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)

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背景

PostgreSQL 在 10的版本,内置了分区表的语法,简化了以前需要写 RULE或TG+继承表功能 来实现分区表的模式。

《PostgreSQL 10.0 preview 功能增强 - 内置分区表》

《PostgreSQL 传统 hash 分区方法和性能》

但是内置分区表的性能还有改进的空间,对比了pg_pathman,性能差异是较大的,主要在plan代码这块。所以对于社区版本的用户,建议使用pg_pathman这个插件来使用分区表的功能。

《PostgreSQL 10 内置分区 vs pg_pathman perf profiling》

作为PostgreSQL的商用发行版本的PPAS,这块有非常大的性能改进。

PPAS分区表性能优化参数

edb_enable_pruning

Parameter Type: Boolean  
  
Default Value: true  
  
Range: {true | false}  
  
Minimum Scope of Effect: Per session  
  
When Value Changes Take Effect: Immediate  
  
Required Authorization to Activate: Session user  
  
When set to TRUE, edb_enable_pruning allows the query planner to early-prune partitioned tables.   
Early-pruning means that the query planner can “prune” (i.e., ignore) partitions that would   
not be searched in a query before generating query plans.   
This helps improve performance time as it eliminates the generation of query plans of   
partitions that would not be searched.  
  
Conversely, late-pruning means that the query planner prunes partitions after   
generating query plans for each partition.   
(The constraint_exclusion configuration parameter controls late-pruning.)  
  
The ability to early-prune depends upon the nature of the query in the WHERE clause.   
Early-pruning can be utilized in only simple queries with constraints of the type    
WHERE column = literal (e.g., WHERE deptno = 10).  
  
Early-pruning is not used for more complex queries such as   
WHERE column = expression (e.g., WHERE deptno = 10 + 5).  

edb_enable_pruning这个参数的功能是在生成执行计划之前,过滤掉不需要访问的对象,从而减少执行计划的开销。

注意,目前只适用于 “常量值” 的过滤。即使是immutable函数也不支持。

支持优化  
WHERE deptno = 10  
  
不支持优化  
WHERE deptno = 10 + 5  

对于不能过滤的分区,最后会在生成执行计划后,使用constraint_exclusion参数来过滤不需要访问的分区。

功能测试

创建分区表

postgres=# create table t (id int, info text) partition by range (id);  
CREATE TABLE  
  
postgres=# create table t0 PARTITION OF t for values from (0) to (100);  
CREATE TABLE  
postgres=# create table t1 PARTITION OF t for values from (100) to (200);  
CREATE TABLE  

开启edb_enable_pruning参数,关闭constraint_exclusion参数

postgres=# show edb_enable_pruning ;  
 edb_enable_pruning   
--------------------  
 on  
(1 row)  
  
postgres=# set constraint_exclusion =off;  
SET  

简单SQL,可以看到edb_enable_pruning起作用了,过滤了不需要访问的分区。

postgres=# explain select * from t where id=1;  
                        QUERY PLAN                          
----------------------------------------------------------  
 Append  (cost=0.00..25.88 rows=6 width=36)  
   ->  Seq Scan on t0  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 1)  
(3 rows)  

但是对于非常量,无法优化,没有起到过滤效果。

postgres=# explain select * from t where id=1+1;  
                        QUERY PLAN                          
----------------------------------------------------------  
 Append  (cost=0.00..51.75 rows=12 width=36)  
   ->  Seq Scan on t0  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 2)  
   ->  Seq Scan on t1  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 2)  
(5 rows)  

打开 constraint_exclusion 参数,它会对复杂SQL进行过滤(仅限于immutable、stable的函数和操作符。)

postgres=# set constraint_exclusion =on;  
SET  
postgres=# explain select * from t where id=1+1;  
                        QUERY PLAN                          
----------------------------------------------------------  
 Append  (cost=0.00..25.88 rows=6 width=36)  
   ->  Seq Scan on t0  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 2)  
(3 rows)  

将edb_enable_pruning关闭,过滤不受影响。只是没有起到优化效果。

postgres=# set edb_enable_pruning =off;  
SET  
postgres=# explain select * from t where id=1+1;  
                        QUERY PLAN                          
----------------------------------------------------------  
 Append  (cost=0.00..25.88 rows=6 width=36)  
   ->  Seq Scan on t0  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 2)  
(3 rows)  
  
postgres=# explain select * from t where id=1;  
                        QUERY PLAN                          
----------------------------------------------------------  
 Append  (cost=0.00..25.88 rows=6 width=36)  
   ->  Seq Scan on t0  (cost=0.00..25.88 rows=6 width=36)  
         Filter: (id = 1)  
(3 rows)  

性能测试

为了体现优化效果,加到2000个分区。

postgres=# do language plpgsql $$  
declare  
begin  
  for i in 2..2000 loop  
    execute 'create table t'||i||' PARTITION OF t for values from ('||200+i||') to ('||200+i+1||')';  
  end loop;  
end;  
$$;  
DO  

测试简单SQL(起到优化效果的SQL)

vi test.sql  
  
select * from t where id=1;  

TPS达到了100万。

pgbench -M prepared -n -r -P 1 -f ./test.sql -c 56 -j 56 -T 120  
progress: 1.0 s, 1031487.3 tps, lat 0.053 ms stddev 0.328  
progress: 2.0 s, 1098419.2 tps, lat 0.051 ms stddev 0.009  
progress: 3.0 s, 1075788.5 tps, lat 0.052 ms stddev 0.014  
progress: 4.0 s, 1090429.9 tps, lat 0.051 ms stddev 0.010  
progress: 5.0 s, 1091784.5 tps, lat 0.051 ms stddev 0.010  
progress: 6.0 s, 1084007.3 tps, lat 0.052 ms stddev 0.012  
progress: 7.0 s, 1094544.1 tps, lat 0.051 ms stddev 0.009  

