<!-- doc/src/sgml/parallel.sgml -->

 <chapter id="parallel-query">
  <title>Parallel Query</title>

  <indexterm zone="parallel-query">
   <primary>parallel query</primary>
  </indexterm>

  <para>
   <productname>PostgreSQL</productname> can devise query plans which can leverage
   multiple CPUs in order to answer queries faster.  This feature is known
   as parallel query.  Many queries cannot benefit from parallel query, either
   due to limitations of the current implementation or because there is no
   imaginable query plan which is any faster than the serial query plan.
   However, for queries that can benefit, the speedup from parallel query
   is often very significant.  Many queries can run more than twice as fast
   when using parallel query, and some queries can run four times faster or
   even more.  Queries that touch a large amount of data but return only a
   few rows to the user will typically benefit most.  This chapter explains
   some details of how parallel query works and in which situations it can be
   used so that users who wish to make use of it can understand what to expect.
  </para>

 <sect1 id="how-parallel-query-works">
  <title>How Parallel Query Works</title>

   <para>
    When the optimizer determines that parallel query is the fastest execution
    strategy for a particular query, it will create a query plan which includes
    a <firstterm>Gather</firstterm> or <firstterm>Gather Merge</firstterm>
    node.  Here is a simple example:

<screen>
EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%';
                                     QUERY PLAN                                      
-------------------------------------------------------------------------------------
 Gather  (cost=1000.00..217018.43 rows=1 width=97)
   Workers Planned: 2
   ->  Parallel Seq Scan on pgbench_accounts  (cost=0.00..216018.33 rows=1 width=97)
         Filter: (filler ~~ '%x%'::text)
(4 rows)
</screen>
   </para>

   <para>
    In all cases, the <literal>Gather</literal> or
    <literal>Gather Merge</literal> node will have exactly one
    child plan, which is the portion of the plan that will be executed in
    parallel.  If the <literal>Gather</literal> or <literal>Gather Merge</literal> node is
    at the very top of the plan tree, then the entire query will execute in
    parallel.  If it is somewhere else in the plan tree, then only the portion
    of the plan below it will run in parallel.  In the example above, the
    query accesses only one table, so there is only one plan node other than
    the <literal>Gather</literal> node itself; since that plan node is a child of the
    <literal>Gather</literal> node, it will run in parallel.
   </para>

   <para>
    <link linkend="using-explain">Using EXPLAIN</link>, you can see the number of
    workers chosen by the planner.  When the <literal>Gather</literal> node is reached
    during query execution, the process which is implementing the user's
    session will request a number of <link linkend="bgworker">background
    worker processes</link> equal to the number
    of workers chosen by the planner.  The number of background workers that
    the planner will consider using is limited to at most
    <xref linkend="guc-max-parallel-workers-per-gather"/>.  The total number
    of background workers that can exist at any one time is limited by both
    <xref linkend="guc-max-worker-processes"/> and
    <xref linkend="guc-max-parallel-workers"/>.  Therefore, it is possible for a
    parallel query to run with fewer workers than planned, or even with
    no workers at all.  The optimal plan may depend on the number of workers
    that are available, so this can result in poor query performance.  If this
    occurrence is frequent, consider increasing
    <varname>max_worker_processes</varname> and <varname>max_parallel_workers</varname>
    so that more workers can be run simultaneously or alternatively reducing
    <varname>max_parallel_workers_per_gather</varname> so that the planner
    requests fewer workers.
   </para>

   <para>
    Every background worker process which is successfully started for a given
    parallel query will execute the parallel portion of the plan.  The leader
    will also execute that portion of the plan, but it has an additional
    responsibility: it must also read all of the tuples generated by the
    workers.  When the parallel portion of the plan generates only a small
    number of tuples, the leader will often behave very much like an additional
    worker, speeding up query execution.  Conversely, when the parallel portion
    of the plan generates a large number of tuples, the leader may be almost
    entirely occupied with reading the tuples generated by the workers and
    performing any further processing steps which are required by plan nodes
    above the level of the <literal>Gather</literal> node or
    <literal>Gather Merge</literal> node.  In such cases, the leader will
    do very little of the work of executing the parallel portion of the plan.
   </para>

