标签
PostgreSQL , cpu 并行 , smp 并行 , 并行计算 , gpu 并行 , 并行过程支持
背景
PostgreSQL 11 优化器已经支持了非常多场合的并行。简单估计，已支持27余种场景的并行计算。
parallel seq scan
parallel index scan
parallel index only scan
parallel bitmap scan
parallel filter
parallel hash agg
parallel group agg
parallel cte
parallel subquery
parallel create table
parallel create index
parallel select into
parallel CREATE MATERIALIZED VIEW
parallel 排序 : gather merge
parallel nestloop join
parallel hash join
parallel merge join
parallel 自定义并行聚合
parallel 自定义并行UDF
parallel append
parallel union
parallel fdw table scan
parallel partition join
parallel partition agg
parallel gather
parallel gather merge
parallel rc 并行
parallel rr 并行
parallel GPU 并行
parallel unlogged table
接下来进行一一介绍。
关键知识请先自行了解：
1、优化器自动并行度算法 CBO
《PostgreSQL 11 并行计算算法，参数，强制并行度设置》
parallel 自定义并行聚合
自定义并行聚合
数据量：10亿。
| 场景 | 数据量 | 关闭并行 | 开启并行 | 并行度 | 开启并行性能提升倍数 |
|---|---|---|---|---|---|
| 自定义并行聚合1(求 distinct 数组 字段元素、以及count distinct) | 10 亿 | 298.8 秒 | 8.7 秒 | 36 | 34.3 倍 |
| 自定义并行聚合2(求 distinct 普通 字段元素、以及count distinct) | 10 亿 | 96.5 秒 | 3.43 秒 | 36 | 28 倍 |
自定义聚合函数例子1，合并数组，并去重：
数组去重函数：
postgres=# create or replace function uniq(int2[]) returns int2[] as $$
select array( select unnest($1) group by 1);
$$ language sql strict parallel safe;
CREATE FUNCTION
数组合并与去重函数：
postgres=# create or replace function array_uniq_cat(anyarray,anyarray) returns anyarray as $$
select uniq(array_cat($1,$2));
$$ language sql strict parallel safe;
CREATE FUNCTION
有combinefunc，同时支持并行聚合，并行扫描。
create aggregate arragg (anyarray) (sfunc = array_uniq_cat, stype=anyarray, COMBINEFUNC = array_uniq_cat, PARALLEL=safe);
自定义聚合函数例子2，数据聚合插件：count_distinct 自定义并行聚合加速
https://github.com/tvondra/count_distinct
git clone https://github.com/tvondra/count_distinct
cd count_distinct/
USE_PGXS=1 make
USE_PGXS=1 make install
create extension count_distinct ;
并行聚合函数例子
/* Create the aggregate functions */
CREATE AGGREGATE count_distinct(anyelement) (
SFUNC = count_distinct_append,
STYPE = internal,
FINALFUNC = count_distinct,
COMBINEFUNC = count_distinct_combine,
SERIALFUNC = count_distinct_serial,
DESERIALFUNC = count_distinct_deserial,
PARALLEL = SAFE
);
CREATE AGGREGATE array_agg_distinct(anynonarray) (
SFUNC = count_distinct_append,
STYPE = internal,
FINALFUNC = array_agg_distinct,
FINALFUNC_EXTRA,
COMBINEFUNC = count_distinct_combine,
SERIALFUNC = count_distinct_serial,
DESERIALFUNC = count_distinct_deserial,
PARALLEL = SAFE
);
CREATE AGGREGATE count_distinct_elements(anyarray) (
SFUNC = count_distinct_elements_append,
STYPE = internal,
FINALFUNC = count_distinct,
COMBINEFUNC = count_distinct_combine,
SERIALFUNC = count_distinct_serial,
DESERIALFUNC = count_distinct_deserial,
PARALLEL = SAFE
);
CREATE AGGREGATE array_agg_distinct_elements(anyarray) (
SFUNC = count_distinct_elements_append,
STYPE = internal,
FINALFUNC = array_agg_distinct,
FINALFUNC_EXTRA,
COMBINEFUNC = count_distinct_combine,
SERIALFUNC = count_distinct_serial,
DESERIALFUNC = count_distinct_deserial,
PARALLEL = SAFE
);
测试数据10亿
create unlogged table table3 (i int2[]);
insert into table3 values (array[1,2,3]), (array[3,3,4,6,100,2,3]);
insert into table3 select array[109,209,3223] from generate_series(1,1000000000);
1、关闭并行，耗时： 298.8 秒 , 96.5 秒。
postgres=# explain select array_agg_distinct_elements(i) from table3;
