Lecture # 22-23 Transactions
Rashmi Dutta Baruah
Department of Computer Science & Engineering
Overview of DBMS architecture
Database DBMS User/Application
Read SQL
Write Data
2
• File organization and Indexing
Overview of DBMS – Heap files, sequential files, Indexes
architecture • Role of buffer manager
• Query Processing and Optimizaton
Transaction management
Source: [Anatomy of a DB System. J. Hellerstein & M. Stonebraker. Red Book. 4ed].
3
Outline
• Transaction Concept
• Transaction State
• Concurrent Executions
• Serializability
• Recoverability
• Implementation of Isolation
• Transaction Definition in SQL
• Testing for Serializability.
4
Transaction Concept
• A transaction is a unit of program execution that
accesses and possibly updates various data items.
– Collection of operations forming a single logical unit
• 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 deal with:
– Failures of various kinds, such as hardware failures and
system crashes
– Concurrent execution of multiple transactions
5
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.
6
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
7
Example of Fund Transfer
• 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.
• Durability can be guaranteed by ensuring either:
– The updates carried out by the transaction have been written to
disk before the transaction completes.
– Information about the updates carried out by the transaction is
written to disk, and such information is sufficient to enable the
database to reconstruct the updates when the database system is
restarted after the failure.
8
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.
9
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.
10
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.
11
Transaction State (Cont.)
12
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
• First we will discuss - notion of correctness of concurrent
executions.
13
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
14
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 :
15
Schedule 2
• A serial schedule where T2 is followed by T1
16
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.
17
Schedule 4
• The following concurrent schedule does not
preserve the value of (A + B ).
18
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
19
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.
20
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.
21
Conflict Serializability
• 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
22
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
23
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 >.
24
Testing for Serializability
• Consider some schedule of a set of transactions T1,
T2, ..., Tn
• Precedence graph — a directed 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
25
Test for Conflict Serializability
• A schedule is conflict serializable if and
only if its precedence graph is acyclic.
• 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.
26
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.
27
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.
28
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.
29
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.
30
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
31
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
32
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
• Testing a schedule for serializability after it has
executed is a little too late!
• Goal – to develop concurrency control protocols
that will assure serializability.
33
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.
– We study such protocols in the next chapter.
• 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.
34
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
– Such transactions need not be serializable with respect
to other transactions
• Tradeoff accuracy for performance
35
Levels of Consistency in SQL standard
• Isolation levels in SQL
– 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.
36
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)
37
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)
38
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
39
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”
40
Exercise
Consider the following schedule, is it serializable?
The initial state is A = B = 25, the variables t and s are local variables of T1 and
T2, respectively, they are not database elements. The only consistency on the
database state is that A = B.
T1 T2 A B
read(A, t)
t := t+100
write(A, t)
read(A, s)
s := s*2
write(A, s)
read (B, s)
s := s*2
write(B, s)
read(B, t)
t := t+100
write (B, t)
41
Exercise
Consider the following schedule : (solution is present in slide # 22)
r1(A); w1(A); r2(A); w2(A); r1(B); w1(B); r2(B); w2(B)
Show that the schedule is conflict-serializable i.e. show the sequence
of swaps in which this schedule is converted to the serial schedule <T1,
T2>.
T1 T2
read(A)
write(A)
read(A)
write(A)
read(B)
write(B)
read(B)
write(B)
42
Summary
• We discussed:
– Transaction : a unit of program execution that access and
updates various data items.
– Transactions are required to have the ACID properties:
atomicity, consistency, isolation, and durability.
– Concurrent execution of transactions improves throughput of
transactions and system utilization and also reduces the waiting
time of transactions, however, without concurrency control
mechanisms the database may no longer remain consistent.
– A serial execution of transactions guarantees consistency
preservation.
– Schedule- instructions from multiple transactions in a
chronological order
– Serializable schedule is equivalent to a serial schedule.
43
Summary
• We discussed the following
– Serializability property of schedules
– Conflict serializability and view serializability
– Precedence graph- to test for conflict serializability
– Recoverable and cascadeless schedules
– Isolation levels and weak isolation levels
44
