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><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="TRANSACTION-ISO"
>13.2. Transaction Isolation</A
></H1
><P
> The <ACRONYM
CLASS="ACRONYM"
>SQL</ACRONYM
> standard defines four levels of
transaction isolation. The most strict is Serializable,
which is defined by the standard in a paragraph which says that any
concurrent execution of a set of Serializable transactions is guaranteed
to produce the same effect as running them one at a time in some order.
The other three levels are defined in terms of phenomena, resulting from
interaction between concurrent transactions, which must not occur at
each level. The standard notes that due to the definition of
Serializable, none of these phenomena are possible at that level. (This
is hardly surprising -- if the effect of the transactions must be
consistent with having been run one at a time, how could you see any
phenomena caused by interactions?)
</P
><P
> The phenomena which are prohibited at various levels are:
<P
></P
></P><DIV
CLASS="VARIABLELIST"
><DL
><DT
>dirty read
</DT
><DD
><P
> A transaction reads data written by a concurrent uncommitted transaction.
</P
></DD
><DT
>nonrepeatable read
</DT
><DD
><P
> A transaction re-reads data it has previously read and finds that data
has been modified by another transaction (that committed since the
initial read).
</P
></DD
><DT
>phantom read
</DT
><DD
><P
> A transaction re-executes a query returning a set of rows that satisfy a
search condition and finds that the set of rows satisfying the condition
has changed due to another recently-committed transaction.
</P
></DD
></DL
></DIV
><P>
</P
><P
>
The four transaction isolation levels and the corresponding
behaviors are described in <A
HREF="transaction-iso.html#MVCC-ISOLEVEL-TABLE"
>Table 13-1</A
>.
</P
><DIV
CLASS="TABLE"
><A
NAME="MVCC-ISOLEVEL-TABLE"
></A
><P
><B
>Table 13-1. Standard <ACRONYM
CLASS="ACRONYM"
>SQL</ACRONYM
> Transaction Isolation Levels</B
></P
><TABLE
BORDER="1"
CLASS="CALSTABLE"
><COL><COL><COL><COL><THEAD
><TR
><TH
> Isolation Level
</TH
><TH
> Dirty Read
</TH
><TH
> Nonrepeatable Read
</TH
><TH
> Phantom Read
</TH
></TR
></THEAD
><TBODY
><TR
><TD
> Read uncommitted
</TD
><TD
> Possible
</TD
><TD
> Possible
</TD
><TD
> Possible
</TD
></TR
><TR
><TD
> Read committed
</TD
><TD
> Not possible
</TD
><TD
> Possible
</TD
><TD
> Possible
</TD
></TR
><TR
><TD
> Repeatable read
</TD
><TD
> Not possible
</TD
><TD
> Not possible
</TD
><TD
> Possible
</TD
></TR
><TR
><TD
> Serializable
</TD
><TD
> Not possible
</TD
><TD
> Not possible
</TD
><TD
> Not possible
</TD
></TR
></TBODY
></TABLE
></DIV
><P
> In <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>, you can request any of the
four standard transaction isolation levels. But internally, there are
only three distinct isolation levels, which correspond to the levels Read
Committed, Repeatable Read, and Serializable. When you select the level Read
Uncommitted you really get Read Committed, and phantom reads are not possible
in the <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> implementation of Repeatable
Read, so the actual
isolation level might be stricter than what you select. This is
permitted by the SQL standard: the four isolation levels only
define which phenomena must not happen, they do not define which
phenomena must happen. The reason that <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>
only provides three isolation levels is that this is the only
sensible way to map the standard isolation levels to the multiversion
concurrency control architecture. The behavior of the available
isolation levels is detailed in the following subsections.
</P
><P
> To set the transaction isolation level of a transaction, use the
command <A
HREF="sql-set-transaction.html"
>SET TRANSACTION</A
>.
