PostgreSQL dblink异步调用实现并行hash分片JOIN - 含数据交、并、差提速案例-阿里云开发者社区

背景

数据交、并、差是分析型场景常见的需求。例如用来筛选目标用户、店铺等。

PostgreSQL中交、并、差可以使用SQL语法来实现（union , union all , except , intersect）。其中只有union all是不去重的，其他都会去重。

例子

select generate_series(1,10) except select generate_series(5,12);    
    
select generate_series(1,10) union select generate_series(5,12);    
    
select generate_series(1,10) union all select generate_series(5,12);    
    
select generate_series(1,10) intersect select generate_series(5,12);

当数据量非常庞大时，求交集、差集的话这种方法的效果可能会不够理想。

那么有什么好方法呢？

1、我们自己对数据进行hash切片，然后使用dblink的异步调用接口，一对一的并行操作（求交、差）。

2、PostgreSQL 11 已经支持了parallel hash join，可以解决大数据量求交、差的性能问题。

《PostgreSQL 11 preview - parallel hash join(并行哈希JOIN) 性能极大提升》

原生求交、差性能

测试结构和数据

postgres=# create table tbl(id int, c1 int);  
CREATE TABLE  
postgres=# insert into tbl select generate_series(1,10000000), random()*99;  
INSERT 0 10000000

1、1千万交 1千万

耗时，31.7秒。

postgres=# explain analyze select * from tbl intersect select * from tbl;    
                                                                      QUERY PLAN                                                                          
------------------------------------------------------------------------------------------------------------------------------------------------------    
 HashSetOp Intersect  (cost=0.00..413495.25 rows=9999977 width=12) (actual time=27730.818..30412.898 rows=10000000 loops=1)    
   ->  Append  (cost=0.00..313495.48 rows=19999954 width=12) (actual time=0.402..18889.746 rows=20000000 loops=1)    
         ->  Subquery Scan on "*SELECT* 1"  (cost=0.00..156747.74 rows=9999977 width=12) (actual time=0.401..7744.308 rows=10000000 loops=1)    
               ->  Gather  (cost=0.00..56747.97 rows=9999977 width=8) (actual time=0.397..5947.082 rows=10000000 loops=1)    
                     Workers Planned: 8    
                     Workers Launched: 8    
                     ->  Parallel Seq Scan on tbl  (cost=0.00..56747.97 rows=1249997 width=8) (actual time=0.015..248.653 rows=1111111 loops=9)    
         ->  Subquery Scan on "*SELECT* 2"  (cost=0.00..156747.74 rows=9999977 width=12) (actual time=0.329..8366.856 rows=10000000 loops=1)    
               ->  Gather  (cost=0.00..56747.97 rows=9999977 width=8) (actual time=0.326..6567.651 rows=10000000 loops=1)    
                     Workers Planned: 8    
                     Workers Launched: 8    
                     ->  Parallel Seq Scan on tbl tbl_1  (cost=0.00..56747.97 rows=1249997 width=8) (actual time=0.013..195.661 rows=1111111 loops=9)    
 Planning time: 0.098 ms    
 Execution time: 31691.115 ms    
(14 rows)

