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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 that 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 +-------------------------------------------------------------------&zwsp;------------------ + 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 that 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 that 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 that 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 that 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 that 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> + </itemizedlist> + + <para> + In addition, the system must not be running in single-user mode. Since + the entire database system is running as a 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 following commands, which create a new table and populate + it, can use a parallel plan for the underlying <literal>SELECT</literal> + part of the query: + + <itemizedlist> + <listitem> + <para><command>CREATE TABLE ... AS</command></para> + </listitem> + <listitem> + <para><command>SELECT INTO</command></para> + </listitem> + <listitem> + <para><command>CREATE MATERIALIZED VIEW</command></para> + </listitem> + <listitem> + <para><command>REFRESH MATERIALIZED VIEW</command></para> + </listitem> + </itemizedlist> + </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> + </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> + </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 that 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 into ranges and shared among the cooperating processes. Each + worker process will complete the scanning of its given range of blocks before + requesting an additional range of blocks. + </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 processes 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 that 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 that 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 + that can 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 that 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 that + does not conflict with the use of parallel query. A parallel restricted + operation is one that cannot be performed in a parallel worker, but that + 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 that + contains such a node. A parallel unsafe operation is one that cannot + be performed while parallel query is in use, not even in the leader. + When a query contains anything that 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 that indicates otherwise. + </para> + </listitem> + + <listitem> + <para> + Plan nodes to which an <literal>InitPlan</literal> is attached. + </para> + </listitem> + + <listitem> + <para> + Plan nodes that 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 that 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 that 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 that + 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 that 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 that 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> |