数据库内核月报 - 2017 / 03

HybridDB · 性能优化 · Count Distinct的几种实现方式

前言

最近遇到几个客户在HybridDB上做性能测试时,都遇到Count Distinct的性能调优问题。这里我们总结一下HybridDB中,对Count Distinct的几种处理方式。

我们以一个客户的案例来做说明。客户的典型的业务场景是,在用户行为日志中统计对应类别的行为数,类别有几千个,独立的行为的总量很多,有几千万;为分析行为,要查询一段时间内的基于类别的独立行为数,查询如下(test的建表语句见附录):

select category, count(distinct actionId) as ct from test_user_log
where receivetime between '2017-03-07 11:00:00' and '2017-03-07 12:00:00' group by category
order by ct desc limit 10;

下面我们针对这个查询,来看一下Count Distinct是怎么处理的。

Count Distinct的基本处理方式

利用explain analyze命令,看一下这个查询执行过程的信息:

test=# explain analyze select category, count(distinct actionId) as ct from test_user_log
where receivetime between '2017-03-07 11:00:00' and '2017-03-07 12:00:00' group by category
order by ct desc limit 10;
                                                                                            QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-------------------
 Limit  (cost=0.00..431.00 rows=10 width=16)
 Gather Motion 16:1  (slice2; segments: 16)  (cost=5968.98..5968.99 rows=1 width=40)
   Merge Key: ct
   Rows out:  745 rows at destination with 2469 ms to end, start offset by 77 ms.
   ->  Sort  (cost=5968.98..5968.99 rows=1 width=40)
         Sort Key: ct
         Rows out:  Avg 46.6 rows x 16 workers.  Max 55 rows (seg0) with 2461 ms to end, start offset by 85 ms.
         Executor memory:  58K bytes avg, 58K bytes max (seg0).
         Work_mem used:  58K bytes avg, 58K bytes max (seg0). Workfile: (0 spilling, 0 reused)
         ->  GroupAggregate  (cost=5968.94..5968.97 rows=1 width=40)
               Group By: public.test_user_log.category
               Rows out:  Avg 46.6 rows x 16 workers.  Max 55 rows (seg0) with 2460 ms to end, start offset by 85 ms.
               ->  Sort  (cost=5968.94..5968.94 rows=1 width=40)
                     Sort Key: public.test_user_log.category
                     Rows out:  Avg 461.6 rows x 16 workers.  Max 572 rows (seg4) with 2458 ms to end, start offset by 88 ms.
                     Executor memory:  85K bytes avg, 145K bytes max (seg4).
                     Work_mem used:  85K bytes avg, 145K bytes max (seg4). Workfile: (0 spilling, 0 reused)
                     ->  Redistribute Motion 16:16  (slice1; segments: 16)  (cost=5960.60..5968.93 rows=1 width=40)
                           Hash Key: public.test_user_log.category
                           Rows out:  Avg 461.6 rows x 16 workers at destination.  Max 572 rows (seg4) with 2316 ms to first row, 2458 ms to end, start offset by 88 ms.
                           ->  GroupAggregate  (cost=5960.60..5968.91 rows=1 width=40)
                                 Group By: public.test_user_log.category
                                 Rows out:  Avg 461.6 rows x 16 workers.  Max 472 rows (seg7) with 536 ms to first row, 2455 ms to end, start offset by 89 ms.
                                 Executor memory:  318587K bytes avg, 330108K bytes max (seg7).
                                 Work_mem used:  8544K bytes avg, 8544K bytes max (seg0).
                                 Work_mem wanted: 8414K bytes avg, 8472K bytes max (seg14) to lessen workfile I/O affecting 16 workers.
                                 ->  Sort  (cost=5960.60..5963.37 rows=70 width=64)
                                       Sort Key: public.test_user_log.category
                                       Rows out:  Avg 367982.3 rows x 16 workers.  Max 369230 rows (seg8) with 527 ms to first row, 625 ms to end, start offset by 90 ms.
                                       Executor memory:  61433K bytes avg, 61433K bytes max (seg0).
                                       Work_mem used:  61433K bytes avg, 61433K bytes max (seg0). Workfile: (0 spilling, 0 reused)
                                       ->  Append  (cost=0.00..5904.72 rows=70 width=64)
                                             Rows out:  Avg 367982.3 rows x 16 workers.  Max 369230 rows (seg8) with 2.710 ms to first row, 265 ms to end, start offset by 91 ms
.
                                             ->  Append-only Columnar Scan on test_user_log_1_prt_usual test_user_log  (cost=0.00..0.00 rows=1 width=64)
                                                   Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::timestamp
 without time zone
                                                   Rows out:  0 rows (seg0) with 0.542 ms to end, start offset by 93 ms.
                                             ->  Append-only Columnar Scan on test_user_log_1_prt_157 test_user_log  (cost=0.00..3134.45 rows=37 width=64)
                                                   Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::timestamp
 without time zone
                                                   Rows out:  Avg 367882.1 rows x 16 workers.  Max 369131 rows (seg8) with 2.178 ms to first row, 132 ms to end, start offset by
 91 ms.
                                             ->  Append-only Columnar Scan on test_user_log_1_prt_158 test_user_log  (cost=0.00..2770.27 rows=33 width=64)
                                                   Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::timestamp
 without time zone
                                                   Rows out:  Avg 100.2 rows x 16 workers.  Max 124 rows (seg11) with 2.135 ms to first row, 73 ms to end, start offset by 394 m
s.
 Slice statistics:
   Settings:  effective_cache_size=8GB; gp_statistics_use_fkeys=on; optimizer=off
 Optimizer status: legacy query optimizer
 Total runtime: 2546.862 ms
(51 rows)