</P
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="XACT-READ-COMMITTED"
>13.2.1. Read Committed Isolation Level</A
></H2
><P
> <I
CLASS="FIRSTTERM"
>Read Committed</I
> is the default isolation
level in <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>. When a transaction
uses this isolation level, a <TT
CLASS="COMMAND"
>SELECT</TT
> query
(without a <TT
CLASS="LITERAL"
>FOR UPDATE/SHARE</TT
> clause) sees only data
committed before the query began; it never sees either uncommitted
data or changes committed during query execution by concurrent
transactions. In effect, a <TT
CLASS="COMMAND"
>SELECT</TT
> query sees
a snapshot of the database as of the instant the query begins to
run. However, <TT
CLASS="COMMAND"
>SELECT</TT
> does see the effects
of previous updates executed within its own transaction, even
though they are not yet committed. Also note that two successive
<TT
CLASS="COMMAND"
>SELECT</TT
> commands can see different data, even
though they are within a single transaction, if other transactions
commit changes during execution of the first <TT
CLASS="COMMAND"
>SELECT</TT
>.
</P
><P
> <TT
CLASS="COMMAND"
>UPDATE</TT
>, <TT
CLASS="COMMAND"
>DELETE</TT
>, <TT
CLASS="COMMAND"
>SELECT
FOR UPDATE</TT
>, and <TT
CLASS="COMMAND"
>SELECT FOR SHARE</TT
> commands
behave the same as <TT
CLASS="COMMAND"
>SELECT</TT
>
in terms of searching for target rows: they will only find target rows
that were committed as of the command start time. However, such a target
row might have already been updated (or deleted or locked) by
another concurrent transaction by the time it is found. In this case, the
would-be updater will wait for the first updating transaction to commit or
roll back (if it is still in progress). If the first updater rolls back,
then its effects are negated and the second updater can proceed with
updating the originally found row. If the first updater commits, the
second updater will ignore the row if the first updater deleted it,
otherwise it will attempt to apply its operation to the updated version of
the row. The search condition of the command (the <TT
CLASS="LITERAL"
>WHERE</TT
> clause) is
re-evaluated to see if the updated version of the row still matches the
search condition. If so, the second updater proceeds with its operation
using the updated version of the row. In the case of
<TT
CLASS="COMMAND"
>SELECT FOR UPDATE</TT
> and <TT
CLASS="COMMAND"
>SELECT FOR
SHARE</TT
>, this means it is the updated version of the row that is
locked and returned to the client.
</P
><P
> Because of the above rule, it is possible for an updating command to see an
inconsistent snapshot: it can see the effects of concurrent updating
commands on the same rows it is trying to update, but it
does not see effects of those commands on other rows in the database.
This behavior makes Read Committed mode unsuitable for commands that
involve complex search conditions; however, it is just right for simpler
cases. For example, consider updating bank balances with transactions
like:
</P><PRE
CLASS="SCREEN"
>BEGIN;
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 12345;
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 7534;
COMMIT;</PRE
><P>
If two such transactions concurrently try to change the balance of account
12345, we clearly want the second transaction to start with the updated
version of the account's row. Because each command is affecting only a
predetermined row, letting it see the updated version of the row does
not create any troublesome inconsistency.
</P
><P
> More complex usage can produce undesirable results in Read Committed
mode. For example, consider a <TT
CLASS="COMMAND"
>DELETE</TT
> command
operating on data that is being both added and removed from its
restriction criteria by another command, e.g., assume
<TT
CLASS="LITERAL"
>website</TT
> is a two-row table with
<TT
CLASS="LITERAL"
>website.hits</TT
> equaling <TT
CLASS="LITERAL"
>9</TT
> and
<TT
CLASS="LITERAL"
>10</TT
>:
</P><PRE
CLASS="SCREEN"
>BEGIN;
UPDATE website SET hits = hits + 1;
-- run from another session: DELETE FROM website WHERE hits = 10;
COMMIT;</PRE
><P>
The <TT
CLASS="COMMAND"
>DELETE</TT
> will have no effect even though
there is a <TT
CLASS="LITERAL"
>website.hits = 10</TT
> row before and
after the <TT
CLASS="COMMAND"
>UPDATE</TT
>. This occurs because the
pre-update row value <TT
CLASS="LITERAL"
>9</TT
> is skipped, and when the
<TT
CLASS="COMMAND"
>UPDATE</TT
> completes and <TT
CLASS="COMMAND"
>DELETE</TT
>
obtains a lock, the new row value is no longer <TT
CLASS="LITERAL"
>10</TT
> but
<TT
CLASS="LITERAL"
>11</TT
>, which no longer matches the criteria.
</P
><P
> Because Read Committed mode starts each command with a new snapshot
that includes all transactions committed up to that instant,
subsequent commands in the same transaction will see the effects
of the committed concurrent transaction in any case. The point
at issue above is whether or not a <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>single</I
></SPAN
> command
sees an absolutely consistent view of the database.
