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时序、时序、时序业务(所有VALUE NEW值 7秒到7毫秒的优化之旅) - 阿里云RDS PostgreSQL最佳实践

简介:

标签

PostgreSQL , 时序数据库 , 时序 , 滑动窗口 , 递归查询 , subquery , 窗口查询 , 求最新值


背景

在很多场景中,都会有数据合并、清洗的需求。

例如:

1、记录了表的变更明细(insert,update,delete),需要合并明细,从明细中快速取到每个PK的最新值。

2、有很多传感器,不断的在上报数据,要快速的取出每个传感器的最新状态。

对于这类需求,可以使用窗口查询,但是如何加速,如何快速的取出批量数据呢?

PostgreSQL是最高级的开源数据库,优化方法之多,超乎你的想象。

时序数据取值优化

1、唯一值较少时,并且唯一值范围未知时,使用递归。

方法如下:

《PostgrSQL 递归SQL的几个应用 - 极客与正常人的思维》

《时序数据合并场景加速分析和实现 - 复合索引,窗口分组查询加速,变态递归加速》

2、唯一值较少时,并且唯一值范围确定时,使用subquery。

方法如下:

《PostgreSQL 海量时序数据(任意滑动窗口实时统计分析) - 传感器、人群、物体等对象跟踪》

3、唯一值较多时,1 使用窗口查询,比前面的方法更加适合。

4、唯一值较多时,2 使用流式计算,比方法3更加优秀。

方法如下:

《(流式、lambda、触发器)实时处理大比拼 - 物联网(IoT)\金融,时序处理最佳实践》

本文将对前三种方法做一个比较。

方法4 流计算就不用比了,因为它什么时候都是最强大的。通杀所有场景。等pipelineDB 插件化吧,阿里云RDS PG 10会集成pipelineDB的功能。

递归 vs subquery vs 窗口

以500万数据为例,对比这几种方法的适应场景。

1 递归

场景一、唯一值较多(100万唯一值)

1、建表

\timing  
drop table test;  
create unlogged table test(id int , info text, crt_time timestamp);  

2、构造数据

insert into test select ceil(random()*1000000), md5(random()::text), clock_timestamp() from generate_series(1,5000000);  

3、创建索引

create index idx_test_1 on test (id, crt_time desc);  

4、递归查询效率

explain (analyze,verbose,timing,costs,buffers) with recursive skip as (    
  (    
    select test as v from test where id in (select id from test where id is not null order by id,crt_time desc limit 1) limit 1  
  )    
  union all    
  (    
    select (  
      select t as v from test t where t.id>(s.v).id and t.id is not null order by id,crt_time desc limit 1  
    ) from skip s where (s.v).id is not null  
  )      -- 这里的where (s.v).id is not null 一定要加, 否则就死循环了.   
)     
select (t.v).id, (t.v).info, (t.v).crt_time from skip t where t.* is not null;   
                                                                                      QUERY PLAN                                                                                        
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 CTE Scan on skip t  (cost=54.35..56.37 rows=100 width=44) (actual time=0.042..6626.084 rows=993288 loops=1)  
   Output: (t.v).id, (t.v).info, (t.v).crt_time  
   Filter: (t.* IS NOT NULL)  
   Rows Removed by Filter: 1  
   Buffers: shared hit=3976934  
   CTE skip  
     ->  Recursive Union  (cost=0.91..54.35 rows=101 width=69) (actual time=0.034..6006.615 rows=993289 loops=1)  
           Buffers: shared hit=3976934  
           ->  Limit  (cost=0.91..0.93 rows=1 width=69) (actual time=0.033..0.033 rows=1 loops=1)  
                 Output: test.*  
                 Buffers: shared hit=8  
                 ->  Nested Loop  (cost=0.91..10.19 rows=500 width=69) (actual time=0.032..0.032 rows=1 loops=1)  
                       Output: test.*  
                       Buffers: shared hit=8  
                       ->  HashAggregate  (cost=0.48..0.49 rows=1 width=4) (actual time=0.021..0.021 rows=1 loops=1)  
                             Output: test_1.id  
                             Group Key: test_1.id  
                             Buffers: shared hit=4  
                             ->  Limit  (cost=0.43..0.47 rows=1 width=12) (actual time=0.016..0.016 rows=1 loops=1)  
                                   Output: test_1.id, test_1.crt_time  
                                   Buffers: shared hit=4  
                                   ->  Index Only Scan using idx_test_1 on public.test test_1  (cost=0.43..173279.36 rows=5000002 width=12) (actual time=0.015..0.015 rows=1 loops=1)  
                                         Output: test_1.id, test_1.crt_time  
                                         Index Cond: (test_1.id IS NOT NULL)  
                                         Heap Fetches: 1  
                                         Buffers: shared hit=4  
                       ->  Index Scan using idx_test_1 on public.test  (cost=0.43..9.64 rows=6 width=73) (actual time=0.009..0.009 rows=1 loops=1)  
                             Output: test.*, test.id  
                             Index Cond: (test.id = test_1.id)  
                             Buffers: shared hit=4  
           ->  WorkTable Scan on skip s  (cost=0.00..5.14 rows=10 width=32) (actual time=0.006..0.006 rows=1 loops=993289)  
                 Output: (SubPlan 1)  
                 Filter: ((s.v).id IS NOT NULL)  
                 Rows Removed by Filter: 0  
                 Buffers: shared hit=3976926  
                 SubPlan 1  
                   ->  Limit  (cost=0.43..0.49 rows=1 width=81) (actual time=0.005..0.005 rows=1 loops=993288)  
                         Output: t_1.*, t_1.id, t_1.crt_time  
                         Buffers: shared hit=3976926  
                         ->  Index Scan using idx_test_1 on public.test t_1  (cost=0.43..102425.17 rows=1666667 width=81) (actual time=0.005..0.005 rows=1 loops=993288)  
                               Output: t_1.*, t_1.id, t_1.crt_time  
                               Index Cond: ((t_1.id > (s.v).id) AND (t_1.id IS NOT NULL))  
                               Buffers: shared hit=3976926  
 Planning time: 0.354 ms  
 Execution time: 6706.105 ms  
(45 rows)  