2、1千万差 1千万

耗时，30秒。

postgres=# explain analyze select * from tbl except select * from tbl;    
                                                                      QUERY PLAN                                                                          
------------------------------------------------------------------------------------------------------------------------------------------------------    
 HashSetOp Except  (cost=0.00..413495.25 rows=9999977 width=12) (actual time=30021.111..30021.111 rows=0 loops=1)    
   ->  Append  (cost=0.00..313495.48 rows=19999954 width=12) (actual time=0.415..20454.584 rows=20000000 loops=1)    
         ->  Subquery Scan on "*SELECT* 1"  (cost=0.00..156747.74 rows=9999977 width=12) (actual time=0.414..8500.176 rows=10000000 loops=1)    
               ->  Gather  (cost=0.00..56747.97 rows=9999977 width=8) (actual time=0.409..6696.932 rows=10000000 loops=1)    
                     Workers Planned: 8    
                     Workers Launched: 8    
                     ->  Parallel Seq Scan on tbl  (cost=0.00..56747.97 rows=1249997 width=8) (actual time=0.019..233.999 rows=1111111 loops=9)    
         ->  Subquery Scan on "*SELECT* 2"  (cost=0.00..156747.74 rows=9999977 width=12) (actual time=0.341..9162.134 rows=10000000 loops=1)    
               ->  Gather  (cost=0.00..56747.97 rows=9999977 width=8) (actual time=0.337..7358.837 rows=10000000 loops=1)    
                     Workers Planned: 8    
                     Workers Launched: 8    
                     ->  Parallel Seq Scan on tbl tbl_1  (cost=0.00..56747.97 rows=1249997 width=8) (actual time=0.015..196.848 rows=1111111 loops=9)    
 Planning time: 0.080 ms    
 Execution time: 30358.560 ms    
(14 rows)

优化手段1 - 使用hash切片，然后使用dblink的异步调用接口，求交、差性能

dblink异步调用的例子，参考

《惊天性能！单RDS PostgreSQL实例支撑 2000亿 - 实时标签透视案例》

《阿里云RDS PostgreSQL OSS 外部表 - (dblink异步调用封装)并行写提速案例》

这个方法纯属个人高级玩法。建议咱们还是直接用PG 11。

参与计算相交、差的字段中的任意一个或多个，作为HASH切片字段即可。PostgreSQL内置了好多类型转HASH值得到函数：

postgres=# \df *.hash*    
                                    List of functions    
   Schema   |         Name          | Result data type |   Argument data types    | Type     
------------+-----------------------+------------------+--------------------------+------    
 pg_catalog | hash_aclitem          | integer          | aclitem                  | func    
 pg_catalog | hash_aclitem_extended | bigint           | aclitem, bigint          | func    
 pg_catalog | hash_array            | integer          | anyarray                 | func    
 pg_catalog | hash_array_extended   | bigint           | anyarray, bigint         | func    
 pg_catalog | hash_numeric          | integer          | numeric                  | func    
 pg_catalog | hash_numeric_extended | bigint           | numeric, bigint          | func    
 pg_catalog | hash_range            | integer          | anyrange                 | func    
 pg_catalog | hash_range_extended   | bigint           | anyrange, bigint         | func    
 pg_catalog | hashbpchar            | integer          | character                | func    
 pg_catalog | hashbpcharextended    | bigint           | character, bigint        | func    
 pg_catalog | hashchar              | integer          | "char"                   | func    
 pg_catalog | hashcharextended      | bigint           | "char", bigint           | func    
 pg_catalog | hashenum              | integer          | anyenum                  | func    
 pg_catalog | hashenumextended      | bigint           | anyenum, bigint          | func    
 pg_catalog | hashfloat4            | integer          | real                     | func    
 pg_catalog | hashfloat4extended    | bigint           | real, bigint             | func    
 pg_catalog | hashfloat8            | integer          | double precision         | func    
 pg_catalog | hashfloat8extended    | bigint           | double precision, bigint | func    
 pg_catalog | hashhandler           | index_am_handler | internal                 | func    
 pg_catalog | hashinet              | integer          | inet                     | func    
 pg_catalog | hashinetextended      | bigint           | inet, bigint             | func    
 pg_catalog | hashint2              | integer          | smallint                 | func    
 pg_catalog | hashint2extended      | bigint           | smallint, bigint         | func    
 pg_catalog | hashint4              | integer          | integer                  | func    
 pg_catalog | hashint4extended      | bigint           | integer, bigint          | func    
 pg_catalog | hashint8              | integer          | bigint                   | func    
 pg_catalog | hashint8extended      | bigint           | bigint, bigint           | func    
 pg_catalog | hashmacaddr           | integer          | macaddr                  | func    
 pg_catalog | hashmacaddr8          | integer          | macaddr8                 | func    
 pg_catalog | hashmacaddr8extended  | bigint           | macaddr8, bigint         | func    
 pg_catalog | hashmacaddrextended   | bigint           | macaddr, bigint          | func    
 pg_catalog | hashname              | integer          | name                     | func    
 pg_catalog | hashnameextended      | bigint           | name, bigint             | func    
 pg_catalog | hashoid               | integer          | oid                      | func    
 pg_catalog | hashoidextended       | bigint           | oid, bigint              | func    
 pg_catalog | hashoidvector         | integer          | oidvector                | func    
 pg_catalog | hashoidvectorextended | bigint           | oidvector, bigint        | func    
 pg_catalog | hashtext              | integer          | text                     | func    
 pg_catalog | hashtextextended      | bigint           | text, bigint             | func    
 pg_catalog | hashvarlena           | integer          | internal                 | func    
 pg_catalog | hashvarlenaextended   | bigint           | internal, bigint         | func    
(41 rows)

