HTAP数据库 PostgreSQL 场景与性能测试之 25 - (OLTP) IN , EXISTS 查询

超、凢脫俗 2022-01-30 04:06 124阅读 0赞

### 标签 ###

PostgreSQL , HTAP , OLTP , OLAP , 场景与性能测试

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

## 背景 ##

PostgreSQL是一个历史悠久的数据库，历史可以追溯到1973年，最早由2014计算机图灵奖得主，关系数据库的鼻祖[Michael\_Stonebraker][Michael_Stonebraker] 操刀设计，PostgreSQL具备与Oracle类似的功能、性能、架构以及稳定性。

[![pic][]][pic 1]

PostgreSQL社区的贡献者众多，来自全球各个行业，历经数年，PostgreSQL 每年发布一个大版本，以持久的生命力和稳定性著称。

2017年10月，PostgreSQL 推出10 版本，携带诸多惊天特性，目标是胜任OLAP和OLTP的HTAP混合场景的需求：

[《最受开发者欢迎的HTAP数据库PostgreSQL 10特性》][HTAP_PostgreSQL 10]

1、多核并行增强

2、fdw 聚合下推

3、逻辑订阅

4、分区

5、金融级多副本

6、json、jsonb全文检索

7、还有插件化形式存在的特性，如 向量计算、JIT、SQL图计算、SQL流计算、分布式并行计算、时序处理、基因测序、化学分析、图像分析 等。

[![pic][pic 2]][pic_pic 2]

在各种应用场景中都可以看到PostgreSQL的应用：

[![pic][pic 3]][pic_pic 3]

PostgreSQL近年来的发展非常迅猛，从知名数据库评测网站dbranking的数据库评分趋势，可以看到PostgreSQL向上发展的趋势：

[![pic][pic 4]][pic_pic 4]

从每年PostgreSQL中国召开的社区会议，也能看到同样的趋势，参与的公司越来越多，分享的公司越来越多，分享的主题越来越丰富，横跨了 传统企业、互联网、医疗、金融、国企、物流、电商、社交、车联网、共享XX、云、游戏、公共交通、航空、铁路、军工、培训、咨询服务等 行业。

接下来的一系列文章，将给大家介绍PostgreSQL的各种应用场景以及对应的性能指标。

## 环境 ##

环境部署方法参考：

[《PostgreSQL 10 + PostGIS + Sharding(pg\_pathman) + MySQL(fdw外部表) on ECS 部署指南(适合新用户)》][PostgreSQL 10 _ PostGIS _ Sharding_pg_pathman_ _ MySQL_fdw_ on ECS]

阿里云 ECS：`56核，224G，1.5TB*2 SSD云盘`。

操作系统：`CentOS 7.4 x64`

数据库版本：`PostgreSQL 10`

PS：ECS的CPU和IO性能相比物理机会打一定的折扣，可以按下降1倍性能来估算。跑物理主机可以按这里测试的性能乘以2来估算。

## 场景 - IN , EXISTS 查询 (OLTP) ##

### 1、背景 ###

in 查询，多用在多个输入值的匹配场景。

实际上PostgreSQL支持很多种多个输入值匹配的语法。

1、`in (...)`

2、`in (table or subquery or srf)`

3、`= any (array)`

4、`exists (select 1 from (values (),(),...) as t(id) where x.?=t.id)`

5、`=? or =? or =? or .....`

他们的执行计划分别如下，(`in (values....) or = any (array)`最佳) ：

postgres=# explain select * from a where id in (1,2,3,4,5);  
                               QUERY PLAN                              
    -----------------------------------------------------------------  
     Index Scan using a_pkey on a  (cost=0.43..9.46 rows=5 width=45)  
       Index Cond: (id = ANY ('{1,2,3,4,5}'::integer[]))  
    (2 rows)  
      
    postgres=# explain select * from a where id = any (array[1,2,3,4,5]);  
                               QUERY PLAN                              
    -----------------------------------------------------------------  
     Index Scan using a_pkey on a  (cost=0.43..9.46 rows=5 width=45)  
       Index Cond: (id = ANY ('{1,2,3,4,5}'::integer[]))  
    (2 rows)  
      
