标签
PostgreSQL , 分区表 , native partition , 性能 , pg_pathman , plpgsql , 动态SQL , 服务端绑定变量 , prepare , execute
背景
目前PG的native partition分区性能不佳，一种解决方法是使用pg_pathman插件，另一种方法是业务上直接插分区，还有一种方法是使用UDF函数接口（函数内部使用prepared statement来降低PARSE CPU开销）。
本文提供的是UDF的例子，以及性能比对。
例子
1、创建分区表
create table p (id int , info text, crt_time timestamp) partition by list (abs(mod(id,128)));
2、创建128个分区
do language plpgsql $$
declare
begin
for i in 0..127 loop
execute format('create table p%s partition of p for values in (%s)', i, i);
end loop;
end;
$$;
直接插分区主表
vi test.sql
\set id random(1,2000000000)
insert into p values (:id, 'test', now());
性能
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 64 -j 64 -T 120
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
progress: 1.0 s, 26287.2 tps, lat 1.178 ms stddev 0.418
progress: 2.0 s, 27441.8 tps, lat 1.166 ms stddev 0.393
progress: 3.0 s, 27526.0 tps, lat 1.163 ms stddev 0.398
批量插性能
vi test.sql
insert into p values (1,'test',now()),(2,'test',now()),(3,'test',now()),(4,'test',now()),(5,'test',now()),(6,'test',now()),(7,'test',now()),(8,'test',now()),(9,'test',now()),(10,'test',now());
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
progress: 1.0 s, 26240.5 tps, lat 1.179 ms stddev 0.462
progress: 2.0 s, 28285.8 tps, lat 1.131 ms stddev 0.393
progress: 3.0 s, 28185.1 tps, lat 1.135 ms stddev 0.423
progress: 4.0 s, 28266.1 tps, lat 1.132 ms stddev 0.395
progress: 5.0 s, 28248.9 tps, lat 1.133 ms stddev 0.438
progress: 6.0 s, 26739.0 tps, lat 1.197 ms stddev 1.154
progress: 7.0 s, 28075.1 tps, lat 1.140 ms stddev 0.426
progress: 8.0 s, 28297.8 tps, lat 1.131 ms stddev 0.384
使用UDF+绑定变量插分区
1、绑定变量的语法
postgres=# \h prepare
Command: PREPARE
Description: prepare a statement for execution
Syntax:
PREPARE name [ ( data_type [, ...] ) ] AS statement
postgres=# \h execute
Command: EXECUTE
Description: execute a prepared statement
Syntax:
EXECUTE name [ ( parameter [, ...] ) ]
2、写一个UDF，使用绑定变量插入
create or replace function ins_p(int, text, timestamp) returns void as $$
declare
suffix text := abs(mod($1,128));
begin
execute format('execute ps%s(%s, %L, %L)', suffix, $1, $2, $3);
exception when others then
execute format('prepare ps%s(int,text,timestamp) as insert into p%s (id,info,crt_time) values ($1,$2,$3)', suffix, suffix);
execute format('execute ps%s(%s, %L, %L)', suffix, $1, $2, $3);
end;
$$ language plpgsql strict;
3、性能
vi test.sql
\set id random(1,2000000000)
select ins_p(:id, 'test', now()::timestamp);
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
progress: 1.0 s, 192814.1 tps, lat 0.161 ms stddev 0.092
progress: 2.0 s, 205480.6 tps, lat 0.156 ms stddev 0.061
progress: 3.0 s, 209206.4 tps, lat 0.153 ms stddev 0.058
progress: 4.0 s, 206333.8 tps, lat 0.155 ms stddev 0.061
如果是BATCH写入，可以改一下这个UDF如下
create or replace function ins_p(int, text, timestamp) returns void as $$
declare
suffix text := abs(mod($1,128));
begin
execute format('execute ps%s(%s, %L, %L)', suffix, $1, $2, $3);
exception when others then
execute format('prepare ps%s(int,text,timestamp) as insert into p%s (id,info,crt_time) values ($1,$2,$3)', suffix, suffix);
execute format('execute ps%s(%s, %L, %L)', suffix, $1, $2, $3);
end;
$$ language plpgsql strict;
create or replace function ins_p_batch(p[]) returns void as $$
declare
i p;
begin
