<!-- doc/src/sgml/mvcc.sgml -->
<chapter id="mvcc">
<title>Concurrency Control</title>
<indexterm>
<primary>concurrency</primary>
</indexterm>
<!## XC>
&xconly;
<para>
<productname>Postgres-XC</productname> inherits most of the high availability feature from
<productname>PostgreSQL</productname>.
In this chapter, please read PostgreSQL as Postgres-XC if not described explicitly.
</para>
<!## end>
<!## PG>
<para>
This chapter describes the behavior of the
<productname>PostgreSQL</productname> database system when two or
more sessions try to access the same data at the same time. The
goals in that situation are to allow efficient access for all
sessions while maintaining strict data integrity. Every developer
of database applications should be familiar with the topics covered
in this chapter.
</para>
<!## end>
<!## XC>
&xconly;
<para>
This chapter describes the behavior of the
<productname>Postgres-XC</productname> database system when two or
more sessions try to access the same data at the same time. The
goals in that situation are to allow efficient access for all
sessions while maintaining strict data integrity. Every developer
of database applications should be familiar with the topics covered
in this chapter.
</para>
<para>
<productname>Postgres-XC</> inherited concurrency control
of <productname>PostgreSQL</> and extended it globally to whole
Coordinators and Datanodes involved. Regardless of what
Coordinator to connect to, all the transactions
in <productname>Postgres-XC</> database cluster behaves in
consistent way as if they are running in single database.
</para>
<!## end>
<sect1 id="mvcc-intro">
<title>Introduction</title>
<indexterm>
<primary>Multiversion Concurrency Control</primary>
</indexterm>
<indexterm>
<primary>MVCC</primary>
</indexterm>
<indexterm>
<primary>Serializable Snapshot Isolation</primary>
</indexterm>
<indexterm>
<primary>SSI</primary>
</indexterm>
<para>
<!## PG>
<productname>PostgreSQL</productname> provides a rich set of tools
<!## end>
<!## XC>
<productname>Postgres-XC</productname> provides a rich set of tools
<!## end>
for developers to manage concurrent access to data. Internally,
data consistency is maintained by using a multiversion
model (Multiversion Concurrency Control, <acronym>MVCC</acronym>).
This means that while querying a database each transaction sees
a snapshot of data (a <firstterm>database version</firstterm>)
as it was some
time ago, regardless of the current state of the underlying data.
This protects the transaction from viewing inconsistent data that
could be caused by (other) concurrent transaction updates on the same
data rows, providing <firstterm>transaction isolation</firstterm>
for each database session. <acronym>MVCC</acronym>, by eschewing
the locking methodologies of traditional database systems,
minimizes lock contention in order to allow for reasonable
performance in multiuser environments.
</para>
<para>
The main advantage of using the <acronym>MVCC</acronym> model of
concurrency control rather than locking is that in
<acronym>MVCC</acronym> locks acquired for querying (reading) data
do not conflict with locks acquired for writing data, and so
reading never blocks writing and writing never blocks reading.
<!## PG>
<productname>PostgreSQL</productname> maintains this guarantee
<!## end>
<!## XC>
<productname>Postgres-XC</productname> maintains this guarantee
<!## end>
even when providing the strictest level of transaction
isolation through the use of an innovative <firstterm>Serializable
Snapshot Isolation</firstterm> (<acronym>SSI</acronym>) level.
</para>
<para>
Table- and row-level locking facilities are also available in
<!## PG>
<productname>PostgreSQL</productname> for applications which don't
<!## end>
<!## XC>
<productname>Postgres-XC</productname> for applications which don't
<!## end>
generally need full transaction isolation and prefer to explicitly
manage particular points of conflict. However, proper
use of <acronym>MVCC</acronym> will generally provide better
performance than locks. In addition, application-defined advisory
locks provide a mechanism for acquiring locks that are not tied
to a single transaction.
</para>
<!## XC>
&xconly;
<para>
Please note that current Postgres-XC does not provide snapshot isolation.
</para>
<!## end>
</sect1>
<sect1 id="transaction-iso">
<title>Transaction Isolation</title>
<indexterm>
<primary>transaction isolation</primary>
</indexterm>
<para>
The <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?)
</para>
<para>
The phenomena which are prohibited at various levels are:
<variablelist>
<varlistentry>
<term>
dirty read
<indexterm><primary>dirty read</primary></indexterm>
</term>
<listitem>
<para>
A transaction reads data written by a concurrent uncommitted transaction.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
nonrepeatable read
<indexterm><primary>nonrepeatable read</primary></indexterm>
</term>
<listitem>
<para>
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).
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
phantom read
<indexterm><primary>phantom read</primary></indexterm>
</term>
<listitem>
<para>
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.
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
<para>
<indexterm>
<primary>transaction isolation level</primary>
</indexterm>
The four transaction isolation levels and the corresponding
behaviors are described in <xref linkend="mvcc-isolevel-table">.
