On 9/23/24 05:03, Richard Guo wrote:
> On Sun, Sep 22, 2024 at 1:38 PM David Rowley <dgrowle...@gmail.com> wrote:
>> Just looking at the commit message:
>>
>>> The rationale is based on the assumption that incremental sort is
>>> always faster than full sort when there are presorted keys, a premise
>>> that has been applied in various parts of the code. This assumption
>>> does not always hold, particularly in cases with a large skew in the
>>> number of rows within the presorted groups.
>>
>> My understanding is that the worst case for incremental sort is the
>> same as sort, i.e. only 1 presorted group, which is the same effort to
>> sort. Is there something I'm missing?
>
> I was thinking that when there’s a large skew in the number of rows
> per pre-sorted group, incremental sort might underperform full sort.
> As mentioned in the comments for cost_incremental_sort, it has to
> detect the sort groups, plus it needs to perform tuplesort_reset after
> each group. However, I doubt these factors would have a substantial
> impact on the performance of incremental sort. So maybe in the worst
> skewed groups case, incremental sort may still perform similarly to
> full sort.
>
> Another less obvious reason is that in cases of skewed groups, we may
> significantly underestimate the cost of incremental sort. This could
> result in choosing a more expensive subpath under the sort. Such as
> the example in [1], we end up with IndexScan->IncrementalSort rather
> than SeqScan->FullSort, while the former plan is more expensive to
> execute. However, this point does not affect this patch, because for
> a mergejoin, we only consider outerrel's cheapest-total-cost when we
> assume that an explicit sort atop is needed, i.e., we do not have a
> chance to select which subpath to use in this case.
>
> [1]
> https://postgr.es/m/CAMbWs49+CXsrgbq0LD1at-5jR=ahhn0ytdy9yvgxasmfndz...@mail.gmail.com
>
You don't need any skew. Consider this perfectly uniform dataset:
create table t (a int, b int);
insert into t select 100000 * random(), 100 * random()
from generate_series(1,1000000) s(i);
create index on t (a);
vacuum analyze;
checkpoint;
explain analyze select * from t order by a, b;
QUERY PLAN
-----------------------------------------------------------------
Sort (cost=127757.34..130257.34 rows=1000000 width=8)
(actual time=186.288..275.813 rows=1000000 loops=1)
Sort Key: a, b
Sort Method: external merge Disk: 17704kB
-> Seq Scan on t (cost=0.00..14425.00 rows=1000000 width=8)
(actual time=0.005..35.989 rows=1000000 loops=1)
Planning Time: 0.075 ms
Execution Time: 301.143 ms
(6 rows)
set enable_incremental_sort = on;
explain analyze select * from t order by a, b;
QUERY PLAN
-----------------------------------------------------------------
Incremental Sort (cost=1.03..68908.13 rows=1000000 width=8)
(actual time=0.102..497.143 rows=1000000 loops=1)
Sort Key: a, b
Presorted Key: a
Full-sort Groups: 27039 Sort Method: quicksort
Average Memory: 25kB Peak Memory: 25kB
-> Index Scan using t_a_idx on t
(cost=0.42..37244.25 rows=1000000 width=8)
(actual time=0.050..376.403 rows=1000000 loops=1)
Planning Time: 0.057 ms
Execution Time: 519.301 ms
(7 rows)
Sure, the table is very small, but the example is not crazy. In fact,
this is the "nicest" example for estimation - it's perfectly random, no
correlations, no skew.
regards
--
Tomas Vondra
