Performance problems with multiple layers of functions

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Hi there.

I have hit a edge in the planning and I hope you can help.

The system uses a lot of stored procedures to move as much of the intelligence into the database layer as possible.

My (development) query looks like and runs reasonably fast:

explain analyze select dataset_id, entity, sum(amount) from entrydata_current where flow_direction in (select * from outflow_direction(dataset_id)) and dataset_id in (122,112,125,89,111) group by dataset_id, entity; QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------------------------------------------
HashAggregate (cost=918171.00..918171.30 rows=24 width=19) (actual time=11533.297..11533.340 rows=50 loops=1) -> Bitmap Heap Scan on entrydata_current (cost=676.72..917736.04 rows=57994 width=19) (actual time=23.921..11425.373 rows=37870 loops=1) Recheck Cond: ((dataset_id = 122) OR (dataset_id = 112) OR (dataset_id = 125) OR (dataset_id = 89) OR (dataset_id = 111))
        Filter: (subplan)
-> BitmapOr (cost=676.72..676.72 rows=117633 width=0) (actual time=15.765..15.765 rows=0 loops=1) -> Bitmap Index Scan on entrydata_current_dataset_idx (cost=0.00..83.97 rows=14563 width=0) (actual time=1.881..1.881 rows=13728 loops=1)
                    Index Cond: (dataset_id = 122)
-> Bitmap Index Scan on entrydata_current_dataset_idx (cost=0.00..156.12 rows=27176 width=0) (actual time=3.508..3.508 rows=25748 loops=1)
                    Index Cond: (dataset_id = 112)
-> Bitmap Index Scan on entrydata_current_dataset_idx (cost=0.00..124.24 rows=21498 width=0) (actual time=2.729..2.729 rows=20114 loops=1)
                    Index Cond: (dataset_id = 125)
-> Bitmap Index Scan on entrydata_current_dataset_idx (cost=0.00..102.20 rows=17771 width=0) (actual time=2.351..2.351 rows=17344 loops=1)
                    Index Cond: (dataset_id = 89)
-> Bitmap Index Scan on entrydata_current_dataset_idx (cost=0.00..210.19 rows=36625 width=0) (actual time=5.292..5.292 rows=37118 loops=1)
                    Index Cond: (dataset_id = 111)
        SubPlan
-> Function Scan on outflow_direction (cost=0.00..12.50 rows=1000 width=4) (actual time=0.093..0.095 rows=4 loops=114052)
Total runtime: 11540.506 ms
(18 rows)

The problem is, that the application should not need to know the five dataset_ids (it will always know one - its own). So I make a function to return the five ids and then the query looks like:

explain select dataset_id, entity, sum(amount) from entrydata_current where flow_direction in (select * from outflow_direction(dataset_id)) and dataset_id in (select * from get_dataset_ids(122)) group by dataset_id, entity;
                                                              QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------
HashAggregate  (cost=24672195.68..24672203.88 rows=656 width=19)
  ->  Hash IN Join  (cost=15.00..24660005.45 rows=1625364 width=19)
        Hash Cond: ("outer".dataset_id = "inner".get_dataset_ids)
-> Index Scan using entrydata_current_dataset_idx on entrydata_current (cost=0.00..24558405.20 rows=1625364 width=19)
              Filter: (subplan)
              SubPlan
-> Function Scan on outflow_direction (cost=0.00..12.50 rows=1000 width=4)
        ->  Hash  (cost=12.50..12.50 rows=1000 width=4)
-> Function Scan on get_dataset_ids (cost=0.00..12.50 rows=1000 width=4)
(9 rows)

which does not return within 10 minutes - which is unacceptable.

Is there any way to get a better plan for the second ? The planner should really see the two queries as equal as there is no dependencies between the outer query and get_dataset_ids (isn't it called constant folding?).

Thanks in advance

Svenne

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