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2 <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>15.3. Parallel Plans</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="when-can-parallel-query-be-used.html" title="15.2. When Can Parallel Query Be Used?" /><link rel="next" href="parallel-safety.html" title="15.4. Parallel Safety" /></head><body id="docContent" class="container-fluid col-10"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">15.3. Parallel Plans</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="when-can-parallel-query-be-used.html" title="15.2. When Can Parallel Query Be Used?">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="parallel-query.html" title="Chapter 15. Parallel Query">Up</a></td><th width="60%" align="center">Chapter 15. Parallel Query</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 18.0 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="parallel-safety.html" title="15.4. Parallel Safety">Next</a></td></tr></table><hr /></div><div class="sect1" id="PARALLEL-PLANS"><div class="titlepage"><div><div><h2 class="title" style="clear: both">15.3. Parallel Plans <a href="#PARALLEL-PLANS" class="id_link">#</a></h2></div></div></div><div class="toc"><dl class="toc"><dt><span class="sect2"><a href="parallel-plans.html#PARALLEL-SCANS">15.3.1. Parallel Scans</a></span></dt><dt><span class="sect2"><a href="parallel-plans.html#PARALLEL-JOINS">15.3.2. Parallel Joins</a></span></dt><dt><span class="sect2"><a href="parallel-plans.html#PARALLEL-AGGREGATION">15.3.3. Parallel Aggregation</a></span></dt><dt><span class="sect2"><a href="parallel-plans.html#PARALLEL-APPEND">15.3.4. Parallel Append</a></span></dt><dt><span class="sect2"><a href="parallel-plans.html#PARALLEL-PLAN-TIPS">15.3.5. Parallel Plan Tips</a></span></dt></dl></div><p>
3 Because each worker executes the parallel portion of the plan to
4 completion, it is not possible to simply take an ordinary query plan
5 and run it using multiple workers. Each worker would produce a full
6 copy of the output result set, so the query would not run any faster
7 than normal but would produce incorrect results. Instead, the parallel
8 portion of the plan must be what is known internally to the query
9 optimizer as a <em class="firstterm">partial plan</em>; that is, it must be constructed
10 so that each process that executes the plan will generate only a
11 subset of the output rows in such a way that each required output row
12 is guaranteed to be generated by exactly one of the cooperating processes.
13 Generally, this means that the scan on the driving table of the query
14 must be a parallel-aware scan.
15 </p><div class="sect2" id="PARALLEL-SCANS"><div class="titlepage"><div><div><h3 class="title">15.3.1. Parallel Scans <a href="#PARALLEL-SCANS" class="id_link">#</a></h3></div></div></div><p>
16 The following types of parallel-aware table scans are currently supported.
18 </p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
19 In a <span class="emphasis"><em>parallel sequential scan</em></span>, the table's blocks will
20 be divided into ranges and shared among the cooperating processes. Each
21 worker process will complete the scanning of its given range of blocks before
22 requesting an additional range of blocks.
23 </p></li><li class="listitem"><p>
24 In a <span class="emphasis"><em>parallel bitmap heap scan</em></span>, one process is chosen
25 as the leader. That process performs a scan of one or more indexes
26 and builds a bitmap indicating which table blocks need to be visited.
27 These blocks are then divided among the cooperating processes as in
28 a parallel sequential scan. In other words, the heap scan is performed
29 in parallel, but the underlying index scan is not.
30 </p></li><li class="listitem"><p>
31 In a <span class="emphasis"><em>parallel index scan</em></span> or <span class="emphasis"><em>parallel index-only
32 scan</em></span>, the cooperating processes take turns reading data from the
33 index. Currently, parallel index scans are supported only for
34 btree indexes. Each process will claim a single index block and will
35 scan and return all tuples referenced by that block; other processes can
36 at the same time be returning tuples from a different index block.
37 The results of a parallel btree scan are returned in sorted order
38 within each worker process.
39 </p></li></ul></div><p>
41 Other scan types, such as scans of non-btree indexes, may support
42 parallel scans in the future.
