1 Introduction to Transaction Processing Concepts and Theory.

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Introduction to Transaction Processing Concepts and Theory

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1 Introduction to Transaction Processing (1)

A transaction is a sequence of operations whose execution

transforms a database from one consistent state to another consistent state.

A logical unit of database processing that includes one or more access operations (read -retrieval, write - insert or update, delete).

Transaction boundaries: Begin and End transaction.

Consistent state: the data currently in the database satisfy all integrity constraints defined for the database.

During transaction execution the database may be inconsistent.

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Introduction to Transaction Processing (2)

When the transaction is committed, the database must be consistent.

Two main issues to deal with: Failures of various kinds, such as hardware

failures and system crashes Concurrent execution of multiple transactions

Consistent Database State

Consistent Database State

Execution of transaction

Possible inconsistent state

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Consider a transaction that transfers $200 from account A to account B.

read(A)

A = A - 200 write(A) read(B) B = B + 200 write(B)

System crash

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Single-User System: At most one user at a time can use the system.

Multiuser System: Many users can access the system concurrently.

Concurrency Interleaved processing:

Concurrent execution of processes is interleaved in a single CPU

Parallel processing: Processes are concurrently executed in multiple

CPUs.

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A database is a collection of named data items.

The size of a data item is called granularity. It can be a field, a record , or a whole disk block

Basic operations are read and write read_item(X): Reads a database item named X into

a program variable. To simplify our notation, we assume that the program variable is also named X.

write_item(X): Writes the value of program variable X into the database item named X.

Basic unit of data transfer from the disk to the computer main memory is one block.

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In general, a data item (what is read or written) will be the field of some record in the database, although it may be a larger unit such as a record or even a whole block.

read_item(X) command includes the following steps: Find the address of the disk block that contains item X.

Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer).

Copy item X from the buffer to the program variable named X.

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write_item(X) command includes the following steps: Find the address of the disk block that contains item X.

Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer).

Copy item X from the program variable named X into its correct location in the buffer.

Store the updated block from the buffer back to disk (either immediately or at some later point in time).

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FIGURE 17.2 Two sample transactions: (a) Transaction T1 (b) Transaction T2

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Why Concurrency Control is needed

Multiple transactions are allowed to run concurrently. Advantages are:

increased processor and disk utilization, leading to better transaction throughput one transaction can be using the CPU while another is

reading from or writing to the disk throughput: # of transactions executed in a given

amount if time. reduced average response time for transactions

short transactions need not wait behind long ones. Concurrency control is needed to achieve isolation

i.e., to control the interaction among the concurrent transactions in order to prevent them from destroying the database consistency.

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Why Concurrency Control is needed

Problems occur when concurrent transactions execute in an uncontrolled manner: The Lost Update The Temporary Update (or Dirty Read) The Incorrect Summary

Unrepeatable Read: A transaction T1 may read a given value. If another

transaction later updates that value and T1 reads that value again, then T1 will see a different value.

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The Lost Update Problem

This occurs when two transactions that access the same database items have their operations interleaved in a way that makes the value of some database item incorrect.

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The Temporary Update Problem(Dirty Read)

This occurs when one transaction updates a database item and then the transaction fails for some reason. The updated item is accessed by another

transaction before it is changed back to its original value.

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Incorrect Summary Problem

If one transaction is calculating an aggregate summary function on a number of records while other transactions are updating some of these records, the aggregate function may calculate some values before they are updated and others after they are updated.

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Incorrect Summary Problem

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Why Recovery is needed

What causes a Transaction to fail 1. A computer failure (system crash):

A hardware or software error occurs in the computer system during transaction execution. If the hardware crashes, the contents of the computer’s internal memory may be lost.

2. A transaction or system error: Some operation in the transaction may cause it

to fail, such as integer overflow or division by zero. Transaction failure may also occur because of erroneous parameter values or because of a logical programming error. In addition, the user may interrupt the transaction during its execution.

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3. Local errors or exception conditions detected by the transaction: Certain conditions necessitate cancellation of the

transaction. For example, data for the transaction may not be found. A condition, such as insufficient account balance in a banking database, may cause a transaction, such as a fund withdrawal from that account, to be canceled.

A programmed abort in the transaction causes it to fail.

4. Concurrency control enforcement: The concurrency control method may decide to abort

the transaction, to be restarted later, because it violates serializability or because several transactions are in a state of deadlock (see Chapter 18).

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5. Disk failure: Some disk blocks may lose their data because

of a read or write malfunction or because of a disk read/write head crash. This may happen during a read or a write operation of the transaction.

6. Physical problems and catastrophes: This refers to an endless list of problems that

includes power or air-conditioning failure, fire, theft, sabotage, overwriting disks or tapes by mistake, and mounting of a wrong tape by the operator.

