Transactions and Concurrency Control

Zachary G. IvesUniversity of Pennsylvania

CIS 550 – Database & Information Systems

December 2, 2003

Slide content courtesy of Susan Davidson, Raghu Ramakrishnan & Johannes Gehrke

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From Queries to Updates

We’ve spent a lot of time talking about querying data

Yet updates are a really major part of many DBMS applications Particularly important: ensuring ACID properties

Atomicity: each operation looks atomic to the user Consistency: each operation in isolation keeps the

database in a consistent state (this is the responsibility of the user)

Isolation: should be able to understand what’s going on by considering each separate transaction independently

Durability: updates stay in the DBMS!!!

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What is a Transaction?

A transaction is a sequence of read and write operations on data items that logically functions as one unit of work: should either be done entirely or not at all if it succeeds, the effects of write operations

persist (commit); if it fails, no effects of write operations persist (abort)

these guarantees are made despite concurrent activity in the system, and despite failures that may occur

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How Things Can Go Awry

Suppose we have a table of bank accounts which contains the balance of the account

An ATM deposit of $50 to account # 1234 would be written as:

This reads and writes the account’s balance What if two accountholders make deposits

simultaneously from two ATMs?

update Accountsset balance = balance + $50where account#= ‘1234’;

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Concurrent Deposits

This SQL update code is represented as a sequence of read and write operations on “data items” (which for now should be thought of as individual accounts):

where X is the data item representing the account with account# 1234.

Deposit 1 Deposit 2read(X.bal) read(X.bal)X.bal := X.bal + $50 X.bal:= X.bal + $10write(X.bal) write(X.bal)

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A “Bad” Concurrent Execution

Only one “action” (e.g. a read or a write) can actually happen at a time, and we can interleave deposit operations in many ways:

Deposit 1 Deposit 2read(X.bal) read(X.bal)X.bal := X.bal + $50 X.bal:= X.bal + $10write(X.bal) write(X.bal)

time

BAD!

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A “Good” Execution

Previous execution would have been fine if the accounts were different (i.e. one were X and one were Y), i.e., transactions were independent

The following execution is a serial execution, and executes one transaction after the other:

Deposit 1 Deposit 2read(X.bal) X.bal := X.bal + $50 write(X.bal) read(X.bal) X.bal:= X.bal + $10 write(X.bal)

time

GOOD!

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Good Executions

An execution is “good” if it is serial (transactions are executed atomically and consecutively) or serializable (i.e. equivalent to some serial execution)

Equivalent to executing Deposit 1 then 3, or vice versa Why would we want to do this instead?

Deposit 1 Deposit 3read(X.bal) read(Y.bal)X.bal := X.bal + $50 Y.bal:= Y.bal + $10write(X.bal) write(Y.bal)

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Atomicity

Problems can also occur if a crash occurs in the middle of executing a transaction:

Need to guarantee that the write to X does not persist (ABORT) Default assumption if a transaction doesn’t commit

Transferread(X.bal)read(Y.bal)X.bal= X.bal-$100

Y.bal= Y.bal+$100

CRASH

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Transactions in SQL

A transaction begins when any SQL statement that queries the db begins.

To end a transaction, the user issues a COMMIT or ROLLBACK statement.

TransferUPDATE Accounts SET balance = balance - $100 WHERE account#= ‘1234’;UPDATE Accounts SET balance = balance + $100 WHERE account#= ‘5678’;COMMIT;

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Read-Only Transactions

When a transaction only reads information, we have more freedom to let the transaction execute in parallel with other transactions.

We signal this to the system by stating:

SET TRANSACTION READ ONLY; SELECT * FROM Accounts WHERE account#=‘1234’;...

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Read-Write Transactions

If we state “read-only”, then the transaction cannot perform any updates.

Instead, we must specify that the transaction may update (the default):

SET TRANSACTION READ ONLY; UPDATE AccountsSET balance = balance - $100WHERE account#= ‘1234’; ...

SET TRANSACTION READ WRITE; update Accountsset balance = balance - $100where account#= ‘1234’; ...

ILLEGAL!

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Dirty Reads

Dirty data is data written by an uncommitted transaction; a dirty read is a read of dirty data (WR conflict)

Sometimes we can tolerate dirty reads; other times we cannot:

e.g., if we wished to ensure balances never went negative in the transfer example, we should test that there is enough money first!