QUERY PLAN
-----------------------------------------------------------------------------
Aggregate (cost=19852943.60..19852943.61 rows=1 width=32)
-> Seq Scan on table3 (cost=0.00..17352943.28 rows=1000000128 width=27)
(2 rows)
postgres=# explain select array_agg_distinct(i) from table1;
QUERY PLAN
----------------------------------------------------------------------------
Aggregate (cost=16924779.00..16924779.01 rows=1 width=32)
-> Seq Scan on table1 (cost=0.00..14424779.00 rows=1000000000 width=2)
(2 rows)
postgres=# select array_agg_distinct_elements(i) from table3;
array_agg_distinct_elements
------------------------------
{1,2,3,4,6,100,109,3223,209}
(1 row)
Time: 298794.796 ms (04:58.795)
postgres=# select array_agg_distinct(i) from table1;
array_agg_distinct
--------------------
{1}
(1 row)
Time: 96477.082 ms (01:36.477)
2、开启并行，耗时： 8.75 秒 , 3.43 秒。
postgres=# explain select array_agg_distinct_elements(i) from table3;
QUERY PLAN
-----------------------------------------------------------------------------------------------
Finalize Aggregate (cost=7700164.46..7700164.47 rows=1 width=32)
-> Gather (cost=7700164.27..7700164.28 rows=36 width=32)
Workers Planned: 36
-> Partial Aggregate (cost=7700164.27..7700164.28 rows=1 width=32)
-> Parallel Seq Scan on table3 (cost=0.00..7630719.81 rows=27777781 width=27)
(5 rows)
postgres=# explain select array_agg_distinct(i) from table1;
QUERY PLAN
----------------------------------------------------------------------------------------------
Finalize Aggregate (cost=4772001.42..4772001.43 rows=1 width=32)
-> Gather (cost=4772001.23..4772001.24 rows=36 width=32)
Workers Planned: 36
-> Partial Aggregate (cost=4772001.23..4772001.24 rows=1 width=32)
-> Parallel Seq Scan on table1 (cost=0.00..4702556.78 rows=27777778 width=2)
(5 rows)
postgres=# select array_agg_distinct_elements(i) from table3;
array_agg_distinct_elements
------------------------------
{1,2,3,4,6,100,109,3223,209}
(1 row)
Time: 8752.115 ms (00:08.752)
postgres=# select array_agg_distinct(i) from table1;
array_agg_distinct
--------------------
{1}
(1 row)
Time: 3427.029 ms (00:03.427)
自定义聚合的效率取决于自定义聚合代码本身的效率，SQL语言写的自定义聚合效率比较低，建议使用C语言写这种需要进行大数据量运算的FUNCTION。
其他知识
1、优化器自动并行度算法 CBO
《PostgreSQL 11 并行计算算法，参数，强制并行度设置》
2、function, op 识别是否支持parallel
postgres=# select proparallel,proname from pg_proc;
proparallel | proname
-------------+----------------------------------------------
s | boolin
s | boolout
s | byteain
s | byteaout
3、subquery mapreduce unlogged table
对于一些情况，如果期望简化优化器对非常非常复杂的SQL并行优化的负担，可以自己将SQL拆成几段，中间结果使用unlogged table保存，类似mapreduce的思想。unlogged table同样支持parallel 计算。
4、vacuum，垃圾回收并行。
5、dblink 异步调用并行
《PostgreSQL VOPS 向量计算 + DBLINK异步并行 - 单实例 10亿 聚合计算跑进2秒》
《PostgreSQL 相似搜索分布式架构设计与实践 - dblink异步调用与多机并行(远程 游标+记录 UDF实例)》
暂时不允许并行的场景(将来PG会继续扩大支持范围)：
1、修改行，锁行，除了create table as , select into, create mview这几个可以使用并行。
2、query 会被中断时，例如cursor , loop in PL/SQL ，因为涉及到中间处理，所以不建议开启并行。
3、paralle unsafe udf ，这种UDF不会并行
4、嵌套并行(udf (内部query并行))，外部调用这个UDF的SQL不会并行。（主要是防止large parallel workers ）
5、SSI 隔离级别
参考
https://www.postgresql.org/docs/11/parallel-plans.html
《PostgreSQL 11 并行计算算法，参数，强制并行度设置》
《PostgreSQL 11 preview - 并行计算 增强 汇总》
《PostgreSQL 10 自定义并行计算聚合函数的原理与实践 - (含array_agg合并多个数组为单个一元数组的例子)》
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