与 MS SQL 相比，PostgreSQL 在 Where 和 Group 上非常慢

Question

在尝试解决我们数据库在 Postgre 中的性能问题 SQL 五天后，我决定向您寻求帮助！一周前，我们决定尝试将包含 60M 记录的数据库从 MSSQL 移动到 PostgreSQL，而我们下面的 SQL 在 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

执行计划

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

执行计划URLhttps://explain.depesz.com/s/zLNI

相同的 SQL 在 MSSQL 上花费不到 2 秒，但在 PostgreSQL 上甚至需要 10 秒。日志 table 包含大约 60M 条记录，"UserId" = 7841 AND "DateStamp" > '2019-01-01' AND "DateStamp" < '2020-02-26' where 子句过滤大约 3M 条记录。

Table结构如下

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");

PostgreSQL 服务器是 6CPU 和 16GB ram 服务器，与我们旧的 MSSQL 2CPU 和 8GB RAM 相比，如您所见，PostgreSQL 有更多的计算资源，但是表现更差。两台服务器都有 SSD。

也许问题是PostgreSQL在性能方面不如MSSQL那么先进，这里什么也做不了？

Answer 1

您可以将查询改写为：

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

那么，查询将从索引中受益匪浅：

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

为了进一步提高性能，您可以创建一个覆盖索引:

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

与 MS SQL 相比，PostgreSQL 在 Where 和 Group 上非常慢

PostgreSQL extremely slow on Where and Group comparing to MS SQL

sql

postgresql

query-performance

postgresql-11