首先看看切成小片后，求交、差执行时间需要多久：

不开并行，切成48份，每份的intersect时间，大概是1.9秒。

postgres=# explain analyze select t1.* from tbl t1 where mod(abs(hashint4(id)), 48)=0 intersect select t1.* from tbl t1 where mod(abs(hashint4(id)), 48)=0;    
                                                               QUERY PLAN                                                                   
----------------------------------------------------------------------------------------------------------------------------------------    
 HashSetOp Intersect  (cost=0.00..489995.08 rows=50000 width=12) (actual time=1822.887..1867.381 rows=208902 loops=1)    
   ->  Append  (cost=0.00..489495.08 rows=100000 width=12) (actual time=0.021..1679.633 rows=417804 loops=1)    
         ->  Subquery Scan on "*SELECT* 1"  (cost=0.00..244747.54 rows=50000 width=12) (actual time=0.020..811.669 rows=208902 loops=1)    
               ->  Seq Scan on tbl t1  (cost=0.00..244247.54 rows=50000 width=8) (actual time=0.019..774.864 rows=208902 loops=1)    
                     Filter: (mod(abs(hashint4(id)), 48) = 0)    
                     Rows Removed by Filter: 9791098    
         ->  Subquery Scan on "*SELECT* 2"  (cost=0.00..244747.54 rows=50000 width=12) (actual time=0.027..807.215 rows=208902 loops=1)    
               ->  Seq Scan on tbl t1_1  (cost=0.00..244247.54 rows=50000 width=8) (actual time=0.026..770.958 rows=208902 loops=1)    
                     Filter: (mod(abs(hashint4(id)), 48) = 0)    
                     Rows Removed by Filter: 9791098    
 Planning time: 0.116 ms    
 Execution time: 1887.638 ms    
(12 rows)

也就是说，开48个并行切片的话，最理想的性能是1.9秒。

注意

因为这里面的HASH分片是扫全表得到的，所以开的并发越多，扫描次数越多。最好是扫一次，并均分到N个临时空间，然后再从临时空间中扫，这样就只需要扫一遍。当然会增加复杂度，如果表不大，实际上多扫几次也无所谓。

hash并行切片+异步dblink

1、创建生成dblink连接的函数，重复创建不报错。

create or replace function conn(      
  name,   -- dblink名字      
  text    -- 连接串,URL      
) returns void as $$        
declare        
begin        
  perform dblink_connect($1, $2);       
  return;        
exception when others then        
  return;        
end;        
$$ language plpgsql strict;