43 </p></div><div class="sect2" id="PARALLEL-JOINS"><div class="titlepage"><div><div><h3 class="title">15.3.2. Parallel Joins <a href="#PARALLEL-JOINS" class="id_link">#</a></h3></div></div></div><p>
44 Just as in a non-parallel plan, the driving table may be joined to one or
45 more other tables using a nested loop, hash join, or merge join. The
46 inner side of the join may be any kind of non-parallel plan that is
47 otherwise supported by the planner provided that it is safe to run within
48 a parallel worker. Depending on the join type, the inner side may also be
50 </p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
51 In a <span class="emphasis"><em>nested loop join</em></span>, the inner side is always
52 non-parallel. Although it is executed in full, this is efficient if
53 the inner side is an index scan, because the outer tuples and thus
54 the loops that look up values in the index are divided over the
55 cooperating processes.
56 </p></li><li class="listitem"><p>
57 In a <span class="emphasis"><em>merge join</em></span>, the inner side is always
58 a non-parallel plan and therefore executed in full. This may be
59 inefficient, especially if a sort must be performed, because the work
60 and resulting data are duplicated in every cooperating process.
61 </p></li><li class="listitem"><p>
62 In a <span class="emphasis"><em>hash join</em></span> (without the "parallel" prefix),
63 the inner side is executed in full by every cooperating process
64 to build identical copies of the hash table. This may be inefficient
65 if the hash table is large or the plan is expensive. In a
66 <span class="emphasis"><em>parallel hash join</em></span>, the inner side is a
67 <span class="emphasis"><em>parallel hash</em></span> that divides the work of building
68 a shared hash table over the cooperating processes.
69 </p></li></ul></div></div><div class="sect2" id="PARALLEL-AGGREGATION"><div class="titlepage"><div><div><h3 class="title">15.3.3. Parallel Aggregation <a href="#PARALLEL-AGGREGATION" class="id_link">#</a></h3></div></div></div><p>
70 <span class="productname">PostgreSQL</span> supports parallel aggregation by aggregating in
71 two stages. First, each process participating in the parallel portion of
72 the query performs an aggregation step, producing a partial result for
73 each group of which that process is aware. This is reflected in the plan
74 as a <code class="literal">Partial Aggregate</code> node. Second, the partial results are
75 transferred to the leader via <code class="literal">Gather</code> or <code class="literal">Gather
76 Merge</code>. Finally, the leader re-aggregates the results across all
77 workers in order to produce the final result. This is reflected in the
78 plan as a <code class="literal">Finalize Aggregate</code> node.
80 Because the <code class="literal">Finalize Aggregate</code> node runs on the leader
81 process, queries that produce a relatively large number of groups in
82 comparison to the number of input rows will appear less favorable to the
83 query planner. For example, in the worst-case scenario the number of
84 groups seen by the <code class="literal">Finalize Aggregate</code> node could be as many as
85 the number of input rows that were seen by all worker processes in the
86 <code class="literal">Partial Aggregate</code> stage. For such cases, there is clearly
87 going to be no performance benefit to using parallel aggregation. The
88 query planner takes this into account during the planning process and is
89 unlikely to choose parallel aggregate in this scenario.
91 Parallel aggregation is not supported in all situations. Each aggregate
92 must be <a class="link" href="parallel-safety.html" title="15.4. Parallel Safety">safe</a> for parallelism and must
93 have a combine function. If the aggregate has a transition state of type
94 <code class="literal">internal</code>, it must have serialization and deserialization
95 functions. See <a class="xref" href="sql-createaggregate.html" title="CREATE AGGREGATE"><span class="refentrytitle">CREATE AGGREGATE</span></a> for more details.
96 Parallel aggregation is not supported if any aggregate function call
97 contains <code class="literal">DISTINCT</code> or <code class="literal">ORDER BY</code> clause and is also
98 not supported for ordered set aggregates or when the query involves
99 <code class="literal">GROUPING SETS</code>. It can only be used when all joins involved in
100 the query are also part of the parallel portion of the plan.