    postgres=# explain select * from a where id = any (array(select generate_series(1,10)));  
                                QUERY PLAN                               
    -------------------------------------------------------------------  
     Index Scan using a_pkey on a  (cost=5.45..22.74 rows=10 width=45)  
       Index Cond: (id = ANY ($0))  
       InitPlan 1 (returns $0)  
         ->  ProjectSet  (cost=0.00..5.02 rows=1000 width=4)  
               ->  Result  (cost=0.00..0.01 rows=1 width=0)  
    (5 rows)  
      
    postgres=# explain select * from a where id = any (array(select id from (values (1),(2),(3),(4),(5)) t (id)));  
                                 QUERY PLAN                                
    ---------------------------------------------------------------------  
     Index Scan using a_pkey on a  (cost=0.50..17.79 rows=10 width=45)  
       Index Cond: (id = ANY ($0))  
       InitPlan 1 (returns $0)  
         ->  Values Scan on "*VALUES*"  (cost=0.00..0.06 rows=5 width=4)  
    (4 rows)  
      
    postgres=# explain select * from a where id in (select id from (values (1),(2),(3),(4),(5)) t (id));  
                                   QUERY PLAN                                  
    -------------------------------------------------------------------------  
     Nested Loop  (cost=0.51..14.39 rows=5 width=45)  
       ->  HashAggregate  (cost=0.07..0.12 rows=5 width=4)  
             Group Key: "*VALUES*".column1  
             ->  Values Scan on "*VALUES*"  (cost=0.00..0.06 rows=5 width=4)  
       ->  Index Scan using a_pkey on a  (cost=0.43..2.85 rows=1 width=45)  
             Index Cond: (id = "*VALUES*".column1)  
    (6 rows)  
      
    postgres=# explain select * from a where exists (select 1 from (values (1),(2),(3),(4),(5)) t (id) where t.id=a.id);  
                                   QUERY PLAN                                  
    -------------------------------------------------------------------------  
     Nested Loop  (cost=0.51..14.39 rows=5 width=45)  
       ->  HashAggregate  (cost=0.07..0.12 rows=5 width=4)  
             Group Key: "*VALUES*".column1  
             ->  Values Scan on "*VALUES*"  (cost=0.00..0.06 rows=5 width=4)  
       ->  Index Scan using a_pkey on a  (cost=0.43..2.85 rows=1 width=45)  
             Index Cond: (id = "*VALUES*".column1)  
    (6 rows)  
      
    postgres=# explain select * from a where id=1 or id=2 or id=3 or id=4 or id =5;  
                                     QUERY PLAN                                   
    ----------------------------------------------------------------------------  
     Bitmap Heap Scan on a  (cost=8.22..14.32 rows=5 width=45)  
       Recheck Cond: ((id = 1) OR (id = 2) OR (id = 3) OR (id = 4) OR (id = 5))  
       ->  BitmapOr  (cost=8.22..8.22 rows=5 width=0)  
             ->  Bitmap Index Scan on a_pkey  (cost=0.00..1.64 rows=1 width=0)  
                   Index Cond: (id = 1)  
             ->  Bitmap Index Scan on a_pkey  (cost=0.00..1.64 rows=1 width=0)  
                   Index Cond: (id = 2)  
             ->  Bitmap Index Scan on a_pkey  (cost=0.00..1.64 rows=1 width=0)  
                   Index Cond: (id = 3)  
             ->  Bitmap Index Scan on a_pkey  (cost=0.00..1.64 rows=1 width=0)  
                   Index Cond: (id = 4)  
             ->  Bitmap Index Scan on a_pkey  (cost=0.00..1.64 rows=1 width=0)  
                   Index Cond: (id = 5)  
    (13 rows)

### 2、设计 ###

1亿记录，查询匹配多个输入值的性能。分别输入1,10,100,1000,10000,100000,1000000个值作为匹配条件。

1、`in (...)`

2、`in (table or subquery or srf)`

3、`= any (array)`

4、`exists (select 1 from (values (),(),...) as t(id) where x.?=t.id)`

5、`=? or =? or =? or .....`

### 3、准备测试表 ###

create table t_in_test (id int primary key, info text, crt_time timestamp);