</para>
<table tocentry="1" id="mvcc-isolevel-table">
<title>Standard <acronym>SQL</acronym> Transaction Isolation Levels</title>
<tgroup cols="4">
<thead>
<row>
<entry>
Isolation Level
</entry>
<entry>
Dirty Read
</entry>
<entry>
Nonrepeatable Read
</entry>
<entry>
Phantom Read
</entry>
</row>
</thead>
<tbody>
<row>
<entry>
Read uncommitted
</entry>
<entry>
Possible
</entry>
<entry>
Possible
</entry>
<entry>
Possible
</entry>
</row>
<row>
<entry>
Read committed
</entry>
<entry>
Not possible
</entry>
<entry>
Possible
</entry>
<entry>
Possible
</entry>
</row>
<row>
<entry>
Repeatable read
</entry>
<entry>
Not possible
</entry>
<entry>
Not possible
</entry>
<entry>
Possible
</entry>
</row>
<row>
<entry>
Serializable
</entry>
<entry>
Not possible
</entry>
<entry>
Not possible
</entry>
<entry>
Not possible
</entry>
</row>
</tbody>
</tgroup>
</table>
<para>
In <productname>PostgreSQL</productname>, 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 <productname>PostgreSQL</productname> 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 <productname>PostgreSQL</>
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.
</para>
<!## XC>
&xconly;
<para>
Please note that at present <productname>Postgres-XC</productname> provide only
read committed isolation level.
Other isolation level will be provided in future releases.
</para>
&common;
<!## end>
<para>
To set the transaction isolation level of a transaction, use the
command <xref linkend="sql-set-transaction">.
</para>
<sect2 id="xact-read-committed">
<title>Read Committed Isolation Level</title>
<indexterm>
<primary>transaction isolation level</primary>
<secondary>read committed</secondary>
</indexterm>
<indexterm>
<primary>read committed</primary>
</indexterm>
<para>
<firstterm>Read Committed</firstterm> is the default isolation
level in <productname>PostgreSQL</productname>. When a transaction
uses this isolation level, a <command>SELECT</command> query
(without a <literal>FOR UPDATE/SHARE</> 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 <command>SELECT</command> query sees
a snapshot of the database as of the instant the query begins to
run. However, <command>SELECT</command> does see the effects
of previous updates executed within its own transaction, even
though they are not yet committed. Also note that two successive
<command>SELECT</command> commands can see different data, even
though they are within a single transaction, if other transactions
commit changes during execution of the first <command>SELECT</command>.
</para>
<para>
<command>UPDATE</command>, <command>DELETE</command>, <command>SELECT
FOR UPDATE</command>, and <command>SELECT FOR SHARE</command> commands
behave the same as <command>SELECT</command>
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 <literal>WHERE</> 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
<command>SELECT FOR UPDATE</command> and <command>SELECT FOR
SHARE</command>, this means it is the updated version of the row that is
locked and returned to the client.
</para>
<para>
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:
<screen>
BEGIN;
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 12345;
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 7534;
COMMIT;
</screen>
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.
</para>
<para>
More complex usage can produce undesirable results in Read Committed
mode. For example, consider a <command>DELETE</command> command
operating on data that is being both added and removed from its
restriction criteria by another command, e.g., assume
<literal>website</literal> is a two-row table with
<literal>website.hits</literal> equaling <literal>9</literal> and
<literal>10</literal>:
<screen>
BEGIN;
UPDATE website SET hits = hits + 1;
-- run from another session: DELETE FROM website WHERE hits = 10;
COMMIT;
</screen>
The <command>DELETE</command> will have no effect even though
there is a <literal>website.hits = 10</literal> row before and
after the <command>UPDATE</command>. This occurs because the
pre-update row value <literal>9</> is skipped, and when the
<command>UPDATE</command> completes and <command>DELETE</command>
obtains a lock, the new row value is no longer <literal>10</> but
<literal>11</>, which no longer matches the criteria.
</para>
<para>
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 <emphasis>single</> command
sees an absolutely consistent view of the database.
</para>
<para>
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.
</para>
</sect2>
<sect2 id="xact-repeatable-read">
<title>Repeatable Read Isolation Level</title>
<indexterm>
<primary>transaction isolation level</primary>
<secondary>repeatable read</secondary>
</indexterm>
<indexterm>
<primary>repeatable read</primary>
</indexterm>
<para>
The <firstterm>Repeatable Read</firstterm> 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>SQL</acronym> standard
for this isolation level, and prevents all of the phenomena described
in <xref linkend="mvcc-isolevel-table">. As mentioned above, this is
specifically allowed by the standard, which only describes the
<emphasis>minimum</emphasis> protections each isolation level must
provide.
</para>
<para>
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
<emphasis>transaction</>, not as of the start
of the current query within the transaction. Thus, successive
<command>SELECT</command> commands within a <emphasis>single</>
transaction see the same data, i.e., they do not see changes made by
other transactions that committed after their own transaction started.
</para>
<para>
Applications using this level must be prepared to retry transactions
due to serialization failures.
</para>
<para>
<command>UPDATE</command>, <command>DELETE</command>, <command>SELECT
FOR UPDATE</command>, and <command>SELECT FOR SHARE</command> commands
behave the same as <command>SELECT</command>
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
<screen>
ERROR: could not serialize access due to concurrent update
</screen>
because a repeatable read transaction cannot modify or lock rows changed by
other transactions after the repeatable read transaction began.
</para>
<para>
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.
