Good suggestion. The results are...interesting:
test0=# SET min_parallel_index_scan_size =0;
SET
Time: 0.172 ms
test0=# EXPLAIN ANALYZE SELECT t0.id0, t1.val FROM tab0 AS t0 JOIN tab1
AS t1 ON (t0.id0 = t1.id0) WHERE t0.id0 < 5;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------
Gather (cost=1097.91..40206.84 rows=5000 width=98) (actual
time=0.362..5.565 rows=3500.00 loops=1)
Workers Planned: 1
Workers Launched: 1
Buffers: shared hit=114
-> Nested Loop (cost=97.91..38706.84 rows=2941 width=98) (actual
time=0.034..0.479 rows=1750.00 loops=2)
Buffers: shared hit=114
-> Parallel Index Only Scan using tab0_pkey on tab0 t0
(cost=0.29..4.36 rows=3 width=4) (actual time=0.008..0.009 rows=2.50
loops=2)
Index Cond: (id0 < 5)
Heap Fetches: 0
Index Searches: 1
Buffers: shared hit=3
-> Bitmap Heap Scan on tab1 t1 (cost=97.61..12867.78
rows=3305 width=98) (actual time=0.036..0.140 rows=700.00 loops=5)
Recheck Cond: (t0.id0 = id0)
Heap Blocks: exact=97
Buffers: shared hit=111
-> Bitmap Index Scan on tab1_id0_hash (cost=0.00..96.79
rows=3305 width=0) (actual time=0.030..0.030 rows=700.00 loops=5)
Index Cond: (id0 = t0.id0)
Index Searches: 5
Buffers: shared hit=14
Planning:
Buffers: shared hit=222
Planning Time: 0.763 ms
Execution Time: 5.716 ms
(23 rows)
Time: 7.248 ms
test0=# SET max_parallel_workers_per_gather=0;
SET
Time: 0.131 ms
test0=# EXPLAIN ANALYZE SELECT t0.id0, t1.val FROM tab0 AS t0 JOIN tab1
AS t1 ON (t0.id0 = t1.id0) WHERE t0.id0 < 5;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------
Nested Loop (cost=97.91..64508.51 rows=5000 width=98) (actual
time=0.044..0.903 rows=3500.00 loops=1)
Buffers: shared hit=113
-> Index Only Scan using tab0_pkey on tab0 t0 (cost=0.29..4.38
rows=5 width=4) (actual time=0.003..0.005 rows=5.00 loops=1)
Index Cond: (id0 < 5)
Heap Fetches: 0
Index Searches: 1
Buffers: shared hit=3
-> Bitmap Heap Scan on tab1 t1 (cost=97.61..12867.78 rows=3305
width=98) (actual time=0.028..0.132 rows=700.00 loops=5)
Recheck Cond: (t0.id0 = id0)
Heap Blocks: exact=97
Buffers: shared hit=110
-> Bitmap Index Scan on tab1_id0_hash (cost=0.00..96.79
rows=3305 width=0) (actual time=0.021..0.021 rows=700.00 loops=5)
Index Cond: (id0 = t0.id0)
Index Searches: 5
Buffers: shared hit=13
Planning:
Buffers: shared hit=9
Planning Time: 0.190 ms
Execution Time: 1.025 ms
(19 rows)
Time: 1.459 ms
However disabling gather workers gets a much better plan. Now I can
switch the child index to btree if you think that is significant. Best
wishes
Mark
On 08/01/2026 17:14, David Rowley wrote:
On Thu, 8 Jan 2026 at 17:03, Mark Kirkwood <[email protected]> wrote:
I don't think so - while the case I posted used a hash index on the
child table, exactly the sane behaviour happens if it is a btree (I
probably should have mentioned that sorry). Background is I discovered
this while playing about with hash indexes...which I must say - someone
has done excellent work on as in this *particular cases* they are
getting me better query performance!
Ok, it seems related to the min_parallel_index_scan_size GUC. If you
zero that, do you get a better plan?
I think the problem is that because the best form of plan for joining
this tiny set of rows to the huge table is a parameterised nested
loop, to parallelise that loop, you need a Parallel node on the outer
side of the Nested Loop. If the index's size is below
min_parallel_index_scan_size then we won't build a partial path for
it.
David