可以发现,看似很简单的查询,处理流程却有点复杂。整个处理流程大致如下:

Scan (Columnar Scan + Append) -> Sort(category) -> Group by(category) -> Redistribute -> Sort(category) -> Group by(category) -> Sort -> Gather

从各个节点的实际执行时间的记录可以看出,主要的时间花在了前三步,因为这三步完成后,中间结果只有几百行了。我们重点关注这三个步骤。其实这几个步骤的逻辑比较好理解,扫描出来的数据,直接做排序,排序后,再把结果扫描一遍,同时进行聚合运算。

这里需要注意的一个细节是,查询的表test_user_log是按actionId做分布键的,相同的actionId都会分布在同一个节点上,所以每个节点本地按category做分组后,会在每个分组记录分组中出现的不同actionId值,最终的聚合的结果是category加上一个对应的actionId的计数。

这里有个疑问,其实category的唯一值很少(只有几百个),很适合利用Hash的方式做聚合呢,那么为什么没有选择Hash的方式而是采用了Sort的方式呢?

观察上述查询计划中test_user_log_1_prt_157这个表分区的中间结果估计((cost=0.00..3134.45 rows=37 width=64)),可以发现预估的结果只有37行,而实际是Rows out: Avg 367882.1 rows,即36万多,这说明表的统计信息不准确。

执行一下Analyze来更新统计信息:

test=# analyze test_user_log_1_prt_157;
analyze test_user_log_1_prt_158;ANALYZE
test=# analyze test_user_log_1_prt_158;
ANALYZE

更新统计信息后,再执行一下查询,查询计划果然发生了变化:排序聚合变成了Hash方式聚合!执行时间也由2546.862ms缩短到2099.144ms。

                                                                                               QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-------------------------
 Limit  (cost=0.00..431.00 rows=10 width=16)
   Gather Motion 16:1  (slice2; segments: 16)  (cost=320695.70..320695.92 rows=10 width=40)
         Merge Key: ct
         ->  Limit  (cost=320695.70..320695.72 rows=1 width=40)
               ->  Sort  (cost=320695.70..320695.94 rows=7 width=40)
                     Sort Key (Limit): ct
                     ->  HashAggregate  (cost=320691.23..320692.46 rows=7 width=40)
                           Group By: partial_aggregation.category
                           ->  HashAggregate  (cost=303756.92..311454.34 rows=38488 width=64)
                                 Group By: public.test_user_log.category, public.test_user_log.actionId
                                 ->  Redistribute Motion 16:16  (slice1; segments: 16)  (cost=280664.69..292980.55 rows=38488 width=64)
                                       Hash Key: public.test_user_log.category
                                       ->  HashAggregate  (cost=280664.69..280664.69 rows=38488 width=64)
                                             Group By: public.test_user_log.category, public.test_user_log.actionId
                                             ->  Append  (cost=0.00..236527.23 rows=367813 width=43)
                                                   ->  Append-only Columnar Scan on test_user_log_1_prt_usual test_user_log  (cost=0.00..0.00 rows=1 width=64)
                                                         Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::tim
estamp without time zone
                                                   ->  Append-only Columnar Scan on test_user_log_1_prt_157 test_user_log  (cost=0.00..124267.69 rows=367813 width=42)
                                                         Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::tim
estamp without time zone
                                                   ->  Append-only Columnar Scan on test_user_log_1_prt_158 test_user_log  (cost=0.00..112259.54 rows=1 width=42)
                                                         Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00'::tim
estamp without time zone