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2 Transaction and System Concepts (1)

A transaction is an atomic unit of work that is either completed in its entirety or not done at all. For recovery purposes, the system needs to keep track

of when the transaction starts, terminates, and commits or aborts.

A transaction must be in one of the following states: 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.

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Transaction and System Concepts (2)

Committed: after successful completion.

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:– restart the transaction (only if no internal logical error)

– kill the transaction

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Recovery manager keeps track of the following operations: begin_transaction

This marks the beginning of transaction execution. read or write:

These specify read or write operations on the database items that are executed as part of a transaction.

end_transaction: This specifies that read and write transaction operations have

ended and marks the end limit of transaction execution. At this point it may be necessary to check whether the changes

introduced by the transaction can be permanently applied to the database or whether the transaction has to be aborted because it violates concurrency control or for some other reason.

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Recovery manager keeps track of the following operations (cont): commit_transaction:

This signals a successful end of the transaction so that any changes (updates) executed by the transaction can be safely committed to the database and will not be undone.

rollback (or abort): This signals that the transaction has ended

unsuccessfully, so that any changes or effects that the transaction may have applied to the database must be undone.

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Recovery techniques use the following operators: undo:

Similar to rollback except that it applies to a single operation rather than to a whole transaction.

redo: This specifies that certain transaction operations

must be redone to ensure that all the operations of a committed transaction have been applied successfully to the database.

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State transition diagram illustrating the states for transaction execution

Rollback

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The System Log

Log or Journal: The log keeps track of all transaction operations that affect the values of database items. This information may be needed to permit

recovery from transaction failures.

The log is kept on disk, so it is not affected by any type of failure except for disk failure.

In addition, the log is periodically backed up to archival storage to guard against such failures.

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The System Log

T in the following discussion refers to a unique transaction-id that is generated automatically by the system and is used to identify each transaction:

Types of log record: [start_transaction, T]:

Records that transaction T has started execution. [write_item, T, X, old_value, new_value]:

Records that transaction T has changed the value of database item X from old_value to new_value.

[read_item, T, X]: Records that transaction T has read the value of database item

X. [commit, T]:

Records that transaction T has completed successfully, and affirms that its effect can be committed (recorded permanently) to the database.

[abort, T]: Records that transaction T has been aborted.

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The System Log

Protocols for recovery that avoid cascading rollbacks do not require that read operations be written to the system log, whereas other protocols require these entries for recovery.

Strict protocols require simpler write entries that do not include new value (see Section 17.4).

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Recovery Using Log Records

If the system crashes, we can recover to a consistent database state by examining the log and using one of the techniques described in Chapter 19. Because the log contains a record of every write

operation that changes the value of some database item, it is possible to undo the effect of these write operations of a transaction T by tracing backward through the log and resetting all items changed by a write operation of T to their old_values.

We can also redo the effect of the write operations of a transaction T by tracing forward through the log and setting all items changed by a write operation of T (that did not get done permanently) to their new_values.

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Commit Point of a Transaction

Commit Point: A transaction T reaches its commit point when

all its operations that access the database have been executed successfully and the effect of all the transaction operations on the database has been recorded in the log.

Beyond the commit point, the transaction is said to be committed, and its effect is assumed to be permanently recorded in the database.

The transaction then writes an entry [commit,T] into the log.

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Commit Point of a Transaction

Roll Back of transactions: Needed for transactions that have a [start_transaction,T] entry into

the log but no commit entry [commit,T] into the log. Redoing transactions:

Transactions that have written their commit entry in the log must also have recorded all their write operations in the log; otherwise they would not be committed, so their effect on the database can be redone from the log entries.

Notice that the log file must be kept on disk. At the time of a system crash, only the log entries that have been

written back to disk are considered in the recovery process because the contents of main memory may be lost.

Force writing a log: Before a transaction reaches its commit point, any portion of the

log that has not been written to the disk yet must now be written to the disk.

This process is called force-writing the log file before committing a transaction.

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3 Desirable Properties of Transactions (1)

The ACID properties of a transaction Atomicity

a transaction is an atomic processing unit; it is either performed in its entirety or not performed at all.

Consistencya transaction transforms a database from a consistent state to

another consistent state. Isolation

A transaction should not make its updates visible to other transactions until it is committed; this property, when enforced strictly, solves the temporary update problem.

Durabilitycommitted work must never be lost due to subsequently failure.

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ACID Properties

Example:T1 T2 value of Xread(X) 200 (initial value)X = X + 100 300 (not saved yet)

read(X) 200 X = X - 50 150 (not saved yet) write(X) 300 (saved) write(X) 150 (overwrite 300)

lost $100! Correct value of X should be 250.

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4 Schedules

A schedule S of n transactions T1, T2, ..., Tn is an ordering of all the operations in these transactions subject to the constraint that: for each transaction Ti, the operations of Ti in S

must appear in the same order as they do in Ti.Note, however, that operations from other transactions Tj

can be interleaved with the operations of Ti in S.

