COP 5725 Fall 2012 Database Management Systems

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COP 5725 Fall 2012

Database Management

Systems

University of Florida, CISE Department Prof. Daisy Zhe Wang

Adapted Slides from Prof. Jeff Ullman

and from R. Ramakrishnan and J. Gehrke

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Transaction Management Overview

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Transactions

• A transaction is the DBMS’s abstract view of a user program: a sequence of reads and writes.

• A user’s program may carry out many operations on

the data retrieved from the database, but the DBMS is only concerned about what data is read/written

from/to the database.

•

Concurrent execution of user programs is essential for

good DBMS performance.

– Because disk accesses are frequent, and relatively slow, it is important to keep the cpu humming by working on several user programs concurrently.

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ACID Transactions

• A DBMS is expected to support “

ACID

transactions

,” processes that are:

–

Atomic

: Either the whole process is done or

none is.

–

Consistent

: Database constraints are

preserved.

–

Isolated

: It appears to the user as if only one

process executes at a time.

–

Durable

: Effects of a process do not get lost if

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Concurrency in a DBMS

• Users submit transactions, and can think of each transaction as executing by itself.

– Concurrency is achieved by the DBMS, which interleaves

actions (reads/writes of DB objects) of various transactions.

– Each transaction must leave the database in a consistent state if the DB is consistent when the transaction begins.

• DBMS will enforce some ICs, depending on the ICs declared in CREATE TABLE statements.

• Beyond this, the DBMS does not really understand the semantics of the data. (e.g., it does not understand how the interest on a bank account is computed).

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Atomicity of Transactions

• A transaction might

commit

after completing all

its actions, or it could

abort

(or be aborted by

the DBMS) after executing some actions.

• A very important property guaranteed by the

DBMS for all transactions is that they are

atomic

.

That is, a user can think of a Xact as

always executing all its actions in one step, or

not executing any actions at all.

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Example

• Consider two transactions (

Xacts

):

T1: BEGIN A=A+100, B=B-100 END T2: BEGIN A=1.06*A, B=1.06*B END

 Intuitively, the first transaction is transferring $100 from B’s account to A’s account. The second is

crediting both accounts with a 6% interest payment.

 There is no guarantee that T1 will execute before T2 or

vice-versa, if both are submitted together. However, the net effect must be equivalent to these two

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Example (Contd.)

• Consider a possible interleaving

(

schedule

)

:

T1: A=A+100, B=B-100

T2: A=1.06*A, B=1.06*B

 This is OK. But what about:

T1: A=A+100, B=B-100 T2: A=1.06*A, B=1.06*B

 The DBMS’s view of the second schedule:

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Scheduling Transactions

•

Serial schedule: Schedule that does not interleave the

actions of different transactions.

•

Equivalent schedules

: For any database state, the effect (on the set of objects in the database) of

executing the first schedule is identical to the effect of executing the second schedule.

•

Serializable schedule

: A schedule that is equivalent to some serial execution of the transactions.

(Note: If each transaction preserves consistency, every serializable schedule preserves consistency. )

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Anomalies with Interleaved Execution

• Reading Uncommitted Data (WR

Conflicts, “dirty reads”):

• Unrepeatable Reads (RW Conflicts):

T1: R(A), W(A), R(B), W(B), Abort T2: R(A), W(A), C

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Anomalies (Continued)

• Overwriting Uncommitted Data (WW

Conflicts):

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Lock-Based Concurrency Control

•

Strict Two-phase Locking (Strict 2PL) Protocol:

– Each Xact must obtain a S (shared) lock on object before reading, and an X (exclusive) lock on object before writing.

– All locks held by a transaction are released when the transaction completes

• (Non-strict) 2PL Variant: Release locks anytime, but cannot acquire locks after releasing any lock.

– If an Xact holds an X lock on an object, no other Xact can get a lock (S or X) on that object.

• Strict 2PL allows only serializable schedules.

– Additionally, it simplifies transaction aborts

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Aborting a Transaction

• If a transaction

Ti

is aborted, all its actions have to be undone. Not only that, if

Tj

reads an object last

written by

Ti

,

Tj

must be aborted as well!

• Most systems avoid such cascading aborts by

releasing a transaction’s locks only at commit time.

