SELECT is faster on SQL Server - Mailing list pgsql-general

Hi all

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.

This is the table definition -

                                          Table "prop.ar_totals"
      Column      |     Type      | Collation | Nullable 
|                    Default
-----------------+---------------+-----------+----------+------------------------------------------------
  row_id          | integer       |           | not null | 
nextval('prop.ar_totals_row_id_seq'::regclass)
  created_id      | integer       |           |          | 0
  deleted_id      | integer       |           |          | 0
  ledger_row_id   | integer       |           |          |
  location_row_id | integer       |           |          |
  function_row_id | integer       |           |          |
  source_code_id  | integer       |           |          |
  tran_date       | date          |           |          |
  tran_day        | numeric(21,2) |           |          | 0
  tran_tot        | numeric(21,2) |           |          | 0
Indexes:
     "ar_totals_pkey" PRIMARY KEY, btree (row_id)
     "_ar_totals" UNIQUE, btree (ledger_row_id NULLS FIRST, 
location_row_id NULLS FIRST, function_row_id NULLS FIRST, source_code_id 
NULLS FIRST, tran_date NULLS FIRST) WHERE deleted_id = 0
     "ar_tots_cover" btree (ledger_row_id NULLS FIRST, location_row_id 
NULLS FIRST, function_row_id NULLS FIRST, source_code_id NULLS FIRST, 
tran_date DESC NULLS LAST, tran_day NULLS FIRST, tran_tot NULLS FIRST) 
WHERE deleted_id = 0

This is the SELECT -

SELECT
     '2018-03-01' AS op_date, '2018-03-31' AS cl_date,
     cl_bal.source_code_id, op_bal.op_tot, cl_bal.cl_tot
FROM (
     SELECT a.source_code_id, SUM(a.tran_tot) AS cl_tot FROM (
         SELECT source_code_id, tran_tot,
         ROW_NUMBER() OVER (PARTITION BY
         ledger_row_id, location_row_id, function_row_id, source_code_id
         ORDER BY tran_date DESC) row_num
         FROM prop.ar_totals WHERE deleted_id = 0 AND tran_date <= 
'2018-03-31'
             AND ledger_row_id = 1
         ) AS a
     WHERE a.row_num = 1
     GROUP BY a.source_code_id
     ) as cl_bal
LEFT JOIN (
     SELECT a.source_code_id, SUM(a.tran_tot) AS op_tot FROM (
         SELECT source_code_id, tran_tot,
         ROW_NUMBER() OVER (PARTITION BY
         ledger_row_id, location_row_id, function_row_id, source_code_id
         ORDER BY tran_date DESC) row_num
         FROM prop.ar_totals WHERE deleted_id = 0 AND tran_date < 
'2018-03-01'
             AND ledger_row_id = 1
         ) AS a
     WHERE a.row_num = 1
     GROUP BY a.source_code_id
     ) as op_bal
ON op_bal.source_code_id = cl_bal.source_code_id

This is the EXPLAIN -

QUERY PLAN

------------------------------------------------------------------------------------------------------------------------------------------------------------
  Nested Loop Left Join  (cost=5.66..5.74 rows=1 width=132)
    Join Filter: (a_1.source_code_id = a.source_code_id)
    ->  GroupAggregate  (cost=3.65..3.67 rows=1 width=36)
          Group Key: a.source_code_id
          ->  Sort  (cost=3.65..3.65 rows=1 width=10)
                Sort Key: a.source_code_id
                ->  Subquery Scan on a  (cost=2.36..3.64 rows=1 width=10)
                      Filter: (a.row_num = 1)
                      ->  WindowAgg  (cost=2.36..3.24 rows=32 width=34)
                            ->  Sort  (cost=2.36..2.44 rows=32 width=26)
                                  Sort Key: ar_totals.location_row_id, 
ar_totals.function_row_id, ar_totals.source_code_id, ar_totals.tran_date 
DESC
                                  ->  Seq Scan on ar_totals 
(cost=0.00..1.56 rows=32 width=26)
                                        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)
          Group Key: a_1.source_code_id
          ->  Sort  (cost=2.01..2.02 rows=1 width=10)
                Sort Key: a_1.source_code_id
                ->  Subquery Scan on a_1  (cost=1.68..2.00 rows=1 width=10)
                      Filter: (a_1.row_num = 1)
                      ->  WindowAgg  (cost=1.68..1.90 rows=8 width=34)
                            ->  Sort  (cost=1.68..1.70 rows=8 width=26)
                                  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
                                  ->  Seq Scan on ar_totals ar_totals_1  
(cost=0.00..1.56 rows=8 width=26)
                                        Filter: ((tran_date < 
'2018-03-01'::date) AND (deleted_id = 0) AND (ledger_row_id = 1))
(24 rows)

Maybe SQL Server has a way of optimising this, and there is nothing more 
I can do. I can live with that. But I just thought I would ask the question.

Thanks for any advice.

Frank Millman