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 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.
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 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. For example, it may be an index scan which looks up a value taken from the outer side of the join. Each worker will execute the inner side of the join in full, which for hash join means that an identical hash table is built in each worker process.
PostgreSQL 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 Partial Aggregate 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
Finalize Aggregate node.
Because the Finalize Aggregate 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 Finalize Aggregate node could be as many as
the number of input rows which were seen by all worker processes in the
Partial Aggregate 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.
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 CREATE AGGREGATE for more details.
Parallel aggregation is not supported if any aggregate function call
contains DISTINCT or ORDER BY clause and is also
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 parallel_setup_cost or 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 Section 15.2 and Section 15.4 for information on why this may be the case.
When executing a parallel plan, you can use EXPLAIN (ANALYZE,
VERBOSE) 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.