### 4、准备测试函数(可选) ###

### 5、准备测试数据 ###

insert into t_in_test select generate_series(1,100000000), md5(random()::text), clock_timestamp();

### 6、准备测试脚本 ###

1、`in (...)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

do language plpgsql $$  
    declare  
      arr text;  
      ts timestamp := clock_timestamp();  
      mx int8;  
    begin  
      for i in 0..6 loop  
        mx := (1*(10^i))::int8;  
        select string_agg((random()*100000)::int::text, ',') into arr from generate_series(1, mx);  
        ts := clock_timestamp();  
        execute 'select * from t_in_test where id in ('||arr||')';  
        raise notice '%: %', mx, clock_timestamp()-ts;  
      end loop;  
    end;  
    $$ ;

2、`in (table or subquery or srf)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

do language plpgsql $$  
    declare  
      arr text;  
      ts timestamp := clock_timestamp();  
      mx int8;  
    begin  
      for i in 0..6 loop  
        mx := (1*(10^i))::int8;  
        ts := clock_timestamp();  
        perform * from t_in_test where id in ( select (random()*100000)::int from generate_series(1, mx) );  
        raise notice '%: %', mx, clock_timestamp()-ts;  
      end loop;  
    end;  
    $$ ;

3、`= any (array)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

do language plpgsql $$  
    declare  
      arr int[];  
      ts timestamp := clock_timestamp();  
      mx int8;  
    begin  
      for i in 0..6 loop  
        mx := (1*(10^i))::int8;  
        select array_agg((random()*100000)::int) into arr from generate_series(1, mx);  
        ts := clock_timestamp();  
        perform * from t_in_test where id = any ( arr );  
        raise notice '%: %', mx, clock_timestamp()-ts;  
      end loop;  
    end;  
    $$ ;

4、`exists (select 1 from (values (),(),...) as t(id) where x.?=t.id)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

do language plpgsql $$  
    declare  
      ts timestamp := clock_timestamp();  
      mx int8;  
    begin  
      for i in 0..6 loop  
        mx := (1*(10^i))::int8;  
        ts := clock_timestamp();  
        perform * from t_in_test where exists ( select 1 from ( select (random()*100000)::int id from generate_series(1,mx) ) t where t_in_test.id=t.id );  
        raise notice '%: %', mx, clock_timestamp()-ts;  
      end loop;  
    end;  
    $$ ;

5、压测

匹配1 ~ 100个输入值，求聚合。高并发。

vi test.sql  
      
    \set x random(1,100)  
    select count(*) from t_in_test where id = any(array(select (random()*100000000)::int from generate_series(1,:x)));

压测

CONNECTS=56    
    TIMES=300    
    export PGHOST=$PGDATA    
    export PGPORT=1999    
    export PGUSER=postgres    
    export PGPASSWORD=postgres    
    export PGDATABASE=postgres    
        
    pgbench -M prepared -n -r -f ./test.sql -P 5 -c $CONNECTS -j $CONNECTS -T $TIMES

### 7、测试 ###

1、`in (...)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

NOTICE:  1: 00:00:00.000256  
    NOTICE:  10: 00:00:00.000173  
    NOTICE:  100: 00:00:00.000772  
    NOTICE:  1000: 00:00:00.004445  
    NOTICE:  10000: 00:00:00.024073  
    NOTICE:  100000: 00:00:00.195439  
    NOTICE:  1000000: 00:00:01.638982  
    DO

2、`in (table or subquery or srf)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

NOTICE:  1: 00:00:00.00044  
    NOTICE:  10: 00:00:00.000244  
    NOTICE:  100: 00:00:00.000788  
    NOTICE:  1000: 00:00:00.004455  
    NOTICE:  10000: 00:00:00.028793  
    NOTICE:  100000: 00:00:00.187841  
    NOTICE:  1000000: 00:00:00.583744  
    DO

3、`= any (array)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

NOTICE:  1: 00:00:00.000216  
    NOTICE:  10: 00:00:00.000151  
    NOTICE:  100: 00:00:00.000654  
    NOTICE:  1000: 00:00:00.00399  
    NOTICE:  10000: 00:00:00.021216  
    NOTICE:  100000: 00:00:00.106335  
    NOTICE:  1000000: 00:00:00.386113  
    DO