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“Bad” Dirty ReadEXEC SQL select balance into :bal from Accounts where account#=‘1234’;if (bal > 100) {

EXEC SQL update Accounts set balance = balance - $100 where account#= ‘1234’;

EXEC SQL update Accounts set balance = balance + $100 where account#= ‘5678’;}EXEC SQL COMMIT;

If the initial read (italics) were dirty, the balance could become negative!

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Acceptable Dirty Read

If we are just checking availability of an airline seat, a dirty read might be fine! (Why is that?) Reservation transaction:EXEC SQL select occupied into :occ

from Flights where Num= ‘123’ and date=11-03-99 and seat=‘23f’;if (!occ) {EXEC SQL update Flights set occupied=true where Num= ‘123’ and date=11-03-99 and seat=‘23f’;}else {notify user that seat is unavailable}

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Other Undesirable Phenomena

Unrepeatable read: a transaction reads the same data item twice and gets different values (RW conflict)

Phantom problem: a transaction retrieves a collection of tuples twice and sees different results

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Phantom Problem Example T1: “find the students with best grades who Take

either cis550-f03 or cis570-f02” T2: “insert new entries for student #1234 in the Takes

relation, with grade A for cis570-f02 and cis550-f03”

Suppose that T1 consults all students in the Takes relation and finds the best grades for cis550-f03

Then T2 executes, inserting the new student at the end of the relation, perhaps on a page not seen by T1

T1 then completes, finding the students with best grades for cis570-f02 and now seeing student #1234

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Isolation

The problems we’ve seen are all related to isolation

General rules of thumb w.r.t. isolation: Fully serializable isolation is more expensive

than “no isolation” We can’t do as many things concurrently (or we have

to undo them frequently)

For performance, we generally want to specify the most relaxed isolation level that’s acceptable Note that we’re “slightly” violating a correctness

constraint to get performance!

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Specifying Acceptable Isolation Levels

The default isolation level is SERIALIZABLE (as for the transfer example).

To signal to the system that a dirty read is acceptable,

In addition, there are

SET TRANSACTION READ WRITEISOLATION LEVEL READ UNCOMMITTED;

SET TRANSACTIONISOLATION LEVEL READ COMMITTED;SET TRANSACTIONISOLATION LEVEL REPEATABLE READ;

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READ COMMITTED

Forbids the reading of dirty (uncommitted) data, but allows a transaction T to issue the same query several times and get different answers No value written by T can be modified until T

completes

For example, the Reservation example could also be READ COMMITTED; the transaction could repeatably poll to see if the seat was available, hoping for a cancellation

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REPEATABLE READ

What it is NOT: a guarantee that the same query will get the same answer!

However, if a tuple is retrieved once it will be retrieved again if the query is repeated For example, suppose Reservation were

modified to retrieve all available seats If a tuple were retrieved once, it would be

retrieved again (but additional seats may also become available)

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Implementing Isolation Levels

One approach – use locking at some level (tuple, page, table, etc.): each data item is either locked (in some mode, e.g.

shared or exclusive) or is available (no lock) an action on a data item can be executed if the

transaction holds an appropriate lock consider granularity of locks – how big of an item to lock

Larger granularity = fewer locking operations but more contention!

Appropriate locks: Before a read, a shared lock must be acquired Before a write, an exclusive lock must be acquired

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Lock Compatibility Matrix

Locks on a data item are granted based on a lock compatibility matrix:

When a transaction requests a lock, it must wait (block) until the lock is granted

Mode of Data Item None Shared ExclusiveShared Y Y NExclusive Y N NRequest mode {

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Locks Prevent “Bad” Execution If the system used locking, the first “bad”

execution could have been avoided: Deposit 1 Deposit 2xlock(X)read(X.bal) {xlock(X) is not granted}X.bal := X.bal + $50 write(X.bal) release(X) xlock(X) read(X.bal) X.bal:= X.bal + $10 write(X.bal) release(X)

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Lock Types and Read/Write Modes

When we specify “read-only”, the system only uses shared-mode locks Any transaction that attempts to update will be

illegal

When we specify “read-write”, the system may also acquire locks in exclusive mode Obviously, we can still query in this mode

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Isolation Levels and Locking

