international computer institute, izmir, turkey transactions asst. prof. dr. İlker kocabaş ubİ502...

International Computer Institute, Izmir, TurkeyInternational Computer Institute, Izmir, Turkey

TransactionsTransactions

Asst. Prof. Dr. İlker Kocabaş

UBİ502 at http://ube.ege.edu.tr/~ikocabas/teaching/ubi50

2/index.html

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©Silberschatz, Korth and Sudarshan

Modifications & additions by Cengiz Güngör

UBI 502

Database Management Systems

TransactionsTransactions

1. Introduction

2. Concurrent Execution and Schedules

3. Serializability

4. Recoverability

5. Transaction Definition in SQL

6. Example

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1. Introduction1. Introduction

A transaction is a unit of program execution that accesses and possibly updates various data items

A transaction must see a consistent database

During transaction execution the database may be inconsistent

When the transaction is committed, the database must be consistent

Two main issues to deal with: Failures, e.g. hardware failures and system crashes

Concurrency, for simultaneous execution of multiple transactions

Process-1

Process-2

Process-3

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ACIDACID

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

Durability: After a transaction completes successfully, the changes it has made to the database persist, even if there are system failures

To preserve integrity of data, the database system must ensure:

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Example of Fund TransferExample of Fund Transfer

Transfer $50 from account A to B:

1. read(A)

2. A := A – 50

3. write(A)

4. read(B)

5. B := B + 50

6. write(B)

Consistency: the sum of A and B is unchanged by the execution of the transaction.

Atomicity: if the transaction fails after step 3 and before step 6, the system should ensure that its updates are not reflected in the database, else an inconsistency will result.

Durability: once the user has been notified that the transaction has completed, the updates to the database by the transaction must persist despite failures

Isolation: between steps 3 and 6, no other transaction should access the partially updated database, or else it will see an inconsistent state (the sum A + B will be less than it should be).

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Transaction StateTransaction State

Active, the initial state; the transaction stays in this state while it is executing

Partially committed, after the final statement has been executed.

Committed, after successful completion.

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.

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Naive Approach: Shadow-DatabaseNaive Approach: Shadow-Database

assume only one transaction is active at a time

db_pointer always points to the current consistent copy of the database

all updates are made on a copy of the database, and db_pointer is made to point to the updated copy only after the transaction reaches partial commit and all updated pages have been flushed to disk

if the transaction fails, the old consistent copy pointed to by db_pointer can be used, and the shadow copy can be deleted

Assumes disks to not fail

Useful for text editors, but extremely inefficient for large database -- executing a single transaction requires copying the entire database!

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2. Concurrent Execution and Schedules2. Concurrent Execution and Schedules

Concurrent execution: executing transactions simultaneously has the following advantages: increased processor and disk utilization, leading to better throughput one transaction can be using the CPU while another is reading from or

writing to the disk reduced average response time for transactions: short transactions need

not wait behind long ones

Concurrency control schemes: these are mechanisms to achieve isolation to control the interaction among the concurrent transactions in order to

prevent them from destroying the consistency of the database

Schedules: sequences that indicate 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

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Example SchedulesExample Schedules Let T1 transfer $50 from A to B, and T2 transfer 10% of the balance

from A to B. The following is a serial schedule (Schedule 1 in the text), in which T1 is followed by T2.

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Example Schedule (cont)Example Schedule (cont) Let T1 and T2 be the transactions defined previously. The

following schedule (Schedule 3 in the text) is not a serial schedule, but it is equivalent to Schedule 1.

In both Schedule 1 and 3, the sum A + B is preserved.

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Example Schedules (cont)Example Schedules (cont) The following concurrent schedule (Schedule 4 in the book)

does not preserve the value of the the sum A + B

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3. Serializability3. Serializability Basic Assumption – Each transaction, on its own, preserves

database consistency i.e. serial execution 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 conflict serializability and view serializability

Simplifying assumptions: ignore operations other than read and write instructions

assume that transactions may perform arbitrary computations on data in local buffers between reads and writes

simplified schedules consist only of reads and writes

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Conflict SerializabilityConflict Serializability

Instructions la and lb of transactions Ta and Tb respectively, conflict if and only if there exists some item Q accessed by both la and lb, and at least one of these instructions wrote Q.

1. la = read(Q), lb = read(Q). la and lb don’t conflict.2. la = read(Q), lb = write(Q). They conflict.3. la = write(Q), lb = read(Q). They conflict4. la = write(Q), lb = write(Q). They conflict

Intuitively, a conflict between la and lb forces a (logical) temporal order between them

If la and lb are consecutive in a schedule and they do not conflict, their results would remain the same even if they had been interchanged in the ordering

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Conflict Serializability (cont)Conflict Serializability (cont) 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)Conflict Serializability (cont)

Schedule 3 below can be transformed into Schedule 1, a serial schedule where T2 follows T1, by series of swaps of non-conflicting instructions. Therefore Schedule 3 is conflict serializable.

