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Special actions: commit, abortFor now, assume reads and writes are

on tuples;

A transaction is a unit of work which

completes as a unit or fails as a unit.The ACID Properties

Atomicity: All actions in thetransaction happen, or none happen.

Consistency: If each transaction is

consistent, and the DB starts consistent,

it ends up consistent.

Isolation: Execution of one transaction

is isolated from that of other

transactions.

Durability: If a transaction commits,

its effects persist.

A transaction is a collection of

operations involving data items in a

database. Read, insert, delete, and

update are example operations. There

are four important properties of

transactions that a DBMS must ensure

to maintain data in the face of

concurrent access and system failures:Atomicity: Users should be able to

regard the execution of each transaction

as atomic: either all actions are

executed or none are executed. Users

should not have to worry about the

effect of incomplete transactions

(example, when a system crash occurs).

Consistency: Each transaction, run by

itself with no concurrent execution ofother transactions, must preserve the

consistency of the database. This

property is called consistency. Ensuring

this property of the transaction is the

responsibility of the user.

Isolation: Users should be able to
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4/27

understand the transaction without

considering the effect of other

concurrently executing transactions,

even if the DBMS interleaves the

actions of several transactions.Transactions are isolated, or protected,

from the effects of concurrently

scheduling other transactions.

Durability: Once the DBMS informs

the user that a transaction has been

successfully completed, its effects

should persist even if the system

crashes before all its changes are

reflected on disk.

Passing the ACID Test

Concurrency ControlGuaranteesConsistency and Isolation, given

Atomicity.

Logging and RecoveryGuarantees

Atomicity and Durability.

Atomicity

All-or-nothing, no partial results. An

event either happens and is committed

or fails and is rolled back.e.g. in a money transfer, debit one

account, credit the other. Either both

debiting and crediting operations

succeed, or neither of them do.

Transaction failure is called Abort

Commit and abort are irrevocableactions. There is no undo for these

actions.

An Abort undoes operations that havealready been executed

For database operations, restore the

datas previous value from before thetransaction (Rollback-it); a Rollback

command will undo all actions taken

since the last commit for that user.


5/27

But some real world operations arenot undoable.

Examples - transfer money, print ticket,

fire missile

Consistency Every transaction should maintain DBconsistency

Referential integrity - e.g. each orderreferences an existing customer number

and existing part numbers

The books balance (debits = credits,assets = liabilities)

Consistency preservation is a propertyof a transaction, not of the database

mechanisms for controlling it (unlike

the A, I, and D of ACID)

If each transaction maintains

consistency, then a serial execution of

transactions does also

A database state consists of thecomplete set of data values in the

database

A database state is consistent if the

database obeys all the integrityconstraint

A transaction brings the database fromone consistent state to another

consistent state

Consistent state diagram

IsolationIntuitively, the effect of a set of

transactions should be the same as if

they ran independently. Formally, an interleaved execution of

transactions is serializable if its effect is

equivalent to a serial one.

Implies a user view where the systemruns each users transaction stand-alone.

Of course, transactions in fact run with


6/27

lots of concurrency, to use device

parallelismthis will be covered later.

Transactions can use common data(shared data)

They can use the same data processingmechanisms (time sharing)

Durability

When a transaction commits, itsresults will survive failures (e.g. of the

application, OS, DB system even of

the disk).

Makes it possible for a transaction to

be a legal contract.

Implementation is usually via a log

DB system writes all transaction

updates to a log file

to commit, it adds a record

commit(Ti) to the log

When the commit record is on disk,

the transaction is committed.

system waits for disk ack before

acknowledging to user

Active: transaction is started and is

issuing reads and writes to the database Partially committed: operations aredone and values are ready to be written

to the database

Committed: writing to the database is

permitted and successfully completed

Abort: the transaction or the systemdetects a fatal error

Terminated: transaction leaves the

system A transaction reaches its commit point

when all operations accessing the

database are completed and the result

has been recorded in the log. It then

writes a [commit, ] and terminates

When a system failure occurs, search


7/27

the log file for entries [start, ]

and if there are no logged entries

[commit, ]then undo all operations that

have logged entries [write, , X,

old_value, new_value]Why is Concurrency Control Needed?

