Transcript No Slide Title

Transaction Management,
Concurrency Control and Recovery
Chapter 22
1
Overview
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

2
What is a Transaction?
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)
3
What is a Transaction?

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.
4
Properties of Transactions
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.

5
Schedule
Start with
t0 or t1
Running Transactions
T1
t0
Begin Transaction
t1
Read(Balance1)
Order of execution
Time
t2
Balance1 += 500
t4
Write(Balance1)
t5
6
T2
:
T3
Balance1
Balance2
100
200
100
200
Begin
Transaction
100
200
Read(Balance2)
100
200
600
200
Begin
Transaction
Read(Balance1)
t3
:
Data Items affected
by transactions (optional)
Commit
Read(Balance1)
600
200
:
:
:
:
Schedule Rules

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).
7
Schedule Definitions
Schedule
Sequence of reads/writes by set of concurrent transactions.
Serial Schedule
Schedule 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 Schedule
Schedule where operations from set of concurrent transactions are
interleaved
8
Example of a Serial Schedule
9
Time
T1
t0
Begin Transaction
t1
Read(Balance1)
t2
Balance1 += 500
t3
Commit
T2
t4
Begin Transaction
t5
Read(Balance1)
t6
Commit
Example of a non-Serial Schedule
Time
T1
t0
Begin Transaction
t1
t2
10
T2
Begin Transaction
Read(Balance1)
t3
Read(Balance1)
t4
Commit
t5
Balance1 += 500
t6
Commit
What is Concurrency Control?
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.
11
Why we Need Concurrency Control?
Three
examples
of
potential
problems
concurrency:

Lost update problem.

Uncommitted dependency problem.

Inconsistent analysis problem.
12
caused
by
Lost Update Problem
Time
T1
t1




balx
begin-transaction
100
read(balx)
100
t2
begin-transaction
t3
read(balx)
balx = balx +100
100
t4
balx = balx -10
write(balx)
200
t5
write(balx)
commit
90
t6

T2
commit
90
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
13
Uncommitted Dependency Problem
Time
T3
T4
balx
t1
begin-transaction
100
t2
read(balx)
100
t3
balx = balx +100
100
write(balx)
200
t4
begin_transaction
t5
read(balx)
:
200
t6
balx = balx -10
rollback
100
t7
write(balx)
t8




commit
190
190
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
14
commits or aborts.
Inconsistent Analysis Problem

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).
15
Inconsistent Analysis Problem

Problem avoided by preventing T6 from reading
balx and balz until after T5 completed updates.
16
Serializability

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).
17
Serializability

Two types of seriailizability:

Conflict.

View.
Serializability
Theory
Conflict
Serializability
Test
18
View
Serializability
Test
Practice
Locking
Timestamping
Optimistic
Conflict Serializability
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.
19
Conflict Serializability

Schedule S1 is conflict serializable if it is conflict
equivalent to a serial schedule.

We test a schedule for conflict serialiazibility using a
Precedence Graph.
20
Testing for Conflict Serializability –
Precedence Graph

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.
21
Test Schedule: Is it conflict serializable?
Time
T7
t1
begin-transaction
t2
read(balx)
t3
balx = balx +100
t4
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
22
T8
commit
View Serializability

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.
23
View Serializability

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.
All Schedules
View Serializable
Schedules
Conflict
Serializable
Schedules

It can be shown that any view serializable schedule that is not
conflict serializable contains one or more blind writes.
24
View Serializable Schedule
Time
T7
T8
T7
t1
begin-transaction
t2
read(balx)
read(balx)
t3
write(balx)
write(balx)
t4
read(baly)
t5
write(baly)
t6
T8
begin-transaction
begin_transaction
read(balx)
commit
write(balx)
t7
begin-transaction
read(baly)
t8
read(balx)
write(baly)
t9
write(balx)
t10
read(baly)
read(baly)
t11
write(baly)
write(baly)
t12
commit
25
commit
commit
View Serializable Schedule
Time
t1
T11
T12
T13
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
Is this schedule conflict serializable?
26
Concurrency Control Techniques
Serializability
Theory
Conflict
Serializability
Test
27
View
Serializability
Test
Practice
Locking
Timestamping
Optimistic
Concurrency Control Techniques