</para>
<para>
Note that only updating transactions might need to be retried; read-only
transactions will never have serialization conflicts.
</para>
<para>
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
<emphasis>not</emphasis> 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.
</para>
<note>
<para>
Prior to <productname>PostgreSQL</productname> 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.
</para>
</note>
</sect2>
<sect2 id="xact-serializable">
<title>Serializable Isolation Level</title>
<indexterm>
<primary>transaction isolation level</primary>
<secondary>serializable</secondary>
</indexterm>
<indexterm>
<primary>serializable</primary>
</indexterm>
<indexterm>
<primary>predicate locking</primary>
</indexterm>
<indexterm>
<primary>serialization anomaly</primary>
</indexterm>
<para>
The <firstterm>Serializable</firstterm> 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
<firstterm>serialization anomaly</firstterm> will trigger a
<firstterm>serialization failure</firstterm>.
</para>
<para>
As an example,
consider a table <structname>mytab</>, initially containing:
<screen>
class | value
-------+-------
1 | 10
1 | 20
2 | 100
2 | 200
</screen>
Suppose that serializable transaction A computes:
<screen>
SELECT SUM(value) FROM mytab WHERE class = 1;
</screen>
and then inserts the result (30) as the <structfield>value</> in a
new row with <structfield>class</><literal> = 2</>. Concurrently, serializable
transaction B computes:
<screen>
SELECT SUM(value) FROM mytab WHERE class = 2;
</screen>
and obtains the result 300, which it inserts in a new row with
<structfield>class</><literal> = 1</>. 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:
<screen>
ERROR: could not serialize access due to read/write dependencies among transactions
</screen>
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.
</para>
<para>
To guarantee true serializability <productname>PostgreSQL</productname>
uses <firstterm>predicate locking</>, 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 <productname>PostgreSQL</productname> these locks do not
cause any blocking and therefore can <emphasis>not</> 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 <literal>SELECT FOR
UPDATE</literal> or <literal>SELECT FOR SHARE</literal> which not only
can block other transactions but cause disk access.
</para>
<para>
Predicate locks in <productname>PostgreSQL</productname>, like in most
other database systems, are based on data actually accessed by a
transaction. These will show up in the
<link linkend="view-pg-locks"><structname>pg_locks</structname></link>
system view with a <literal>mode</> of <literal>SIReadLock</>. 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 <literal>READ ONLY</> 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, <literal>READ ONLY</> transactions will often be able to
establish that fact at startup and avoid taking any predicate locks.
If you explicitly request a <literal>SERIALIZABLE READ ONLY DEFERRABLE</>
transaction, it will block until it can establish this fact. (This is
the <emphasis>only</> 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.
</para>
<para>
Consistent use of Serializable transactions can simplify development.
The guarantee that any set of 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. 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 <literal>SELECT FOR UPDATE</> or <literal>SELECT FOR
SHARE</>, Serializable transactions are the best performance choice
for some environments.
</para>
<para>
For optimal performance when relying on Serializable transactions for
concurrency control, these issues should be considered:
<itemizedlist>
<listitem>
<para>
Declare transactions as <literal>READ ONLY</> when possible.
</para>
</listitem>
<listitem>
<para>
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.
</para>
</listitem>
<listitem>
<para>
Don't put more into a single transaction than needed for integrity
purposes.
</para>
</listitem>
<listitem>
<para>
Don't leave connections dangling <quote>idle in transaction</quote>
longer than necessary.
</para>
</listitem>
<listitem>
<para>
Eliminate explicit locks, <literal>SELECT FOR UPDATE</>, and
<literal>SELECT FOR SHARE</> where no longer needed due to the
protections automatically provided by Serializable transactions.
</para>
</listitem>
<listitem>
<para>
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
<xref linkend="guc-max-pred-locks-per-transaction">.
</para>
</listitem>
<listitem>
<para>
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
<xref linkend="guc-random-page-cost"> and/or increasing
<xref linkend="guc-cpu-tuple-cost">. Be sure to weigh any decrease
in transaction rollbacks and restarts against any overall change in
query execution time.
</para>
</listitem>
</itemizedlist>
</para>
<warning>
<para>
Support for the Serializable transaction isolation level has not yet
been added to Hot Standby replication targets (described in
<xref linkend="hot-standby">). The strictest isolation level currently
supported in hot standby mode is Repeatable Read. While performing all
permanent database writes within Serializable transactions on the
master will ensure that all standbys will eventually reach a consistent
state, a Repeatable Read transaction run on the standby can sometimes
see a transient state which is inconsistent with any serial execution
of serializable transactions on the master.
</para>
</warning>
</sect2>
</sect1>
<sect1 id="explicit-locking">
<title>Explicit Locking</title>
<indexterm>
<primary>lock</primary>
</indexterm>
<para>
<productname>PostgreSQL</productname> provides various lock modes
to control concurrent access to data in tables. These modes can
be used for application-controlled locking in situations where
<acronym>MVCC</acronym> does not give the desired behavior. Also,
most <productname>PostgreSQL</productname> commands automatically
acquire locks of appropriate modes to ensure that referenced
tables are not dropped or modified in incompatible ways while the
command executes. (For example, <command>TRUNCATE</> cannot safely be
executed concurrently with other operations on the same table, so it
obtains an exclusive lock on the table to enforce that.)