Several problems occur whenconcurrent transactions execute in an

uncontrolled manner

A schedule of concurrent transactions

is a particular sequence of interleaving

of their read or write operations

In general a transaction, has a set ofdata items it accesses (read set), and a

set of data items it modifies (write set)

Lost Update Problem - Successfully

completed update is overridden by

another user.

What should the final Order Value be?

Which Update has been lost?

Loss of T2s update avoided by

preventing T1 from reading balx until

after update.

Uncommitted Dependency Problem /Temporary Update Problem

Occurs when one transaction can seeintermediate results of another

transaction before it has committed.

What should the final Order Value be?

Where is the temporary update?Uncommitted Dependency Problem

Problem avoided by preventing T3

from reading balx until after T4commits or aborts.

Incorrect Summary Problem

What should the total Order Value be?

Which order was accumulated beforeupdate, and which after?

Schedules - actions of transactions as


8/27

seen by the DBMS

Schedule: An interleaving of actions

from a set of transactions, where the

actions of any one transaction are in the

original order. When two or more transactions arerunning concurrently, the steps of the

transactions would normally be

interleaved. The interleaved execution

of transactions is decided by the

database scheduler which receives a

stream of user requests that arise from

the active transactions. A particular

sequencing (usually interleaved) of the

actions of a set of transactions is called

a schedule.

Represents some actual sequence of

database actions.

Example: R 1 (A), W 1 (A), R 2 (B),

W 2 (B), R 1 (C), W 1 (C)

T1 T2

R(A)

W(A)

R(B)W(B)

R(C)

W(C)

In a complete schedule, each

transaction ends in commit or abort.

Initial State + Schedule Final State

Serial schedule is a schedule in which

all the operations of one transaction are

completed before another transactioncan begin (that is, there is no

interleaving).

One sensible isolated, consistentschedule:

Run transactions one at a time, in aseries.


9/27

NOTE: Different serial schedules can

have different final states; all are OK

-- DBMS makes no guarantees about

the order in which concurrently

submitted transactions are executed. Serializable Schedule

A schedule whose effect on the DBstate is the same as that of some serialschedule

All serial schedules are serializable.

But the reverse may not be true

Let T be a set of n transactions . If the

n transactions are executed serially (call

this execution S), we assume they

terminate properly and leave the

database in a consistent state. A

concurrent execution of the n

transactions in T (call this execution C)

is called serializable if the execution is

computationally equivalent to a serial

execution. There may be more than one

such serial execution. That is, the

concurrent execution C always

produces exactly the same effect on thedatabase as some serial execution S

does. (Note that S is some serial

execution of T, not necessarily the

order). A serial schedule is always

correct since we assume transactions do

not depend on each other and

furthermore, we assume, that each

transaction when run in isolation

transforms a consistent database into anew consistent state and therefore a set

of transactions executed one at a time

(i.e. serially) must also be correct.

Final state is what some serial

schedule would have produced.

Aborted Xacts are not part of


10/27

schedule; ignore them for now (they are

made to `disappear by using logging).

In general, serializability is a good

propertyBut difficult to achieve due

to lack of effective algorithms. Serializability

Objective of a concurrency controlprotocol is to schedule transactions in

such a way as to avoid any interference.

Could run transactions serially, but

this limits degree of concurrency or

parallelism in system.

Serializability identifies thoseexecutions of transactions guaranteed to

ensure consistency.

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.

Nonserial Schedule Schedule where operations from set ofconcurrent transactions are interleaved.

Objective of serializability is to findnonserial schedules that allow

transactions to execute concurrently

without interfering with one another.

In other words, want to find nonserial

schedules that are equivalent to some

serial schedule. Such a schedule iscalled serializable.

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.


11/27

(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 itemand another reads or writes same data

item, order of execution is important.

Serializability Violations

Two transactions T1 and T2 are said

to conflict if some action t1 of T1 and

an action t2 of T2 access the same

object and at least one of the actions is a

write. The conflict is called a RW-

conflict if the write set of one

transaction intersects with the read set

of another. A WW-conflict occurs if the

conflict is between two writes.

Result is not equal to any serialexecution!

W- R conflict: T2 reads something T1wrote previously (dirty read).

RW Conflicts (Unrepeatable Read)

T2 overwrites what T1 read.

If T1 reads it again, it will seesomething new!

