Chapter 14: Transactions
■ Transaction Concept
■ Transaction State
■ Concurrent Executions
■ Serializability
■ Recoverability
■ Implementation of Isolation
■ Transaction Definition in SQL
■ Testing for Serializability.
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Transaction Concept
■ A transaction is a unit of program execution (a sequence of operations and queries) that accesses and possibly updates various data items.
■ For example, changes data if certain conditions satisfied
■ E.g. transaction to transfer $50 from account A to account B:
1. read(A)
2. A:=A–50 3. write(A)
4. read(B)
5. B:=B+50 6. write(B)
■ Two main issues to address, to ensure transactions are performed properly
● Failuresofvariouskinds,suchashardwarefailuresandsystem crashes
● Concurrentexecutionofmultipletransactions
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Big Picture: OLAP vs. OLTP
Database Systems
Transaction Processing
● keepingtrackofthings ● manyqueries
● manyupdates ● smallqueries
Online Analytical Processing
● analyzingthings ● fewqueries
● fewupdates
● largequeries
… but often done by separate specialized systems
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Applications of OLTP
■ Retail
■ Banking
■ Electronic Trading
■ Credit cards
■ Travel reservations
■ Telephony: phone cards, 1-800 services, billing
■ E-commerce and many other web services (facebook etc)
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Examples of Transactions
■ Withdrawal at ATM:
1. Check if requested amount is available 2. Deduct amount from balance
3. Dispense cash
■ Two withdrawals from same account around the same time:
– suppose both transactions do step 1 at same time, before step 2
.
■ Or a withdrawal while a check is being deposited:
– initial account balance $1000
– check of $400 is deposited while withdrawal of $1200 is processed
One of two things could happen ..
Database does not care which one (by and large)
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Example of Fund Transfer
■ Transaction to transfer $50 from account A to account B: 1. read(A)
2. A:=A–50 3. write(A)
4. read(B)
5. B:=B+50 6. write(B)
■ Atomicity requirement
● if the transaction fails after step 3 and before step 6, money will be “lost”
leading to an inconsistent database state
! Failure could be due to software or hardware
● the system should ensure that updates of a partially executed transaction are not reflected in the database
■ Durability requirement — once the user has been notified that the transaction has completed (i.e., the transfer of the $50 has taken place), the updates to the database by the transaction must persist even if there are software or hardware failures.
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Example of Fund Transfer (Cont.)
■ Transaction to transfer $50 from account A to account B: 1. read(A)
2. A:=A–50 3. write(A)
4. read(B)
5. B:=B+50 6. write(B)
■ Consistency requirement in above example:
● the sum of A and B is unchanged by the execution of the transaction
■ In general, consistency requirements include
! Explicitly specified integrity constraints such as primary keys and foreign
keys
! Implicit integrity constraints
– e.g. sum of balances of all accounts, minus sum of loan amounts must equal value of cash-in-hand
● A transaction must see a consistent database.
● During transaction execution the database may be temporarily inconsistent.
● When the transaction completes successfully the database must be consistent
! Erroneous transaction logic can lead to inconsistency
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Example of Fund Transfer (Cont.)
■ Isolation requirement — if between steps 3 and 6, another transaction T2 is allowed to access the partially updated database, it will see an inconsistent database (the sum A + B will be less than it should be).
T1 T2 1. read(A)
2. A:=A–50 3. write(A)
4. read(B)
5. B:=B+50 6. write(B
read(A), read(B), print(A+B)
■ Isolation can be ensured trivially by running transactions serially ● that is, one after the other.
■ However, executing multiple transactions concurrently has significant benefits, as we will see later.
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ACID Properties
A transaction is a unit of program execution that accesses and possibly updates various data items.To preserve the integrity of data the database system must ensure:
■ Atomicity. Either all operations of the transaction are properly reflected in the database or none are.
■ Consistency. Execution of a transaction in isolation preserves the consistency of the database.
■ Isolation. Although multiple transactions may execute concurrently, each transaction must be unaware of other concurrently executing transactions. Intermediate transaction results must be hidden from other concurrently executed transactions.
● That is, for every pair of transactions Ti and Tj, it appears to Ti that either Tj, finished execution before Ti started, or Tj started execution after Ti finished.
■ Durability. After a transaction completes successfully, the changes it has made to the database persist, even if there are system failures.
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Transaction State
■ Active – the initial state; the transaction stays in this state while it is executing
■ Partially committed – after the final statement has been executed.
■ Failed — after the discovery that normal execution can no longer
proceed.
■ Aborted – after the transaction has been rolled back and the database restored to its state prior to the start of the transaction. Two options after it has been aborted:
● restartthetransaction
! can be done only if no internal logical error
● killthetransaction
■ Committed – after successful completion.
