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$PostgreSQL: pgsql/doc/src/sgml/mvcc.sgml,v 2.46 2004/12/23 23:07:38 tgl Exp $
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 <chapter id="mvcc">
  <title>Concurrency Control</title>

  <indexterm>
   <primary>concurrency</primary>
  </indexterm>

  <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>

  <sect1 id="mvcc-intro">
   <title>Introduction</title>

   <indexterm>
    <primary>MVCC</primary>
   </indexterm>

   <para>
    Unlike traditional database systems which use locks for concurrency control,
    <productname>PostgreSQL</productname>
    maintains data consistency 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.
   </para>

   <para>
    The main advantage to 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.
   </para>

   <para>
    Table- and row-level locking facilities are also available in
    <productname>PostgreSQL</productname> for applications that cannot
    adapt easily to <acronym>MVCC</acronym> behavior.  However, proper
    use of <acronym>MVCC</acronym> will generally provide better
    performance than locks.
   </para>
  </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 in terms of three phenomena that must be
    prevented between concurrent transactions.  These undesirable
    phenomena 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><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 two distinct isolation levels, which correspond to the levels Read
    Committed and Serializable.  When you select the level Read
    Uncommitted you really get Read Committed, and when you select
    Repeatable Read you really get Serializable, so the actual
    isolation level may 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 two 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>

   <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>

   <para>
    <firstterm>Read Committed</firstterm>
    is the default isolation level in <productname>PostgreSQL</productname>. 
    When a transaction runs on this isolation level,
    a <command>SELECT</command> query sees only data committed before the
    query began; it never sees either uncommitted data or changes committed
    during query execution by concurrent transactions.  (However, the
    <command>SELECT</command> does see the effects of previous updates
    executed within its own transaction, even though they are not yet
    committed.)  In effect, a <command>SELECT</command> query
    sees a snapshot of the database as of the instant that that query
    begins to run.  Notice 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>, and <command>SELECT
    FOR UPDATE</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 may have already been updated (or deleted or marked for update) 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,
    starting from the updated version of the row.
   </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 that affected 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 from 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>
    Since in Read Committed mode each new command starts 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 here is whether
    or not within a <emphasis>single</> command we see 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, for applications that do complex queries and updates, it may
    be necessary to guarantee a more rigorously consistent view of the
    database than the Read Committed mode provides.
   </para>
  </sect2>

  <sect2 id="xact-serializable">
   <title>Serializable Isolation Level</title>

   <indexterm>
    <primary>transaction isolation level</primary>
    <secondary>serializable</secondary>
   </indexterm>

   <para>
    The level <firstterm>Serializable</firstterm> 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, applications using this level must
    be prepared to retry transactions due to serialization failures.
   </para>

   <para>
    When a transaction is on the serializable level,
    a <command>SELECT</command> query sees only data committed before the
    transaction began; it never sees either uncommitted data or changes
    committed
    during transaction execution by concurrent transactions.  (However, the
    <command>SELECT</command> does see the effects of previous updates
    executed within its own transaction, even though they are not yet
    committed.)  This is different from Read Committed in that the
    <command>SELECT</command>
    sees a snapshot as of the start of the transaction, not as of the start
    of the current query within the transaction.  Thus, successive
    <command>SELECT</command> commands within a single transaction always see the same
    data.
   </para>

   <para>
    <command>UPDATE</command>, <command>DELETE</command>, and <command>SELECT
    FOR UPDATE</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 may have already been updated (or deleted or marked for update) by
    another concurrent transaction by the time it is found.  In this case, the
    serializable 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 serializable transaction can proceed
    with updating the originally found row.  But if the first updater commits
    (and actually updated or deleted the row, not just selected it for update)
    then the serializable transaction will be rolled back with the message

<screen>
ERROR:  could not serialize access due to concurrent update
</screen>

    because a serializable transaction cannot modify rows changed by
    other transactions after the serializable transaction began.
   </para>