Example when this would happen? The

increment/ decrement example.

Again, not equivalent to a serial

execution.

WW Conflicts (Overwriting

Uncommitted Data)

T2 overwrites what T1 wrote.

Example: 2 transactions to update itemsto be kept equal.

Usually occurs in conjunction with

other anomalies.

Aborted Transactions: All actions of

aborted transactions are to be undone

as if aborted transactions never


12/27

happened.

Two Issues:

How does one undo the effects of a

transaction? Well cover this in logging/

recoveryWhat if another transaction sees theseeffects?? Must undo that transaction as

well!

Cascading Aborts

T1 T2

R(A)

W(A)

R(A)

W(A)

abort

Abort of T1 requires abort of T2!Cascading Abort

What about WW conflicts & aborts?

T2 overwrites a value that T1 writes.

T1 aborts: its remembered valuesare restored.

Lose T2s write! We will see how tosolve this, too.

Recoverable ScheduleA 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.

Unrecoverable Schedule

T1 T2

R(A)

W(A)R(A)

W(A)

commit

abort

Recoverable Schedule

T1 T2


13/27

R(A)

W(A)

R(A)

W(A)

commitcommit

Abort of T1 in first figure requires abort

of T2!

But T2 has already committed and

hence cannot undo its actions! This is

unrecoverable schedule. A recoverable

schedule is one in which this cannot

happen.

i. e. a transaction commits only after

all the transactions it depends on (i. e.

it reads from or overwrites) commit.

An ACA (avoids cascading abort)

schedule is one in which cascading

abort cannot arise.

A transaction only reads/ writes datafrom committed transactions.

Recoverable implies ACA (but notvice- versa!). Real systems typically

ensure that only recoverable schedulesarise (through locking).

Concurrency Control Techniques

Two basic concurrency control

techniques:

Locking,

Timestamping. Both are conservative approaches:

delay transactions in case they conflict

with other transactions. Optimisticmethods assume conflict is rare and

only check for conflicts at commit.

LockingThe concept of locking data items is one

of the main techniques for controlling

the concurrent execution of


14/27

transactions.

A lock is a variable associated with a

data item in the database.

Generally there is a lock for each data

item in the database. A lock describes the status of the dataitem with respect to possible operations

that can be applied to that item

used for synchronising the access by

concurrent transactions to the database

items.

A transaction locks an object before

using it

When an object is locked by another

transaction, the requesting transaction

must wait

Binary locks have two possible states: 1.locked (lock_item (X) operation)and

2.unlocked (unlock (X) operation

Multiple-mode locks allow concurrent

access to the same item by several

transactions. Three possible states:

1.read locked or shared locked (othertransactions are allowed to read the

item)

2.write locked or exclusive locked (asingle transaction exclusively holds the

lock on the item) and

3.unlocked. Locks are held in a lock table.

upgrade lock: read lock to write lock

downgrade lock: write lock to readlock

TYPES OF LOCKS

SHARED LOCKS

EXCLUSIVE LOCKSTransaction uses locks to deny access to


15/27

other transactions and so prevent

incorrect updates.

Most widely used approach to ensureserializability.

Generally, a transaction must claim ashared (read) or exclusive (write) lock

on a data item before read or write.

Lock prevents another transactionfrom modifying item or even reading it,

in the case of a write lock.

Locking - Basic Rules If transaction has shared lock on item,

can read but not update item.

If transaction has exclusive lock on

item, can both read and update item.

Reads cannot conflict, so more thanone transaction can hold shared locks

simultaneously on same item.

Exclusive lock gives transactionexclusive access to that item.

Some systems allow transaction to

upgrade read lock to an exclusive lock,

or downgrade exclusive lock to a shared

lock. Two-Phase Locking (2PL)

Strict 2PL:

If T wants to read an object, first

obtains an S lock.

If T wants to modify an object, first

obtains X lock.

Hold all locks until end of transaction.

Guarantees serializability, and

recoverable schedule, too! also avoidsWW problems!

2PL:

Slight variant of strict 2PL

transactions can release locks before

the end (commit or abort)

? But after releasing any lock it can


16/27

acquire no new locksGuaranteesserializability

A two-phase locking ( 2PL) scheme is a

locking scheme in which a transaction

cannot request a new lock afterreleasing a lock. Two phase locking

therefore involves two phases:

Growing Phase ( Locking Phase) -When locks are acquired and none

released.