foreach i in array $1 loop
perform ins_p(i.id, i.info, i.crt_time);
end loop;
end;
$$ language plpgsql strict;
batch使用举例
postgres=# select count(*) from p;
count
----------
28741670
(1 row)
Time: 390.775 ms
postgres=# select ins_p_batch((select array_agg(p) from (select p from p limit 10000) t));
ins_p_batch
-------------
(1 row)
Time: 247.861 ms
postgres=# select count(*) from p;
count
----------
28751670
(1 row)
Time: 383.485 ms
postgres=# select array_agg(p) from (select p from p limit 10) t;
-[ RECORD 1 ]-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
array_agg | {"(1269675648,test,\"2019-01-09 17:08:35.432933\")","(1515917568,test,\"2019-01-09 17:08:35.435001\")","(137413760,test,\"2019-01-09 17:08:35.438484\")","(1750920192,test,\"2019-01-09 17:08:35.443544\")","(849316096,test,\"2019-01-09 17:08:35.448552\")","(891638016,test,\"2019-01-09 17:08:35.449074\")","(320902144,test,\"2019-01-09 17:08:35.449142\")","(95829120,test,\"2019-01-09 17:08:35.453658\")","(358048256,test,\"2019-01-09 17:08:35.454924\")","(1009512320,test,\"2019-01-09 17:08:35.457164\")"}
Time: 1.771 ms
postgres=# select ins_p_batch('{"(1269675648,test,\"2019-01-09 17:08:35.432933\")","(1515917568,test,\"2019-01-09 17:08:35.435001\")","(137413760,test,\"2019-01-09 17:08:35.438484\")","(1750920192,test,\"2019-01-09 17:08:35.443544\")","(849316096,test,\"2019-01-09 17:08:35.448552\")","(891638016,test,\"2019-01-09 17:08:35.449074\")","(320902144,test,\"2019-01-09 17:08:35.449142\")","(95829120,test,\"2019-01-09 17:08:35.453658\")","(358048256,test,\"2019-01-09 17:08:35.454924\")","(1009512320,test,\"2019-01-09 17:08:35.457164\")"}');
ins_p_batch
-------------
(1 row)
Time: 0.841 ms
性能
vi test.sql
select ins_p_batch('{"(1269675648,test,\"2019-01-09\")","(1515917568,test,\"2019-01-09\")","(137413760,test,\"2019-01-09\")","(1750920192,test,\"2019-01-09\")","(849316096,test,\"2019-01-09\")","(891638016,test,\"2019-01-09\")","(320902144,test,\"2019-01-09\")","(95829120,test,\"2019-01-09\")","(358048256,test,\"2019-01-09\")","(1009512320,test,\"2019-01-09\")"}');
一次插10行
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
progress: 1.0 s, 41637.4 tps, lat 0.745 ms stddev 0.742
progress: 2.0 s, 42862.5 tps, lat 0.746 ms stddev 0.614
progress: 3.0 s, 42417.1 tps, lat 0.754 ms stddev 0.689
progress: 4.0 s, 42389.5 tps, lat 0.755 ms stddev 0.691
应用程序直接写分区
性能
vi test.sql
\set id random(1,2000000000)
insert into p2 values (2,'test',now());
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
progress: 1.0 s, 364350.5 tps, lat 0.085 ms stddev 0.208
progress: 2.0 s, 379071.4 tps, lat 0.084 ms stddev 0.215
progress: 3.0 s, 384452.1 tps, lat 0.083 ms stddev 0.188
性能对比
| 方法 | 每秒插入多少行 |
|---|---|
| 插分区主表(单条) | 2.7万 |
| 插分区主表(10条) | 28万 |
| 应用直接插分区(单条) | 38万 |
| 使用UDF+动态绑定变量插分区(单条) | 20万 |
| 使用UDF+动态绑定变量批量查(10条) | 42万 |
另外需要注意，并发越高，直接插主表的性能越差，例如使用64个并发插入时，只有2.1万行/s。
参考
《PostgreSQL 9.x, 10, 11 hash分区表 用法举例》
《分区表锁粒度差异 - pg_pathman VS native partition table》
《PostgreSQL 商用版本EPAS(阿里云ppas(Oracle 兼容版)) - 分区表性能优化 (堪比pg_pathman)》
《PostgreSQL 10 内置分区 vs pg_pathman perf profiling》
《PostgreSQL 9.6 sharding based on FDW & pg_pathman》
《PostgreSQL 9.5+ 高效分区表实现 - pg_pathman》
《PostgreSQL 查询涉及分区表过多导致的性能问题 - 性能诊断与优化(大量BIND, spin lock, SLEEP进程)》
免费领取阿里云RDS PostgreSQL实例、ECS虚拟机
