Re: Re: Using indexes for ORDER BY and PARTITION BY clause in windowing functions

> Agree that windowing function will return all the rows compared to max and
> group by returing only max rows per group. But even while arriving at the
> aggregate/sorting windowing function seems to spend more effort than group
> by/order by.

(I'll apologise in advance for possible misreading..)

The most cause of the difference in time comes from sorting. Over
90% of total execution time has elapsed while sorting
(49ms->2733ms) for the one using windowing function. If this sort
were useless, the execution time would be less than 300 ms -
seems comparable enough to group-by query.

| Subquery Scan on __unnamed_subquery_0
|          (actual time=2606.075..2953.937 rows=558 loops=1)
|   Filter: (__unnamed_subquery_0.rn = 1)
|   ->  WindowAgg  (actual time=2606.063..2928.061 rows=122880 loops=1)
|         ->  Sort (actual time=2606.020..2733.677 rows=122880 loops=1)
|               Sort Key: student_score.course, student_score.score
|               ->  Seq Scan on student_score
|                   (actual time=0.009..49.026 rows=122880 loops=1)

As you see in above plan, sorting key is (course, score). If your
point is the overall performance but not reusing a kind of
'hash', there's a big chance to eliminate this sorting if you are
able to have an additional index, say,

=# create index idx_co_sc on student_score using btree (course, score);

With this index, you will get a different plan like this,

> uniontest=# explain analyze select student_name from (select student_name, dense_rank() over(partition by course order by score) rn, score from student_score) rnn where rn=2;
>                                       QUERY PLAN
> -------------------------------------------------------------------------------
>  Subquery Scan on rnn  (actual time=0.088..319.403 rows=135 loops=1)
>    Filter: (rnn.rn = 2)
>    Rows Removed by Filter: 122746
>    ->  WindowAgg  (actual time=0.037..296.851 rows=122881 loops=1)
>          ->  Index Scan using idx_co_sc on student_score
>            (actual time=0.027..111.333 rows=122881 loops=1)
>  Total runtime: 319.483 ms

Does this satisfies your needs?

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> Another thing, (I may be stupid and naive here) does PostgreSQL
> re-uses the hash which has been already created for sort. In
> this case the inner query must have created a hash for windoing
> aggregate. Can't we use that same one while applying the the
> filter "rn=1" ?

Generally saying, hashes cannot yield ordered output by its
nature, I believe.

Windowing function (execnode) always receives tuples sequentially
in the window-defined order (as you see in the explained plan
above) then processes the tuples in semi tuple-by-tuple manner to
perform per-frame aggregaion, and finally outputs tuples of the
same number to input. And furthermore, dense_rank() doesn't even
need per-frame aggregations. So none of the planners so far seems
to have chance to use a kind of hash tables to culculate/execute
windowing fucntions. On the another point, automatically
preserving some internal data within a query beyond the end of
the query brings in 'when to discard it?' problem.