2、创建一个函数，用于跑并行求交

create or replace function get_intersect(    
  conn text,         -- 连接串    
  OUT id int,    
  OUT c1 int    
) returns setof record as $$       
declare      
begin      
for i in 0..47 loop       
  perform conn('link'||i,  conn);       
  perform 1 from dblink_get_result('link'||i) as t(id int, c1 int);      
  perform dblink_send_query('link'||i, format('select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s intersect select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s', i, i));      
end loop;      
      
for i in 0..47 loop      
  return query SELECT * FROM dblink_get_result('link'||i) as t(id int, c1 int);      
end loop;      
end;      
$$ language plpgsql strict;

使用这个方法，可以看到执行时间大概3秒。但是耗费了很多时间在将1000万条记录从所有的远端返回给调用端。总共差不多8秒。

如果改成返回游标，响应速度就快得不得了了，比如在图计算中，用游标流式返回：

《金融风控、公安刑侦、社会关系、人脉分析等需求分析与数据库实现 - PostgreSQL图数据库场景应用》

create or replace function get_intersect1()   
  returns setof refcursor as $$       
declare   
  ref refcursor[];    
  res refcursor;  
begin      
for i in 0..47 loop       
  ref[i] := 'cur'||i;  
  res := ref[i];  
  open res for execute format('select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s intersect select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s', i, i);    
  return next res;  
end loop;      
  return;    
end;      
$$ language plpgsql strict;

用法

postgres=# begin;  
BEGIN  
postgres=# select * from get_intersect1();  
 get_intersect1   
----------------  
 cur0  
 cur1  
 cur2  
 cur3  
 cur4  
 cur5  
 cur6  
 cur7  
 cur8  
 cur9  
 cur10  
 cur11  
 cur12  
 cur13  
 cur14  
 cur15  
 cur16  
 cur17  
 cur18  
 cur19  
 cur20  
 cur21  
 cur22  
 cur23  
 cur24  
 cur25  
 cur26  
 cur27  
 cur28  
 cur29  
 cur30  
 cur31  
 cur32  
 cur33  
 cur34  
 cur35  
 cur36  
 cur37  
 cur38  
 cur39  
 cur40  
 cur41  
 cur42  
 cur43  
 cur44  
 cur45  
 cur46  
 cur47  
(48 rows)  
  
Time: 46.471 ms  
  
-- 第一页比较慢  
  
postgres=# fetch 10 from cur1;  
   id    | c1   
---------+----  
 3591658 | 70  
 6100015 | 17  
 3222328 | 90  
 5500150 | 23  
 9087335 | 45  
 2463228 | 86  
  870261 | 51  
 9276428 | 85  
 7672240 | 32  
 6828314 | 41  
(10 rows)  
  
Time: 1645.906 ms (00:01.646)  
  
-- 后面就飞快了。  
  
postgres=# fetch 10 from cur1;  
   id    | c1   
---------+----  
 7335851 |  5  
 8007430 | 10  
 6230301 | 27  
 9111491 | 91  
 1400805 | 65  
 3651088 | 33  
 3292697 | 65  
 1431682 | 66  
 2959698 | 66  
 4580225 | 39  
(10 rows)  
  
Time: 0.187 ms

是不是飞快了呢，使用游标，从用户发出请求，到获取数据，大概的延迟是1.7秒。.

求差与之类似，只是改一下SQL。

create or replace function get_except1()   
  returns setof refcursor as $$       
declare   
  ref refcursor[];    
  res refcursor;  
begin      
for i in 0..47 loop       
  ref[i] := 'cur'||i;  
  res := ref[i];  
  open res for execute format('select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s except select * from tbl t1 where mod(abs(hashint4(id)), 48)=%s', i, i);    
  return next res;  
end loop;      
  return;    
end;      
$$ language plpgsql strict;

postgres=# begin;  
BEGIN  
Time: 0.169 ms  
postgres=# select * from get_except1();  
 get_except1   
-------------  
 cur0  
 cur1  
 ..........  
 cur44  
 cur45  
 cur46  
 cur47  
(48 rows)  
  
Time: 46.482 ms  
  
postgres=# fetch 10 from cur1;  
 id | c1   
----+----  
(0 rows)  
  