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View SerializabilityView 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, where Q is a data item and Ti is a transaction: If Ti reads the initial value of Q in schedule S, then Ti must, in

schedule S´, also read the initial value of Q

If Ti executes read(Q) in schedule S, and that value was produced by transaction Tj (if any), then transaction Ti must in schedule S´ also read the value of Q that was produced by transaction Tj

The transaction (if any) that performs the final write(Q) operation in schedule S (for any data item Q) must perform the final write(Q) operation in schedule S´

NB. view equivalence is also based purely on reads and writes

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View Serializability (cont)View Serializability (cont)

A schedule S is view serializable it is view equivalent to a serial schedule

Every conflict serializable schedule is also view serializable Schedule 9 (from book) — a schedule which is view-serializable

but not conflict serializable

Every view serializable schedule that is not conflict serializable has blind writes

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Other Notions of SerializabilityOther Notions of Serializability

This schedule produces the same outcome as the serial schedule < T1, T5 >

However it is not conflict equivalent or view equivalent to it

Determining such equivalence requires analysis of operations other than read and write

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Testing for SerializabilityTesting for Serializability

Consider some schedule of a set of transactions T1, T2, ..., Tn

Precedence graph: a directed graph where the vertices are transaction names

We draw an arc from Ta to Tb if the two transaction conflict, and Ta accessed the data item before Tb

We may label the arc by the item that was accessed

Example:x

y

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Example Schedule and Precedence GraphExample Schedule and Precedence GraphT1 T2 T3 T4 T5

read(X)read(Y)read(Z)

read(V)read(W)read(W)

read(Y)write(Y)

write(Z)read(U)

read(Y)write(Y)read(Z)write(Z)

read(U)write(U)

T3

T4

T1 T2

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Testing for Serializability (cont)Testing for Serializability (cont)

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

If precedence graph is acyclic, the serializability ordercan be obtained by a topological sorting of the graph.This is a linear order consistent with the partial orderof the graph. For example, a serializability order forthis graph is T2 T1 T3 T4 T5

The precedence graph test for conflict serializability must be modified to apply to a test for view serializability The problem of checking if a schedule is view serializable is

NP-complete. Thus existence of an efficient algorithm is unlikely. However practical algorithms that just check some sufficient conditions for view serializability can still be used

Example of an acyclic precedence graph

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Concurrency Control vs. Serializability TestsConcurrency Control vs. Serializability Tests

Goal – to develop concurrency control protocols that will ensure serializability

These protocols will impose a discipline that avoids nonseralizable schedules

A common concurrency control protocol uses locks while one transaction is accessing a data item, no other

transaction can modify it

require a transaction to lock the item before accessing it

two standard lock modes are “shared” (read-only) and “exclusive” (read-write)

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4. Recoverability4. Recoverability

Recoverable schedule: if a transaction Tj reads a data item previously written by a transaction Ti , 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

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

Need to address the effect of transaction failures on concurrently running transactions

Commit

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Recoverability (cont)Recoverability (cont)

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)

If T10 fails, T11 and T12 must also be rolled back

Can lead to the undoing of a significant amount of work

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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Transaction Definition in SQLTransaction 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

Levels of consistency specified by SQL-92: Serializable — default

Repeatable read

Read committed

Read uncommitted

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6. Example6. Example

T1:read(A);read(B);if A = 0 then B := B+1;write(B).

T2:read(B);read(A);if B = 0 then A := A+1;write(A).

Consistency requirement:A=0 or B=0

Initial values:A=0; B=0;

1. Show that every serial execution involving T1 and T2 preserves the consistency of the database.

2. Show a concurrent execution of T1 and T2 that produces a nonserializable schedule.

3. Is there a concurrent execution of T1 and T2 that produces a serializable schedule?

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Example (cont): serial executionsExample (cont): serial executions

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Example (cont): a non-serializable scheduleExample (cont): a non-serializable schedule

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Example (cont)Example (cont)

T1:read(A);read(B);if A = 0 then B := B+1;write(B).

T2:read(B);read(A);if B = 0 then A := A+1;write(A).

No concurrent execution of T1 and T2 produces a serializable schedule

Suppose we start with T1 read(A)

When the schedule ends, regardless of when we run the steps of T2, B=1

Before T1 is finished we must start T2.

T2 read(B) will give B a value of 0.

When T2 completes, A=1

At the end of execution, A=1, B=1, violating the consistency requirement

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ExampleExample

During execution, a transaction passes through several states, until it finally commits or aborts. List all possible sequences of states through which a transaction may pass. Explain why each state transition may occur

1. active -> partially committed -> committed Once all the transaction’s statements have been executed it enters the

partially committed state, and once enough recovery information has been written to disk, the transaction enters the committed state.

2. active -> partially committed -> aborted Once all the transaction’s statements have been executed it enters the

partially committed state, but before enough recovery information is written to disk there is a hardware failure. When the system is restarted, all changes made by this transaction are undone, after which the transaction enters the aborted state.

3. active -> failed -> aborted After the transaction starts it is discovered that normal execution cannot

continue (e.g. an update violates an integrity constraint), so the transaction enters the failed state. It is then rolled back, after which it enters the aborted state.

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SummarySummary

ACID Atomicity: all operations reflected in the DB, or else none are

Consistency: Executing transaction in isolation preserves consistency

Isolation: Concurrent transactions unaware of each other

Durability: Changes made by successful transactions persist

Serializability Conflict serializability

View serializability

Testing for serializability, precedence graphs

Recoverability handling the effects of a transaction failure on running transactions

cascading rollback

international computer institute, izmir, turkey transactions asst. prof. dr. İlker kocabaş ubİ502...

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