parallel query PostgreSQL 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. 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 Gather node. Here is a simple example: 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) In all cases, the Gather node will have exactly one child plan, which is the portion of the plan that will be executed in parallel. If the Gather 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 that portion of the query will run in parallel. In the example above, the query accesses only one table, so there is only one plan node other than the Gather node itself; since that plan node is a child of the Gather node, it will run in parallel. Using EXPLAIN, you can see the number of workers chosen by the planner. When the Gather node is reached during query execution, the process which is implementing the user's session will request a number of background worker processes equal to the number of workers chosen by the planner. The total number of background workers that can exist at any one time is limited by , so 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, considering increasing max_worker_processes so that more workers can be run simultaneously or alternatively reducing so that the planner requests fewer workers. Every background worker process which is successfully started for a given parallel query will execute the portion of the plan which is a descendent of the Gather node. 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 Gather node. In such cases, the leader will do very little of the work of executing the parallel portion of the plan. 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. 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 max_parallel_workers_per_gather. must be set to a value other than none. Parallel query requires dynamic shared memory in order to pass data between cooperating processes. 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. 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: 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. This is a limitation of the current implementation which could be lifted in a future release. 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 DECLARE CURSOR will never use a parallel plan. Similarly, a PL/pgsql loop of the form FOR x IN query LOOP .. END LOOP 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. The query uses any function marked PARALLEL UNSAFE. Most system-defined functions are PARALLEL SAFE, but user-defined functions are marked PARALLEL UNSAFE by default. See the discussion of . 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. The transaction isolation level is serializable. This is a limitation of the current implementation. 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 between below the Gather node entirely by itself, almost as if the Gather node were not present. This will happen if any of the following conditions are met: No background workers can be obtained because of the limitation that the total number of background workers cannot exceed . The client sends an Execute message with a non-zero fetch count. See the discussion of the extended query protocol. Since libpq 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 in sessions where it is likely, so as to avoid generating query plans that may be suboptimal when run serially. 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. 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 partial plan; that is, it must constructed so that each process will 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. Currently, the only type of scan which has been modified to work with parallel query is a sequential scan. Therefore, the driving table in a parallel plan will always be scanned using a Parallel Seq Scan. The relation's blocks will be divided among the cooperating processes. Blocks are handed out one at a time, so that access to the relation remains sequential. Each process will visit every tuple on the page assigned to it before requesting a new page. The driving table may be joined to one or more other tables using nested loops or hash joins. The outer 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. For example, it may be an index scan which looks up a value based on a column taken from the inner table. Each worker will execute the outer side of the plan in full, which is why merge joins are not supported here. The outer side of a merge join will often involve sorting the entire inner table; even if it involves an index, it is unlikely to be productive to have multiple processes each conduct a full index scan of the inner table. It is not possible to perform the aggregation portion of a query entirely in parallel. For example, if a query involves selecting COUNT(*), each worker could compute a total, but those totals would need to combined in order to produce a final answer. If the query involved a GROUP BY clause, a separate total would need to be computed for each group. Even though aggregation can't be done entirely in parallel, queries involving aggregation are often excellent candidates for parallel query, because they typically read many rows but return only a few rows to the client. Queries that return many rows to the client are often limited by the speed at which the client can read the data, in which case parallel query cannot help very much. PostgreSQL supports parallel aggregation by aggregating twice. 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 PartialAggregate node. Second, the partial results are transferred to the leader via the Gather node. 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 FinalizeAggregate node. Parallel aggregation is not supported in all situations. Each aggregate must be safe for parallelism and must have a combine function. If the aggregate has a transition state of type internal, it must have serialization and deserialization functions. See for more details. Parallel aggregation is not supported for ordered set aggregates or when the query involves GROUPING SETS. It can only be used when all joins involved in the query are also part of the parallel portion of the plan. If a query that is expected to do so does not produce a parallel plan, you can try reducing or . 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 and for information on why this may be the case. When executing a parallel plan, you can use EXPLAIN (ANALYZE, VERBOSE) will 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. The planner classifies operations involved in a query as either parallel safe, parallel restricted, or parallel unsafe. 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 Gather node, but can occur elsewhere in a plan which contains a Gather 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. The following operations are always parallel restricted. Scans of common table expressions (CTEs). Scans of temporary tables. Scans of foreign tables, unless the foreign data wrapper has an IsForeignScanParallelSafe API which indicates otherwise. Access to an InitPlan or SubPlan. 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 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 or , markings can be set by specifying PARALLEL SAFE, PARALLEL RESTRICTED, or PARALLEL UNSAFE as appropriate. When using , the PARALLEL option can be specified with SAFE, RESTRICTED, or UNSAFE as the corresponding value. Functions and aggregates must be marked PARALLEL UNSAFE if they write to the database, access sequences, change the transaction state even temporarily (e.g. a PL/pgsql function which establishes an EXCEPTION block to catch errors), or make persistent changes to settings. Similarly, functions must be marked PARALLEL RESTRICTED 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, setseed and random are parallel restricted for this last reason. 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 UNSAFE. 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 PARALLEL RESTRICTED to ensure that they execute only in the leader. 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 WHERE clause applied to a particular table is parallel restricted, the query planner will not consider placing the scan of that table below a Gather node. 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 WHERE clause so that it happens above the Gather node. However, the planner does not do this.