测试不能优化的SQL,只能走传统的constraint_exclusion参数过滤的,性能下降到了1000多TPS

vi test.sql  
  
select * from t where id=1+1;  
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 56 -j 56 -T 120  
progress: 1.0 s, 0.0 tps, lat -nan ms stddev -nan  
progress: 2.0 s, 412.2 tps, lat 247.149 ms stddev 591.770  
progress: 3.0 s, 1196.0 tps, lat 53.604 ms stddev 112.786  
progress: 4.0 s, 1198.0 tps, lat 46.672 ms stddev 5.575  

pg_pathman 的对比性能

pg_pathman实际上以前已经对比过,性能非常好。

《PostgreSQL 10 内置分区 vs pg_pathman perf profiling》

同样创建2000个分区,测试简单和不简单的查询。

postgres=# CREATE EXTENSION pg_pathman;      
CREATE EXTENSION      
      
postgres=# create table tbl_range(id int not null, info text, crt_time timestamp);      
CREATE TABLE      
      
postgres=# select create_range_partitions('tbl_range', 'id', 0, 100, 2000);      
 create_range_partitions       
-------------------------      
                    2000      
(1 row)  
  
  
postgres=# \d tbl_range  
                        Table "public.tbl_range"  
  Column  |            Type             | Collation | Nullable | Default   
----------+-----------------------------+-----------+----------+---------  
 id       | integer                     |           | not null |   
 info     | text                        |           |          |   
 crt_time | timestamp without time zone |           |          |   
Number of child tables: 2000 (Use \d+ to list them.)  

pg_pathman不依赖传统的constraint_exclusion参数,简单和不简单的SQL,都被过滤了。

postgres=# set constraint_exclusion =off;  
SET  
  
postgres=# explain select * from tbl_range where id=1;  
                            QUERY PLAN                               
-------------------------------------------------------------------  
 Append  (cost=0.00..24.12 rows=6 width=44)  
   ->  Seq Scan on tbl_range_1  (cost=0.00..24.12 rows=6 width=44)  
         Filter: (id = 1)  
(3 rows)  
  
postgres=# explain select * from tbl_range where id=1+1;  
                            QUERY PLAN                               
-------------------------------------------------------------------  
 Append  (cost=0.00..24.12 rows=6 width=44)  
   ->  Seq Scan on tbl_range_1  (cost=0.00..24.12 rows=6 width=44)  
         Filter: (id = 2)  
(3 rows)  

性能测试

pgbench -M prepared -n -r -P 1 -f ./test.sql -c 56 -j 56 -T 120  
  
  
-- 简单SQL  
  
progress: 3.0 s, 947237.9 tps, lat 0.059 ms stddev 0.010  
progress: 4.0 s, 949539.4 tps, lat 0.059 ms stddev 0.009  
progress: 5.0 s, 948459.0 tps, lat 0.059 ms stddev 0.010  
progress: 6.0 s, 947355.4 tps, lat 0.059 ms stddev 0.010  
progress: 7.0 s, 947789.2 tps, lat 0.059 ms stddev 0.010  
progress: 8.0 s, 949380.5 tps, lat 0.059 ms stddev 0.010  
progress: 9.0 s, 944190.6 tps, lat 0.059 ms stddev 0.023  
progress: 10.0 s, 947677.8 tps, lat 0.059 ms stddev 0.010  
  
-- 非简单SQL  
  
progress: 3.0 s, 951051.2 tps, lat 0.059 ms stddev 0.012  
progress: 4.0 s, 960237.6 tps, lat 0.058 ms stddev 0.010  
progress: 5.0 s, 961659.2 tps, lat 0.058 ms stddev 0.009  
progress: 6.0 s, 946538.5 tps, lat 0.059 ms stddev 0.012  
progress: 7.0 s, 956382.1 tps, lat 0.059 ms stddev 0.011  
progress: 8.0 s, 961674.0 tps, lat 0.058 ms stddev 0.009  
progress: 9.0 s, 957060.6 tps, lat 0.059 ms stddev 0.010  
progress: 10.0 s, 950707.1 tps, lat 0.059 ms stddev 0.013  
progress: 11.0 s, 955766.4 tps, lat 0.059 ms stddev 0.010  

pg_pathman对简单和非简单SQL的优化效果一样,都非常的好。

性能对比

分区特性 | TPS —|— PPAS native分区 edb_enable_pruning=on 常量条件过滤 | 1031487 PPAS native分区 edb_enable_pruning=on 条件无法过滤 | 1196 PG pg_pathman分区 | 957060

小结

对于PPAS用户,建议能常量输入的,就使用常量输入,这样能够用到分区过滤的优化特性。(特别是在分区表非常多的情况下,优化效果非常明显)。

对于PG用户,使用pg_pathman作为分区组件,在分区很多的情况下,性能比native的分区好很多很多。

PostgreSQL 社区正在改进这块的代码,PATCH如下(PostgreSQL 11可能会包含这部分优化):

https://www.postgresql.org/message-id/flat/098b9c71-1915-1a2a-8d52-1a7a50ce79e8@lab.ntt.co.jp#098b9c71-1915-1a2a-8d52-1a7a50ce79e8@lab.ntt.co.jp

https://commitfest.postgresql.org/17/1272/

目前分区越多,在高并发访问时可能导致BIND性能问题:

《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)》

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