Justin,
I did try changing min_parallel_index_scan_size / min_parallel_table_scan_size and didn't see any change (the below is with force_parallel_mode = off):
postgres=# set min_parallel_index_scan_size = 0;
SET
postgres=# set min_parallel_table_scan_size = 0;
SET
postgres=# explain select * from testing where id = ANY(array[1608377,5449811, ... <removed for brevity> ... ,4654284,3558460]::integer[]);
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------
Index Scan using testing_pkey on testing (cost=0.43..6138.81 rows=1000 width=74)
Index Cond: (id = ANY ('{1608377,5449811, ... < removed for brevity > ... 4654284,3558460}'::integer[]))
(2 rows)
As for 'force_parallel_mode', while this isn't "debugging PG", it isn't something that I would actually turn on production, just something I was playing with to see the cost of parallel queries when the planner might not think they are the most efficient.
Thomas,
Thanks for the explanation. Yes, that is the query plan I was imagining. I do see how chopping it up could result in an unfair distribution. But my counter to that would be that wouldn't chopping it up still be better than not. If things do happen to work out to be fair, now it's X times as fast, if things are very unfair, then you haven't really lost much (besides the parallel overhead) compared to the non-parallel query. Or maybe it should be possible to do the parallel query if there were some statistics (either normal ones or extended ones) that told the planner that the result would probably be fair?
Though I do agree that the "work stealing" option would be the most efficient, but would be a lot more complicated to code up.
I tried out inserting into a separate table, and as you guessed that worked. For my production scenario that isn't really feasible, but still cool to see it work.
postgres=# create table ids(
probe_id int PRIMARY KEY
);
insert into ids(probe_id) values (774494);
insert into ids(probe_id) values (9141914);
...
postgres=# select count(*) from ids;
count
-------
1000
(1 row)
postgres=# explain select * from testing where id in (select * from ids);
QUERY PLAN
-----------------------------------------------------------------------------------------
Gather (cost=0.43..3504.67 rows=1000 width=74)
Workers Planned: 2
-> Nested Loop (cost=0.43..3504.67 rows=417 width=74)
-> Parallel Seq Scan on ids (cost=0.00..9.17 rows=417 width=4)
-> Index Scan using testing_pkey on testing (cost=0.43..8.37 rows=1 width=74)
Index Cond: (id = ids.probe_id)
(6 rows)
Thanks,
Alex Kaiser
I did try changing min_parallel_index_scan_size / min_parallel_table_scan_size and didn't see any change (the below is with force_parallel_mode = off):
postgres=# set min_parallel_index_scan_size = 0;
SET
postgres=# set min_parallel_table_scan_size = 0;
SET
postgres=# explain select * from testing where id = ANY(array[1608377,5449811, ... <removed for brevity> ... ,4654284,3558460]::integer[]);
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------
Index Scan using testing_pkey on testing (cost=0.43..6138.81 rows=1000 width=74)
Index Cond: (id = ANY ('{1608377,5449811, ... < removed for brevity > ... 4654284,3558460}'::integer[]))
(2 rows)
As for 'force_parallel_mode', while this isn't "debugging PG", it isn't something that I would actually turn on production, just something I was playing with to see the cost of parallel queries when the planner might not think they are the most efficient.
Thomas,
Thanks for the explanation. Yes, that is the query plan I was imagining. I do see how chopping it up could result in an unfair distribution. But my counter to that would be that wouldn't chopping it up still be better than not. If things do happen to work out to be fair, now it's X times as fast, if things are very unfair, then you haven't really lost much (besides the parallel overhead) compared to the non-parallel query. Or maybe it should be possible to do the parallel query if there were some statistics (either normal ones or extended ones) that told the planner that the result would probably be fair?
Though I do agree that the "work stealing" option would be the most efficient, but would be a lot more complicated to code up.
I tried out inserting into a separate table, and as you guessed that worked. For my production scenario that isn't really feasible, but still cool to see it work.
postgres=# create table ids(
probe_id int PRIMARY KEY
);
insert into ids(probe_id) values (774494);
insert into ids(probe_id) values (9141914);
...
postgres=# select count(*) from ids;
count
-------
1000
(1 row)
postgres=# explain select * from testing where id in (select * from ids);
QUERY PLAN
-----------------------------------------------------------------------------------------
Gather (cost=0.43..3504.67 rows=1000 width=74)
Workers Planned: 2
-> Nested Loop (cost=0.43..3504.67 rows=417 width=74)
-> Parallel Seq Scan on ids (cost=0.00..9.17 rows=417 width=4)
-> Index Scan using testing_pkey on testing (cost=0.43..8.37 rows=1 width=74)
Index Cond: (id = ids.probe_id)
(6 rows)
Thanks,
Alex Kaiser
On Wed, Feb 1, 2023 at 1:52 PM Thomas Munro <thomas.munro@xxxxxxxxx> wrote:
On Wed, Feb 1, 2023 at 6:39 PM Alex Kaiser <alextkaiser@xxxxxxxxx> wrote:
> select * from testing where id in (1608377,5449811, ... <1000 random ids> ,4654284,3558460);
>
> Essentially I have a list of 1000 ids and I would like the rows for all of those ids.
>
> This seems like it would be pretty easy to parallelize, if you have X threads then you would split the list of IDs into 1000/X sub lists and give one to each thread to go find the rows for ids in the given list. Even when I use the following configs I don't get a query plan that actually uses any parallelism:
It sounds like the plan you are imagining is something like:
Gather
Nested Loop Join
Outer side: <partial scan of your set of constant values>
Inner side: Index scan of your big table
Such a plan would only give the right answer if each process has a
non-overlapping subset of the constant values to probe the index with,
and together they have the whole set. Hypothetically, a planner could
chop that set up beforehand and and give a different subset to each
process (just as you could do that yourself using N connections and
separate queries), but that might be unfair: one process might find
lots of matches, and the others might find none, because of the
distribution of data. So you'd ideally want some kind of "work
stealing" scheme, where each worker can take more values to probe from
whenever it needs more, so that they all keep working until the values
run out. We don't have a thing that can do that. You might imagine
that a CTE could do it, so WITH keys_to_look_up AS (VALUES (1), (2),
...) SELECT ... JOIN ON ..., but that also doesn't work because we
don't have a way to do "partial" scans of CTEs either (though someone
could invent that). Likewise for temporary tables: they are invisible
to parallel workers, so they can't help us. I have contemplated
"partial function scans" for set-returning functions, where a function
could be given a bit of shared memory and various other infrastructure
to be able to be "parallel aware" (= able to coordinate across
processes so that each process gets a subset of the data), and one
could imagine that that would allow various solutions to the problem,
but that's vapourware.
But you can get a plan like that if you insert all those values into a
regular table, depending on various settings, stats and
min_parallel_table_scan_size (try 0, I guess that'll definitely do
it). Which probably isn't the answer you wanted to hear.