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.
28
Concurrency Control Techniques Overview
Timestamping
Locking
Basic
Rules
2PL
Rigorous
Regular
Strict
29
Deadlock
Prevention
Time
outs
Deadlock
Detection
WaitDie
Wound
-Wait
Basic Timestamp
Ordering
Wait-for
Graph
Optimistic
Multi-version
Time-stamp
Ordering
Thomas’s Write
Rule
Locking
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.
30
Locking – Basic Rules

Shared Lock:



Exclusive 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.
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:


31
upgrade read lock to an exclusive lock.
downgrade exclusive lock to a shared lock.
Locking -- Commands

To acquire a shared (read) lock on X:





To acquire an exclusive (write) lock on X:





Read_Lock(x)
RLock(X)
Shared_Lock(X)
SLock(X)
Write_Lock(X)
WLock(X)
Exclusive_Lock(X)
XLock(X)
To release a lock on X:

32
Unlock(X)
Time
t1
t2
t3
t4
t5
t6
t7
t8
t9
t10
t11
t12
t13
t14
t15
t16
t17
t18
t19
t20
t21
t22
T9
begin-transaction
write_lock(balx)
read(balx)
balx = balx + 100
write(balx)
unlock(balx)
T10
begin_transaction
write_lock(balx)
read(balx)
balx = balx * 1.1
write(balx)
unlock(balx)
write_lock(baly)
read(baly)
baly = baly * 1.1
write(baly)
commit/unlock(baly)
write_lock(baly)
read(baly)
baly = baly - 100
write(baly)
commit/unlock(baly)
Correct use of locks. But is the execution correct?
Two-Phase Locking (2PL)


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.
34
Original Lost Update Problem
Time
T1
t1
T2
balx
begin-transaction
100
read(balx)
100
t2
begin-transaction
t3
read(balx)
balx = balx +100
100
t4
balx = balx -10
write(balx)
200
t5
write(balx)
commit
90
t6
35
commit
90
Preventing Lost Update Problem
Time
T1
t1
t2
T2
begin-transaction
begin_transaction
write_lock(balx)
balx
100
100
t3
write_lock(balx)
read(balx)100
t4
WAIT
balx = balx +100
100
t5
WAIT
write(balx)
200
t6
WAIT
t7
read(balx)
200
t8
balx = balx -10
200
t9
write(balx)
190
t10
36
commit/unlock(balx)
commit/unlock(balx)
200
190
Original Uncommitted Dependency Problem
Time
T3
T4
balx
t1
begin-transaction
100
t2
read(balx)
100
t3
balx = balx +100
100
write(balx)
200
t4
begin_transaction
t5
read(balx)
:
200
t6
balx = balx -10
rollback
100
t7
write(balx)
t8
commit
37
190
190
Preventing Uncommitted Dependency
Problem
Time
T3
t1
T4
begin-transaction
t2
write_lock(balx)
t3
read(balx)100
balx
100
100
t4
begin_transaction
t5
write_lock(balx)
t6
WAIT
t7
read(balx)
200
t8
balx = balx -10
200
t9
write(balx)
190
t10
38
commit/unlock(balx)
balx = balx +100
100
write(balx)
200
commit/unlock(balx)
200
190
Original Inconsistent Analysis Problem
39
Preventing Inconsistent Analysis Problem
40
A Potential Problem with 2PL
41
Cascading Rollbacks

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.
42
Problems with 2PL
Cascading Rollbacks:
1.

Solved with strict or rigorous 2PL.
Dead Locks:
2.


43
Happen in regular 2PL, and also in strict and rigorous 2PL.
Handled using deadlock detection and prevention
techniques.
Deadlocks
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).
44
Example Deadlock
Time
t1
T10
begin-transaction
begin-transaction
t2
write_lock(balx)
t3
read(balx)
write_lock(baly)
t4
balx = balx - 10
read(baly)
t5
write(balx)
baly = baly + 100
t6
write_lock(baly)
write(baly)
t7
WAIT
wait_lock(balx)
t8
WAIT
WAIT
t9
WAIT
WAIT
t10
WAIT
WAIT
t11
45
T9
:
:
Deadlock Handling

Two general techniques for handling deadlock:

Deadlock prevention: DBMS doesn’t allow deadlock to
happen.




Deadlock detection and recovery: DBMS allows
deadlocks to happens but detects and recovers from
them.