Example: Given T1 = R1(Q) W1(Q) & T2 = R2(Q) W2(Q)

a schedule: R1(Q) R2(Q) W1(Q) W2(Q)

not a schedule: W1(Q) R1(Q) R2(Q) W2(Q)

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Schedules

Sa: r1(X); r2(X); w1(X); r1(Y); w2(X); w1(Y);

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Schedules

Sb: r1(X); w1(X); r2(X); w2(X); r1(Y); a1;

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Conflict Operations

Instructions (Operations) 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 conflict2. li = Read(Q), lj = Write(Q). They conflict3. li = Write(Q), lj = Read(Q). They conflict4. li = Write(Q), lj = Write(Q). They conflict

Two operations in a schedule are conflict if:1) They belong to different transactions,2) They access the same item Q, and3) At least one them is a Write(Q) operation.

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Recoverable Schedule

Recoverable schedule: One where no committed transaction needs to be

rolled back. A schedule S is recoverable if no transaction T in S

commits until all transactions T’ that have written an item that T reads have committed.

T reads from T’ in S if X is first written by T’ and later read by T. T’ should not have been aborted before T reads X There should be no transaction Ti that writes X

after T’ writes it before T reads it (unless Ti, if any, has aborted before T reads X).

Sa, Sb and Sa’ are recoverable: Sa’: r1(X); r2(X); w1(X); r1(Y); w2(X); c2; w1(Y); c1;

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Recoverable Schedule

Consider the following schedules: Sc: r1(X); w1(X); r2(X); r1(Y); w2(X); c2; a1; Sd: r1(X); w1(X); r2(X); r1(Y); w2(X); w1(Y); c1; c2; Se: r1(X); w1(X); r2(X); r1(Y); w2(X); w1(Y); a1; a2;

Sc is not recoverable because: T2 reads item X from T1, and then T2 commits before T1

commits. If T1 aborts after c2 operation in Sc, then the value of X

that T2 read is no longer valid and T2 must be aborted after it is committed, leading to a schedule that is not recoverable.

For the schedule to be recoverable c2 operation in Sc must be postponed until after T1 commits, as shown in Sd;

If T1 aborts instead of committing, then T2 should also abort as shown in Se, because the value of X it read is no longer valid.

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Cascade-less Schedule

Schedules requiring cascaded rollback: A schedule in which uncommitted transactions

that read an item from a failed transaction must be rolled back. As shown in schedule Se

Cascadeless Schedule: One where every transaction reads only the

items that are written by committed transactions. r2(X) in Sd and Se must be postponed until after T1

has committed (or aborted), thus delaying T2 but ensuring no cascading rollback if T1 aborts.

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Cascade-less Schedule

Strict Schedules: A schedule in which a transaction can neither

read or write an item X until the last transaction that wrote X has committed.

Consider the following schedule: Sf: w1(X, 5); w2(X , 8); a1; Suppose the value of X was originally 9. If T1 aborts, as in Sf, the recovery system will

restore the value of X to 9, even though it has already been changed to 8 by T2, thus leading to incorrect results.

Although Sf is cascade-less, it is not strict It permits T2 to write X even though T1 that last

wrote X had not yet committed (or aborted).

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Schedules Classification

In term of: 1. Recoverability 2. Avoidance of cascading rollback 3. Strictness

Condition 2 implies condition 1, and condition 3 implies both 1 and 2.

Thus, all strict schedules are cascade-less, and All cascade-less schedules are recoverable

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Recoverability

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

Recoverable schedule if a transaction Tj reads a data items 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

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Recoverability

If T8 should abort, T9 would have read (dirty read) an inconsistent database state. Hence database must ensure that schedules are recoverable.

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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.

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Recoverability (Cont.)

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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Schedules

A schedule S is serial, if for every transaction T participating in the

schedule, all the operations of T are executed consecutively if operations from different transactions are not interleaved.

otherwise the schedule is called nonserial.

Serial schedules: R1(Q) W1(Q) R2(Q) W2(Q) R2(Q) W2(Q) R1(Q) W1(Q)

Non-serial schedule: R1(Q) R2(Q) W1(Q) W2(Q)

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Example Schedules

The following is a serial schedule (Schedule 1), in which T1 is followed by T2.

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Example Schedule (Cont.)

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

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

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Example Schedules (Cont.)

The following concurrent schedule (Schedule 4) does not preserve the value of the the sum A + B.

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Example Schedules

(a) Serial schedule A: T1 followed by T2. (b) Serial schedules B: T2 followed by T1.

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Example Schedule (Cont.)

(c) Two nonserial schedules C and D with interleaving of operations.

