I'm surprised at the effort pgsql requires to run one of my queries. I
don't know how to tune this query.
Column | Type | Modifiers
------------+--------------+-----------
the_id | integer | not null
the_date | date | not null
num1 | numeric(9,4) |
num2 | numeric(9,4) |
num3 | numeric(9,4) |
num4 | numeric(9,4) |
int1 | integer |
Indexes:
"the_table_pkey" primary key, btree (the_id, the_date)
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The query I want to run is
select stock_id, min(price_date) from day_ends group by stock_id;
---------------------------------------
Here's the plan that I get.
GroupAggregate (cost=3711244.30..3838308.31 rows=6732 width=8)
-> Sort (cost=3711244.30..3753593.36 rows=16939624 width=8)
Sort Key: stock_id
-> Seq Scan on day_ends (cost=0.00..361892.24 rows=16939624
width=8)
If I set enable_seqscan = false, the plan changes to
GroupAggregate (cost=0.00..67716299.91 rows=6732 width=8)
-> Index Scan using day_ends_pkey on day_ends
(cost=0.00..67631584.96 rows=16939624 width=8)
---------------------------------------
Now... the first plan uses up tons of temporary space for sorting. The
second one just runs and runs and runs. I've tried setting the
statistics to 1000 with little effect.
So the query can get everything it needs from the index, and a full scan
of the index should be faster (the index file is less than half the size
of the data file.) So why does the optimizer estimate so high?
Also, to get the MIN for a given group, not all values of the index need
to be seen. Must pgsql do a full scan because it treats all aggregates
in the same way? Are MIN and MAX used often enough to justify special
treatment, and could that be cleanly implemented? Perhaps the aggregate
function can request the data in a certain order, be told that it is
being passed data in a certain order, and return before seeing the
entire set of data.
Food for thought...
Thanks,
Ken Geis
