标签
PostgreSQL , 聚合函数 , 自定义 , AGGREGATE , 并行 , COMBINEFUNC
背景
PostgreSQL 9.6开始就支持并行计算了，意味着聚合、扫描、排序、JOIN等都开始支持并行计算。对于聚合操作来说，并行计算与非并行计算是有差异的。
例如avg聚合，对一张表进行计算时，一个任务中操作和多个并行任务操作，算法是不一样的。
PostgreSQL提供了一套标准的接口，可以支持聚合函数的并行操作。
自定义并行聚合的原理和例子
创建聚合函数的语法如下：
CREATE AGGREGATE name ( [ argmode ] [ argname ] arg_data_type [ , ... ] ) (
SFUNC = sfunc,
STYPE = state_data_type
[ , SSPACE = state_data_size ]
[ , FINALFUNC = ffunc ]
[ , FINALFUNC_EXTRA ]
[ , COMBINEFUNC = combinefunc ]
[ , SERIALFUNC = serialfunc ]
[ , DESERIALFUNC = deserialfunc ]
[ , INITCOND = initial_condition ]
[ , MSFUNC = msfunc ]
[ , MINVFUNC = minvfunc ]
[ , MSTYPE = mstate_data_type ]
[ , MSSPACE = mstate_data_size ]
[ , MFINALFUNC = mffunc ]
[ , MFINALFUNC_EXTRA ]
[ , MINITCOND = minitial_condition ]
[ , SORTOP = sort_operator ]
[ , PARALLEL = { SAFE | RESTRICTED | UNSAFE } ]
)
相比非并行，多了一个过程，那就是combinefunc的过程(也叫partial agg)。
非并行模式的聚合流程大致如下：
循环
sfunc( internal-state, next-data-values ) ---> next-internal-state
最后调用一次(可选)
ffunc( internal-state ) ---> aggregate-value
并行模式的聚合流程大致如下，如果没有写combinefunc，那么实际上聚合过程并没有实现并行而只是扫描并行：
下面这个例子，我们可以观察到一个COUNT操作的并行聚合。
postgres=# set max_parallel_workers=4;
SET
postgres=# set max_parallel_workers_per_gather =4;
SET
postgres=# set parallel_setup_cost =0;
SET
postgres=# set parallel_tuple_cost =0;
SET
postgres=# alter table test set (parallel_workers =4);
ALTER TABLE
postgres=# explain (analyze,verbose,timing,costs,buffers) select count(*) from test;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------------
-- final并行，可有可无，看具体的聚合算法
Finalize Aggregate (cost=15837.02..15837.03 rows=1 width=8) (actual time=57.296..57.296 rows=1 loops=1)
Output: count(*)
Buffers: shared hit=3060
-> Gather (cost=15837.00..15837.01 rows=4 width=8) (actual time=57.287..57.292 rows=5 loops=1)
Output: (PARTIAL count(*))
Workers Planned: 4
Workers Launched: 4
Buffers: shared hit=3060
-- 一下就是combinefunc完成的聚合并行(显示为PARTIAL agg)
-> Partial Aggregate (cost=15837.00..15837.01 rows=1 width=8) (actual time=52.333..52.333 rows=1 loops=5)
Output: PARTIAL count(*)
Buffers: shared hit=12712
Worker 0: actual time=50.917..50.918 rows=1 loops=1
Buffers: shared hit=2397
Worker 1: actual time=51.293..51.294 rows=1 loops=1
Buffers: shared hit=2423
Worker 2: actual time=51.062..51.063 rows=1 loops=1
Buffers: shared hit=2400
Worker 3: actual time=51.436..51.436 rows=1 loops=1
Buffers: shared hit=2432
-> Parallel Seq Scan on public.test (cost=0.00..15212.00 rows=250000 width=0) (actual time=0.010..30.499 rows=200000 loops=5)
Buffers: shared hit=12712
Worker 0: actual time=0.013..30.343 rows=190269 loops=1
Buffers: shared hit=2397
Worker 1: actual time=0.010..30.401 rows=192268 loops=1
Buffers: shared hit=2423
Worker 2: actual time=0.013..30.467 rows=190350 loops=1
Buffers: shared hit=2400
Worker 3: actual time=0.009..30.221 rows=192861 loops=1
Buffers: shared hit=2432
Planning time: 0.074 ms
Execution time: 60.169 ms
(31 rows)
了解了并行聚合的原理后，我们就可以写自定义聚合函数的并行计算了。
例子
例如我们要支持一个数组的聚合，并且在聚合过程中我们要实现对元素去重。
1、创建测试表
create table test(id int, col int[]);
2、生成测试数据
CREATE OR REPLACE FUNCTION public.gen_arr(integer, integer)
RETURNS integer[]
LANGUAGE sql
STRICT
AS $function$
select array(select ($1*random())::int from generate_series(1,$2));
$function$;
insert into test select random()*1000, gen_arr(500,10) from generate_series(1,10000);
3、创建聚合函数
例子1，没有combinefunc，只支持扫描并行。
数组去重函数
postgres=# create or replace function uniq(int[]) returns int[] 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
聚合函数(不带COMBINEFUNC)