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Transaction State (Cont.)
partially commied
commied
active
failed
aborted
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Concurrent Executions
■ Multiple transactions are allowed to run concurrently in the system. Advantages are:
● increasedprocessoranddiskutilization,leadingtobetter transaction throughput
! E.g. one transaction can be using the CPU while another is reading from or writing to the disk
! Also, modern CPUs have multiple cores
! Elevator algorithm on disk
! More important: many transactions committed in one disk write to log
● reducedaverageresponsetimefortransactions:short transactions need not wait behind long ones.
■ Concurrency control schemes – mechanisms to achieve isolation
● that is, to control the interaction among the concurrent transactions in order to prevent them from destroying the consistency of the database
! Will study in Chapter 15, after studying notion of correctness of concurrent executions.
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Implementing Transaction Processing
■ Two main ingredients:
concurrency control and crash recovery
■ Concurrency control (Chapter 15)
– make sure no interference between Xactions – typically uses LOCKS
■ Crash recoyery (Chapter 16)
– make sure crashes do not corrupt data
– uses a LOG
– i.e., we append records for all actions to a log file
– this is done BEFORE a transaction can commit
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Concurrency Control
■ Schedule: ordering of the steps of several transactions ■ Serializability of a schedule: equivalence to some
serial execution of the transaction
■ Conflict Serializability versus View Serializability
u Concurrency control protocols: mechanisms/rules that achieve serializability
■ Difference CC methods take different amounts of risk: – forcing serial execution (inefficient)
– strict 2-phase locking (lock based, widely used)
– 2-phase locking (lock based, may result in aborts) – optimistic concurrence control (checks at commit)
u Compare: traffic rules, traffic lights
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Schedules
■ Schedule – a sequences of instructions that specify the chronological order in which instructions of concurrent transactions are executed
● a schedule for a set of transactions must consist of all instructions of those transactions
● must preserve the order in which the instructions appear in each individual transaction.
■ Atransactionthatsuccessfullycompletesitsexecutionwillhave a commit instructions as the last statement
● by default transaction assumed to execute commit instruction as its last step
■ Atransactionthatfailstosuccessfullycompleteitsexecutionwill have an abort instruction as the last statement
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Schedule 1
■ Let T1 transfer $50 from A to B, and T2 transfer 10% of the balance from A to B.
■ A serial schedule in which T1 is followed by T2 :
T1
T2
read (A) A:= A–50 write (A) read (B) B:= B+50 write (B) commit
read (A)
temp := A * 0.1 A:=A temp write (A) read (B)
B:= B+temp write (B) commit
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Schedule 2
• A serial schedule where T2 is followed by T1
T1
T2
read (A) A:= A–50 write (A) read (B) B:= B+50 write (B) commit
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read (A) temp:= A*0.1 A:=A temp write (A) read (B)
B:= B+temp write (B) commit
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Schedule 3
■ Let T1 and T2 be the transactions defined previously. The following schedule is not a serial schedule, but it is equivalent to Schedule 1.
T1
T2
read (A) A:= A–50 write (A)
read (B) B:= B+50 write (B) commit
read (A) temp:= A*0.1 A:=A temp write (A)
read (B)
B:= B+temp write (B) commit
In Schedules 1, 2 and 3, the sum A + B is preserved.
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Schedule 4
■ The following concurrent schedule does not preserve the value of (A + B ).
T1
T2
read (A) A:= A–50
read (A) temp:= A*0.1 A:=A temp write (A) read (B)
write (A) read (B) B:= B+50 write (B) commit
B:= B+temp write (B) commit
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The Problem with Serializability
■ Serializable: equivalent to some serial schedule
■ But this is very hard to understand/prove in the general case
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The Problem with Serializability
■ Serializable: equivalent to some serial schedule
■ But this is very hard to understand/prove in the general case ■ The following schedule is not conflict or view serializable
■ but produces same outcome as < T1, T5 >, and thus serializable
T1
T5
read (A) A:= A–50 write (A)
read (B) B:= B+50 write (B)
read (B) B:=B 10 write (B)
read (A) A := A + 10 write (A)
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Database System Concepts – 6th Edition
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The Problem with Serializability
■ Serializable: equivalent to some serial schedule
■ But this is very hard to understand/prove in the general case ■ The following schedule is not conflict or view serializable
■ but produces same outcome as < T1, T5 >, and thus serializable
T1
T5
read (A) A:= A–50 write (A)
read (B) B:= B+50 write (B)
read (B) B:=B 10 write (B)
read (A) A := A + 10 write (A)
replace with /
replace with *
■ But not serializable if you replace the + and – in T5 with * and /
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The Problem with Serializability
■ Figuring out if two schedule produce same result is very hard!