</para>
<para>
To examine a list of the currently outstanding locks in a database
server, use the
<link linkend="view-pg-locks"><structname>pg_locks</structname></link>
system view. For more information on monitoring the status of the lock
manager subsystem, refer to <xref linkend="monitoring">.
</para>
<sect2 id="locking-tables">
<title>Table-level Locks</title>
<indexterm zone="locking-tables">
<primary>LOCK</primary>
</indexterm>
<para>
The list below shows the available lock modes and the contexts in
which they are used automatically by
<productname>PostgreSQL</productname>. You can also acquire any
of these locks explicitly with the command <xref
linkend="sql-lock">.
Remember that all of these lock modes are table-level locks,
even if the name contains the word
<quote>row</quote>; the names of the lock modes are historical.
To some extent the names reflect the typical usage of each lock
mode — but the semantics are all the same. The only real difference
between one lock mode and another is the set of lock modes with
which each conflicts (see <xref linkend="table-lock-compatibility">).
Two transactions cannot hold locks of conflicting
modes on the same table at the same time. (However, a transaction
never conflicts with itself. For example, it might acquire
<literal>ACCESS EXCLUSIVE</literal> lock and later acquire
<literal>ACCESS SHARE</literal> lock on the same table.) Non-conflicting
lock modes can be held concurrently by many transactions. Notice in
particular that some lock modes are self-conflicting (for example,
an <literal>ACCESS EXCLUSIVE</literal> lock cannot be held by more than one
transaction at a time) while others are not self-conflicting (for example,
an <literal>ACCESS SHARE</literal> lock can be held by multiple transactions).
</para>
<variablelist>
<title>Table-level Lock Modes</title>
<varlistentry>
<term>
<literal>ACCESS SHARE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>ACCESS EXCLUSIVE</literal> lock
mode only.
</para>
<para>
The <command>SELECT</command> command acquires a lock of this mode on
referenced tables. In general, any query that only <emphasis>reads</> a table
and does not modify it will acquire this lock mode.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>ROW SHARE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>EXCLUSIVE</literal> and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
</para>
<para>
The <command>SELECT FOR UPDATE</command> and
<command>SELECT FOR SHARE</command> commands acquire a
lock of this mode on the target table(s) (in addition to
<literal>ACCESS SHARE</literal> locks on any other tables
that are referenced but not selected
<option>FOR UPDATE/FOR SHARE</option>).
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>ROW EXCLUSIVE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>SHARE</literal>, <literal>SHARE ROW
EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
</para>
<para>
The commands <command>UPDATE</command>,
<command>DELETE</command>, and <command>INSERT</command>
acquire this lock mode on the target table (in addition to
<literal>ACCESS SHARE</literal> locks on any other referenced
tables). In general, this lock mode will be acquired by any
command that <emphasis>modifies data</> in a table.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>SHARE UPDATE EXCLUSIVE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>SHARE UPDATE EXCLUSIVE</literal>,
<literal>SHARE</literal>, <literal>SHARE ROW
EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
This mode protects a table against
concurrent schema changes and <command>VACUUM</> runs.
</para>
<para>
Acquired by <command>VACUUM</command> (without <option>FULL</option>),
<command>ANALYZE</>, <command>CREATE INDEX CONCURRENTLY</>, and
some forms of <command>ALTER TABLE</command>.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>SHARE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>ROW EXCLUSIVE</literal>,
<literal>SHARE UPDATE EXCLUSIVE</literal>, <literal>SHARE ROW
EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
This mode protects a table against concurrent data changes.
</para>
<para>
Acquired by <command>CREATE INDEX</command>
(without <option>CONCURRENTLY</option>).
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>SHARE ROW EXCLUSIVE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>ROW EXCLUSIVE</literal>,
<literal>SHARE UPDATE EXCLUSIVE</literal>,
<literal>SHARE</literal>, <literal>SHARE ROW
EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
This mode protects a table against concurrent data changes, and
is self-exclusive so that only one session can hold it at a time.
</para>
<para>
This lock mode is not automatically acquired by any
<productname>PostgreSQL</productname> command.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>EXCLUSIVE</literal>
</term>
<listitem>
<para>
Conflicts with the <literal>ROW SHARE</literal>, <literal>ROW
EXCLUSIVE</literal>, <literal>SHARE UPDATE
EXCLUSIVE</literal>, <literal>SHARE</literal>, <literal>SHARE
ROW EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal> lock modes.
This mode allows only concurrent <literal>ACCESS SHARE</literal> locks,
i.e., only reads from the table can proceed in parallel with a
transaction holding this lock mode.
</para>
<para>
This lock mode is not automatically acquired on tables by any
<productname>PostgreSQL</productname> command.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<literal>ACCESS EXCLUSIVE</literal>
</term>
<listitem>
<para>
Conflicts with locks of all modes (<literal>ACCESS
SHARE</literal>, <literal>ROW SHARE</literal>, <literal>ROW
EXCLUSIVE</literal>, <literal>SHARE UPDATE
EXCLUSIVE</literal>, <literal>SHARE</literal>, <literal>SHARE
ROW EXCLUSIVE</literal>, <literal>EXCLUSIVE</literal>, and
<literal>ACCESS EXCLUSIVE</literal>).
