HTAP数据库 PostgreSQL 场景与性能测试之 25 - (OLTP) IN , EXISTS 查询-阿里云开发者社区

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 操刀设计，PostgreSQL具备与Oracle类似的功能、性能、架构以及稳定性。

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》

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。

在各种应用场景中都可以看到PostgreSQL的应用：

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：

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)》

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

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

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

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

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

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

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

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

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。

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

参考

《PostgreSQL、Greenplum 应用案例宝典《如来神掌》 - 目录》

《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》

《数据库界的华山论剑 tpc.org》

https://www.postgresql.org/docs/10/static/pgbench.html

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

标签

背景

环境

场景 - IN , EXISTS 查询 (OLTP)

1、背景

2、设计

3、准备测试表

4、准备测试函数(可选)

5、准备测试数据

6、准备测试脚本

7、测试

TPS： 46384

平均响应时间： 1.207 毫秒

1到100万个输入值的响应时间

参考

关系型数据库

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