   <para>
    When the application receives this error message, it should abort
    the current transaction and then retry the whole transaction from
    the beginning.  The second time through, the transaction sees 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 may need to be retried; read-only
    transactions will never have serialization conflicts.
   </para>

   <para>
    The Serializable mode provides a rigorous guarantee that each
    transaction sees a wholly consistent view of the database.  However,
    the application has to be prepared to retry transactions when concurrent
    updates make it impossible to sustain the illusion of serial execution.
    Since the cost of redoing complex transactions may be significant,
    this mode is recommended only when updating transactions contain logic
    sufficiently complex that they may give wrong answers in Read
    Committed mode.  Most commonly, Serializable mode is necessary when
    a transaction executes several successive commands that must see
    identical views of the database.
   </para>

   <sect3 id="mvcc-serializability">
    <title>Serializable Isolation versus True Serializability</title>

   <indexterm>
    <primary>serializability</primary>
   </indexterm>

   <indexterm>
    <primary>predicate locking</primary>
   </indexterm>

   <para>
    The intuitive meaning (and mathematical definition) of
    <quote>serializable</> execution is that any two successfully committed
    concurrent transactions will appear to have executed strictly serially,
    one after the other &mdash; although which one appeared to occur first may
    not be predictable in advance.  It is important to realize that forbidding
    the undesirable behaviors listed in <xref linkend="mvcc-isolevel-table">
    is not sufficient to guarantee true serializability, and in fact
    <productname>PostgreSQL</productname>'s Serializable mode <emphasis>does
    not guarantee serializable execution in this sense</>.  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</> = 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</> = 1.  Then both transactions commit.  None of
    the listed undesirable behaviors have occurred, yet we have a result
    that could not have occurred in either order serially.  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 mathematical serializability, it is necessary for
    a database system to enforce <firstterm>predicate locking</>, which
    means that a transaction cannot insert or modify a row that would
    have matched the <literal>WHERE</> condition of a query in another concurrent
    transaction.  For example, once transaction A has executed the query
    <literal>SELECT ... WHERE class = 1</>, a predicate-locking system
    would forbid transaction B from inserting any new row with class 1
    until A has committed.
     <footnote>
      <para>
       Essentially, a predicate-locking system prevents phantom reads
       by restricting what is written, whereas MVCC prevents them by
       restricting what is read.
      </para>
     </footnote>
    Such a locking system is complex to
    implement and extremely expensive in execution, since every session must
    be aware of the details of every query executed by every concurrent
    transaction.  And this large expense is mostly wasted, since in
    practice most applications do not do the sorts of things that could
    result in problems.  (Certainly the example above is rather contrived
    and unlikely to represent real software.)  Accordingly,
    <productname>PostgreSQL</productname> does not implement predicate
    locking, and so far as we are aware no other production DBMS does either.
   </para>

   <para>
    In those cases where the possibility of nonserializable execution
    is a real hazard, problems can be prevented by appropriate use of
    explicit locking.  Further discussion appears in the following
    sections.
   </para>
  </sect3>
  </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>ALTER TABLE</> cannot be
    executed concurrently with other operations on the same table.)
   </para>

   <para>
    To examine a list of the currently outstanding locks in a database
    server, use the <structname>pg_locks</structname> system view
    (<xref linkend="view-pg-locks">). 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 &mdash; 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.  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 may acquire
    <literal>ACCESS EXCLUSIVE</literal> lock and later acquire
    <literal>ACCESS SHARE</literal> lock on the same table.)  Non-conflicting
    lock modes may 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).
    Once acquired, a lock is held till end of transaction.
   </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 commands <command>SELECT</command> and
	 <command>ANALYZE</command> acquire a lock of this mode on
	 referenced tables.  In general, any query that only 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> command acquires 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</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 modifies the 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>).
	</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>.
	</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.
	</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 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>, <command>DROP
	 TABLE</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</option>)
       statement.
      </para>
     </tip>