</P
><P
> The partial transaction isolation provided by Read Committed mode
is adequate for many applications, and this mode is fast and simple
to use; however, it is not sufficient for all cases. Applications
that do complex queries and updates might require a more rigorously
consistent view of the database than Read Committed mode provides.
</P
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="XACT-REPEATABLE-READ"
>13.2.2. Repeatable Read Isolation Level</A
></H2
><P
> The <I
CLASS="FIRSTTERM"
>Repeatable Read</I
> isolation level only sees
data committed before the transaction began; it never sees either
uncommitted data or changes committed during transaction execution
by concurrent transactions. (However, the query does see the
effects of previous updates executed within its own transaction,
even though they are not yet committed.) This is a stronger
guarantee than is required by the <ACRONYM
CLASS="ACRONYM"
>SQL</ACRONYM
> standard
for this isolation level, and prevents all of the phenomena described
in <A
HREF="transaction-iso.html#MVCC-ISOLEVEL-TABLE"
>Table 13-1</A
>. As mentioned above, this is
specifically allowed by the standard, which only describes the
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>minimum</I
></SPAN
> protections each isolation level must
provide.
</P
><P
> This level is different from Read Committed in that a query in a
repeatable read transaction sees a snapshot as of the start of the
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>transaction</I
></SPAN
>, not as of the start
of the current query within the transaction. Thus, successive
<TT
CLASS="COMMAND"
>SELECT</TT
> commands within a <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>single</I
></SPAN
>
transaction see the same data, i.e., they do not see changes made by
other transactions that committed after their own transaction started.
</P
><P
> Applications using this level must be prepared to retry transactions
due to serialization failures.
</P
><P
> <TT
CLASS="COMMAND"
>UPDATE</TT
>, <TT
CLASS="COMMAND"
>DELETE</TT
>, <TT
CLASS="COMMAND"
>SELECT
FOR UPDATE</TT
>, and <TT
CLASS="COMMAND"
>SELECT FOR SHARE</TT
> commands
behave the same as <TT
CLASS="COMMAND"
>SELECT</TT
>
in terms of searching for target rows: they will only find target rows
that were committed as of the transaction start time. However, such a
target row might have already been updated (or deleted or locked) by
another concurrent transaction by the time it is found. In this case, the
repeatable read transaction will wait for the first updating transaction to commit or
roll back (if it is still in progress). If the first updater rolls back,
then its effects are negated and the repeatable read transaction can proceed
with updating the originally found row. But if the first updater commits
(and actually updated or deleted the row, not just locked it)
then the repeatable read transaction will be rolled back with the message
</P><PRE
CLASS="SCREEN"
>ERROR: could not serialize access due to concurrent update</PRE
><P>
because a repeatable read transaction cannot modify or lock rows changed by
other transactions after the repeatable read transaction began.
</P
><P
> When an application receives this error message, it should abort
the current transaction and retry the whole transaction from
the beginning. The second time through, the transaction will see the
previously-committed change as part of its initial view of the database,
so there is no logical conflict in using the new version of the row
as the starting point for the new transaction's update.
</P
><P
> Note that only updating transactions might need to be retried; read-only
transactions will never have serialization conflicts.
</P
><P
> The Repeatable Read mode provides a rigorous guarantee that each
transaction sees a completely stable view of the database. However,
this view will not necessarily always be consistent with some serial
(one at a time) execution of concurrent transactions of the same level.
For example, even a read only transaction at this level may see a
control record updated to show that a batch has been completed but
<SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>not</I
></SPAN
> see one of the detail records which is logically
part of the batch because it read an earlier revision of the control
record. Attempts to enforce business rules by transactions running at
this isolation level are not likely to work correctly without careful use
of explicit locks to block conflicting transactions.
</P
><DIV
CLASS="NOTE"
><BLOCKQUOTE
CLASS="NOTE"
><P
><B
>Note: </B
> Prior to <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> version 9.1, a request
for the Serializable transaction isolation level provided exactly the
same behavior described here. To retain the legacy Serializable
behavior, Repeatable Read should now be requested.
