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In recent versions of PostgreSQL, can we share a query between two or more cores to get a performance boost? Or should we get faster cores?

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Have you read this? – Jack Douglas Dec 14 '13 at 15:09
You may also be interested in PL/Proxy. – dezso Dec 14 '13 at 21:12
@JackDouglas tnx, but it's not about query distribution among more than one core. – Alireza Hos Dec 15 '13 at 6:15
@john.locke the brief list headed "On the server-side, there is already some parallelism" doesn't include any mention of parallel query (and in fact there is no parallelism of the sort you are asking about yet). Of course if you can break your query down into multiple queries you can run them in parallel. – Jack Douglas Dec 15 '13 at 7:45
Good point about breaking queries into multiple ones to distribute them between cores. tnx – Alireza Hos Dec 15 '13 at 9:32
up vote 26 down vote accepted

No. Please see the PostgresSQL FAQ: How does PostgreSQL use CPU resources?

The PostgreSQL server is process-based (not threaded), and uses one operating system process per database session. A single database session (connection) cannot utilize more than one CPU. Of course, multiple sessions are automatically spread across all available CPUs by your operating system. Client applications can easily use threads and create multiple database connections from each thread.

A single complex and CPU-intensive query is unable to use more than one CPU to do the processing for the query. The OS may still be able to use others for disk I/O etc, but you won't see much benefit from more than one spare core.

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No, but there is a workaround. :)

I found parsel (parallel select) plpgsql function, which splits your query based on primary key, then connects to the database via dblink extension and waits for all subqueries.

Author also wrote article about this function:

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No. Each connections spawn a separate process on server.

You can "emulate" some parallelism using a threaded procedural language like pljava. Create a java procedure(function) that launches several threads and create the output result using several workers. The backend is syncronized so each worker can update the output asynchronous.

Java has good support for thread coordination/cooperation.

As examples, this would be nice for CPU intensive operations or network length operations.

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