– If Ti writes an object, Tj can read this only after Ti commits.

• In order to

undo

the actions of an aborted

transaction, the DBMS maintains a

log

in which every write is recorded.

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The Log

• The following actions are recorded in the log:

– Ti writes an object: the old value and the new value.

• Log record must go to disk before the changed page!

– Ti commits/aborts: a log record indicating this action.

• Log records are chained together by Xact id, so it’s easy to undo a specific Xact.

• Log is often

duplexed

and

archived

on stable storage.

• All log related activities (and in fact, all CC related

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Recovering From a Crash

• There are 3 phases in the

Aries

recovery algorithm:

– Analysis: Scan the log forward (from the most recent checkpoint) to identify all Xacts that were active, and all dirty pages in the buffer pool at the time of the crash.

– Redo: Redoes all updates to dirty pages in the buffer pool, as needed, to ensure that all logged updates are in fact

carried out and written to disk.

– Undo: The writes of all Xacts that were active at the crash are undone (by restoring the before value of the update, which is in the log record for the update), working

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Summary

• Concurrency control and recovery are among the most important functions provided by a DBMS.

• Users need not worry about concurrency.

– System automatically inserts lock/unlock requests and schedules actions of different Xacts in such a way as to ensure that the resulting execution is equivalent to

executing the Xacts one after the other in some order.

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Transactions in SQL

• SQL supports transactions, often behind

the scenes.

– Each statement issued at the generic query interface is a transaction by itself.

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COMMIT

• The SQL statement COMMIT causes a

transaction to complete.

– It’s database modifications are now

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ROLLBACK

• The SQL statement ROLLBACK also

causes the transaction to end, but by

aborting

.

– No effects on the database.

• Failures like division by 0 or a

constraint violation can also cause

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An Example: Interacting Processes

• Assume the usual

Sells(bar,beer,price)

relation, and suppose that Joe’s Bar sells

only Bud for $2.50 and Miller for $3.00.

• Sally is querying

Sells

for the highest and

lowest price Joe charges.

• Joe decides to stop selling Bud and

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Sally’s Program

• Sally executes the following two SQL

statements, which we call

(min)

and

(max)

, to help remember what they do.

(max)

SELECT MAX(price) FROM Sells

WHERE bar = ’Joe’’s Bar’;

(min)

SELECT MIN(price) FROM Sells

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Joe’s Program

• At about the same time, Joe executes the

following steps, which have the mnemonic

names

(del)

and

(ins)

.

(del)

DELETE FROM Sells

WHERE bar = ’Joe’’s Bar’;

(ins)

INSERT INTO Sells

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Interleaving of Statements

• Although

(max)

must come before

(min)

, and

(del)

must come before

(ins)

, there are no other constraints on

the order of these statements, unless

we group Sally’s and/or Joe’s

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Example: Strange Interleaving

• Suppose the steps execute in the order

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Fixing the Problem by Using Transactions

• If we group Sally’s statements

(max)(min)

into one transaction, then

she cannot see this inconsistency.

• She sees Joe’s prices at some fixed

time.

– Either before or after he changes prices, or in the middle, but the MAX and MIN are

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Another Problem: Rollback

• Suppose Joe executes

(del)(ins)

, not as

a transaction, but after executing these

statements, thinks better of it and

issues a ROLLBACK statement.

• If Sally executes her statements after

(ins)

but before the rollback, she sees a

value, 3.50, that never existed in the

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Solution

• If Joe executes

(del)(ins)

as a

transaction, its effect cannot be seen by

others until the transaction executes

COMMIT.

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Isolation Levels

• SQL defines four

isolation levels

=

choices about what interactions are

allowed by transactions that execute at

about the same time.

• How a DBMS implements these isolation

levels is highly complex, and a typical

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Choosing the Isolation Level

• Within a transaction, we can say:

SET TRANSACTION ISOLATION LEVEL

X

where

X

=

1. SERIALIZABLE

2. REPEATABLE READ 3. READ COMMITTED

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Serializable Transactions

• If Sally = (max)(min) and Joe = (del)(ins)

are each transactions, and Sally runs with isolation level SERIALIZABLE, then she will see the database either before or after Joe runs, but not in the middle.