   <para>
    When the node at the top of the parallel portion of the plan is
    <literal>Gather Merge</literal> rather than <literal>Gather</literal>, it indicates that
    each process executing the parallel portion of the plan is producing
    tuples in sorted order, and that the leader is performing an
    order-preserving merge.  In contrast, <literal>Gather</literal> reads tuples
    from the workers in whatever order is convenient, destroying any sort
    order that may have existed.
   </para>   
 </sect1>

 <sect1 id="when-can-parallel-query-be-used">
  <title>When Can Parallel Query Be Used?</title>

  <para>
    There are several settings which can cause the query planner not to
    generate a parallel query plan under any circumstances.  In order for
    any parallel query plans whatsoever to be generated, the following
    settings must be configured as indicated.
  </para>

  <itemizedlist>
    <listitem>
      <para>
        <xref linkend="guc-max-parallel-workers-per-gather"/> must be set to a
        value which is greater than zero. This is a special case of the more
        general principle that no more workers should be used than the number
        configured via <varname>max_parallel_workers_per_gather</varname>.
      </para>
    </listitem>

    <listitem>
      <para>
        <xref linkend="guc-dynamic-shared-memory-type"/> must be set to a
        value other than <literal>none</literal>.  Parallel query requires dynamic
        shared memory in order to pass data between cooperating processes.
      </para>
    </listitem>
  </itemizedlist>

  <para>
    In addition, the system must not be running in single-user mode.  Since
    the entire database system is running in single process in this situation,
    no background workers will be available.
  </para>

  <para>
    Even when it is in general possible for parallel query plans to be
    generated, the planner will not generate them for a given query
    if any of the following are true:
  </para>

  <itemizedlist>
    <listitem>
      <para>
        The query writes any data or locks any database rows. If a query
        contains a data-modifying operation either at the top level or within
        a CTE, no parallel plans for that query will be generated.  As an
        exception, the commands <literal>CREATE TABLE ... AS</literal>, <literal>SELECT
        INTO</literal>, and <literal>CREATE MATERIALIZED VIEW</literal> which create a new
        table and populate it can use a parallel plan.
      </para>
    </listitem>

    <listitem>
      <para>
        The query might be suspended during execution. In any situation in
        which the system thinks that partial or incremental execution might
        occur, no parallel plan is generated. For example, a cursor created
        using <link linkend="sql-declare">DECLARE CURSOR</link> will never use
        a parallel plan. Similarly, a PL/pgSQL loop of the form
        <literal>FOR x IN query LOOP .. END LOOP</literal> will never use a
        parallel plan, because the parallel query system is unable to verify
        that the code in the loop is safe to execute while parallel query is
        active.
      </para>
    </listitem>

    <listitem>
      <para>
        The query uses any function marked <literal>PARALLEL UNSAFE</literal>.
        Most system-defined functions are <literal>PARALLEL SAFE</literal>,
        but user-defined functions are marked <literal>PARALLEL
        UNSAFE</literal> by default. See the discussion of
        <xref linkend="parallel-safety"/>.
      </para>
    </listitem>

    <listitem>
      <para>
        The query is running inside of another query that is already parallel.
        For example, if a function called by a parallel query issues an SQL
        query itself, that query will never use a parallel plan. This is a
        limitation of the current implementation, but it may not be desirable
        to remove this limitation, since it could result in a single query
        using a very large number of processes.
      </para>
    </listitem>

    <listitem>
      <para>
        The transaction isolation level is serializable.  This is
        a limitation of the current implementation.
      </para>
    </listitem>
  </itemizedlist>