场景二、唯一值较少(1000唯一值)

1、建表

\timing  
drop table test;  
create unlogged table test(id int , info text, crt_time timestamp);  

2、构造数据

insert into test select ceil(random()*1000), md5(random()::text), clock_timestamp() from generate_series(1,5000000);  

3、创建索引

create index idx_test_1 on test (id, crt_time desc);  

4、递归查询效率

查询语句不变  
                                                                                      QUERY PLAN                                                                                        
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 CTE Scan on skip t  (cost=55.09..57.11 rows=100 width=44) (actual time=0.046..8.859 rows=1000 loops=1)  
   Output: (t.v).id, (t.v).info, (t.v).crt_time  
   Filter: (t.* IS NOT NULL)  
   Rows Removed by Filter: 1  
   Buffers: shared hit=4007  
   CTE skip  
     ->  Recursive Union  (cost=0.91..55.09 rows=101 width=69) (actual time=0.039..8.203 rows=1001 loops=1)  
           Buffers: shared hit=4007  
           ->  Limit  (cost=0.91..1.67 rows=1 width=69) (actual time=0.038..0.038 rows=1 loops=1)  
                 Output: test.*  
                 Buffers: shared hit=8  
                 ->  Nested Loop  (cost=0.91..6335.47 rows=8333 width=69) (actual time=0.038..0.038 rows=1 loops=1)  
                       Output: test.*  
                       Buffers: shared hit=8  
                       ->  HashAggregate  (cost=0.48..0.49 rows=1 width=4) (actual time=0.021..0.021 rows=1 loops=1)  
                             Output: test_1.id  
                             Group Key: test_1.id  
                             Buffers: shared hit=4  
                             ->  Limit  (cost=0.43..0.47 rows=1 width=12) (actual time=0.016..0.017 rows=1 loops=1)  
                                   Output: test_1.id, test_1.crt_time  
                                   Buffers: shared hit=4  
                                   ->  Index Only Scan using idx_test_1 on public.test test_1  (cost=0.43..173279.55 rows=5000002 width=12) (actual time=0.015..0.015 rows=1 loops=1)  
                                         Output: test_1.id, test_1.crt_time  
                                         Index Cond: (test_1.id IS NOT NULL)  
                                         Heap Fetches: 1  
                                         Buffers: shared hit=4  
                       ->  Index Scan using idx_test_1 on public.test  (cost=0.43..6284.98 rows=5000 width=73) (actual time=0.015..0.015 rows=1 loops=1)  
                             Output: test.*, test.id  
                             Index Cond: (test.id = test_1.id)  
                             Buffers: shared hit=4  
           ->  WorkTable Scan on skip s  (cost=0.00..5.14 rows=10 width=32) (actual time=0.008..0.008 rows=1 loops=1001)  
                 Output: (SubPlan 1)  
                 Filter: ((s.v).id IS NOT NULL)  
                 Rows Removed by Filter: 0  
                 Buffers: shared hit=3999  
                 SubPlan 1  
                   ->  Limit  (cost=0.43..0.49 rows=1 width=81) (actual time=0.007..0.007 rows=1 loops=1000)  
                         Output: t_1.*, t_1.id, t_1.crt_time  
                         Buffers: shared hit=3999  
                         ->  Index Scan using idx_test_1 on public.test t_1  (cost=0.43..102425.80 rows=1666667 width=81) (actual time=0.007..0.007 rows=1 loops=1000)  
                               Output: t_1.*, t_1.id, t_1.crt_time  
                               Index Cond: ((t_1.id > (s.v).id) AND (t_1.id IS NOT NULL))  
                               Buffers: shared hit=3999  
 Planning time: 0.353 ms  
 Execution time: 8.980 ms  
(45 rows)  

