Re: Introducing coarse grain parallelism by postgres_fdw.

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От Ashutosh Bapat
Тема Re: Introducing coarse grain parallelism by postgres_fdw.
Дата
Msg-id CAFjFpRe3DMxnqqmL09yeyuSmtMLnJ8saDkwVKQdB2TOwppqg0w@mail.gmail.com
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Ответ на Re: Introducing coarse grain parallelism by postgres_fdw.  (Kyotaro HORIGUCHI <horiguchi.kyotaro@lab.ntt.co.jp>)
Ответы Re: Introducing coarse grain parallelism by postgres_fdw.  (Robert Haas <robertmhaas@gmail.com>)
Re: Introducing coarse grain parallelism by postgres_fdw.  (Kyotaro HORIGUCHI <horiguchi.kyotaro@lab.ntt.co.jp>)
Список pgsql-hackers
Hi Kyotaro,
I looked at the patches and felt that the approach taken here is too intrusive, considering that the feature is only for foreign scans.

There are quite a few members added to the generic Path, Plan structures, whose use is is induced only through foreign scans. Each path now stores two sets of costs, one with parallelism and one without. The parallel values will make sense only when there is a foreign scan, which uses parallelism, in the plan tree. So, those costs are maintained unnecessarily or the memory for those members is wasted in most of the cases, where the tables involved are not foreign. Also, not many foreign tables will be able to use the parallelism, e.g. file_fdw. Although, that's my opinion; I would like hear from others.

Instead, an FDW which can use parallelism can add two paths one with and one without parallelism with appropriate costs and let the logic choosing the cheapest path take care of the actual choice. In fact, I thought, parallelism would be always faster than the non-parallel one, except when the foreign server is too much loaded. But we won't be able to check that anyway. Can you point out a case where the parallelism may not win over serial execution?

BTW, the name parallelism seems to be misleading here. All, it will be able to do is fire the queries (or data fetch requests) asynchronously. So, we might want to change the naming appropriately.


On Fri, Aug 1, 2014 at 2:48 PM, Kyotaro HORIGUCHI <horiguchi.kyotaro@lab.ntt.co.jp> wrote:
Hello,

> Hello, this is the new version which is complete to some extent
> of parallelism based on postgres_fdw.
>
> This compares the costs for parallel and non-parallel execution
> and choose parallel one if it is faster by some extent specified
> by GUCs. The attached files are,
>
>  0001_parallel_exec_planning_v0.patch:
>    - PostgreSQL body stuff for parallel execution planning.
>
>  0002_enable_postgres_fdw_to_run_in_parallel_v0.patch:
>    - postgres_fdw parallelization.
>
>  0003_file_fdw_changes_to_avoid_error.patch:
>    - error avoidig stuff for file_fdw (not necessary for this patch)
>
>  env.sql:
>    - simple test script to try this patch.
>
> =====
>
>  - planner stuff to handle cost of parallel execution. Including
>    indication of parallel execution.
>
>  - GUCs to control how easy to go parallel.
>
>    parallel_cost_threshold is the threshold of path total cost
>    where to enable parallel execution.
>
>    prallel_ratio_threshond is the threshold of the ratio of
>    parallel cost to non-parallel cost where to choose the
>    parallel path.
>
>  - postgres_fdw which can run in multiple sessions using snapshot
>    export and fetches in parallel for foreign scans on dedicated
>    connections.

But now the effect of async execution of FETCH'es is omitted
during planning.

>    foreign server has a new option 'max_aux_connections', which
>    limits the number of connections for parallel execution per
>    (server, user) pairs.
>
>  - change file_fdw to follow the changes of planner stuff.
>
>
> Whth the patch attached, the attached sql script shows the
> following result (after some line breaks are added).
>
> postgres=# EXPLAIN ANALYZE SELECT a.a, a.b, b.c
>            FROM fvs1 a join fvs1_2 b on (a.a = b.a);
>                                 QUERY PLAN
> ----------------------------------------------------------------------------
> Hash Join  (cost=9573392.96..9573393.34 rows=1 width=40 parallel)
>            (actual time=2213.400..2213.407 rows=12 loops=1)
>  Hash Cond: (a.a = b.a)
>  ->  Foreign Scan on fvs1 a
>            (cost=9573392.96..9573393.29 rows=10 width=8 parallel)
>            (actual time=2199.992..2199.993 rows=10 loops=1)
>  ->  Hash  (cost=9573393.29..9573393.29 rows=10 width=36)
>            (actual time=13.388..13.388 rows=10 loops=1)
>        Buckets: 1024  Batches: 1  Memory Usage: 6kB
>        ->  Foreign Scan on fvs1_2 b
>                    (cost=9573392.96..9573393.29 rows=10 width=36 parallel)
>                    (actual time=13.376..13.379 rows=10 loops=1)
>  Planning time: 4.761 ms
>  Execution time: 2227.462 ms
> (8 rows)
> postgres=# SET parallel_ratio_threshold to 0.0;
> postgres=# EXPLAIN ANALYZE SELECT a.a, a.b, b.c
>            FROM fvs1 a join fvs1 b on (a.a = b.a);
>                                 QUERY PLAN
> ------------------------------------------------------------------------------
>  Hash Join  (cost=318084.32..318084.69 rows=1 width=40)
>             (actual time=4302.913..4302.928 rows=12 loops=1)
>    Hash Cond: (a.a = b.a)
>    ->  Foreign Scan on fvs1 a  (cost=159041.93..159042.26 rows=10 width=8)
>                                (actual time=2122.989..2122.992 rows=10 loops=1)
>    ->  Hash  (cost=159042.26..159042.26 rows=10 width=500)
>              (actual time=2179.900..2179.900 rows=10 loops=1)
>          Buckets: 1024  Batches: 1  Memory Usage: 6kB
>          ->  Foreign Scan on fvs1 b
>                    (cost=159041.93..159042.26 rows=10 width=500)
>                    (actual time=2179.856..2179.864 rows=10 loops=1)
>  Planning time: 5.085 ms
>  Execution time: 4303.728 ms
> (8 rows)
>
> Where, "parallel" indicates that the node includes nodes run in
> parallel. The latter EXPLAIN shows the result when parallel
> execution is inhibited.
>
> Since the lack of time, sorry that the details for this patch is
> comming later.
>
> Is there any suggestions or opinions?

regards,

--
Kyotaro Horiguchi
NTT Open Source Software Center



--
Best Wishes,
Ashutosh Bapat
EnterpriseDB Corporation
The Postgres Database Company

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