Stacy,
> Each set of test tables holds 1,000,000 tuples with a partition value of
> '1', and 1,000,000 with a partition value of '2'. *The bar* columns are all
> set to non-null values. *The 'one_big_foo' table stores all 2M rows in one
> table. *'super_foo' and 'union_foo' split the data into two tables, and use
> inheritance and union views (respectively) to tie them together, as
> described in my previous message.
>
> Query timings and 'EXPLAIN ANALYZE' results for full table scans and for
> partition scans follow:
Hmmm .... interesting. I think you've demonstrated that pseudo-partitioning
doesn't pay for having only 2 partitions. Examine this:
* * * * *-> *Index Scan using idx_sub_foo2_partition on sub_foo2
super_foo *(cost=0.00..2.01 rows=1 width=4) (actual time=0.221..0.221
rows=0 loops=1)
* * * * * * * *Index Cond: (partition = 1::numeric)
*Total runtime: 15670.463 ms
As you see, even though the aggregate operation requires a seq scan, the
planner is still able to scan, and discard, sub_foo2, using its index in 0.2
seconds. Unfortunately, super_foo still needs to contend with:
* *-> *Append *(cost=0.00..28376.79 rows=1000064 width=4) (actual
time=6.699..12072.483 rows=1000000 loops=1)
Right there, in the Append, you lose 6 seconds. This means that
pseudo-partitioning via inheritance will become a speed gain once you can
"make up" that 6 seconds by being able to discard more partitions. If you
want, do a test with 6 partitions instead of 2 and let us know how it comes
out.
Also, keep in mind that there are other reasons to do pseudo-partitioning than
your example. Data write performance, expiring partitions, and vacuum are
big reasons that can motivate partitioning even in cases when selects are
slower.
--
Josh Berkus
Aglio Database Solutions
San Francisco
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