READ UNCOMMITTED allows queries in the transaction to read data without acquiring any lock For updates, exclusive locks must be obtained

and held to end of transaction

READ COMMITTED requires a read-lock to be obtained for all tuples touched by queries, but it releases the locks immediately after the read Exclusive locks must be obtained for updates

and held to end of transaction

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Isolation levels and locking, cont. REPEATABLE READ places shared locks on

tuples retrieved by queries, holds them until the end of the transaction Exclusive locks must be obtained for updates

and held to end of transaction SERIALIZABLE places shared locks on

tuples retrieved by queries as well as the index, holds them until the end of the transaction Exclusive locks must be obtained for updates

and held to end of transaction Holding locks to the end of a transaction is

called “strict”

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Summary of Isolation Levels

Level Dirty Read Unrepeatable Read Phantoms

READ UN- Maybe Maybe MaybeCOMMITTED

READ No Maybe MaybeCOMMITTED

REPEATABLE No No MaybeREAD

SERIALIZABLE No No No

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Theory of Serializability

A schedule of a set of transactions is a linear ordering of their actions e.g. for the simultaneous deposits example:

R1(X.bal) R2(X.bal) W1(X.bal) W2(X.bal) A serial schedule is one in which all the steps of

each transaction occur consecutively A serializable schedule is one which is equivalent

to some serial schedule (i.e. given any initial state, the final state is the same as one produced by some serial schedule) The example above is neither serial nor serializable

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Questions to Address

Given a schedule S, is it serializable? How can we "restrict" transactions in

progress to guarantee that only serializable schedules are produced?

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Conflicting Actions

Consider a schedule S in which there are two consecutive actions Ii and Ij of transactions Ti and Tj respectively

If Ii and Ij refer to different data items, then swapping Ii and Ij does not matter

If Ii and Ij refer to the same data item Q, then swapping Ii and Ij matters if and only if one of the actions is a write Ri(Q) Wj(Q) produces a different final value for Q than

Wj(Q) Ri(Q)

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Testing for Serializability

Given a schedule S, we can construct a di-graph G=(V,E) called a precedence graph V : all transactions in S E : Ti Tj whenever an action of Ti precedes

and conflicts with an action of Tj in S Theorem:

A schedule S is conflict serializable if and only if its precedence graph contains no cycles

Note that testing for a cycle in a digraph can be done in time O(|V|2)

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An Example

T1 T2 T3 R(X,Y,Z) R(X) W(X) R(Y) W(Y) R(Y) R(X) W(Z)

T1 T2 T3

Cyclic: Not serializable.

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Another Example

T1 T2 T3 R(X) W(X) R(X) W(X) R(Y) W(Y) R(Y) W(Y)

T1 T2 T3

Acyclic: serializable

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Producing the Equivalent Serial Schedule

If the precedence graph for a schedule is acyclic, then an equivalent serial schedule can be found by a topological sort of the graph

For the second example, the equivalent serial schedule is:

R1(Y)W1(Y)R2(X)W2(X) R2(Y)W2(Y) R3(X)W3(X)

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Locking and Serializability

We said that a transaction must hold all locks until it terminates (a condition called strict locking)

It turns out that this is crucial to guarantee serializability Note that the first (bad) example could have

been produced if transactions acquired and immediately released locks.

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Well-Formed, Two-Phased Transactions

A transaction is well-formed if it acquires at least a shared lock on Q before reading Q or an exclusive lock on Q before writing Q and doesn’t release the lock until the action is performed Locks are also released by the end of the transaction

A transaction is two-phased if it never acquires a lock after unlocking one i.e., there are two phases: a growing phase in which

the transaction acquires locks, and a shrinking phase in which locks are released

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Two-Phased Locking Theorem

If all transactions are well-formed and two-phase, then any schedule in which conflicting locks are never granted ensures serializability i.e., there is a very simple scheduler!

However, if some transaction is not well-formed or two-phase, then there is some schedule in which conflicting locks are never granted but which fails to be serializable i.e., one bad apple spoils the bunch.

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Summary

Transactions are all-or-nothing units of work guaranteed despite concurrency or failures in the system

Theoretically, the “correct” execution of transactions is serializable (i.e. equivalent to some serial execution)

Practically, this may adversely affect throughput isolation levels

With isolation levels, users can specify the level of “incorrectness” they are willing to tolerate