Shrinking Phase ( Unlocking Phase) -When locks are released and none

acquired.

The attraction of the two-phase

algorithm derives from a theorem which

proves that the two-phase locking

algorithm always leads to serializable

schedules. This is a sufficient condition

for serializability although it is not

necessary.

Strict two-phase locking ( Strict 2PL) is

the most widely used locking protocol,

and has following two rules:

If a transaction wants to read(respectively, modify) an object, it first

requests a shared (respectively,

exclusive) lock on the object.

All locks held by a transaction are

released when the transaction is

completed

In effect the locking protocol allows

only safe interleavings of transactions.

Q) Three transactions A, B and C arrivein the time sequence A, then B and then

C. The transactions are run concurrently

on the database. Can we predict what

the result would be if 2PL is used?

No, we cannot do that since we arenot able to predict which serial schedule


17/27

the 2PL schedule is going to be

equivalent to. The 2PL schedule could

be equivalent to any of the following six

serial schedules: ABC, ACB, BAC,

BCA, CAB, CBA. Two-Phase Locking (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 butcannot release any locks.

Shrinking phase - releases locks butcannot acquire any new locks.

Preventing Lost Update Problem using

2PL Eg in slides

Preventing Uncommitted DependencyProblem using 2PL

Locking Granularity

A database item which can be locked

could be

a database record

a field value of a database record

the whole database Trade-offs

?coarse granularity - the larger the data

item size, the lower the degree of

concurrency

?fine granularity - the smaller the data

item size, the more locks to be managed

and stored, and the more lock/unlock

operations needed.

DeadlockAn impasse that may result when two

(or more) transactions are each waiting

for locks held by the other to be

released. Eg in slides

Conditions For Deadlock

Mutual Exclusion


18/27

Hold And Wait Non Preemption

Circular Wait

Recovery

Occurs in case of transaction failures. Database (DB) is restored to the mostrecent consistent state just before the

time of failure.

To do this, the DB system needs

information about changes applied by

various transactions. It is the system

log.

Contents of System Log:

[start_transaction, T]: Indicates that

transaction T has started execution.

[write_item, T, X, old_value,new_value]: Indicates that transaction T

has changed the value of DB item X

from old_value to new_value.

[read_item, T, X]: Indicates thattransaction T has read the value of DB

item X.

[commit, T]: Indicates that transaction

T has completed successfully, andaffirms that its effect can be committed

(recorded permenantly) to the database.

[abort, T]: Indicates that transaction Thas been aborted.

Deadlock

Only one way to break deadlock: abortone or more of the transactions.

Deadlock should be transparent to

user, so DBMS should restarttransaction(s).

Three general techniques for handlingdeadlock:

Timeouts.

Deadlock prevention.

Deadlock detection and recovery.


19/27

Timeouts

Transaction that requests lock will

only wait for a system-defined period of

time.

If lock has not been granted withinthis period, lock request times out.

In this case, DBMS assumestransaction may be deadlocked, even

though it may not be, and it aborts and

automatically restarts the transaction.

Deadlock Prevention

DBMS looks ahead to see if

transaction would cause deadlock and

never allows deadlock to occur.

Could order transactions using

transaction timestamps:

Wait-Die - only an older transaction

can wait for younger one, otherwise

transaction is aborted (dies) and

restarted with same timestamp.

Wound-Wait - only a younger

transaction can wait for an older one. If

older transaction requests lock held by

younger one, younger one is aborted(wounded).

Deadlock Detection and Recovery

DBMS allows deadlock to occur but

recognizes it and breaks it.

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 tolock item locked by Tj.

Deadlock exists if and only if WFG

contains cycle.