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The Problem with Serializability
■ Figuring out if two schedule produce same result is very hard! ■ Example: you know that cos^2(x) + sin^2(x) = 1 for all x
■ Thus, the following code leaves the value of y unchanged
x ß randomNumber();
y ß y * (cos^2(x) + sin^2(x));
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The Problem with Serializability
■ Figuring out if two schedule produce same result is very hard! ■ Example: you know that cos^2(x) + sin^2(x) = 1 for all x
■ Thus, the following code leaves the value of y unchanged
x ß randomNumber();
y ß y * (cos^2(x) + sin^2(x));
■ But how to reason about this in general?
■ Thus, general serializability is not that useful as a concept
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Serializability
■ Basic Assumption – Each transaction preserves database consistency.
■ Thus serial execution of a set of transactions preserves database consistency.
■ A (possibly concurrent) schedule is serializable if it is equivalent to a serial schedule. Different forms of schedule equivalence give rise to the notions of:
1. conflict serializability 2. view serializability
Simplified view of transactions
● We ignore operations other than read and write instructions
● We assume that transactions may perform arbitrary computations on
data in local buffers in between reads and writes.
● Our simplified schedules consist of only read and write instructions.
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Conflicting Instructions
■ Instructions li and lj of transactions Ti and Tj respectively, conflict if and only if there exists some item Q accessed by both li and lj, and at least one of these instructions wrote Q.
1. li = read(Q), lj = read(Q). li and lj don’t conflict.
2. li = read(Q), lj = write(Q). They conflict.
3. li = write(Q), lj = read(Q). They conflict
4. li = write(Q), lj = write(Q). They conflict
■ Intuitively, a conflict between li and lj forces a (logical) temporal order between them.
● If li and lj are consecutive in a schedule and they do not conflict, their results would remain the same even if they had been interchanged in the schedule.
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■
■
■
■
Conflict Serializability
If a schedule S can be transformed into a schedule S ́ by a series of swaps of non-conflicting instructions, we say that S and S ́ are conflict equivalent.
We say that a schedule S is conflict serializable if it is conflict equivalent to a serial schedule
Example of a schedule that is not conflict serializable:
T3
T4
read (Q) write (Q)
write (Q)
We are unable to swap instructions in the above schedule to obtain
either the serial schedule < T3, T4 >, or the serial schedule < T4, T3 >.
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Conflict Serializability (Cont.)
■ Schedule 3 can be transformed into Schedule 6, a serial schedule where T2 follows T1, by series of swaps of non- conflicting instructions. Therefore Schedule 3 is conflict serializable.
T1
T2
read (A) write (A)
read (B) write (B)
read (A) write (A)
read (B) write (B)
Schedule 3
read (A) write (A) read (B) write (B)
read (A) write (A) read (B) write (B)
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T1
Schedule 6
T2
View Serializability
■ Let S and S ́ be two schedules with the same set of transactions. S and S ́ are view equivalent if the following three conditions are met, for each data item Q,
1. If in schedule S, transaction Ti reads the initial value of Q, then in schedule S’ also transaction Ti must read the initial value of Q.
2. If in schedule S transaction Ti executes read(Q), and that value was produced by transaction Tj (if any), then in schedule S’ also transaction Ti must read the value of Q that was produced by the same write(Q) operation of transaction Tj .
3. The transaction (if any) that performs the final write(Q) operation in schedule S must also perform the final write(Q) operation in schedule S’.
As can be seen, view equivalence is also based purely on reads and writes alone.
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View Serializability (Cont.)
■ A schedule S is view serializable if it is view equivalent to a serial schedule.
■ Every conflict serializable schedule is also view serializable.
■ Below is a schedule which is view-serializable but not conflict
serializable.
read (Q)
write (Q)
write (Q)
■ What serial schedule is above equivalent to?
T27
T28
T29
write (Q)
■ Every view serializable schedule that is not conflict serializable has
blind writes.
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Testing for Serializability
■ Consider some schedule of a set of transactions T1, T2, …, Tn
■ Precedence graph — a direct graph where the vertices are
the transactions (names).
■ We draw an arc from Ti to Tj if the two transaction conflict, and Ti accessed the data item on which the conflict arose earlier.
■ We may label the arc by the item that was accessed.
■ Example 1
T1 T2
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Test for Conflict Serializability
■ A schedule is conflict serializable if and only if its precedence graph is acyclic.
■ Cycle-detection algorithms exist which take order n2 time, where n is the number of vertices in the graph.
● (Betteralgorithmstakeordern+e where e is the number of edges.)