This mode guarantees that the
holder is the only transaction accessing the table in any way.
</para>
<para>
Acquired by the <command>ALTER TABLE</>, <command>DROP TABLE</>,
<command>TRUNCATE</command>, <command>REINDEX</command>,
<command>CLUSTER</command>, and <command>VACUUM FULL</command>
commands.
This is also the default lock mode for <command>LOCK TABLE</command>
statements that do not specify a mode explicitly.
</para>
</listitem>
</varlistentry>
</variablelist>
<tip>
<para>
Only an <literal>ACCESS EXCLUSIVE</literal> lock blocks a
<command>SELECT</command> (without <option>FOR UPDATE/SHARE</option>)
statement.
</para>
</tip>
<para>
Once acquired, a lock is normally held till end of transaction. But if a
lock is acquired after establishing a savepoint, the lock is released
immediately if the savepoint is rolled back to. This is consistent with
the principle that <command>ROLLBACK</> cancels all effects of the
commands since the savepoint. The same holds for locks acquired within a
<application>PL/pgSQL</> exception block: an error escape from the block
releases locks acquired within it.
</para>
<table tocentry="1" id="table-lock-compatibility">
<title> Conflicting Lock Modes</title>
<tgroup cols="9">
<colspec colnum="2" colname="lockst">
<colspec colnum="9" colname="lockend">
<spanspec namest="lockst" nameend="lockend" spanname="lockreq">
<thead>
<row>
<entry morerows="1">Requested Lock Mode</entry>
<entry spanname="lockreq">Current Lock Mode</entry>
</row>
<row>
<entry>ACCESS SHARE</entry>
<entry>ROW SHARE</entry>
<entry>ROW EXCLUSIVE</entry>
<entry>SHARE UPDATE EXCLUSIVE</entry>
<entry>SHARE</entry>
<entry>SHARE ROW EXCLUSIVE</entry>
<entry>EXCLUSIVE</entry>
<entry>ACCESS EXCLUSIVE</entry>
</row>
</thead>
<tbody>
<row>
<entry>ACCESS SHARE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
</row>
<row>
<entry>ROW SHARE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>ROW EXCLUSIVE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>SHARE UPDATE EXCLUSIVE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>SHARE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>SHARE ROW EXCLUSIVE</entry>
<entry align="center"></entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>EXCLUSIVE</entry>
<entry align="center"></entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
<row>
<entry>ACCESS EXCLUSIVE</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
<entry align="center">X</entry>
</row>
</tbody>
</tgroup>
</table>
</sect2>
<sect2 id="locking-rows">
<title>Row-level Locks</title>
<para>
In addition to table-level locks, there are row-level locks, which
can be exclusive or shared locks. An exclusive row-level lock on a
specific row is automatically acquired when the row is updated or
deleted. The lock is held until the transaction commits or rolls
back, just like table-level locks. Row-level locks do
not affect data querying; they block only <emphasis>writers to the same
row</emphasis>.
</para>
<para>
To acquire an exclusive row-level lock on a row without actually
modifying the row, select the row with <command>SELECT FOR
UPDATE</command>. Note that once the row-level lock is acquired,
the transaction can update the row multiple times without
fear of conflicts.
</para>
<para>
To acquire a shared row-level lock on a row, select the row with
<command>SELECT FOR SHARE</command>. A shared lock does not prevent
other transactions from acquiring the same shared lock. However,
no transaction is allowed to update, delete, or exclusively lock a
row on which any other transaction holds a shared lock. Any attempt
to do so will block until the shared lock(s) have been released.
</para>
<para>
<productname>PostgreSQL</productname> doesn't remember any
information about modified rows in memory, so there is no limit on
the number of rows locked at one time. However, locking a row
might cause a disk write, e.g., <command>SELECT FOR
UPDATE</command> modifies selected rows to mark them locked, and so
will result in disk writes.
</para>
<para>
In addition to table and row locks, page-level share/exclusive locks are
used to control read/write access to table pages in the shared buffer
pool. These locks are released immediately after a row is fetched or
updated. Application developers normally need not be concerned with
page-level locks, but they are mentioned here for completeness.
</para>
</sect2>
<sect2 id="locking-deadlocks">
<title>Deadlocks</title>
<indexterm zone="locking-deadlocks">
<primary>deadlock</primary>
</indexterm>
<para>
The use of explicit locking can increase the likelihood of
<firstterm>deadlocks</>, wherein two (or more) transactions each
hold locks that the other wants. For example, if transaction 1
acquires an exclusive lock on table A and then tries to acquire
an exclusive lock on table B, while transaction 2 has already
exclusive-locked table B and now wants an exclusive lock on table
A, then neither one can proceed.
<productname>PostgreSQL</productname> automatically detects
deadlock situations and resolves them by aborting one of the
transactions involved, allowing the other(s) to complete.
(Exactly which transaction will be aborted is difficult to
predict and should not be relied upon.)
