Transaction Management, Concurrency Control and Recovery

Transaction Management, Concurrency Control and Recovery

Chapter 22

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


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.

We test a schedule for conflict serialiazibility using a Precedence Graph.

Testing for Conflict Serializability – Precedence Graph

21

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?

22

Time T7 T8


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

23

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

24

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

25

Time T7 T8


t2 read(balx)

t3 write(balx)

t4 read(baly)

t5 write(baly)

t6 commit


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

26

Time T11 T12 T13


t2 read(balx)


t4 write(balx)

t5 commit

t6 write(balx)

t7 commit

t8

begin_transaction

t9

write(balx)

t10 commitIs this schedule conflict serializable?

Concurrency Control Techniques

27

Serializability

Theory Practice

Conflict Serializabili

ty Test

View Serializabili

ty TestLocking

Time-stamping

Optimistic

Concurrency Control Techniques

28

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

29

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

30

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

31

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

32

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


t2 write_lock(balx)

t3 read(balx)

t4 balx = balx + 100

t5 write(balx)

t6 unlock(balx)


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)

34

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

35

Time T1 T2

balx


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

36

Time T1 T2

balx


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

37

Time T3 T4

balx


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

38

Time T3 T4

balx


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

39

Preventing Inconsistent Analysis Problem

40

A Potential Problem with 2PL

41

Cascading Rollbacks

42

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

43

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

44

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

45

Time T9


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

46

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

47

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

48

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

49

TS(T11) = 1 TS(T12) = 3 TS(T13) = 8

Time T11 T12 T13


t2 read(balx)


t4 write(balx)

t5 commit

t6 write(balx)

t7 commit

t8

begin_transaction

t9

write(balx)

t10 commit

Wait-Die Technique

50

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

51

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

52

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

53

Time T9


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

54

WFG is created at regular intervals.

Several issues when recovering from a deadlock:

choice of deadlock victim;

avoiding starvation.

Self-read: pages 593-594


55


Optimistic

Basic Rule

s2PL

Deadlock Prevention

Basic Time-stamp

Ordering

Multi-version

Time-stamp Ordering

Deadlock Detection

Wait-Die

Wound-

Wait

Wait-for

Graph


Regular

Strict

Rigorous Time

outs

Timestamping

56

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

57

2 Techniques:

1.Basic Timestamp Ordering. Thomas’s Write Rule.

2.Multiversion Timestamp Ordering.

Basic Timestamp Ordering

58

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)

59

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)

60

TS(T) < read_timestamp(x)

x already read by younger transaction.


timestamp.


x already written by younger transaction.


timestamp.

Otherwise, operation is accepted and executed.

Write_timestamp(x) = TS(T)

Basic Timestamp Ordering

61


62

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.


timestamp.


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

63

All Schedules

View Serializable Schedules

Conflict Serializable Schedules

2PL TS

Multiversion Timestamp Ordering

64

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)

65

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)

66

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)

67

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)

68

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


69


Optimistic

Basic Rule

s2PL

Deadlock Prevention

Basic Time-stamp

Ordering

Multi-version

Time-stamp Ordering

Deadlock Detection

Wait-Die

Wound-

Wait

Wait-for

Graph


Regular

Strict

Rigorous Time

outs

Optimistic Techniques

70

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.

Database Recovery

71

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

72

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

73

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

74

Contains information about all updates to database:

Transaction records.

Checkpoint records.

Often used for other purposes (for example, auditing).

3. Checkpoint Facility

75

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

76

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

77

Main Recovery Techniques

78

Three main recovery techniques:

Deferred Update.

Immediate Update.

Deferred Update

79

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

80

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

81

Using Deferred Update: Which transactions to undo? Redo?

Using Immediate Update: Which transactions to undo? Redo?

Transaction Management, Concurrency Control and Recovery

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