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Several Observations

Serial schedule guarantees database consistency. n transactions may form n! different serial

schedules. Different serial schedule may produce different

result. Suppose Q = 20 initially. R1(Q), Q=Q+10, W1(Q), R2(Q), Q=Q*2, W2(Q)

produces Q = 60 R2(Q), Q=Q*2, W2(Q), R1(Q), Q=Q+10, W1(Q)

produces Q = 50

Allowing only serial schedule may cause poor system performance (i.e., low throughput)

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Several Observations

Serial schedule is not a must for guaranteeing transaction consistency.

If X and Y are independent, then the following two schedules always produces the same result: non-serial schedule:

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

serial schedule:R1(X) W1(X) R1(Y) W1(Y) R2(X) W2(X)

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Serializability

A schedule S of n transactions is serializable if it is equivalent to some serial schedule of the same n transactions.

Basic Assumption each transaction preserves database consistency

We ignore operations other than read and write instructions schedules consist of only read and write

instructions

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Serializability

One way to ensure correctness of concurrent transactions is to enforce serializability of transactions that is the interleaved execution of the

transactions must be equivalent to some serial execution of those transactions.

The interleaved execution of a set of transactions is considered correct iff it is serializable.

A nonserial but serializable schedule often permits higher degree of concurrency than a serial schedule.

Different forms of schedule equivalence give rise to the notions of: conflict serializability view serializability

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Serializability

Two schedules that are result equivalent for the initial value of X = 100, but are not result equivalent in general.

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Conflict Serializability

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.

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. Two schedules are called conflict equivalent if

the order of any two conflicting operations is the same in both schedules

We say that a schedule S is conflict serializable if it is conflict equivalent to a serial schedule

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Example

Consider two transactions: T1 = R1(X) W1(X) R1(Y) W1(Y)

T2 = R2(X) W2(X)

The following two schedules are equivalent: S1: R1(X) W1(X) R2(X) W2(X) R1(Y) W1(Y)

S2: R1(X) W1(X) R1(Y) W1(Y) R2(X) W2(X)

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Conflict Serializability (Cont.)

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 schedules T3, T4 or T4, T3

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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 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:

1. For each data item Q, if transaction Ti reads the initial value of Q in schedule S, then transaction Ti must, in schedule S´, also read the initial value of Q.

2. For each data item Q, if transaction 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.

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View Serializability

3. For each data item Q, the transaction (if any) that performs the final write(Q) operation in schedule S must perform the final write(Q) operation in schedule S´.

As can be seen, view equivalence is also based purely on reads and writes alone.

Conditions 1 and 2 ensure that each transaction reads the same values in both

schedules. Condition 3, coupled with conditions 1 and 2,

ensures that both schedules results in the same final state

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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.

Schedule 9 is view-serializable but not conflict serializable. Every view serializable schedule that is not conflict

serializable has blind writes. a write operation without having performed a read

operation

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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 edge from Ti to Tj if the two transaction conflict, and Ti accessed the data item on which the conflict arose earlier.

The edge may be labeled by the item that was accessed

x

y

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Example Schedule (Schedule A)

T1 T2 T3 T4 T5

R(X)R(Y)R(Z)

R(B)R(A)R(A)

R(Y)W(Y)

W(Z)R(U)

R(Y)W(Y)R(Z)W(Z)

R(U)W(U)

T3T4

T1 T2

T5

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Test for Conflict Serializability

A schedule is conflict serializable iff its precedence graph is acyclic.

If the precedence graph of schedule S has no cycle, then S is equivalent to any serial schedule that can be generated by a topological sort of the precedence graph.

For example, a serializability order for schedule A would be T5 T1 T3 T2 T4

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Test Schedule Serializability

Consider the following schedule S R1(X) R2(Y) W1(X) R2(X) W2(Y) W2(X)

R3(Y) W3(Y) R4(X) W4(X)

Two possible orders of topological sorting: T1 T2 T3 T4 & T1 T2 T4 T3 S is equivalent to both of the above two serial

schedules

T1 T2

T4

T3

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Test for View Serializability

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 falls in the class of NP-complete problems. Thus existence of an efficient algorithm is

unlikely.

However practical algorithms that just check some sufficient conditions for view serializability can still be used.

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Other Notions of Serializability

Schedule 8 given below produces same outcome as the serial schedule T1, T5, yet is not conflict equivalent or view equivalent to it.

Because addition and subtraction are commutitiave (they can be applied in any order), it is possible to produce correct schedules that are not serializable.

Determining such equivalence requires additional knowledge or sematics of operations other than read and write.

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Implementation of Isolation

Schedules must be conflict or view serializable, and recoverable, for the sake of database consistency, and preferably cascadeless.

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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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. READ Section 17.6

1 Introduction to Transaction Processing Concepts and Theory.

Documents

transaction execution

database item

transaction boundaries

end transaction

item x

disk block basic operations

main memory buffer

theory slide