</para>
<para>
Note that deadlocks can also occur as the result of row-level
locks (and thus, they can occur even if explicit locking is not
used). Consider the case in which two concurrent
transactions modify a table. The first transaction executes:
<screen>
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 11111;
</screen>
This acquires a row-level lock on the row with the specified
account number. Then, the second transaction executes:
<screen>
UPDATE accounts SET balance = balance + 100.00 WHERE acctnum = 22222;
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 11111;
</screen>
The first <command>UPDATE</command> statement successfully
acquires a row-level lock on the specified row, so it succeeds in
updating that row. However, the second <command>UPDATE</command>
statement finds that the row it is attempting to update has
already been locked, so it waits for the transaction that
acquired the lock to complete. Transaction two is now waiting on
transaction one to complete before it continues execution. Now,
transaction one executes:
<screen>
UPDATE accounts SET balance = balance - 100.00 WHERE acctnum = 22222;
</screen>
Transaction one attempts to acquire a row-level lock on the
specified row, but it cannot: transaction two already holds such
a lock. So it waits for transaction two to complete. Thus,
transaction one is blocked on transaction two, and transaction
two is blocked on transaction one: a deadlock
condition. <productname>PostgreSQL</productname> will detect this
situation and abort one of the transactions.
</para>
<para>
The best defense against deadlocks is generally to avoid them by
being certain that all applications using a database acquire
locks on multiple objects in a consistent order. In the example
above, if both transactions
had updated the rows in the same order, no deadlock would have
occurred. One should also ensure that the first lock acquired on
an object in a transaction is the most restrictive mode that will be
needed for that object. If it is not feasible to verify this in
advance, then deadlocks can be handled on-the-fly by retrying
transactions that abort due to deadlocks.
</para>
<para>
So long as no deadlock situation is detected, a transaction seeking
either a table-level or row-level lock will wait indefinitely for
conflicting locks to be released. This means it is a bad idea for
applications to hold transactions open for long periods of time
(e.g., while waiting for user input).
</para>
<!## XC>
&xconly;
<para>
Please note that <productname>Postgres-XC</> does not detect
deadlocks which spreads among multiple Coordinators and/or
Datanodes, know as global deadlocks. There are many discussions
whether global deadlocks should be detected or not, mainly
because of the cost of the detection. So
far, <productname>Postgres-XC</> leave global deadlock detection
to timeout.
</para>
<!## end>
</sect2>
<sect2 id="advisory-locks">
<title>Advisory Locks</title>
<indexterm zone="advisory-locks">
<primary>advisory lock</primary>
</indexterm>
<indexterm zone="advisory-locks">
<primary>lock</primary>
<secondary>advisory</secondary>
</indexterm>
<para>
<productname>PostgreSQL</productname> provides a means for
creating locks that have application-defined meanings. These are
called <firstterm>advisory locks</>, because the system does not
enforce their use — it is up to the application to use them
correctly. Advisory locks can be useful for locking strategies
that are an awkward fit for the MVCC model.
For example, a common use of advisory locks is to emulate pessimistic
locking strategies typical of so called <quote>flat file</> data
management systems.
While a flag stored in a table could be used for the same purpose,
advisory locks are faster, avoid table bloat, and are automatically
cleaned up by the server at the end of the session.
</para>
<para>
There are two ways to acquire an advisory lock in
<productname>PostgreSQL</productname>: at session level or at
transaction level.
Once acquired at session level, an advisory lock is held until
explicitly released or the session ends. Unlike standard lock requests,
session-level advisory lock requests do not honor transaction semantics:
a lock acquired during a transaction that is later rolled back will still
be held following the rollback, and likewise an unlock is effective even
if the calling transaction fails later. A lock can be acquired multiple
times by its owning process; for each completed lock request there must
be a corresponding unlock request before the lock is actually released.
Transaction-level lock requests, on the other hand, behave more like
regular lock requests: they are automatically released at the end of the
transaction, and there is no explicit unlock operation. This behavior
is often more convenient than the session-level behavior for short-term
usage of an advisory lock.
Session-level and transaction-level lock requests for the same advisory
lock identifier will block each other in the expected way.
If a session already holds a given advisory lock, additional requests by
it will always succeed, even if other sessions are awaiting the lock; this
statement is true regardless of whether the existing lock hold and new
request are at session level or transaction level.
</para>
<para>
Like all locks in
<productname>PostgreSQL</productname>, a complete list of advisory locks
currently held by any session can be found in the <link
linkend="view-pg-locks"><structname>pg_locks</structname></link> system
view.
</para>
<para>
Both advisory locks and regular locks are stored in a shared memory
pool whose size is defined by the configuration variables
<xref linkend="guc-max-locks-per-transaction"> and
<xref linkend="guc-max-connections">.
Care must be taken not to exhaust this
memory or the server will be unable to grant any locks at all.
This imposes an upper limit on the number of advisory locks
grantable by the server, typically in the tens to hundreds of thousands
depending on how the server is configured.
</para>
<!## XC>
&xconly;
<para>
Please note that <productname>Postgres-XC</>'s advisory lock is
local to Coordinator or Datanode. If you wish to acquire
advisory locks on different Coordinator, you should do it manually
using <type>EXECUTE DIRECT</> statement.