101 </p></div><div class="sect2" id="PARALLEL-APPEND"><div class="titlepage"><div><div><h3 class="title">15.3.4. Parallel Append <a href="#PARALLEL-APPEND" class="id_link">#</a></h3></div></div></div><p>
102 Whenever <span class="productname">PostgreSQL</span> needs to combine rows
103 from multiple sources into a single result set, it uses an
104 <code class="literal">Append</code> or <code class="literal">MergeAppend</code> plan node.
105 This commonly happens when implementing <code class="literal">UNION ALL</code> or
106 when scanning a partitioned table. Such nodes can be used in parallel
107 plans just as they can in any other plan. However, in a parallel plan,
108 the planner may instead use a <code class="literal">Parallel Append</code> node.
110 When an <code class="literal">Append</code> node is used in a parallel plan, each
111 process will execute the child plans in the order in which they appear,
112 so that all participating processes cooperate to execute the first child
113 plan until it is complete and then move to the second plan at around the
114 same time. When a <code class="literal">Parallel Append</code> is used instead, the
115 executor will instead spread out the participating processes as evenly as
116 possible across its child plans, so that multiple child plans are executed
117 simultaneously. This avoids contention, and also avoids paying the startup
118 cost of a child plan in those processes that never execute it.
120 Also, unlike a regular <code class="literal">Append</code> node, which can only have
121 partial children when used within a parallel plan, a <code class="literal">Parallel
122 Append</code> node can have both partial and non-partial child plans.
123 Non-partial children will be scanned by only a single process, since
124 scanning them more than once would produce duplicate results. Plans that
125 involve appending multiple result sets can therefore achieve
126 coarse-grained parallelism even when efficient partial plans are not
127 available. For example, consider a query against a partitioned table
128 that can only be implemented efficiently by using an index that does
129 not support parallel scans. The planner might choose a <code class="literal">Parallel
130 Append</code> of regular <code class="literal">Index Scan</code> plans; each
131 individual index scan would have to be executed to completion by a single
132 process, but different scans could be performed at the same time by
135 <a class="xref" href="runtime-config-query.html#GUC-ENABLE-PARALLEL-APPEND">enable_parallel_append</a> can be used to disable
137 </p></div><div class="sect2" id="PARALLEL-PLAN-TIPS"><div class="titlepage"><div><div><h3 class="title">15.3.5. Parallel Plan Tips <a href="#PARALLEL-PLAN-TIPS" class="id_link">#</a></h3></div></div></div><p>
138 If a query that is expected to do so does not produce a parallel plan,
139 you can try reducing <a class="xref" href="runtime-config-query.html#GUC-PARALLEL-SETUP-COST">parallel_setup_cost</a> or
140 <a class="xref" href="runtime-config-query.html#GUC-PARALLEL-TUPLE-COST">parallel_tuple_cost</a>. Of course, this plan may turn
141 out to be slower than the serial plan that the planner preferred, but
142 this will not always be the case. If you don't get a parallel
143 plan even with very small values of these settings (e.g., after setting
144 them both to zero), there may be some reason why the query planner is
145 unable to generate a parallel plan for your query. See
146 <a class="xref" href="when-can-parallel-query-be-used.html" title="15.2. When Can Parallel Query Be Used?">Section 15.2</a> and
147 <a class="xref" href="parallel-safety.html" title="15.4. Parallel Safety">Section 15.4</a> for information on why this may be
150 When executing a parallel plan, you can use <code class="literal">EXPLAIN (ANALYZE,
151 VERBOSE)</code> to display per-worker statistics for each plan node.
152 This may be useful in determining whether the work is being evenly
153 distributed between all plan nodes and more generally in understanding the
154 performance characteristics of the plan.
155 </p></div></div><div class="navfooter"><hr /><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="when-can-parallel-query-be-used.html" title="15.2. When Can Parallel Query Be Used?">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="parallel-query.html" title="Chapter 15. Parallel Query">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="parallel-safety.html" title="15.4. Parallel Safety">Next</a></td></tr><tr><td width="40%" align="left" valign="top">15.2. When Can Parallel Query Be Used? </td><td width="20%" align="center"><a accesskey="h" href="index.html" title="PostgreSQL 18.0 Documentation">Home</a></td><td width="40%" align="right" valign="top"> 15.4. Parallel Safety</td></tr></table></div></body></html>
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