Re: Partition-wise aggregation/grouping

On 11.11.2017 23:29, Konstantin Knizhnik wrote:
> On 10/27/2017 02:01 PM, Jeevan Chalke wrote:
>> Hi,
>>
>> Attached new patch-set here. Changes include:
>>
>> 1. Added separate patch for costing Append node as discussed up-front
>> in the
>> patch-set.
>> 2. Since we now cost Append node, we don't need
>> partition_wise_agg_cost_factor
>> GUC. So removed that. The remaining patch hence merged into main
>> implementation
>> patch.
>> 3. Updated rows in test-cases so that we will get partition-wise plans.
>>
>> Thanks
>
> I applied partition-wise-agg-v6.tar.gz patch to  the master and use
> shard.sh example from
> https://www.postgresql.org/message-id/14577.1509723225%40localhost
> Plan for count(*) is the following:
>
> shard=# explain select count(*) from orders;
>                                       QUERY PLAN
> ---------------------------------------------------------------------------------------
>
>  Finalize Aggregate  (cost=100415.29..100415.30 rows=1 width=8)
>    ->  Append  (cost=50207.63..100415.29 rows=2 width=8)
>          ->  Partial Aggregate  (cost=50207.63..50207.64 rows=1 width=8)
>                ->  Foreign Scan on orders_0 (cost=101.00..50195.13
> rows=5000 width=0)
>          ->  Partial Aggregate  (cost=50207.63..50207.64 rows=1 width=8)
>                ->  Foreign Scan on orders_1 (cost=101.00..50195.13
> rows=5000 width=0)
>
>
> We really calculate partial aggregate for each partition, but to do we
> still have to fetch all data from remote host.
> So for foreign partitions such plans is absolutely inefficient.
> Amy be it should be combined with some other patch?
> For example, with  agg_pushdown_v4.tgz patch
> https://www.postgresql.org/message-id/14577.1509723225%40localhost ?
> But it is not applied after partition-wise-agg-v6.tar.gz patch.
> Also postgres_fdw in 11dev is able to push down aggregates without
> agg_pushdown_v4.tgz patch.
>
> In 0009-Teach-postgres_fdw-to-push-aggregates-for-child-rela.patch
> there is the following check:
>
>  /* Partial aggregates are not supported. */
> +       if (extra->isPartial)
> +           return;
>
> If we just comment this line then produced plan will be the following:
>
> shard=# explain select sum(product_id) from orders;
>                            QUERY PLAN
> ----------------------------------------------------------------
>  Finalize Aggregate  (cost=308.41..308.42 rows=1 width=8)
>    ->  Append  (cost=144.18..308.41 rows=2 width=8)
>          ->  Foreign Scan  (cost=144.18..154.20 rows=1 width=8)
>                Relations: Aggregate on (public.orders_0 orders)
>          ->  Foreign Scan  (cost=144.18..154.20 rows=1 width=8)
>                Relations: Aggregate on (public.orders_1 orders)
> (6 rows)
>
> And it is actually desired plan!
> Obviously such approach will not always work. FDW really doesn't
> support partial aggregates now.
> But for most frequently used aggregates: sum, min, max, count
> aggtype==aggtranstype and there is no difference
> between partial and normal aggregate calculation.
> So instead of (extra->isPartial) condition we can add more complex
> check which will traverse pathtarget expressions and
> check if it can be evaluated in this way. Or... extend FDW API to
> support partial aggregation.
>
> But even the last plan is not ideal: it will calculate predicates at
> each remote node sequentially.
> There is parallel append patch:
> https://www.postgresql.org/message-id/CAJ3gD9ctEcrVUmpY6fq_JUB6WDKGXAGd70EY68jVFA4kxMbKeQ%40mail.gmail.com
>
> but ... FDW doesn't support parallel scan, so parallel append can not
> be applied in this case.
> And we actually do not need parallel append with all its dynamic
> workers here.
> We just need to start commands at all remote servers and only after it
> fetch results (which can be done sequentially).
>
> I am investigating problem of efficient execution of OLAP queries on
> sharded tables (tables with remote partitions).
> After reading all this threads and corresponding  patches, it seems to me
> that we already have most of parts of the puzzle, what we need is to
> put them on right places and may be add missed ones.
> I wonder if somebody is busy with it and can I somehow help here?
>
> Also I am not quite sure about the best approach with parallel
> execution of distributed query at all nodes.
> Should we make postgres_fdw parallel safe and use parallel append? How
> difficult it will be?
> Or in addition to parallel append we should also have "asynchronous
> append" which will be able to initiate execution at all nodes?
> It seems to be close to merge append, because it should simultaneously
> traverse all cursors.
>
> Looks like second approach is easier for implementation. But in case
> of sharded table, distributed query may need to traverse both remote
> and local shards and this approach doesn't allow to processed several
> local shards in parallel.
>

I attach small patch for postgres_fdw.c which allows concurrent
execution of aggregates by all remote servers (when them are accessed
through postgres_fdw).
I have added "postgres_fdw.use_prefetch" GUC to enable/disable
prefetching data in postgres_fdw.
This patch should be applied after of partition-wise-agg-v6.tar.gz patch.
With shard example and the following two GUCs set:

shard=# set postgres_fdw.use_prefetch=on;
shard=# set enable_partition_wise_agg=on;
shard=# select sum(product_id) from orders;
   sum
---------
 9965891
(1 row)

shard=# explain select sum(product_id) from orders;
                           QUERY PLAN
----------------------------------------------------------------
 Finalize Aggregate  (cost=308.41..308.42 rows=1 width=8)
   ->  Append  (cost=144.18..308.41 rows=2 width=8)
         ->  Foreign Scan  (cost=144.18..154.20 rows=1 width=8)
               Relations: Aggregate on (public.orders_0 orders)
         ->  Foreign Scan  (cost=144.18..154.20 rows=1 width=8)
               Relations: Aggregate on (public.orders_1 orders)
(6 rows)

sum aggregate is calculated in parallel by both servers.

I have not tested it much, it is just prove of concept.

--
Konstantin Knizhnik
Postgres Professional: http://www.postgrespro.com
The Russian Postgres Company