Re: Async execution of postgres_fdw.

I revised the patch so that async scan will be done more
aggressively, and took execution time for two very simple cases.

As the result, simple seq scan gained 5% and hash join of two
foreign tables gained 150%. (2.4 times faster).

While measuring the performance, I noticed that each scan in a
query runs at once rather than alternating with each other in
many cases such as hash join or sorted joins and so. So I
modified the patch so that async fetch is done more
aggressively. The new v4 patch is attached. The following numbers
are taken based on it.

========
Simple seq scan for the first test.

> CREATE TABLE lt1 (a int, b timestamp, c text);
> CREATE SERVER sv1 FOREIGN DATA WRAPPER postgres_fdw OPTIONS (host 'localhost');
> CREATE USER MAPPING FOR PUBLIC SERVER sv1;
> CREATE FOREIGN TABLE ft1 () SERVER sv1 OPTIONS (table_name 'lt1');
> INSERT INTO lt1 (SELECT a, now(), repeat('x', 128) FROM generate_series(0, 999999) a);

On this case, I took the the 10 times average of exec time of the
following query for both master head and patched version. The
fetch size is 100.

> postgres=# EXPLAIN (ANALYZE ON, COSTS OFF) SELECT * FROM ft1;
> QUERY PLAN
> ------------------------------------------------------------------
> Foreign Scan on ft1 (actual time=0.79 5..4175.706 rows=1000000 loops=1)
> Planning time: 0.060 ms
> Execution time: 4276.043 ms

master head : avg = 4256.621, std dev = 17.099
patched pgfdw: avg = 4036.463, std dev = 2.608

The patched version is faster by about 5%. This should be pure
result of asynchronous fetching, not including the effect of
early starting of remote execution in ExecInit.

Interestingly, as fetch_count gets larger, the gain raises in
spite of the decrease of the number of query sending.

master head : avg = 2622.759, std dev = 38.379
patched pgfdw: avg = 2277.622, std dev = 27.269

About 15% gain. And for 10000,

master head : avg = 2000.980, std dev = 6.434
patched pgfdw: avg = 1616.793, std dev = 13.192

19%.. It is natural that exec time reduces along with increase of
fetch size, but I haven't found the reason why the patch's gain
also increases.

======================

The second case is a simple join of two foreign tables sharing
one connection.

The master head runs this query in about 16 seconds with almost
no fluctuation among multiple tries.

> =# EXPLAIN (ANALYZE ON, COSTS OFF) SELECT x.a, x.c, y.c
> FROM ft1 AS x JOIN ft1 AS y on x.a = y.a;
> QUERY PLAN
> ----------------------------------------------------------------------------
> Hash Join (actual time=7541.831..15924.631 rows=1000000 loops=1)
> Hash Cond: (x.a = y.a)
> -> Foreign Scan on ft1 x (actual time=1.176..6553.480 rows=1000000 loops=1)
> -> Hash (actual time=7539.761..7539.761 rows=1000000 loops=1)
> Buckets: 32768 Batches: 64 Memory Usage: 2829kB
> -> Foreign Scan on ft1 y (actual time=1.067..6529.165 rows=1000000 loops=1)
> Planning time: 0.223 ms
> Execution time: 15973.916 ms

But the v4 patch mysteriously accelerates this query, 6.5 seconds.

> =# EXPLAIN (ANALYZE ON, COSTS OFF) SELECT x.a, x.c, y.c
> FROM ft1 AS x JOIN ft1 AS y on x.a = y.a;
> QUERY PLAN
> ----------------------------------------------------------------------------
> Hash Join (actual time=2556.977..5812.937 rows=1000000 loops=1)
> Hash Cond: (x.a = y.a)
> -> Foreign Scan on ft1 x (actual time=32.689..1936.565 rows=1000000 loops=1)
> -> Hash (actual time=2523.810..2523.810 rows=1000000 loops=1)
> Buckets: 32768 Batches: 64 Memory Usage: 2829kB
> -> Foreign Scan on ft1 y (actual time=50.345..1928.411 rows=1000000 loops=1)
> Planning time: 0.220 ms
> Execution time: 6512.043 ms

The result data seems not broken. I don't know the reason yet but
I'll investigate it.

regards,

--
Kyotaro Horiguchi
NTT Open Source Software Center