  <para>
    Even when parallel query plan is generated for a particular query, there
    are several circumstances under which it will be impossible to execute
    that plan in parallel at execution time.  If this occurs, the leader
    will execute the portion of the plan below the <literal>Gather</literal>
    node entirely by itself, almost as if the <literal>Gather</literal> node were
    not present.  This will happen if any of the following conditions are met:
  </para>

  <itemizedlist>
    <listitem>
      <para>
        No background workers can be obtained because of the limitation that
        the total number of background workers cannot exceed
        <xref linkend="guc-max-worker-processes"/>.
      </para>
    </listitem>

    <listitem>
      <para>
        No background workers can be obtained because of the limitation that
        the total number of background workers launched for purposes of
        parallel query cannot exceed <xref linkend="guc-max-parallel-workers"/>.
      </para>
    </listitem>

    <listitem>
      <para>
        The client sends an Execute message with a non-zero fetch count.
        See the discussion of the
        <link linkend="protocol-flow-ext-query">extended query protocol</link>.
        Since <link linkend="libpq">libpq</link> currently provides no way to
        send such a message, this can only occur when using a client that
        does not rely on libpq.  If this is a frequent
        occurrence, it may be a good idea to set
        <xref linkend="guc-max-parallel-workers-per-gather"/> to zero in
        sessions where it is likely, so as to avoid generating query plans
        that may be suboptimal when run serially.
      </para>
    </listitem>

    <listitem>
      <para>
        The transaction isolation level is serializable.  This situation
        does not normally arise, because parallel query plans are not
        generated when the transaction isolation level is serializable.
        However, it can happen if the transaction isolation level is changed to
        serializable after the plan is generated and before it is executed.
      </para>
    </listitem>
  </itemizedlist>
 </sect1>

 <sect1 id="parallel-plans">
  <title>Parallel Plans</title>

  <para>
    Because each worker executes the parallel portion of the plan to
    completion, it is not possible to simply take an ordinary query plan
    and run it using multiple workers.  Each worker would produce a full
    copy of the output result set, so the query would not run any faster
    than normal but would produce incorrect results.  Instead, the parallel
    portion of the plan must be what is known internally to the query
    optimizer as a <firstterm>partial plan</firstterm>; that is, it must be constructed
    so that each process which executes the plan will generate only a
    subset of the output rows in such a way that each required output row
    is guaranteed to be generated by exactly one of the cooperating processes.
    Generally, this means that the scan on the driving table of the query
    must be a parallel-aware scan.
  </para>

 <sect2 id="parallel-scans">
  <title>Parallel Scans</title>

  <para>
    The following types of parallel-aware table scans are currently supported.

  <itemizedlist>
    <listitem>
      <para>
        In a <emphasis>parallel sequential scan</emphasis>, the table's blocks will
        be divided among the cooperating processes.  Blocks are handed out one
        at a time, so that access to the table remains sequential.
      </para>
    </listitem>
    <listitem>
      <para>
        In a <emphasis>parallel bitmap heap scan</emphasis>, one process is chosen
        as the leader.  That process performs a scan of one or more indexes
        and builds a bitmap indicating which table blocks need to be visited.
        These blocks are then divided among the cooperating processes as in
        a parallel sequential scan.  In other words, the heap scan is performed
        in parallel, but the underlying index scan is not.
      </para>
    </listitem>
    <listitem>
      <para>
        In a <emphasis>parallel index scan</emphasis> or <emphasis>parallel index-only
        scan</emphasis>, the cooperating processes take turns reading data from the
        index.  Currently, parallel index scans are supported only for
        btree indexes.  Each process will claim a single index block and will
        scan and return all tuples referenced by that block; other process can
        at the same time be returning tuples from a different index block.
        The results of a parallel btree scan are returned in sorted order
        within each worker process.
      </para>
    </listitem>
  </itemizedlist>

    Other scan types, such as scans of non-btree indexes, may support
    parallel scans in the future.
  </para>
 </sect2>