4、`exists (select 1 from (values (),(),...) as t(id) where x.?=t.id)`

1,10,100,1000,10000,100000,1000000 个输入值的测试性能

NOTICE:  1: 00:00:00.000458
    NOTICE:  10: 00:00:00.000224
    NOTICE:  100: 00:00:00.000687
    NOTICE:  1000: 00:00:00.003916
    NOTICE:  10000: 00:00:00.02734
    NOTICE:  100000: 00:00:00.187671
    NOTICE:  1000000: 00:00:00.570389
    DO

5、匹配1 ~ 100个输入值，求聚合。高并发。

transaction type: ./test.sql  
    scaling factor: 1  
    query mode: prepared  
    number of clients: 56  
    number of threads: 56  
    duration: 300 s  
    number of transactions actually processed: 13913566  
    latency average = 1.207 ms  
    latency stddev = 0.840 ms  
    tps = 46378.142149 (including connections establishing)  
    tps = 46384.723274 (excluding connections establishing)  
    script statistics:  
     - statement latencies in milliseconds:  
             0.002  \set x random(1,100)  
             1.207  select count(*) from t_in_test where id = any(array(select (random()*100000000)::int from generate_series(1,:x)));

#### TPS： 46384 ####

5、匹配1 ~ 100个输入值，求聚合。高并发。

#### 平均响应时间： 1.207 毫秒 ####

5、匹配1 ~ 100个输入值，求聚合。高并发。

### 1到100万个输入值的响应时间 ###

1亿条记录，匹配100万个输入值( `= any (array)` )，只需要386毫秒。

## 参考 ##

[《PostgreSQL、Greenplum 应用案例宝典《如来神掌》 - 目录》][PostgreSQL_Greenplum _ -]

[《数据库选型之 - 大象十八摸 - 致 架构师、开发者》][- _ - _]

[《PostgreSQL 使用 pgbench 测试 sysbench 相关case》][PostgreSQL _ pgbench _ sysbench _case]

[《数据库界的华山论剑 tpc.org》][tpc.org]

[https://www.postgresql.org/docs/10/static/pgbench.html][https_www.postgresql.org_docs_10_static_pgbench.html]

[Michael_Stonebraker]: https://en.wikipedia.org/wiki/Michael_Stonebraker
[pic]: /images/20220130/85d599ab878e43208899645105be61d6.png
[pic 1]: https://github.com/digoal/blog/blob/master/201711/20171107_02_pic_003.jpg
[HTAP_PostgreSQL 10]: https://github.com/digoal/blog/blob/master/201710/20171029_01.md
[pic 2]: https://github.com/digoal/blog/raw/master/201711/20171107_02_pic_001.jpg
[pic_pic 2]: /images/20220130/244e6461c962432aa8415b9ce52d4663.png
[pic 3]: https://github.com/digoal/blog/raw/master/201706/20170601_02_pic_002.png
[pic_pic 3]: /images/20220130/54a9bdc7660841dbb96362e60629cda0.png
[pic 4]: https://github.com/digoal/blog/raw/master/201711/20171107_02_pic_002.jpg
[pic_pic 4]: /images/20220130/c1c684d9c4ec4de4a805fd445c5e42fe.png
[PostgreSQL 10 _ PostGIS _ Sharding_pg_pathman_ _ MySQL_fdw_ on ECS]: https://github.com/digoal/blog/blob/master/201710/20171018_01.md
[PostgreSQL_Greenplum _ -]: https://github.com/digoal/blog/blob/master/201706/20170601_02.md
[- _ - _]: https://github.com/digoal/blog/blob/master/201702/20170209_01.md
[PostgreSQL _ pgbench _ sysbench _case]: https://github.com/digoal/blog/blob/master/201610/20161031_02.md
[tpc.org]: https://github.com/digoal/blog/blob/master/201701/20170125_01.md
[https_www.postgresql.org_docs_10_static_pgbench.html]: https://www.postgresql.org/docs/10/static/pgbench.html