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<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 that 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 that 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 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, change the transaction state (other than by
using a subtransaction for error recovery), access sequences, 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>
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