46
Timeouts.
Wait-Die.
Wound-wait.
Wait-for Graphs (WFG).
Timeouts

Transaction that requests lock will only wait for a systemdefined 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.
47
Timestamps

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.
48
Timestamps
Time
T11
t1
T12
T13
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
TS(T11) = 1
TS(T12) = 3
TS(T13) = 8
49
Wait-Die Technique

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:


50
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

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:


51
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

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.
52
Example Schedule with WFG
Time
t1
T9
T10
begin-transaction
begin-transaction
t2
write_lock(balx)
t3
read(balx)
write_lock(baly)
t4
balx = balx - 10
read(baly)
t5
write(balx)
baly = baly + 100
t6
write_lock(baly)
write(baly)
t7
WAIT
wait_lock(balx)
t8
WAIT
WAIT
t9
WAIT
WAIT
t10
WAIT
WAIT
t11
:
53
:
T9
T10
Deadlock Detection and Recovery

WFG is created at regular intervals.

Several issues when recovering from a deadlock:


choice of deadlock victim;

avoiding starvation.
Self-read: pages 593-594
54
Concurrency Control Techniques Overview
Timestamping
Locking
Basic
Rules
2PL
Rigorous
Regular
Strict
55
Deadlock
Prevention
Time
outs
Deadlock
Detection
WaitDie
Wound
-Wait
Basic Timestamp
Ordering
Wait-for
Graph
Optimistic
Multi-version
Time-stamp
Ordering
Thomas’s Write
Rule
Timestamping

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
56
Timestamping

2 Techniques:
1.
Basic Timestamp Ordering.

2.
Thomas’s Write Rule.
Multiversion Timestamp Ordering.
57
Basic Timestamp Ordering

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.
58
Basic Timestamping – Read(x)

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)
59
Basic Timestamping – Write(x)
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)
60
Basic Timestamp Ordering
61
Thomas’s Write Rule

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)
62
Comparison of Methods
All Schedules
View Serializable Schedules
Conflict Serializable Schedules
2PL
63
TS
Multiversion Timestamp Ordering




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.
64
Multiversion Timestamping – Read(x)

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:


After xi is found and read by T, we need to record that
xi was read by T:


TS(T)  write_timestamp(xi)
read_timestamp(xi) = max(read_timestamp(xi), TS(T))
Absolutely no aborts on read.
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Multiversion Timestamping – Write(x)

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.
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Multiversion Timestamping – Write(x)
Find the correct version, xi: is the latest version written
by an older transaction:
1.

TS(T)  write_timestamp(xi)

Test it: make sure that no younger transaction has
already read xi:
TS(T) < read_timestamp(xi)?
2.


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If yes: Abort T.
If no: create a new version xj of x:
read_timestamp(xj) = write_timestamp(xj) = TS(T)
Time
0
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T1
T2
T3
Begin
T4
T5
Begin
Read(x)
x=x+20
Write(x)
Begin
Read(x)
Begin
Read(x)
Read(y)
Read(y)
Begin
Read(y)
y=y/2
Write(y)
y=y+x-100
Write(y)
x=x+0.10x
Write(x)
Commit
Commit
Commit
Commit
Commit
Concurrency Control Techniques Overview
Timestamping
Locking
Basic
Rules
2PL
Rigorous
Regular
Strict
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Deadlock
Prevention
Time
outs
Deadlock
Detection
WaitDie
Wound
-Wait
Basic Timestamp
Ordering
Wait-for
Graph
Optimistic
Multi-version
Time-stamp
Ordering
Thomas’s Write
Rule
Optimistic Techniques

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.
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Database Recovery

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.
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Example
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.
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Recovery Facilities

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.
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2. Logging Facilities: The Log File


Contains information about all updates to database:

Transaction records.

Checkpoint records.
Often used for other purposes (for example, auditing).
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3. Checkpoint Facility
Checkpoint
Point of synchronization between database and log file. All buffers
are written to secondary storage.
4.
A checkpoint consists of the following actions:
Suspend execution of transactions temporarily.
Write all updated buffers to disk.
Write a checkpoint log record to disk.
Resume executing transactions.

When failure occurs, the recovery manager performs the following:

1.
2.
3.


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Redo all transactions that committed since the checkpoint.
Undo all transactions active at time of crash (if using immediate update).
Example
T1
T2
T3
T4
T5
T6
t0

T1 and T6: undo.

T2 and T3: do nothing.

T4 and T5: redo.
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tc
tf
Checkpoint in Log File
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Main Recovery Techniques

Three main recovery techniques:

Deferred Update.

Immediate Update.
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Deferred Update

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.
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Immediate Update

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.
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Example

Using Deferred Update: Which transactions to undo? Redo?

Using Immediate Update: Which transactions to undo? Redo?
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