上面的计划主要流程如下。两次Group by(Hash(category,actionId))都是为了去除重复值,保证(category,actionId)组合字段值唯一。

Scan (Columnar Scan + Append) -> Group by(Hash(category,actionId)) -> Redistribute(category) -> Group by(Hash(category, acitonId)) -> Group by(Hash(category)) -> Sort -> Gather

那么还有其他可能的处理方式吗?我们知道,HybridDB支持新型的orca优化器,orca考虑更多的查询执行方式。我们下面试试使用orca来生成查询计划。

test=# set optimizer=on;
SET
test=# explain analyze select category, count(distinct actionId) as ct from test_user_log
where receivetime between '2017-03-07 11:00:00' and '2017-03-07 12:00:00' group by category
order by ct desc limit 10;
                                                                                                  QUERY PLAN

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-------------------------------
 Limit  (cost=0.00..431.00 rows=1 width=16)
   Rows out:  10 rows with 2690 ms to end, start offset by 0.500 ms.
   ->  Gather Motion 16:1  (slice2; segments: 16)  (cost=0.00..431.00 rows=1 width=16)
         Merge Key: ct
         Rows out:  10 rows at destination with 2690 ms to end, start offset by 0.501 ms.
         ->  Sort  (cost=0.00..431.00 rows=1 width=16)
               Sort Key: ct
               Rows out:  Avg 46.6 rows x 16 workers.  Max 55 rows (seg0) with 2688 ms to end, start offset by 2.356 ms.
               Executor memory:  33K bytes avg, 33K bytes max (seg0).
               Work_mem used:  33K bytes avg, 33K bytes max (seg0). Workfile: (0 spilling, 0 reused)
               ->  GroupAggregate  (cost=0.00..431.00 rows=1 width=16)
                     Group By: category
                     Rows out:  Avg 46.6 rows x 16 workers.  Max 55 rows (seg0) with 2687 ms to first row, 2688 ms to end, start offset by 2.372 ms.
                     ->  Sort  (cost=0.00..431.00 rows=1 width=16)
                           Sort Key: category
                           Rows out:  Avg 461.6 rows x 16 workers.  Max 572 rows (seg4) with 2687 ms to end, start offset by 3.104 ms.
                           Executor memory:  85K bytes avg, 145K bytes max (seg4).
                           Work_mem used:  85K bytes avg, 145K bytes max (seg4). Workfile: (0 spilling, 0 reused)
                           ->  Redistribute Motion 16:16  (slice1; segments: 16)  (cost=0.00..431.00 rows=1 width=16)
                                 Hash Key: category
                                 Rows out:  Avg 461.6 rows x 16 workers at destination.  Max 572 rows (seg4) with 2442 ms to first row, 2687 ms to end, start offset by 3.113 ms
.
                                 ->  Result  (cost=0.00..431.00 rows=1 width=16)
                                       Rows out:  Avg 461.6 rows x 16 workers.  Max 472 rows (seg7) with 1070 ms to first row, 2583 ms to end, start offset by 3.898 ms.
                                       ->  GroupAggregate  (cost=0.00..431.00 rows=1 width=16)
                                             Group By: category
                                             Rows out:  Avg 461.6 rows x 16 workers.  Max 472 rows (seg7) with 1070 ms to first row, 2583 ms to end, start offset by 3.898 ms.
                                             Executor memory:  316808K bytes avg, 328245K bytes max (seg7).
                                             Work_mem used:  8184K bytes avg, 8184K bytes max (seg0).
                                             Work_mem wanted: 8414K bytes avg, 8472K bytes max (seg14) to lessen workfile I/O affecting 16 workers.
                                             ->  Sort  (cost=0.00..431.00 rows=1 width=16)
                                                   Sort Key: category, actionId
                                                   Rows out:  Avg 367982.3 rows x 16 workers.  Max 369230 rows (seg8) with 1064 ms to first row, 1143 ms to end, start offset by
 3.812 ms.
                                                   Executor memory:  143353K bytes avg, 143353K bytes max (seg0).
                                                   Work_mem used:  143353K bytes avg, 143353K bytes max (seg0). Workfile: (0 spilling, 0 reused)
                                                   ->  Sequence  (cost=0.00..431.00 rows=1 width=24)
                                                         Rows out:  Avg 367982.3 rows x 16 workers.  Max 369230 rows (seg8) with 1.905 ms to first row, 241 ms to end, start off
set by 4.032 ms.
                                                         ->  Partition Selector for test_user_log (dynamic scan id: 1)  (cost=10.00..100.00 rows=7 width=4)
                                                               Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00
'::timestamp without time zone
                                                               Partitions selected:  3 (out of 745)
                                                               Rows out:  0 rows (seg0) with 0.013 ms to end, start offset by 4.033 ms.
                                                         ->  Dynamic Table Scan on test_user_log (dynamic scan id: 1)  (cost=0.00..431.00 rows=1 width=24)
                                                               Filter: receivetime >= '2017-03-07 11:00:00'::timestamp without time zone AND receivetime <= '2017-03-07 12:00:00
'::timestamp without time zone
                                                               Rows out:  Avg 367982.3 rows x 16 workers.  Max 369230 rows (seg8) with 1.891 ms to first row, 202 ms to end, sta
rt offset by 4.046 ms.
                                                               Partitions scanned:  Avg 3.0 (out of 745) x 16 workers.  Max 3 parts (seg0).
 Slice statistics:
   (slice0)    Executor memory: 351K bytes.
   (slice1)  * Executor memory: 152057K bytes avg x 16 workers, 152057K bytes max (seg0).  Work_mem: 143353K bytes max, 8472K bytes wanted.
   (slice2)    Executor memory: 363K bytes avg x 16 workers, 423K bytes max (seg4).  Work_mem: 145K bytes max.
 Statement statistics:
   Memory used: 2047000K bytes
   Memory wanted: 34684K bytes
 Settings:  effective_cache_size=8GB; gp_statistics_use_fkeys=on; optimizer=on
 Optimizer status: PQO version 1.609
 Total runtime: 2690.675 ms
(54 rows)