 <sect2 id="parallel-joins">
  <title>Parallel Joins</title>

  <para>
    Just as in a non-parallel plan, the driving table may be joined to one or
    more other tables using a nested loop, hash join, or merge join.  The
    inner side of the join may be any kind of non-parallel plan that is
    otherwise supported by the planner provided that it is safe to run within
    a parallel worker.  Depending on the join type, the inner side may also be
    a parallel plan.
  </para>

  <itemizedlist>
    <listitem>
      <para>
        In a <emphasis>nested loop join</emphasis>, the inner side is always
        non-parallel.  Although it is executed in full, this is efficient if
        the inner side is an index scan, because the outer tuples and thus
        the loops that look up values in the index are divided over the
        cooperating processes.
      </para>
    </listitem>
    <listitem>
      <para>
        In a <emphasis>merge join</emphasis>, the inner side is always
        a non-parallel plan and therefore executed in full.  This may be
        inefficient, especially if a sort must be performed, because the work
        and resulting data are duplicated in every cooperating process.
      </para>
    </listitem>
    <listitem>
      <para>
        In a <emphasis>hash join</emphasis> (without the "parallel" prefix),
        the inner side is executed in full by every cooperating process
        to build identical copies of the hash table.  This may be inefficient
        if the hash table is large or the plan is expensive.  In a
        <emphasis>parallel hash join</emphasis>, the inner side is a
        <emphasis>parallel hash</emphasis> that divides the work of building
        a shared hash table over the cooperating processes.
      </para>
    </listitem>
  </itemizedlist>
 </sect2>

 <sect2 id="parallel-aggregation">
  <title>Parallel Aggregation</title>
  <para>
    <productname>PostgreSQL</productname> supports parallel aggregation by aggregating in
    two stages.  First, each process participating in the parallel portion of
    the query performs an aggregation step, producing a partial result for
    each group of which that process is aware.  This is reflected in the plan
    as a <literal>Partial Aggregate</literal> node.  Second, the partial results are
    transferred to the leader via <literal>Gather</literal> or <literal>Gather
    Merge</literal>.  Finally, the leader re-aggregates the results across all
    workers in order to produce the final result.  This is reflected in the
    plan as a <literal>Finalize Aggregate</literal> node.
  </para>
  
  <para>
    Because the <literal>Finalize Aggregate</literal> node runs on the leader
    process, queries which produce a relatively large number of groups in
    comparison to the number of input rows will appear less favorable to the
    query planner. For example, in the worst-case scenario the number of
    groups seen by the <literal>Finalize Aggregate</literal> node could be as many as
    the number of input rows which were seen by all worker processes in the
    <literal>Partial Aggregate</literal> stage. For such cases, there is clearly
    going to be no performance benefit to using parallel aggregation. The
    query planner takes this into account during the planning process and is
    unlikely to choose parallel aggregate in this scenario.
  </para>

  <para>
    Parallel aggregation is not supported in all situations.  Each aggregate
    must be <link linkend="parallel-safety">safe</link> for parallelism and must
    have a combine function.  If the aggregate has a transition state of type
    <literal>internal</literal>, it must have serialization and deserialization
    functions.  See <xref linkend="sql-createaggregate"/> for more details.
    Parallel aggregation is not supported if any aggregate function call
    contains <literal>DISTINCT</literal> or <literal>ORDER BY</literal> clause and is also
    not supported for ordered set aggregates or when  the query involves
    <literal>GROUPING SETS</literal>.  It can only be used when all joins involved in
    the query are also part of the parallel portion of the plan.
  </para>

 </sect2>

 <sect2 id="parallel-append">
  <title>Parallel Append</title>

  <para>
    Whenever <productname>PostgreSQL</productname> needs to combine rows
    from multiple sources into a single result set, it uses an
    <literal>Append</literal> or <literal>MergeAppend</literal> plan node.
    This commonly happens when implementing <literal>UNION ALL</literal> or
    when scanning a partitioned table.  Such nodes can be used in parallel
    plans just as they can in any other plan.  However, in a parallel plan,
    the planner may instead use a <literal>Parallel Append</literal> node.
  </para>