   </sect2>

   <sect2 id="locking-rows">
    <title>Row-Level Locks</title>

    <para>
     In addition to table-level locks, there are row-level locks.
     A row-level lock on a specific row is automatically acquired when the
     row is updated (or deleted or marked for update).  The lock is held
     until the transaction commits or rolls back.
     Row-level locks do not affect data
     querying; they block <emphasis>writers to the same row</emphasis>
     only.  To acquire a row-level lock on a row without actually
     modifying the row, select the row with <command>SELECT FOR
     UPDATE</command>.  Note that once a particular row-level lock is
     acquired, the transaction may update the row multiple times without
     fear of conflicts.
    </para>

    <para>
     <productname>PostgreSQL</productname> doesn't remember any
     information about modified rows in memory, so it has no limit to
     the number of rows locked at one time.  However, locking a row
     may cause a disk write; thus, for example, <command>SELECT FOR
     UPDATE</command> will modify selected rows to mark them 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 we mention them 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 on.)
    </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 there are two concurrent
     transactions modifying 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 highest mode that will be
     needed for that object.  If it is not feasible to verify this in
     advance, then deadlocks may be handled on-the-fly by retrying
     transactions that are aborted due to deadlock.
    </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>
   </sect2>
  </sect1>

  <sect1 id="applevel-consistency">
   <title>Data Consistency Checks at the Application Level</title>

   <para>
    Because readers in <productname>PostgreSQL</productname>
    do not lock data, regardless of
    transaction isolation level, data read by one transaction can be
    overwritten by another concurrent transaction. In other words,
    if a row is returned by <command>SELECT</command> it doesn't mean that
    the row is still current at the instant it is returned (i.e., sometime
    after the current query began).  The row might have been modified or
    deleted by an already-committed transaction that committed after this one
    started.
    Even if the row is still valid <quote>now</quote>, it could be changed or
    deleted
    before the current transaction does a commit or rollback.
   </para>

   <para>
    Another way to think about it is that each
    transaction sees a snapshot of the database contents, and concurrently
    executing transactions may very well see different snapshots.  So the
    whole concept of <quote>now</quote> is somewhat ill-defined anyway.
    This is not normally
    a big problem if the client applications are isolated from each other,
    but if the clients can communicate via channels outside the database
    then serious confusion may ensue.
   </para>

   <para>
    To ensure the current validity of a row and protect it against
    concurrent updates one must use <command>SELECT FOR
    UPDATE</command> or an appropriate <command>LOCK TABLE</command>
    statement.  (<command>SELECT FOR UPDATE</command> locks 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.
    (Before version 6.5 <productname>PostgreSQL</productname> used
    read locks, and so this above consideration is also relevant when
    upgrading from <productname>PostgreSQL</productname> versions
    prior to 6.5.)
   </para>

   <para>
    Global validity checks require extra thought under <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 under
    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 serializable transaction will give an accurate picture of the
    effects of transactions that committed before the serializable transaction
    started &mdash; but one might legitimately wonder whether the answer is still
    relevant by the time it is delivered.  If the serializable 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 use
    Read Committed mode, or in Serializable mode be careful to obtain the
    lock(s) before performing queries.  A lock obtained by a
    serializable 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 may predate some now-committed
    changes in the table.  A serializable 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's possible to obtain locks explicitly before the snapshot is
    frozen.
   </para>
  </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 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.  B-tree indexes provide
	the highest concurrency without deadlock conditions.
       </para>
      </listitem>
     </varlistentry>

     <varlistentry>
      <term>
       <acronym>GiST</acronym> and R-tree indexes
      </term>
      <listitem>
       <para>
	Share/exclusive index-level locks are used for read/write access.
	Locks are released after the command is done.
       </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>
    </variablelist>
   </para>

   <para>
    In short, 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 obviously cannot be used; in that
    situation, application developers should be aware of the
    relatively poor concurrent performance of GiST and R-tree
    indexes.
   </para>
  </sect1>
 </chapter>

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