On 2021-03-19 10:56 AM, Pavel Stehule
wrote:
pá 19. 3. 2021 v 9:53 odesílatel Frank Millman <frank@xxxxxxxxxxxx> napsal:
On 2021-03-19 10:29 AM, Thomas Kellerer wrote:
> Frank Millman schrieb am 19.03.2021 um 09:19:
>> This may be a non-issue, and I don't want to waste your time. But perhaps someone can have a look to see if there is anything obvious I have missed.
>>
>> I am writing a cross-platform accounting app, and I test using Sql
>> Server on Windows 10 and PostgreSql on Fedora 31. Performance is
>> usually very similar, with a slight edge to PostgreSql. Now I have a
>> SELECT which runs over twice as fast on Sql Server compared to
>> PostgreSql.
>>
> Can you change the SELECT statement?
>
> Very often "distinct on ()" is faster in Postgres compared to the equivalent solution using window functions
>
> Something along the lines (for the first derived table):
>
> SELECT ...
> FROM (
> SELECT a.source_code_id, SUM(a.tran_tot) AS cl_tot
> FROM (
> SELECT distinct on (location_row_id, function_row_id, source_code_id) source_code_id, tran_tot
> FROM prop.ar_totals
> WHERE deleted_id = 0
> AND tran_date <= '2018-03-31'
> AND ledger_row_id = 1
> ORDER BY location_row_id, function_row_id, source_code_id, tran_date DESC
> ) AS a
> GROUP BY a.source_code_id
> ) as cl_bal
> ...
Thanks, Thomas
I tried that, and it ran about 10% faster. Every little helps, but SQL
Server appears to have some secret sauce!
can you send a result of EXPLAIN ANALYZE?QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------------------------
Nested Loop Left Join (cost=5.66..5.74 rows=1 width=132) (actual time=0.213..0.248 rows=5 loops=1)
Join Filter: (a_1.source_code_id = a.source_code_id)
Rows Removed by Join Filter: 4
-> GroupAggregate (cost=3.65..3.67 rows=1 width=36) (actual time=0.144..0.157 rows=5 loops=1)
Group Key: a.source_code_id
-> Sort (cost=3.65..3.65 rows=1 width=10) (actual time=0.131..0.135 rows=29 loops=1)
Sort Key: a.source_code_id
Sort Method: quicksort Memory: 26kB
-> Subquery Scan on a (cost=2.36..3.64 rows=1 width=10) (actual time=0.063..0.116 rows=29 loops=1)
Filter: (a.row_num = 1)
Rows Removed by Filter: 3
-> WindowAgg (cost=2.36..3.24 rows=32 width=34) (actual time=0.062..0.107 rows=32 loops=1)
-> Sort (cost=2.36..2.44 rows=32 width=26) (actual time=0.054..0.059 rows=32 loops=1)
Sort Key: ar_totals.location_row_id, ar_totals.function_row_id, ar_totals.source_code_id, ar_totals.tran_date DESC
Sort Method: quicksort Memory: 27kB
-> Seq Scan on ar_totals (cost=0.00..1.56 rows=32 width=26) (actual time=0.014..0.028 rows=32 loops=1)
Filter: ((tran_date <= '2018-03-31'::date) AND (deleted_id = 0) AND (ledger_row_id = 1))
-> GroupAggregate (cost=2.01..2.03 rows=1 width=36) (actual time=0.017..0.017 rows=1 loops=5)
Group Key: a_1.source_code_id
-> Sort (cost=2.01..2.02 rows=1 width=10) (actual time=0.012..0.013 rows=8 loops=5)
Sort Key: a_1.source_code_id
Sort Method: quicksort Memory: 25kB
-> Subquery Scan on a_1 (cost=1.68..2.00 rows=1 width=10) (actual time=0.032..0.047 rows=8 loops=1)
Filter: (a_1.row_num = 1)
-> WindowAgg (cost=1.68..1.90 rows=8 width=34) (actual time=0.031..0.043 rows=8 loops=1)
-> Sort (cost=1.68..1.70 rows=8 width=26) (actual time=0.023..0.024 rows=8 loops=1)
Sort Key: ar_totals_1.location_row_id, ar_totals_1.function_row_id, ar_totals_1.source_code_id, ar_totals_1.tran_date DESC
Sort Method: quicksort Memory: 25kB
-> Seq Scan on ar_totals ar_totals_1 (cost=0.00..1.56 rows=8 width=26) (actual time=0.006..0.013 rows=8 loops=1)
Filter: ((tran_date < '2018-03-01'::date) AND (deleted_id = 0) AND (ledger_row_id = 1))
Rows Removed by Filter: 24
Planning Time: 0.479 ms
Execution Time: 0.344 ms
(33 rows)