 

使用orca生成的查询计划,又回到了使用Sort+Groupby的方式来做聚合(这是因为,我们使用Analyze只更新了子分区表的统计信息,而orca只会考虑主表上的统计信息,要想是orca的计划转为使用Hash方式,需要在主表上使用Analyze,这里我们不继续讨论)。而上述使用orca生成的计划,与使用缺省优化器有很大不同。orca的查询计划采用了下面的流程:

Scan (Dynamic Scan) -> Sort (category, actionId) -> Group by (category) -> Redistribute -> Sort (category) -> Group by(category) -> Sort -> Gather

注意,第一次Sort用了(category, actionId)两个字段的组合,但后面的Group by时只适应了category一个字段!这是一种特殊的聚合方式。在做这种聚合时,对应一个不同的category,只需保留一个actionId的计数即可,而不是像在缺省优化器计划中那样,对每个不同的category,需要保留所有不同的actionId值,这样省去了建立类似Hash表的数据结构的时间。但由于Sort的时候用了两个字段,时间消耗比使用一个字段高,导致整个查询计划的性能不如缺省优化器产生的计划。

延伸

上面的讨论所举的例子中的表,正好是以Count Distinct的字段(即actionId)作为分布键的。如果以其他字段作为分布键,会产生不一样的查询计划,但基本原理都是类似的。

另外,我们没有涉及一个查询中涉及多个字段上有Count Distinct的情况,读者可以自行尝试。

附录

  • 建表语句
create  table test_user_log
(
        actionId text,
        code text,
        receiveTime timestamp,
        gmtCreate timestamp,
        category text,
        version text,
        tag text,
        siteId int4
) 
with  (APPENDONLY=true, ORIENTATION=column, BLOCKSIZE=524288)
distributed by (actionId)
PARTITION BY RANGE (receivetime) 
(START ('2017-03-07') INCLUSIVE END ('2017-03-07') EXCLUSIVE EVERY (INTERVAL '1 hour' ), DEFAULT PARTITION usual);