create aggregate arragg (anyarray) (sfunc = array_uniq_cat, stype=anyarray, PARALLEL=safe);
并行查询例子：
postgres=# set max_parallel_workers=4;
SET
postgres=# set max_parallel_workers_per_gather =4;
SET
postgres=# set parallel_setup_cost =0;
SET
postgres=# set parallel_tuple_cost =0;
SET
postgres=# alter table test set (parallel_workers =4);
ALTER TABLE
postgres=# explain (analyze,verbose,timing,costs,buffers) select id, arragg(col) from test group by id ;
很明显没有设置COMBINEFUNC时，未使用并行聚合。
postgres=# explain (analyze,verbose,timing,costs,buffers) select id, arragg(col) from test group by id ;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------
HashAggregate (cost=4139.74..4141.74 rows=200 width=36) (actual time=602.957..603.195 rows=1001 loops=1)
Output: id, arragg(col)
Group Key: test.id
Buffers: shared hit=6
-> Gather (cost=0.00..163.37 rows=15748 width=36) (actual time=0.328..43.734 rows=10000 loops=1)
Output: id, col
Workers Planned: 4
Workers Launched: 4
Buffers: shared hit=6
-- 只有并行扫描，没有并行聚合。
-> Parallel Seq Scan on public.test (cost=0.00..163.37 rows=3937 width=36) (actual time=0.017..0.891 rows=2000 loops=5)
Output: id, col
Buffers: shared hit=124
Worker 0: actual time=0.019..0.177 rows=648 loops=1
Buffers: shared hit=8
Worker 1: actual time=0.022..0.180 rows=648 loops=1
Buffers: shared hit=8
Worker 2: actual time=0.017..3.772 rows=7570 loops=1
Buffers: shared hit=94
Worker 3: actual time=0.015..0.189 rows=648 loops=1
Buffers: shared hit=8
Planning time: 0.084 ms
Execution time: 603.450 ms
(22 rows)
例子2，有combinefunc，支持并行聚合。
drop aggregate arragg(anyarray);
create aggregate arragg (anyarray) (sfunc = array_uniq_cat, stype=anyarray, COMBINEFUNC = array_uniq_cat, PARALLEL=safe);
使用了并行聚合。
postgres=# explain (analyze,verbose,timing,costs,buffers) select id, arragg(col) from test group by id ;
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------------------
Finalize HashAggregate (cost=1361.46..1363.46 rows=200 width=36) (actual time=285.489..285.732 rows=1001 loops=1)
Output: id, arragg(col)
Group Key: test.id
Buffers: shared hit=36
-> Gather (cost=1157.46..1159.46 rows=800 width=36) (actual time=63.654..74.163 rows=4297 loops=1)
Output: id, (PARTIAL arragg(col))
Workers Planned: 4
Workers Launched: 4
Buffers: shared hit=36
-- 并行聚合
-> Partial HashAggregate (cost=1157.46..1159.46 rows=200 width=36) (actual time=57.367..57.727 rows=859 loops=5)
Output: id, PARTIAL arragg(col)
Group Key: test.id
Buffers: shared hit=886
Worker 0: actual time=54.788..54.997 rows=857 loops=1
Buffers: shared hit=213
Worker 1: actual time=56.881..57.255 rows=861 loops=1
Buffers: shared hit=213
Worker 2: actual time=55.415..55.813 rows=856 loops=1
Buffers: shared hit=212
Worker 3: actual time=56.453..56.854 rows=838 loops=1
Buffers: shared hit=212
-> Parallel Seq Scan on public.test (cost=0.00..163.37 rows=3937 width=36) (actual time=0.011..0.736 rows=2000 loops=5)
Output: id, col
Buffers: shared hit=124
Worker 0: actual time=0.009..0.730 rows=1981 loops=1
Buffers: shared hit=25
Worker 1: actual time=0.012..0.773 rows=2025 loops=1
Buffers: shared hit=25
Worker 2: actual time=0.015..0.741 rows=1944 loops=1
Buffers: shared hit=24
Worker 3: actual time=0.012..0.751 rows=1944 loops=1
Buffers: shared hit=24
Planning time: 0.073 ms
Execution time: 285.949 ms
(34 rows)
实际上并行聚合与分布式数据库聚合阶段原理是一样的，分布式数据库自定义聚合可以参考末尾的文章。
参考
https://www.postgresql.org/docs/10/static/sql-createaggregate.html
https://www.postgresql.org/docs/10/static/xaggr.html#XAGGR-PARTIAL-AGGREGATES
《PostgreSQL aggregate function customize》