2 subquery

场景一、唯一值较多(100万唯一值)

1、subquery查询效率

如果ID的取值范围特别广,SUBQUERY就很不划算。

需要维护一张唯一ID表,这里使用generate_series来代替这张表,用于测试。

explain (analyze,verbose,timing,costs,buffers) select (select test from test where id=t.id order by crt_time desc limit 1) from generate_series(1,1000000) t(id);  
                                                                 QUERY PLAN                                                                    
---------------------------------------------------------------------------------------------------------------------------------------------  
 Function Scan on pg_catalog.generate_series t  (cost=0.00..1976.65 rows=1000 width=32) (actual time=70.682..2835.109 rows=1000000 loops=1)  
   Output: (SubPlan 1)  
   Function Call: generate_series(1, 1000000)  
   Buffers: shared hit=3997082  
   SubPlan 1  
     ->  Limit  (cost=0.43..1.97 rows=1 width=77) (actual time=0.002..0.002 rows=1 loops=1000000)  
           Output: test.*, test.crt_time  
           Buffers: shared hit=3997082  
           ->  Index Scan using idx_test_1 on public.test  (cost=0.43..9.64 rows=6 width=77) (actual time=0.002..0.002 rows=1 loops=1000000)  
                 Output: test.*, test.crt_time  
                 Index Cond: (test.id = t.id)  
                 Buffers: shared hit=3997082  
 Planning time: 0.119 ms  
 Execution time: 2892.712 ms  
(14 rows)  

场景二、唯一值较少(1000唯一值)

1、subquery查询效率

查询语句有变

explain (analyze,verbose,timing,costs,buffers) select (select test from test where id=t.id order by crt_time desc limit 1) from generate_series(1,1000) t(id);  
                                                                   QUERY PLAN                                                                     
------------------------------------------------------------------------------------------------------------------------------------------------  
 Function Scan on pg_catalog.generate_series t  (cost=0.00..1699.41 rows=1000 width=32) (actual time=0.107..7.041 rows=1000 loops=1)  
   Output: (SubPlan 1)  
   Function Call: generate_series(1, 1000)  
   Buffers: shared hit=4000  
   SubPlan 1  
     ->  Limit  (cost=0.43..1.69 rows=1 width=77) (actual time=0.006..0.007 rows=1 loops=1000)  
           Output: test.*, test.crt_time  
           Buffers: shared hit=4000  
           ->  Index Scan using idx_test_1 on public.test  (cost=0.43..6284.98 rows=5000 width=77) (actual time=0.006..0.006 rows=1 loops=1000)  
                 Output: test.*, test.crt_time  
                 Index Cond: (test.id = t.id)  
                 Buffers: shared hit=4000  
 Planning time: 0.131 ms  
 Execution time: 7.126 ms  
(14 rows)  

3 窗口查询

场景一、唯一值较多(100万唯一值)