Time: 1681.922 ms (00:01.682)

优化手段2 - PostgreSQL 11 求交、差性能

使用PostgreSQL 11，JOIN的手法来求交、差。语义相同。

1、求交

select * from tbl intersect select * from tbl;

相当于

select t1.* from tbl t1 join tbl t2 on (t1.id=t2.id and t1.c1=t2.c1);  -- 所有参与求交的字段都加到JOIN ON里面

2、求差

select * from tbl except select * from tbl;

相当于

select * from tbl t1 where not exists     
  ( select 1 from      
      (select t1.id,t1.c1 from tbl t1 join tbl t2 on (t1.id=t2.id and t1.c1=t2.c1) ) t   -- 所有参与求交的字段都加到JOIN ON里面    
    where t.id=t1.id and t.c1=t1.c1    
  );

PostgreSQL 11 求交、差性能如下

1、求交集，3.3秒。

postgres=# explain analyze select t1.* from tbl t1 join tbl t2 on (t1.id = t2.id and t1.c1 = t2.c1);    
                                                                QUERY PLAN                                                                     
-------------------------------------------------------------------------------------------------------------------------------------------    
 Gather  (cost=52060.48..101778.20 rows=100921 width=8) (actual time=407.118..2395.421 rows=10000000 loops=1)    
   Workers Planned: 32    
   Workers Launched: 32    
   ->  Parallel Hash Join  (cost=52060.48..101778.20 rows=3154 width=8) (actual time=378.294..691.692 rows=303030 loops=33)    
         Hash Cond: ((t1.id = t2.id) AND (t1.c1 = t2.c1))    
         ->  Parallel Seq Scan on tbl t1  (cost=0.00..47372.99 rows=312499 width=8) (actual time=0.014..41.780 rows=303030 loops=33)    
         ->  Parallel Hash  (cost=47372.99..47372.99 rows=312499 width=8) (actual time=374.931..374.931 rows=303030 loops=33)    
               Buckets: 16777216  Batches: 1  Memory Usage: 522848kB    
               ->  Parallel Seq Scan on tbl t2  (cost=0.00..47372.99 rows=312499 width=8) (actual time=0.022..48.013 rows=303030 loops=33)    
 Planning time: 0.137 ms    
 Execution time: 3316.010 ms    
(11 rows)

2、求差集，1.9秒

postgres=# explain analyze select * from tbl t1 where not exists     
  ( select 1 from      
      (select t1.id,t1.c1 from tbl t1 join tbl t2 on (t1.id=t2.id and t1.c1=t2.c1) ) t   -- 所有参与求交的字段都加到JOIN ON里面    
    where t.id=t1.id and t.c1=t1.c1    
  );    
                                                                      QUERY PLAN                                                                           
-------------------------------------------------------------------------------------------------------------------------------------------------------    
 Gather  (cost=101825.51..153939.67 rows=9899056 width=8) (actual time=1557.867..1557.867 rows=0 loops=1)    
   Workers Planned: 32    
   Workers Launched: 32    
   ->  Parallel Hash Anti Join  (cost=101825.51..153939.67 rows=309346 width=8) (actual time=1495.529..1495.529 rows=0 loops=33)    
         Hash Cond: ((t1.id = t1_1.id) AND (t1.c1 = t1_1.c1))    
         ->  Parallel Seq Scan on tbl t1  (cost=0.00..47372.99 rows=312499 width=8) (actual time=0.013..44.749 rows=303030 loops=33)    
         ->  Parallel Hash  (cost=101778.20..101778.20 rows=3154 width=8) (actual time=1260.916..1260.916 rows=303030 loops=33)    
               Buckets: 16777216 (originally 131072)  Batches: 1 (originally 1)  Memory Usage: 652800kB    
               ->  Parallel Hash Join  (cost=52060.48..101778.20 rows=3154 width=8) (actual time=387.651..740.551 rows=303030 loops=33)    
                     Hash Cond: ((t1_1.id = t2.id) AND (t1_1.c1 = t2.c1))    
                     ->  Parallel Seq Scan on tbl t1_1  (cost=0.00..47372.99 rows=312499 width=8) (actual time=0.013..46.111 rows=303030 loops=33)    
                     ->  Parallel Hash  (cost=47372.99..47372.99 rows=312499 width=8) (actual time=384.666..384.666 rows=303030 loops=33)    
                           Buckets: 16777216  Batches: 1  Memory Usage: 522784kB    
                           ->  Parallel Seq Scan on tbl t2  (cost=0.00..47372.99 rows=312499 width=8) (actual time=0.024..47.326 rows=303030 loops=33)    
 Planning time: 0.251 ms    
 Execution time: 1939.745 ms    
(16 rows)