</P
></BLOCKQUOTE
></DIV
></DIV
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="XACT-SERIALIZABLE"
>13.2.3. Serializable Isolation Level</A
></H2
><P
> The <I
CLASS="FIRSTTERM"
>Serializable</I
> isolation level provides the strictest transaction
isolation. This level emulates serial transaction execution,
as if transactions had been executed one after another, serially,
rather than concurrently. However, like the Repeatable Read level,
applications using this level must
be prepared to retry transactions due to serialization failures.
In fact, this isolation level works exactly the same as Repeatable
Read except that it monitors for conditions which could make
execution of a concurrent set of serializable transactions behave
in a manner inconsistent with all possible serial (one at a time)
executions of those transactions. This monitoring does not
introduce any blocking beyond that present in repeatable read, but
there is some overhead to the monitoring, and detection of the
conditions which could cause a
<I
CLASS="FIRSTTERM"
>serialization anomaly</I
> will trigger a
<I
CLASS="FIRSTTERM"
>serialization failure</I
>.
</P
><P
> As an example,
consider a table <TT
CLASS="STRUCTNAME"
>mytab</TT
>, initially containing:
</P><PRE
CLASS="SCREEN"
> class | value
-------+-------
1 | 10
1 | 20
2 | 100
2 | 200</PRE
><P>
Suppose that serializable transaction A computes:
</P><PRE
CLASS="SCREEN"
>SELECT SUM(value) FROM mytab WHERE class = 1;</PRE
><P>
and then inserts the result (30) as the <TT
CLASS="STRUCTFIELD"
>value</TT
> in a
new row with <TT
CLASS="STRUCTFIELD"
>class</TT
><TT
CLASS="LITERAL"
> = 2</TT
>. Concurrently, serializable
transaction B computes:
</P><PRE
CLASS="SCREEN"
>SELECT SUM(value) FROM mytab WHERE class = 2;</PRE
><P>
and obtains the result 300, which it inserts in a new row with
<TT
CLASS="STRUCTFIELD"
>class</TT
><TT
CLASS="LITERAL"
> = 1</TT
>. Then both transactions try to commit.
If either transaction were running at the Repeatable Read isolation level,
both would be allowed to commit; but since there is no serial order of execution
consistent with the result, using Serializable transactions will allow one
transaction to commit and will roll the other back with this message:
</P><PRE
CLASS="SCREEN"
>ERROR: could not serialize access due to read/write dependencies among transactions</PRE
><P>
This is because if A had
executed before B, B would have computed the sum 330, not 300, and
similarly the other order would have resulted in a different sum
computed by A.
</P
><P
> To guarantee true serializability <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>
uses <I
CLASS="FIRSTTERM"
>predicate locking</I
>, which means that it keeps locks
which allow it to determine when a write would have had an impact on
the result of a previous read from a concurrent transaction, had it run
first. In <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> these locks do not
cause any blocking and therefore can <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>not</I
></SPAN
> play any part in
causing a deadlock. They are used to identify and flag dependencies
among concurrent Serializable transactions which in certain combinations
can lead to serialization anomalies. In contrast, a Read Committed or
Repeatable Read transaction which wants to ensure data consistency may
need to take out a lock on an entire table, which could block other
users attempting to use that table, or it may use <TT
CLASS="LITERAL"
>SELECT FOR
UPDATE</TT
> or <TT
CLASS="LITERAL"
>SELECT FOR SHARE</TT
> which not only
can block other transactions but cause disk access.
</P
><P
> Predicate locks in <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>, like in most
other database systems, are based on data actually accessed by a
transaction. These will show up in the
<A
HREF="view-pg-locks.html"
><TT
CLASS="STRUCTNAME"
>pg_locks</TT
></A
>
system view with a <TT
CLASS="LITERAL"
>mode</TT
> of <TT
CLASS="LITERAL"
>SIReadLock</TT
>. The
particular locks
acquired during execution of a query will depend on the plan used by
the query, and multiple finer-grained locks (e.g., tuple locks) may be
combined into fewer coarser-grained locks (e.g., page locks) during the
course of the transaction to prevent exhaustion of the memory used to
track the locks. A <TT
CLASS="LITERAL"
>READ ONLY</TT
> transaction may be able to
release its SIRead locks before completion, if it detects that no
conflicts can still occur which could lead to a serialization anomaly.
In fact, <TT
CLASS="LITERAL"
>READ ONLY</TT
> transactions will often be able to
establish that fact at startup and avoid taking any predicate locks.