1、窗口查询效率

explain (analyze,verbose,timing,costs,buffers) select id,info,crt_time from (select row_number() over (partition by id order by crt_time desc) as rn, * from test) t where rn=1;  
postgres=# explain (analyze,verbose,timing,costs,buffers) select id,info,crt_time from (select row_number() over (partition by id order by crt_time desc) as rn, * from test) t where rn=1;  
                                                                       QUERY PLAN                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------  
 Subquery Scan on t  (cost=0.43..310779.41 rows=25000 width=45) (actual time=0.027..6398.308 rows=993288 loops=1)  
   Output: t.id, t.info, t.crt_time  
   Filter: (t.rn = 1)  
   Rows Removed by Filter: 4006712  
   Buffers: shared hit=5018864  
   ->  WindowAgg  (cost=0.43..248279.39 rows=5000002 width=53) (actual time=0.026..5973.497 rows=5000000 loops=1)  
         Output: row_number() OVER (?), test.id, test.info, test.crt_time  
         Buffers: shared hit=5018864  
         ->  Index Scan using idx_test_1 on public.test  (cost=0.43..160779.35 rows=5000002 width=45) (actual time=0.019..4058.476 rows=5000000 loops=1)  
               Output: test.id, test.info, test.crt_time  
               Buffers: shared hit=5018864  
 Planning time: 0.121 ms  
 Execution time: 6446.901 ms  
(13 rows)  

场景二、唯一值较少(1000唯一值)

1、窗口查询效率

查询语句不变  
                                                                       QUERY PLAN                                                                          
---------------------------------------------------------------------------------------------------------------------------------------------------------  
 Subquery Scan on t  (cost=0.43..310779.61 rows=25000 width=45) (actual time=0.027..6176.801 rows=1000 loops=1)  
   Output: t.id, t.info, t.crt_time  
   Filter: (t.rn = 1)  
   Rows Removed by Filter: 4999000  
   Buffers: shared hit=4744850 read=18157  
   ->  WindowAgg  (cost=0.43..248279.58 rows=5000002 width=53) (actual time=0.026..5822.576 rows=5000000 loops=1)  
         Output: row_number() OVER (?), test.id, test.info, test.crt_time  
         Buffers: shared hit=4744850 read=18157  
         ->  Index Scan using idx_test_1 on public.test  (cost=0.43..160779.55 rows=5000002 width=45) (actual time=0.020..4175.082 rows=5000000 loops=1)  
               Output: test.id, test.info, test.crt_time  
               Buffers: shared hit=4744850 read=18157  
 Planning time: 0.108 ms  
 Execution time: 6176.924 ms  
(13 rows)  

横向效率对比图

数据量 唯一值个数 窗口查询(ms) subquery(ms) 递归查询(ms)
500 万 100万 6446 2892 6706
500 万 100万 6176 7 9

云端产品

阿里云 RDS PostgreSQL

阿里云 HybridDB for PostgreSQL

类似案例

《PostgrSQL 递归SQL的几个应用 - 极客与正常人的思维》

《时序数据合并场景加速分析和实现 - 复合索引,窗口分组查询加速,变态递归加速》

《(流式、lambda、触发器)实时处理大比拼 - 物联网(IoT)\金融,时序处理最佳实践》

《PostgreSQL 海量时序数据(任意滑动窗口实时统计分析) - 传感器、人群、物体等对象跟踪》

《车联网案例,轨迹清洗 - 阿里云RDS PostgreSQL最佳实践 - 窗口函数》

《PostgreSQL 时序最佳实践 - 证券交易系统数据库设计 - 阿里云RDS PostgreSQL最佳实践》

http://www.timescale.com/

小结

随着物联网的发展,越来越多的时序数据产生,求时序数据的最新值,滑动窗口内的最新值,已经成为时序业务里面非常场景的需求。

PostgreSQL是最先进的开源数据库,它不像很多数据库解决问题往往只有一种方法。PostgreSQL解决一个问题有多种方法,什么方法最优,就看你对它的了解了。

1、唯一值较少时,并且唯一值范围未知时,使用递归。

2、唯一值较少时,并且唯一值范围确定时(例如范围是100万,但是此批数据只出现了50万个,那么如果你有这50万个的ID,性能是最好的,否则需要扫100万。例如用户数是1亿,一个区间的活跃用户可能只有几万。),使用subquery。

3、唯一值较多时,1 使用窗口查询,比前面的方法更加适合。

4、唯一值较多时,2 使用流式计算,比方法3更加优秀。

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