小结

1000万与 1000万求交、差的性能指标：

方法	求交	求差
原生intersect, except	31.7秒	30秒
自定义切片+dblink异步调用	1.7秒	1.7秒
PostgreSQL 11 并行hashjoin	3.3秒	1.9秒

通过改写SQL，PostgreSQL 11可以利用并行计算，更好的支撑求数据交、差的性能。(但是需要注意，NULL值在except, intersect中会视为相同，而join时取等的话，是匹配不到的。这个特别需要注意。（所以语义上不完全一样）)

postgres=# select 1,null except select 1,null;    
 ?column? | ?column?     
----------+----------    
(0 rows)    
    
postgres=# select 1,null intersect select 1,null;    
 ?column? | ?column?     
----------+----------    
        1 |     
(1 row)

如果要让语义完全一样，可以用这种写法，但是就用不到hashjoin了。

即:  等号改成 is not distinct from    
    
select t1.* from tbl t1 join tbl t2 on ((t1.id is not distinct from t2.id) and (t1.c1 is not distinct from t2.c1));

而使用dblink异步的方式，需要注意:

因为我们使用dblink的方法进行HASH分片是扫全表得到的，所以开的并发越多，扫描次数越多。最好是扫一次，并均分到N个临时空间，然后再从临时空间中扫，这样就只需要扫一遍。当然会增加复杂度，如果表不大，实际上多扫几次也无所谓。

参考

《惊天性能！单RDS PostgreSQL实例支撑 2000亿 - 实时标签透视案例》

《PostgreSQL 11 preview - parallel hash join(并行哈希JOIN) 性能极大提升》

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

《阿里云RDS PostgreSQL OSS 外部表 - (dblink异步调用封装)并行写提速案例》

《金融风控、公安刑侦、社会关系、人脉分析等需求分析与数据库实现 - PostgreSQL图数据库场景应用》

PostgreSQL dblink异步调用实现并行hash分片JOIN - 含数据交、并、差提速案例

标签

背景

原生求交、差性能

优化手段1 - 使用hash切片，然后使用dblink的异步调用接口，求交、差性能

注意

hash并行切片+异步dblink

优化手段2 - PostgreSQL 11 求交、差性能

PostgreSQL 11 求交、差性能如下

小结

参考

关系型数据库

热门文章

最新文章

相关产品

相关课程

相关电子书

相关实验场景

推荐镜像

PostgreSQL dblink异步调用实现 并行hash分片JOIN - 含数据交、并、差 提速案例

标签

背景

原生求交、差性能

优化手段1 - 使用hash切片，然后使用dblink的异步调用接口，求交、差性能

注意

hash并行切片+异步dblink

优化手段2 - PostgreSQL 11 求交、差性能

PostgreSQL 11 求交、差性能如下

小结

参考

关系型数据库

热门文章

最新文章

相关产品

相关课程

相关电子书

相关实验场景

推荐镜像

PostgreSQL dblink异步调用实现并行hash分片JOIN - 含数据交、并、差提速案例