WFG is created at regular intervals. Recovery Outline

Restore to most recent consistent


20/27

state just before time of failure

Use data in the log file

Catastrophic Failure

Restore database from backup

Replay transactions from log file Database becomes inconsistent (non-catastrophic errors)

Undo or Redo last transactions untilconsistent state is restored

Recovery Algorithms for Non-

catastrophic Errors:

Deferred Update (NO-UNDO/REDO):

Data written to buffersNot physically updated until after

commit point reached and logs have

been updated

No undo is even necessary

Redo might be necessary ontransactions that have been logged but

not physically updated

Known as the NO-UNDO/REDO

algorithm

Immediate Update (UNDO/REDO):

Database being updated as transactionoccurs

However, log always force writtenfirst

Partially completed transactions will

have to be undone

Committed transactions might have tobe redone

Known as the UNDO/REDO

algorithmVariation on the scheme:

Data is physically updated beforecommit

Only requires UNDO

Known as the UNDO/NO-REDOalgorithm


21/27

Logging Record REDO and UNDO

information, for every update, in a log.

Sequential writes to log (put it on a

separate disk).Minimal info (diff) written to log, somultiple updates fit in a single log page.

Log: An ordered list of REDO/ UNDO

actions

Log record contains:

and additional control infoCaching of Disk Blocks:

Disk blocks typically cached to mainmemory

Changes made to cache block which

is then written back at some later time

Many DBMSs even handle the low-

level I/O

DBMS Caching:

All database accesses check to see ifrequired item is in the cache first. If not,

item is loaded into cache

Dirty bit: Determines if cache block

has been updated and needs to bewritten back to disk

Pin/Unpin bit: Is it OK to write blockback to disk yet?

In-place updating: Block is written

back out to same location. Overwrite

original

Shadowing: Block is written to new

location

? Old copy is kept? Before Image (BFIM) & After Image

(AFIM)

The Write- Ahead Logging Protocol:

Must force the log record for an

update before the corresponding data

page gets to disk.


22/27

Must write all log records for atransaction before commit

The rule that all transactions follow in

the WAL protocol is "Write the log

before you write the data. When atransaction wants to update a record, it

pins the page containing the record in

the main-memory buffer pool, modifies

the page in memory, generates an

undo/redo record, forces the undo/redo

record to the log, and unpins the page in

the buffer pool. At some later time, the

page replacement algorithm or a

checkpoint will write the page back to

the database.

WAL protocol:

#1 guarantees Atomicity.

#2 guarantees Durability.

Each log record has a unique LogSequence Number (LSN). LSNs always

increasing.

Each data page contains a pageLSN.The LSN of the most recent log record

for an update to that page. System keeps track of flushedLSN.The max LSN flushed so far.

WAL: Before a page is written,pageLSN


23/27

all updated DB buffers written out to

disk

Any committed transaction occurringbefore checkpoint in log can be

considered permanent (wont have to beredone after crash)

Actions

suspend execution of all transactions

force-write all modified buffers to

disk

write checkpoint entry in log andforce write log

resume transactions Checkpointing: Periodically, the

DBMS creates a checkpoint, in order to

minimize the time taken to recover in

the event of a system crash. It quiesces

the system (makes all currently

executing transactions pause), writes all

dirty buffers to disk, and then allows

transactions to resume normal

processing.

The problem with this simple

checkpoint is that it makes the dataunavailable for too long, possibly

several minutes, while the checkpoint is

being done. There is another technique

called Fuzzy checkpoint, which does

not have this problem, because it does

not quiesce the system. Instead, for each

buffer, the fuzzy checkpoint procedure

latches the buffer (gets an exclusive

semaphore on it), writes it to disk if it isdirty, and then unlatches the buffer. In

addition, fuzzy checkpoint writes the

IDs of the currently active transactionsto the log. Fuzzy checkpoint just locks

buffers one at a time and releases them.

How does the system recovers from a


24/27

crash: The crash recovery algorithm

reads the most recent checkpoint

information from the log, which yields a

set of transaction IDs that were active

at the time of the checkpoint. Then itscans the log forward from the

checkpoint, reapplying every undo/redo

record to the database (this is called

REDO ALL). During the forward pass,

it analyzes the log to determine which

transactions did not commit or abort

before the crash. These transactions are

called the "losers." Then, the recovery

algorithm scans the log in reverse,

undoing log records for all the losers

(this is called UNDO LOSERS).

Additional Crash Issues: Whathappens if system crashes during

Analysis? During REDO? How do you

limit the amount of work in REDO ?

Flush asynchronously in the

background.

Watch hot spots!

How do you limit the amount of workin UNDO ?

Avoid long- running transactions.

Responses

Author:sri phani

kumari 19 May 2008

Member

Level:

Silver Poin

ts : 2
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