Is it possible to configure Postgres to split out a full text search over multiple CPU processes in an attempt to complete quicker?

I'm running a full text search on 2 million records on a GIN indexed tsvector column, where the source text is about 10,000 characters long.

I have way more CPU than is being used during the search, so I feel like splitting the search over 4 processes in batches of 500k would allow it to run the search concurrently and therefore complete faster.

I'd be interested to know if anyone has tried this or implemented their own equivalent programatically in SQL.

  • Which Postgres version are you using? (select version(); will tell you)
    – user1822
    Oct 24, 2019 at 7:54
  • I have the option to upgrade but currently: PostgreSQL 10.7 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-36), 64-bit Oct 24, 2019 at 7:55
  • 1
    Postgres 10 should be able to parallelize a query - if it makes sense. Check the execution plan.
    – user1822
    Oct 24, 2019 at 7:57
  • Thanks, I'll look into parallelisation I've not come across that before. Oct 24, 2019 at 7:59
  • 1
    postgresql.org/docs/current/parallel-query.html Postgres 11 and 12 improved on that
    – user1822
    Oct 24, 2019 at 8:01

1 Answer 1


The default setting of "max_parallel_workers_per_gather" is 2, which won't spread work over all 4 CPUs for any one query. But that doesn't matter if you aren't getting parallel plans in the first place.

Parallel query is a relatively new feature to PostgreSQL, and is still being improved. You should use the newest version you can to give yourself the best chance of benefiting from it.

I believe the index consultation cannot be parallelized (in any version). The table consultation can be, but it often doesn't make sense to.

If the indexed part of the query is highly selective and returns few rows, then "parallel_setup_cost" will exceed the benefit of parallelizing the table access for just a few rows.

On the other hand, if you return a lot of rows, then "parallel_tuple_cost" (multiplied by rows returned) will exceed the benefit. If you access a lot of rows, but don't return them (like count(*) or some other aggregate, or a filter which the index is unable to address) that is the optimal case for parallelization to work well.

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