If you explicitly request a <TT
CLASS="LITERAL"
>SERIALIZABLE READ ONLY DEFERRABLE</TT
>
transaction, it will block until it can establish this fact. (This is
the <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>only</I
></SPAN
> case where Serializable transactions block but
Repeatable Read transactions don't.) On the other hand, SIRead locks
often need to be kept past transaction commit, until overlapping read
write transactions complete.
</P
><P
> Consistent use of Serializable transactions can simplify development.
The guarantee that any set of successfully committed concurrent
Serializable transactions will have the same effect as if they were run
one at a time means that if you can demonstrate that a single transaction,
as written, will do the right thing when run by itself, you can have
confidence that it will do the right thing in any mix of Serializable
transactions, even without any information about what those other
transactions might do, or it will not successfully commit. It is
important that an environment which uses this technique have a
generalized way of handling serialization failures (which always return
with a SQLSTATE value of '40001'), because it will be very hard to
predict exactly which transactions might contribute to the read/write
dependencies and need to be rolled back to prevent serialization
anomalies. The monitoring of read/write dependencies has a cost, as does
the restart of transactions which are terminated with a serialization
failure, but balanced against the cost and blocking involved in use of
explicit locks and <TT
CLASS="LITERAL"
>SELECT FOR UPDATE</TT
> or <TT
CLASS="LITERAL"
>SELECT FOR
SHARE</TT
>, Serializable transactions are the best performance choice
for some environments.
</P
><P
> While <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>'s Serializable transaction isolation
level only allows concurrent transactions to commit if it can prove there
is a serial order of execution that would produce the same effect, it
doesn't always prevent errors from being raised that would not occur in
true serial execution. In particular, it is possible to see unique
constraint violations caused by conflicts with overlapping Serializable
transactions even after explicitly checking that the key isn't present
before attempting to insert it. This can be avoided by making sure
that <SPAN
CLASS="emphasis"
><I
CLASS="EMPHASIS"
>all</I
></SPAN
> Serializable transactions that insert potentially
conflicting keys explicitly check if they can do so first. For example,
imagine an application that asks the user for a new key and then checks
that it doesn't exist already by trying to select it first, or generates
a new key by selecting the maximum existing key and adding one. If some
Serializable transactions insert new keys directly without following this
protocol, unique constraints violations might be reported even in cases
where they could not occur in a serial execution of the concurrent
transactions.
</P
><P
> For optimal performance when relying on Serializable transactions for
concurrency control, these issues should be considered:
<P
></P
></P><UL
><LI
><P
> Declare transactions as <TT
CLASS="LITERAL"
>READ ONLY</TT
> when possible.
</P
></LI
><LI
><P
> Control the number of active connections, using a connection pool if
needed. This is always an important performance consideration, but
it can be particularly important in a busy system using Serializable
transactions.
</P
></LI
><LI
><P
> Don't put more into a single transaction than needed for integrity
purposes.
</P
></LI
><LI
><P
> Don't leave connections dangling <SPAN
CLASS="QUOTE"
>"idle in transaction"</SPAN
>
longer than necessary.
</P
></LI
><LI
><P
> Eliminate explicit locks, <TT
CLASS="LITERAL"
>SELECT FOR UPDATE</TT
>, and
<TT
CLASS="LITERAL"
>SELECT FOR SHARE</TT
> where no longer needed due to the
protections automatically provided by Serializable transactions.
</P
></LI
><LI
><P
> When the system is forced to combine multiple page-level predicate
locks into a single relation-level predicate lock because the predicate
lock table is short of memory, an increase in the rate of serialization
failures may occur. You can avoid this by increasing
<A
HREF="runtime-config-locks.html#GUC-MAX-PRED-LOCKS-PER-TRANSACTION"
>max_pred_locks_per_transaction</A
>.
</P
></LI
><LI
><P
> A sequential scan will always necessitate a relation-level predicate
lock. This can result in an increased rate of serialization failures.
It may be helpful to encourage the use of index scans by reducing
<A
HREF="runtime-config-query.html#GUC-RANDOM-PAGE-COST"
>random_page_cost</A
> and/or increasing
<A
HREF="runtime-config-query.html#GUC-CPU-TUPLE-COST"
>cpu_tuple_cost</A
>. Be sure to weigh any decrease
in transaction rollbacks and restarts against any overall change in
query execution time.
</P
></LI
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><P>
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