  <para>
    When an <literal>Append</literal> node is used in a parallel plan, each
    process will execute the child plans in the order in which they appear,
    so that all participating processes cooperate to execute the first child
    plan until it is complete and then move to the second plan at around the
    same time.  When a <literal>Parallel Append</literal> is used instead, the
    executor will instead spread out the participating processes as evenly as
    possible across its child plans, so that multiple child plans are executed
    simultaneously.  This avoids contention, and also avoids paying the startup
    cost of a child plan in those processes that never execute it.
  </para>

  <para>
    Also, unlike a regular <literal>Append</literal> node, which can only have
    partial children when used within a parallel plan, a <literal>Parallel
    Append</literal> node can have both partial and non-partial child plans.
    Non-partial children will be scanned by only a single process, since
    scanning them more than once would produce duplicate results.  Plans that
    involve appending multiple results sets can therefore achieve
    coarse-grained parallelism even when efficient partial plans are not
    available.  For example, consider a query against a partitioned table
    which can be only be implemented efficiently by using an index that does
    not support parallel scans.  The planner might choose a <literal>Parallel
    Append</literal> of regular <literal>Index Scan</literal> plans; each
    individual index scan would have to be executed to completion by a single
    process, but different scans could be performed at the same time by
    different processes.
  </para>

  <para>
    <xref linkend="guc-enable-parallel-append" /> can be used to disable
    this feature.
  </para>
 </sect2>

 <sect2 id="parallel-plan-tips">
  <title>Parallel Plan Tips</title>

  <para>
    If a query that is expected to do so does not produce a parallel plan,
    you can try reducing <xref linkend="guc-parallel-setup-cost"/> or
    <xref linkend="guc-parallel-tuple-cost"/>.  Of course, this plan may turn
    out to be slower than the serial plan which the planner preferred, but
    this will not always be the case.  If you don't get a parallel
    plan even with very small values of these settings (e.g. after setting
    them both to zero), there may be some reason why the query planner is
    unable to generate a parallel plan for your query.  See
    <xref linkend="when-can-parallel-query-be-used"/> and
    <xref linkend="parallel-safety"/> for information on why this may be
    the case.
  </para>

  <para>
    When executing a parallel plan, you can use <literal>EXPLAIN (ANALYZE,
    VERBOSE)</literal> to display per-worker statistics for each plan node.
    This may be useful in determining whether the work is being evenly
    distributed between all plan nodes and more generally in understanding the
    performance characteristics of the plan.
  </para>

 </sect2>
 </sect1>

 <sect1 id="parallel-safety">
  <title>Parallel Safety</title>

  <para>
    The planner classifies operations involved in a query as either
    <firstterm>parallel safe</firstterm>, <firstterm>parallel restricted</firstterm>,
    or <firstterm>parallel unsafe</firstterm>.  A parallel safe operation is one which
    does not conflict with the use of parallel query.  A parallel restricted
    operation is one which cannot be performed in a parallel worker, but which
    can be performed in the leader while parallel query is in use.  Therefore,
    parallel restricted operations can never occur below a <literal>Gather</literal>
    or <literal>Gather Merge</literal> node, but can occur elsewhere in a plan which
    contains such a node.  A parallel unsafe operation is one which cannot
    be performed while parallel query is in use, not even in the leader.
    When a query contains anything which is parallel unsafe, parallel query
    is completely disabled for that query.
  </para>

  <para>
    The following operations are always parallel restricted.
  </para>

  <itemizedlist>
    <listitem>
      <para>
        Scans of common table expressions (CTEs).
      </para>
    </listitem>

