PostgreSQL extremely slow on Where and Group comparing to MS SQL

Question

After five days of trying to solve the performance problem of our database in PostgreSQL, I decided to ask you a help! One week ago we have decided to try to move our database with 60M records from MSSQL to PostgreSQL and our SQL below is extremely slow on PostgreSQL.

set random_page_cost=1;
set seq_page_cost=5;
set enable_seqscan=on;
set work_mem = '100MB';

SELECT 
       DATE("DateStamp"), "Result", Count(*), Sum("ConversionCost")
FROM 
     "Log"
WHERE 
      "UserId" = 7841 AND "DateStamp" > '2019-01-01' AND "DateStamp" < '2020-02-26'
GROUP BY 
         1,2

The execution plan

Finalize GroupAggregate  (cost=1332160.59..1726394.02 rows=3093547 width=21) (actual time=2929.936..3157.049 rows=714 loops=1) "  Output: (date(""DateStamp"")), ""Result"", count(*), sum(""ConversionCost"")" "  Group Key: (date(""Log"".""DateStamp"")), ""Log"".""Result"""   Buffers: shared hit=2292 read=345810   ->  Gather Merge  (cost=1332160.59..1661945.12 rows=2577956 width=21) (actual time=2929.783..3156.616 rows=2037 loops=1) "        Output: (date(""DateStamp"")), ""Result"", (PARTIAL count(*)), (PARTIAL sum(""ConversionCost""))"
        Workers Planned: 2
        Workers Launched: 2
        Buffers: shared hit=6172 read=857125
        ->  Partial GroupAggregate  (cost=1331160.56..1363385.01 rows=1288978 width=21) (actual time=2906.450..3089.056 rows=679 loops=3) "              Output: (date(""DateStamp"")), ""Result"", PARTIAL count(*), PARTIAL sum(""ConversionCost"")" "              Group Key: (date(""Log"".""DateStamp"")), ""Log"".""Result"""
              Buffers: shared hit=6172 read=857125
              Worker 0: actual time=2895.531..3058.852 rows=675 loops=1
                Buffers: shared hit=1930 read=255687
              Worker 1: actual time=2894.513..3052.916 rows=673 loops=1
                Buffers: shared hit=1950 read=255628
              ->  Sort  (cost=1331160.56..1334383.01 rows=1288978 width=9) (actual time=2906.435..2968.562 rows=1064916 loops=3) "       Output: (date(""DateStamp"")), ""Result"", ""ConversionCost""" "       Sort Key: (date(""Log"".""DateStamp"")), ""Log"".""Result"""
                    Sort Method: quicksort  Memory: 94807kB
                    Worker 0:  Sort Method: quicksort  Memory: 69171kB
                    Worker 1:  Sort Method: quicksort  Memory: 69063kB
                    Buffers: shared hit=6172 read=857125
                    Worker 0: actual time=2895.518..2951.406 rows=951356 loops=1
                      Buffers: shared hit=1930 read=255687
                    Worker 1: actual time=2894.494..2947.892 rows=949038 loops=1
                      Buffers: shared hit=1950 read=255628
                    ->  Parallel Index Scan using "IX_Log_UserId" on public."Log"  (cost=0.56..1200343.50 rows=1288978 width=9) (actual time=0.087..2634.603 rows=1064916 loops=3) "                          Output: date(""DateStamp""), ""Result"", ""ConversionCost"""
                          Index Cond: ("Log"."UserId" = 7841)
                          Filter: (("Log"."DateStamp" > '2019-01-01 00:00:00'::timestamp without time zone) AND ("Log"."DateStamp" < '2020-02-26 00:00:00'::timestamp without time zone))
                          Buffers: shared hit=6144 read=857123
                          Worker 0: actual time=0.077..2653.065 rows=951356 loops=1
                            Buffers: shared hit=1917 read=255685
                          Worker 1: actual time=0.107..2654.640 rows=949038 loops=1
                            Buffers: shared hit=1935 read=255628 Planning Time: 0.330 ms Execution Time: 3163.850 ms

Execution plan URL https://explain.depesz.com/s/zLNI

The same SQL on MSSQL takes under 2 seconds but on PostgreSQL it takes even 10 seconds. The Log table contains about 60M records and "UserId" = 7841 AND "DateStamp" > '2019-01-01' AND "DateStamp" < '2020-02-26' where clause filters about 3M records.

The Table structure is below

create table "Log"
(
    "Id"                    integer generated by default as identity
        constraint "PK_Log"
            primary key,
    "Result"                boolean   not null,
    "DateStamp"             timestamp not null,
    "ConversionCost"        integer   not null,
    "UserId"                integer   not null
        constraint "FK_Log_User_UserId"
            references "User"
            on delete cascade,
);


create index "IX_Log_ConversionCost"
    on "Log" ("ConversionCost");

create index "IX_Log_DateStamp"
    on "Log" ("DateStamp");

create index "IX_Log_Result"
    on "Log" ("Result");

create index "IX_Log_UserId"
    on "Log" ("UserId");

The PostgreSQL server is 6CPU and 16GB of ram server comparing to our old MSSQL 2CPU and 8GB of RAM, as you see PostgreSQL has more computing resources but performs much worse. Both servers have SSD.

Maybe the problem is that PostgreSQL is not so advanced in a performance like MS SQL and nothing can be done here?

Answer 1

You can rephrase the query as:

SELECT 
  DATE("DateStamp"), "Result", Count(*), Sum("ConversionCost")
FROM "Log"
WHERE "UserId" = 7841 
  AND "DateStamp" >= '2019-01-02' 
  AND "DateStamp" < '2020-02-26'
GROUP BY 1,2

Then, the query would greatly benefit from the index:

create index "IX_Log_UserId" on "Log" ("UserId", "DateStamp"));

For further performance you can create a covering index:

create index "IX_Log_UserId" on "Log" (
  "UserId",
  "DateStamp",
  "Result",
  "ConversionCost"
);