Read(x)3 x=x+204 Write(x)5 Begin6 Read(x)7 Begin8 Read(x)9 Read(y)
10 Read(y)11 Begin12 Read(y)13 y=y/214 Write(y)15 y=y+x-10016 Write(y)17 x=x+0.10x18 Write(x)19 Commit20 Commit21 Commit22 Commit23 Commit
Concurrency Control Techniques Overview
73
Locking Time-stamping
Optimistic
Basic Rule
s2PL
Deadlock Prevention
Basic Time-stamp
Ordering
Multi-version
Time-stamp Ordering
Deadlock Detection
Wait-Die
Wound-
Wait
Wait-for
Graph
Thomas’s Write Rule
Regular
Strict
Rigorous Time
outs
Optimistic Techniques
74
Main Idea: conflict is rare and it is more efficient to let transactions proceed without delays to ensure serializability. At commit, check is made to determine whether conflict has
occurred. If there is a conflict, transaction must be rolled back and restarted.
Potentially allows greater concurrency than traditional protocols.
Three phases: Read. Validation. Write.
Optimistic Techniques – Read Phase
75
Extends from start until immediately before
commit.
Transaction reads values from database and
stores them in local variables. Updates are
applied to a local copy of the data.
DB is not changed during read phase.
Optimistic Techniques – Validation Phase
76
Follows the read phase – just before the transaction
commits.
For read-only transaction:
check that data read are still current values.
If no interference, transaction is committed.
Else, transaction is aborted and restarted.
For update transaction:
check transaction leaves database in a consistent state,
with serializability maintained.
Optimistic Techniques – Write Phase
77
Follows successful validation phase for update
transactions.
Updates made to local copy are applied to the
database.
Database Recovery
78
Process of restoring database to a correct state in the event of a failure.
Transactions represent basic unit of recovery.
Recovery manager responsible for atomicity and durability. If failure occurs between commit and database buffers being flushed to
secondary storage then, to ensure durability, recovery manager has to redo (rollforward) transaction’s updates.
If transaction had not committed at failure time, recovery manager has to undo (rollback) any effects of that transaction for atomicity.
Partial undo - only one transaction has to be undone. Global undo - all transactions have to be undone.
Example
79
T1
T2
T3
T4
T5
T6
t0 tc tf
DBMS starts at time t0, but fails at time tf. Assume data for transactions T2
and T3 have been written to secondary storage.
T1 and T6 have to be undone. In absence of any other information, recovery
manager has to redo T2, T3, T4, and T5.
Recovery Facilities
80
DBMS should provide following facilities to assist with recovery:
1. Backup mechanism: which makes periodic backup copies of
database.
2. Logging facilities: which keep track of current state of transactions
and database changes.
3. Checkpoint facility: which enables updates to database in
progress to be made permanent.
4. Recovery manager: which allows DBMS to restore database to
consistent state following a failure.
2. Logging Facilities: The Log File
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Contains information about all updates to database:
Transaction records.
Checkpoint records.
Often used for other purposes (for example, auditing).
3. Checkpoint Facility
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Checkpoint
Point of synchronization between database and log file. All buffers are written to secondary storage.
A checkpoint consists of the following actions:
1. Suspend execution of transactions temporarily.
2. Write all updated buffers to disk.
3. Write a checkpoint log record to disk.
4. Resume executing transactions.
When failure occurs, the recovery manager performs the following: Redo all transactions that committed since the checkpoint. Undo all transactions active at time of crash (if using immediate update).
Example
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T1
T2
T3
T4
T5
T6
t0 tc tf
T1 and T6: undo.
T2 and T3: do nothing.
T4 and T5: redo.
Checkpoint in Log File
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Main Recovery Techniques
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Three main recovery techniques:
Deferred Update.
Immediate Update.
Shadow Paging.
Deferred Update
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Updates are not written to the database until after a transaction has reached its commit point.
Start from the last checkpoint: If a transaction has committed before checkpoint
Do nothing. If a transaction has committed after checkpoint
Redo it. If a transaction has not committed after checkpoint
Do nothing.
Immediate Update
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Updates are applied to database as they occur.
Start from the last checkpoint: If a transaction has committed before checkpoint Do nothing. If a transaction has committed after checkpoint Redo it. If a transaction has not committed after checkpoint Undo it.
Undo is done in reverse order: from bottom of log file to top.
Redo is done in order: from top of log file to bottom.
Example
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Using Deferred Update: Which transactions to undo? Redo?
Using Immediate Update: Which transactions to undo? Redo?
Shadow Paging
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Maintain two page tables during life of a transaction: Current page table. Shadow page table.
When transaction starts, two tables are the same.
Shadow page table is never changed thereafter and is used to restore database in event of failure.
During transaction, current page table records all updates to database.
When transaction completes, current page table becomes shadow page table.