Re: Is there any good optimization solution to improve the query efficiency? - Mailing list pgsql-general

Hi everyone,

I'm running into some performance issues with my SQL query.

The following SQL query is taking a long time to execute.

Execution Plan：

explain analyse

select * from TBL_RES

left outer join(select T_CUST.RSNO RSNO2 ,

T_CUST.KNO ,

T_CUST.AGE ,

T_CUST.GST

from TBL_CUST T_CUST ,

(select T_CUST.RSNO ,

T_CUST.KNO ,

MIN(T_CUST.GSTSEQ) GSTSEQ

from TBL_CUST T_CUST ,

TBL_POV T_POV ,

TBL_RES T_RES

where T_CUST.STSFLG = 'T'

and T_CUST.DISPSEQ <> 9999

AND T_CUST.KFIX = '0'

and T_POV.CRSNO = T_RES.CRSNO

and T_RES.RSNO = T_CUST.RSNO

group by T_CUST.RSNO , T_CUST.KNO) T_POV2

where T_POV2.RSNO = T_CUST.RSNO

and T_POV2.KNO = T_CUST.KNO

and T_POV2.GSTSEQ = T_CUST.GSTSEQ) T_POV3 on TBL_RES.RSNO = T_POV3.RSNO2

and TBL_RES.KNO = T_POV3.KNO

where TBL_RES.CID >= to_date('2022/07/01', 'YYYY/MM/DD')

and TBL_RES.CID <= to_date('2022/07/31', 'YYYY/MM/DD')

and TBL_RES.COD >= to_date('2022/07/01', 'YYYY/MM/DD')

and TBL_RES.COD <= to_date('2022/07/31', 'YYYY/MM/DD')

----- Execution Plan -----

Nested Loop Left Join  (cost=254388.44..452544.70 rows=473 width=3545) (actual time=3077.312..996048.714 rows=15123 loops=1)

  Join Filter: ((TBL_RES.RSNO = T_CUST.RSNO) AND ((TBL_RES.KNO)::text = (T_CUST.KNO)::text))

  Rows Removed by Join Filter: 4992268642

  ->  Gather  (cost=1000.00..58424.35 rows=473 width=3489) (actual time=0.684..14.158 rows=15123 loops=1)

        Workers Planned: 2

        Workers Launched: 2

        ->  Parallel Seq Scan on TBL_RES  (cost=0.00..57377.05 rows=197 width=3489) (actual time=0.096..279.504 rows=5041 loops=3)

              Filter: ((CID >= to_date('2022/07/01'::text, 'YYYY/MM/DD'::text)) AND (CID <= to_date('2022/07/31'::text, 'YYYY/MM/DD'::text)) AND (COD >= to_date('2022/07/01'::text, 'YYYY/MM/DD'::text)) AND (COD <= to_date('2022/07/31'::text, 'YYYY/MM/DD'::text)))

              Rows Removed by Filter: 161714

  ->  Materialize  (cost=253388.44..394112.08 rows=1 width=56) (actual time=0.081..26.426 rows=330111 loops=15123)

        ->  Hash Join  (cost=253388.44..394112.07 rows=1 width=56) (actual time=1197.484..2954.084 rows=330111 loops=1)

              Hash Cond: ((T_CUST.RSNO = T_CUST_1.RSNO) AND ((T_CUST.KNO)::text = (T_CUST_1.KNO)::text) AND (T_CUST.gstseq = (min(T_CUST_1.gstseq))))

              ->  Seq Scan on TBL_CUST T_CUST  (cost=0.00..79431.15 rows=2000315 width=61) (actual time=0.015..561.005 rows=2000752 loops=1)

              ->  Hash  (cost=246230.90..246230.90 rows=262488 width=50) (actual time=1197.025..1209.957 rows=330111 loops=1)

                    Buckets: 65536  Batches: 8  Memory Usage: 2773kB

                    ->  Finalize GroupAggregate  (cost=205244.84..243606.02 rows=262488 width=50) (actual time=788.552..1116.074 rows=330111 loops=1)

                          Group Key: T_CUST_1.RSNO, T_CUST_1.KNO

                          ->  Gather Merge  (cost=205244.84..238964.80 rows=268846 width=50) (actual time=788.547..982.479 rows=330111 loops=1)

                                Workers Planned: 2

                                Workers Launched: 1

                                ->  Partial GroupAggregate  (cost=204244.81..206933.27 rows=134423 width=50) (actual time=784.032..900.979 rows=165056 loops=2)

                                      Group Key: T_CUST_1.RSNO, T_CUST_1.KNO

                                      ->  Sort  (cost=204244.81..204580.87 rows=134423 width=23) (actual time=784.019..833.791 rows=165061 loops=2)

                                            Sort Key: T_CUST_1.RSNO, T_CUST_1.KNO

                                            Sort Method: external merge  Disk: 5480kB

                                            Worker 0:  Sort Method: external merge  Disk: 5520kB

                                            ->  Parallel Hash Join  (cost=111758.80..190036.38 rows=134423 width=23) (actual time=645.302..716.247 rows=165061 loops=2)

                                                  Hash Cond: (T_CUST_1.RSNO = T_RES.RSNO)

                                                  ->  Parallel Seq Scan on TBL_CUST T_CUST_1  (cost=0.00..74013.63 rows=204760 width=23) (actual time=0.018..264.390 rows=165058 loops=2)

                                                        Filter: ((dispseq <> '9999'::numeric) AND ((stsflg)::text = 'T'::text) AND ((KFIX)::text = '0'::text))

                                                        Rows Removed by Filter: 835318

                                                  ->  Parallel Hash  (cost=109508.52..109508.52 rows=137142 width=8) (actual time=343.593..343.896 rows=165058 loops=2)

                                                        Buckets: 131072  Batches: 8  Memory Usage: 3008kB

                                                        ->  Parallel Hash Join  (cost=51834.70..109508.52 rows=137142 width=8) (actual time=256.732..314.368 rows=165058 loops=2)

                                                              Hash Cond: ((T_RES.crsno)::text = (T_POV.crsno)::text)

                                                              ->  Parallel Seq Scan on TBL_RES T_RES  (cost=0.00..53199.02 rows=208902 width=17) (actual time=0.007..100.510 rows=250132 loops=2)

                                                              ->  Parallel Hash  (cost=49450.42..49450.42 rows=137142 width=9) (actual time=122.308..122.309 rows=165054 loops=2)

                                                                    Buckets: 131072  Batches: 8  Memory Usage: 2976kB

                                                                    ->  Parallel Seq Scan on TBL_POV T_POV  (cost=0.00..49450.42 rows=137142 width=9) (actual time=0.037..89.470 rows=165054 loops=2)

Planning Time: 1.064 ms

Execution Time: 996062.382 ms

--------------------------------------------------------------------------------

The amount of data in the table is as follows.

TBL_RES    500265

TBL_CUST   2000752

TBL_POV    330109

Any suggestions for improving the performance of the query would be greatly appreciated.

Thanks in advance!