■ If precedence graph is acyclic, the serializability order can be obtained by a topological sorting of the graph.
● This is a linear order consistent with the partial order of the graph.
● Forexample,aserializabilityorderfor Schedule A would be
T5 →T1 →T3 →T2 →T4
Tj
Ti
Tm
(a)
Tk
! Are there others?
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Ti Tj
Tk Tm
(b)
Ti
Tk Tj
Tm
(c)
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Test for View Serializability
■ The precedence graph test for conflict serializability cannot be used directly to test for view serializability.
● Extension to test for view serializability has cost exponential in the size of the precedence graph.
■ The problem of checking if a schedule is view serializable falls in the class of NP-complete problems.
● Thus existence of an efficient algorithm is extremely unlikely.
■ However practical algorithms that just check some sufficient conditions for view serializability can still be used.
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Recoverable Schedules
Need to address the effect of transaction failures on concurrently
running transactions.
■ Recoverable schedule — if a transaction Tj reads a data item previously written by a transaction Ti , then the commit operation of Ti appears before the commit operation of Tj.
■ The following schedule (Schedule 11) is not recoverable if T9 commits immediately after the read
T8
T9
read (A) write (A)
read (B)
read (A) commit
■ If T8 should abort, T9 would have read (and possibly shown to the user) an inconsistent database state. Hence, database must ensure that schedules are recoverable.
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Cascading Rollbacks
■ Cascading rollback – a single transaction failure leads to a series of transaction rollbacks. Consider the following schedule where none of the transactions has yet committed (so the schedule is recoverable)
T10
T11
T12
read (A) read (B) write (A)
read (A)
If T10 fails, T11 and T12 must also be rolled back.
abort
read (A) write (A)
■ Can lead to the undoing of a significant amount of work
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Cascadeless Schedules
■ Cascadeless schedules — cascading rollbacks cannot occur; for each pair of transactions Ti and Tj such that Tj reads a data item previously written by Ti, the commit operation of Ti appears before the read operation of Tj.
■ Every cascadeless schedule is also recoverable
■ It is desirable to restrict the schedules to those that are
cascadeless
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Concurrency Control
■ A database must provide a mechanism that will ensure that all possible schedules are
● either conflict or view serializable, and
● are recoverable and preferably cascadeless
■ A policy in which only one transaction can execute at a time generates serial schedules, but provides a poor degree of concurrency
● Are serial schedules recoverable/cascadeless?
■ Testing a schedule for serializability after it has executed
is a little too late!
■ Goal – to develop concurrency control protocols that will assure serializability.
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Concurrency Control (Cont.)
■ Schedules must be conflict or view serializable, and recoverable, for the sake of database consistency, and preferably cascadeless.
■ A policy in which only one transaction can execute at a time generates serial schedules, but provides a poor degree of concurrency.
■ Concurrency-control schemes tradeoff between the amount of concurrency they allow and the amount of overhead that they incur.
■ Some schemes allow only conflict-serializable schedules to be generated, while others allow view- serializable schedules that are not conflict- serializable.
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Concurrency Control vs. Serializability Tests
■ Concurrency-control protocols allow concurrent schedules, but ensure that the schedules are conflict/ view serializable, and are recoverable and cascadeless .
■ Concurrency control protocols generally do not examine the precedence graph as it is being created
● Instead a protocol imposes a discipline that avoids nonseralizable schedules.
● We study such protocols in Chapter 15.
■ Different concurrency control protocols provide different tradeoffs between the amount of concurrency they allow and the amount of overhead that they incur.
■ Tests for serializability help us understand why a concurrency control protocol is correct.
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■ ■
Levels of Consistency in SQL-92
■ Serializable — default
■ Repeatable read — only committed records to be read, repeated reads of same record must return same value. However, a transaction may not be serializable – it may find some records inserted by a transaction but not find others.
■ Read committed — only committed records can be read, but successive reads of record may return different (but committed) values.
■ Read uncommitted — even uncommitted records may be read. Lower degrees of consistency useful for gathering approximate
information about the database
Warning: some database systems do not ensure serializable schedules by default
● E.g.OracleandPostgreSQLbydefaultsupportalevelof consistency called snapshot isolation (not part of the SQL standard)
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Transaction Definition in SQL
■ Data manipulation language must include a construct for specifying the set of actions that comprise a transaction.
■ In SQL, a transaction begins implicitly.
■ A transaction in SQL ends by:
● Commit work commits current transaction and begins a new one.
● Rollback work causes current transaction to abort.
■ In almost all database systems, by default, every SQL
statement also commits implicitly if it executes successfully ● Implicit commit can be turned off by a database directive
! E.g. in JDBC, connection.setAutoCommit(false);
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