</para>
<!## end>
&common;
<para>
In certain cases using advisory locking methods, especially in queries
involving explicit ordering and <literal>LIMIT</> clauses, care must be
taken to control the locks acquired because of the order in which SQL
expressions are evaluated. For example:
<screen>
SELECT pg_advisory_lock(id) FROM foo WHERE id = 12345; -- ok
SELECT pg_advisory_lock(id) FROM foo WHERE id > 12345 LIMIT 100; -- danger!
SELECT pg_advisory_lock(q.id) FROM
(
SELECT id FROM foo WHERE id > 12345 LIMIT 100
) q; -- ok
</screen>
In the above queries, the second form is dangerous because the
<literal>LIMIT</> is not guaranteed to be applied before the locking
function is executed. This might cause some locks to be acquired
that the application was not expecting, and hence would fail to release
(until it ends the session).
From the point of view of the application, such locks
would be dangling, although still viewable in
<structname>pg_locks</structname>.
</para>
<para>
The functions provided to manipulate advisory locks are described in
<xref linkend="functions-advisory-locks">.
</para>
</sect2>
</sect1>
<sect1 id="applevel-consistency">
<title>Data Consistency Checks at the Application Level</title>
<para>
It is very difficult to enforce business rules regarding data integrity
using Read Committed transactions because the view of the data is
shifting with each statement, and even a single statement may not
restrict itself to the statement's snapshot if a write conflict occurs.
</para>
<para>
While a Repeatable Read transaction has a stable view of the data
throughout its execution, there is a subtle issue with using
<acronym>MVCC</acronym> snapshots for data consistency checks, involving
something known as <firstterm>read/write conflicts</firstterm>.
If one transaction writes data and a concurrent transaction attempts
to read the same data (whether before or after the write), it cannot
see the work of the other transaction. The reader then appears to have
executed first regardless of which started first or which committed
first. If that is as far as it goes, there is no problem, but
if the reader also writes data which is read by a concurrent transaction
there is now a transaction which appears to have run before either of
the previously mentioned transactions. If the transaction which appears
to have executed last actually commits first, it is very easy for a
cycle to appear in a graph of the order of execution of the transactions.
When such a cycle appears, integrity checks will not work correctly
without some help.
</para>
<para>
As mentioned in <xref linkend="xact-serializable">, Serializable
transactions are just Repeatable Read transactions which add
non-blocking monitoring for dangerous patterns of read/write conflicts.
When a pattern is detected which could cause a cycle in the apparent
order of execution, one of the transactions involved is rolled back to
break the cycle.
</para>
<sect2 id="serializable-consistency">
<title>Enforcing Consistency With Serializable Transactions</title>
<para>
If the Serializable transaction isolation level is used for all writes
and for all reads which need a consistent view of the data, no other
effort is required to ensure consistency. Software from other
environments which is written to use serializable transactions to
ensure consistency should <quote>just work</quote> in this regard in
<productname>PostgreSQL</productname>.
</para>
<para>
When using this technique, it will avoid creating an unnecessary burden
for application programmers if the application software goes through a
framework which automatically retries transactions which are rolled
back with a serialization failure. It may be a good idea to set
<literal>default_transaction_isolation</> to <literal>serializable</>.
It would also be wise to take some action to ensure that no other
transaction isolation level is used, either inadvertently or to
subvert integrity checks, through checks of the transaction isolation
level in triggers.
</para>
<para>
See <xref linkend="xact-serializable"> for performance suggestions.
</para>
<warning>
<para>
This level of integrity protection using Serializable transactions
does not yet extend to hot standby mode (<xref linkend="hot-standby">).
Because of that, those using hot standby may want to use Repeatable
Read and explicit locking.on the master.
</para>
</warning>
</sect2>
<sect2 id="non-serializable-consistency">
<title>Enforcing Consistency With Explicit Blocking Locks</title>
<para>
When non-serializable writes are possible,
to ensure the current validity of a row and protect it against
concurrent updates one must use <command>SELECT FOR UPDATE</command>,
<command>SELECT FOR SHARE</command>, or an appropriate <command>LOCK
TABLE</command> statement. (<command>SELECT FOR UPDATE</command>
and <command>SELECT FOR SHARE</command> lock just the
returned rows against concurrent updates, while <command>LOCK
TABLE</command> locks the whole table.) This should be taken into
account when porting applications to
<productname>PostgreSQL</productname> from other environments.
</para>
<para>
Also of note to those converting from other environments is the fact
that <command>SELECT FOR UPDATE</command> does not ensure that a
concurrent transaction will not update or delete a selected row.
To do that in <productname>PostgreSQL</productname> you must actually
update the row, even if no values need to be changed.
<command>SELECT FOR UPDATE</command> <emphasis>temporarily blocks</emphasis>
other transactions from acquiring the same lock or executing an
<command>UPDATE</command> or <command>DELETE</command> which would
affect the locked row, but once the transaction holding this lock
commits or rolls back, a blocked transaction will proceed with the
conflicting operation unless an actual <command>UPDATE</command> of
the row was performed while the lock was held.
</para>
<para>
Global validity checks require extra thought under
non-serializable <acronym>MVCC</acronym>.