    <listitem>
      <para>
        Scans of temporary tables.
      </para>
    </listitem>

    <listitem>
      <para>
        Scans of foreign tables, unless the foreign data wrapper has
        an <literal>IsForeignScanParallelSafe</literal> API which indicates otherwise.
      </para>
    </listitem>

    <listitem>
      <para>
        Plan nodes to which an <literal>InitPlan</literal> is attached.
      </para>
    </listitem>

    <listitem>
      <para>
        Plan nodes which reference a correlated <literal>SubPlan</literal>.
      </para>
    </listitem>
  </itemizedlist>

 <sect2 id="parallel-labeling">
  <title>Parallel Labeling for Functions and Aggregates</title>

  <para>
    The planner cannot automatically determine whether a user-defined
    function or aggregate is parallel safe, parallel restricted, or parallel
    unsafe, because this would require predicting every operation which the
    function could possibly perform.  In general, this is equivalent to the
    Halting Problem and therefore impossible.  Even for simple functions
    where it could conceivably be done, we do not try, since this would be expensive
    and error-prone.  Instead, all user-defined functions are assumed to
    be parallel unsafe unless otherwise marked.  When using
    <xref linkend="sql-createfunction"/> or
    <xref linkend="sql-alterfunction"/>, markings can be set by specifying
    <literal>PARALLEL SAFE</literal>, <literal>PARALLEL RESTRICTED</literal>, or
    <literal>PARALLEL UNSAFE</literal> as appropriate.  When using
    <xref linkend="sql-createaggregate"/>, the
    <literal>PARALLEL</literal> option can be specified with <literal>SAFE</literal>,
    <literal>RESTRICTED</literal>, or <literal>UNSAFE</literal> as the corresponding value.
  </para>

  <para>
    Functions and aggregates must be marked <literal>PARALLEL UNSAFE</literal> if
    they write to the database, access sequences, change the transaction state
    even temporarily (e.g. a PL/pgSQL function which establishes an
    <literal>EXCEPTION</literal> block to catch errors), or make persistent changes to
    settings.  Similarly, functions must be marked <literal>PARALLEL
    RESTRICTED</literal> if they access temporary tables, client connection state,
    cursors, prepared statements, or miscellaneous backend-local state which
    the system cannot synchronize across workers. For example,
    <literal>setseed</literal> and <literal>random</literal> are parallel restricted for
    this last reason.
  </para>

  <para>
    In general, if a function is labeled as being safe when it is restricted or
    unsafe, or if it is labeled as being restricted when it is in fact unsafe,
    it may throw errors or produce wrong answers when used in a parallel query.
    C-language functions could in theory exhibit totally undefined behavior if
    mislabeled, since there is no way for the system to protect itself against
    arbitrary C code, but in most likely cases the result will be no worse than
    for any other function. If in doubt, it is probably best to label functions
    as <literal>UNSAFE</literal>.
  </para>

  <para>
    If a function executed within a parallel worker acquires locks which are
    not held by the leader, for example by querying a table not referenced in
    the query, those locks will be released at worker exit, not end of
    transaction. If you write a function which does this, and this behavior
    difference is important to you, mark such functions as
    <literal>PARALLEL RESTRICTED</literal>
    to ensure that they execute only in the leader.
  </para>

  <para>
    Note that the query planner does not consider deferring the evaluation of
    parallel-restricted functions or aggregates involved in the query in
    order to obtain a superior plan.  So, for example, if a <literal>WHERE</literal>
    clause applied to a particular table is parallel restricted, the query
    planner will not consider performing a scan of that table in the parallel
    portion of a plan.  In some cases, it would be
    possible (and perhaps even efficient) to include the scan of that table in
    the parallel portion of the query and defer the evaluation of the
    <literal>WHERE</literal> clause so that it happens above the <literal>Gather</literal>
    node.  However, the planner does not do this.
  </para>

 </sect2>

 </sect1>

 </chapter>
