Transaction Processing
Concepts
Dr. Avdhesh Gupta
Outline
• Transaction Concept
• Transaction State
• Concurrent Executions
• Serializability
• Recoverability
• Implementation of Isolation
• Transaction Definition in SQL
• Testing for Serializability.
Transaction Concept
• A transaction is a unit of program execution that accesses and
possibly updates various data items.
• E.g., transaction to transfer INR50 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 deal with:
• Failures of various kinds, such as hardware failures and system crashes
• Concurrent execution of multiple transactions
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.
Example of Fund Transfer (Cont.)
• 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
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)
read(A), read(B), print(A+B)
4. read(B)
5. B := B + 50
6. write(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.
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.
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:
• Restart the transaction
• Can be done only if no internal logical error
• Kill the transaction
• Committed – after successful completion.
Transaction State
Concurrent Executions
• Multiple transactions are allowed to run concurrently in the system.
Advantages are:
• Increased processor and disk utilization, leading to better transaction
throughput
• E.g., 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 – 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.
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.
• A transaction that successfully completes its execution will have a
commit instructions as the last statement
• By default transaction assumed to execute commit instruction as its last step
• A transaction that fails to successfully complete its execution will have
an abort instruction as the last statement
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 :
Schedule 2
• A serial schedule where T is followed by T1
2
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
• In Schedules 1, 2 and 3, the sum A + B is preserved .
Schedule 4
• The following concurrent schedule does not preserve the value of
(A + B ).
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.
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.
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
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.
Schedule 3 Schedule 6
Conflict Serializability (Cont.)
• Example of a schedule that is not conflict serializable:
• We are unable to swap instructions in the above schedule to obtain
either the serial schedule < T3, T4 >, or the serial schedule < T4, T3 >.
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.
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.
• What serial schedule is above equivalent to?
• Every view serializable schedule that is not conflict serializable has blind
writes.
Other Notions of Serializability
• The schedule below produces same outcome as the serial schedule < T1, T5 >,
yet is not conflict equivalent or view equivalent to it.
• Determining such equivalence requires analysis of operations other than read
and write.
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 of a precedence graph
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.
• (Better algorithms take order n + 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.
• For example, a serializability order for
Schedule A would be
T5 → T1 → T3 → T2 → T4
• Are there others?
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.
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 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.
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)
If T10 fails, T11 and T12 must also be rolled back.
• Can lead to the undoing of a significant amount of work
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
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.
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.
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 non-serializable
schedules.
• 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.
Weak Levels of Consistency
• Some applications are willing to live with weak levels of
consistency, allowing schedules that are not serializable
• E.g., a read-only transaction that wants to get an approximate
total balance of all accounts
• E.g., database statistics computed for query optimization can be
approximate (why?)
• Such transactions need not be serializable with respect to other
transactions
• Tradeoff accuracy for performance
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.
• Successive reads of record may return different (but committed)
values.
• Read uncommitted — even uncommitted records may be
read.
Levels of Consistency
• 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., Oracle (and PostgreSQL prior to version 9) by default
support a level of consistency called snapshot isolation
(not part of the SQL standard)
Transaction Definition in SQL
• 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);
• Isolation level can be set at database level
• Isolation level can be changed at start of transaction
• E.g. In SQL set transaction isolation level serializable
• E.g. in JDBC -- connection.setTransactionIsolation(
Connection.TRANSACTION_SERIALIZABLE)
Implementation of Isolation Levels
• Locking
• Lock on whole database vs lock on items
• How long to hold lock?
• Shared vs exclusive locks
• Timestamps
• Transaction timestamp assigned e.g. when a transaction begins
• Data items store two timestamps
• Read timestamp
• Write timestamp
• Timestamps are used to detect out of order accesses
• Multiple versions of each data item
• Allow transactions to read from a “snapshot” of the database
Transactions as SQL Statements
• E.g., Transaction 1:
select ID, name from instructor where salary > 90000
• E.g., Transaction 2:
insert into instructor values ('11111', 'James', 'Marketing',
100000)
• Suppose
• T1 starts, finds tuples salary > 90000 using index and locks them
• And then T2 executes.
• Do T1 and T2 conflict? Does tuple level locking detect the conflict?
• Instance of the phantom phenomenon
• Also consider T3 below, with Wu’s salary = 90000
update instructor
set salary = salary * 1.1
where name = 'Wu’
• Key idea: Detect “predicate” conflicts, and use some form of
“predicate locking”