For example, a banking application might wish to check that the sum of
all credits in one table equals the sum of debits in another table,
when both tables are being actively updated. Comparing the results of two
successive <literal>SELECT sum(...)</literal> commands will not work reliably in
Read Committed mode, since the second query will likely include the results
of transactions not counted by the first. Doing the two sums in a
single repeatable read transaction will give an accurate picture of only the
effects of transactions that committed before the repeatable read transaction
started — but one might legitimately wonder whether the answer is still
relevant by the time it is delivered. If the repeatable read transaction
itself applied some changes before trying to make the consistency check,
the usefulness of the check becomes even more debatable, since now it
includes some but not all post-transaction-start changes. In such cases
a careful person might wish to lock all tables needed for the check,
in order to get an indisputable picture of current reality. A
<literal>SHARE</> mode (or higher) lock guarantees that there are no
uncommitted changes in the locked table, other than those of the current
transaction.
</para>
<para>
Note also that if one is relying on explicit locking to prevent concurrent
changes, one should either use Read Committed mode, or in Repeatable Read
mode be careful to obtain
locks before performing queries. A lock obtained by a
repeatable read transaction guarantees that no other transactions modifying
the table are still running, but if the snapshot seen by the
transaction predates obtaining the lock, it might predate some now-committed
changes in the table. A repeatable read transaction's snapshot is actually
frozen at the start of its first query or data-modification command
(<literal>SELECT</>, <literal>INSERT</>,
<literal>UPDATE</>, or <literal>DELETE</>), so
it is possible to obtain locks explicitly before the snapshot is
frozen.
</para>
</sect2>
</sect1>
<sect1 id="locking-indexes">
<title>Locking and Indexes</title>
<indexterm zone="locking-indexes">
<primary>index</primary>
<secondary>locks</secondary>
</indexterm>
<para>
Though <productname>PostgreSQL</productname>
provides nonblocking read/write access to table
data, nonblocking read/write access is not currently offered for every
index access method implemented
in <productname>PostgreSQL</productname>.
The various index types are handled as follows:
<variablelist>
<varlistentry>
<term>
B-tree, <acronym>GiST</acronym> and <acronym>SP-GiST</acronym> indexes
</term>
<listitem>
<para>
Short-term share/exclusive page-level locks are used for
read/write access. Locks are released immediately after each
index row is fetched or inserted. These index types provide
the highest concurrency without deadlock conditions.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
Hash indexes
</term>
<listitem>
<para>
Share/exclusive hash-bucket-level locks are used for read/write
access. Locks are released after the whole bucket is processed.
Bucket-level locks provide better concurrency than index-level
ones, but deadlock is possible since the locks are held longer
than one index operation.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>
<acronym>GIN</acronym> indexes
</term>
<listitem>
<para>
Short-term share/exclusive page-level locks are used for
read/write access. Locks are released immediately after each
index row is fetched or inserted. But note that insertion of a
GIN-indexed value usually produces several index key insertions
per row, so GIN might do substantial work for a single value's
insertion.
</para>
</listitem>
</varlistentry>
</variablelist>
</para>
<para>
Currently, B-tree indexes offer the best performance for concurrent
applications; since they also have more features than hash
indexes, they are the recommended index type for concurrent
applications that need to index scalar data. When dealing with
non-scalar data, B-trees are not useful, and GiST, SP-GiST or GIN
indexes should be used instead.
</para>
</sect1>
<!## XC>
<sect1 id="global-transaction-management">
<title><productname>Postgres-XC</productname>'s Global Transaction Management</title>
<indexterm>
<primary>Global Transaction Management</primary>
</indexterm>
&xconly;
<para>
The above sections
described <productname>Postgres-XC</productname>'s concurrency
control and MVCC common to <productname>PostgreSQL</productname>.
This section describes how <productname>Postgres-XC</productname>
implements global concurrency control and MVCC among multiple
Coordinators and Datanodes.
</para>
<para>
In conventional replication clusters, you can run read
transactions in parallel in multiple standby, or slave
servers. Replication servers provide read scalability. However,
you cannot issue write transactions to standby servers because
they don't have means to propagate changes safely. They cannot
maintain consistent view of database to applications for write
operations, unless you issue write transactions to single master
server.
</para>
<para>
Postgres-XC is different.
</para>
<para>
As described
in <xref linkend="intro-whatis">, <productname>Postgres-XC</productname>
is composed of GTM (Global Transaction Manager), Coordinators and
Datanodes.
</para>
<para>
In <productname>Postgres-XC</productname>, any Coordinator can
accept any transaction, regardless whether it is read only or
read/write. Transaction integrity is enforced by GTM (global
transaction manager). Because we have multiple Coordinators, each
of them can handle incoming transactions and statements in
parallel.
</para>
<para>
Analyzed statements are converted into internal plans, which
include SQL statements targeted to Datanodes. They're proxied to
each target Datanode, handled and the result will be sent back to
originating Coordinator where all the results from target
Datanodes will be combined into the result to be sent back to the
application.
</para>
<para>
Each table can be distributed or replicated as described
in <xref linkend="intro-whatis">. If you design each table's
distribution carefully, most of the statements may need to target
to just one Datanode. In this way, Coordinators and Datanodes
runs transactions in parallel which scales out both read and write
operations.
</para>
<para>
More detailed internals
about <productname>Postgres-XC</productname>'s transaction
management will be found in <xref linkend="overview">.
</para>
</sect1>
<!## end>
</chapter>