背景

在分布式数据库中，计算count(distinct xxx)，需要对distinct 的字段，

1、去重，

2、重分布去重后的数据，（这一步，如果distinct值特别多，那么就会比较耗时）

3、然后再去重，

4、最后count (xxx)，

5、求所有节点的count SUM。

例如，以下是Greenplum的执行计划例子


postgres=# explain analyze select count(distinct c_acctbal) from customer;  
                                                                             QUERY PLAN                                                                               
--------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Aggregate  (cost=182242.41..182242.42 rows=1 width=8)  
   Rows out:  1 rows with 0.006 ms to first row, 69 ms to end, start offset by 23 ms.  
   ->  Gather Motion 16:1  (slice2; segments: 16)  (cost=53392.85..173982.82 rows=660767 width=8)  
         Rows out:  818834 rows at destination with 3.416 ms to first row, 447 ms to end, start offset by 23 ms.  
         ->  HashAggregate  (cost=53392.85..61652.43 rows=41298 width=8)  
               Group By: customer.c_acctbal  
               Rows out:  Avg 51177.1 rows x 16 workers.  Max 51362 rows (seg3) with 0.004 ms to first row, 33 ms to end, start offset by 25 ms.  
               ->  Redistribute Motion 16:16  (slice1; segments: 16)  (cost=30266.00..43481.34 rows=41298 width=8)  
                     Hash Key: customer.c_acctbal  
                     Rows out:  Avg 89865.6 rows x 16 workers at destination.  Max 90305 rows (seg3) with 18 ms to first row, 120 ms to end, start offset by 25 ms.  
                     ->  HashAggregate  (cost=30266.00..30266.00 rows=41298 width=8)  
                           Group By: customer.c_acctbal  
                           Rows out:  Avg 89865.6 rows x 16 workers.  Max 89929 rows (seg2) with 0.007 ms to first row, 33 ms to end, start offset by 26 ms.  
                           ->  Append-only Columnar Scan on customer  (cost=0.00..22766.00 rows=93750 width=8)  
                                 Rows out:  Avg 93750.0 rows x 16 workers.  Max 93751 rows (seg4) with 20 ms to first row, 30 ms to end, start offset by 26 ms.  
 Slice statistics:  
   (slice0)    Executor memory: 387K bytes.  
   (slice1)    Executor memory: 6527K bytes avg x 16 workers, 6527K bytes max (seg0).  
   (slice2)    Executor memory: 371K bytes avg x 16 workers, 371K bytes max (seg0).  
 Statement statistics:  
   Memory used: 1280000K bytes  
 Optimizer status: legacy query optimizer  
 Total runtime: 723.143 ms  
(23 rows)

以下是citus的例子


postgres=# explain analyze select count(distinct bid) from pgbench_accounts ;  
                                                                            QUERY PLAN                                                                              
------------------------------------------------------------------------------------------------------------------------------------------------------------------  
 Aggregate  (cost=0.00..0.00 rows= width=) (actual time=31.748..31.749 rows=1 loops=1)  
   ->  Custom Scan (Citus Real-Time)  (cost=0.00..0.00 rows= width=) (actual time=31.382..31.510 rows=1280 loops=1)  
         Task Count: 128  
         Tasks Shown: One of 128  
         ->  Task  
               Node: host=172.24.211.224 port=1921 dbname=postgres  
               ->  HashAggregate  (cost=231.85..231.95 rows=10 width=4) (actual time=3.700..3.702 rows=10 loops=1)  
                     Group Key: bid  
                     ->  Seq Scan on pgbench_accounts_106812 pgbench_accounts  (cost=0.00..212.48 rows=7748 width=4) (actual time=0.017..2.180 rows=7748 loops=1)  
                   Planning time: 0.445 ms  
                   Execution time: 3.781 ms  
 Planning time: 1.399 ms  
 Execution time: 32.159 ms  
(13 rows)

对于可估值计算的场景，即不需要精确distinct值的场景，PostgreSQL提供了一个名为hll的插件，可以用来估算distinct元素个数。

citus 结合hll，可以实现超高速的count(distinct xxx)，即使distinct值非常非常多，也不慢。


SET citus.count_distinct_error_rate to 0.005;  
  
0.005表示失真度

hll加速citus count(distinct xxx)使用举例

部署

1、所有节点（coordinator 与 worker节点），安装hll软件


yum install -y gcc-c++  
  
cd ~/  
  
git clone https://github.com/citusdata/postgresql-hll  
  
cd postgresql-hll  
  
. /var/lib/pgsql/.bash_profile   
  
USE_PGXS=1 make  
USE_PGXS=1 make install

2、所有节点（coordinator 与 worker节点），在需要用到HLL的DB中增加插件


su - postgres -c "psql -d postgres -c 'create extension hll;'"  
  
su - postgres -c "psql -d newdb -c 'create extension hll;'"

使用举例

1、创建测试表，128 shard


create table test (id int primary key, a int, b int, c int);  
  
set citus.shard_count =128;   
  
select create_distributed_table('test', 'id');

2、写入10亿测试数据，a字段10唯一值，b字段100唯一值，c字段100万唯一值

insert into test select id, random()*9, random()*99, random()*999999 from generate_series(1,1000000000) t(id);

3、（coordinator节点）设置全局或当前会话级参数，指定失真度，越小失真度越小


SET citus.count_distinct_error_rate to 0.005;  
  
newdb=# explain select count(distinct bid) from pgbench_accounts group by bid;  
                                                          QUERY PLAN                                                             
-------------------------------------------------------------------------------------------------------------------------------  
 HashAggregate  (cost=0.00..0.00 rows= width=)  
   Group Key: remote_scan.worker_column_2  
   ->  Custom Scan (Citus Real-Time)  (cost=0.00..0.00 rows= width=)  
         Task Count: 128  
         Tasks Shown: One of 128  
         ->  Task  
               Node: host=172.24.211.224 port=8001 dbname=newdb  
               ->  GroupAggregate  (cost=97272.79..105102.29 rows=1000 width=36)  
                     Group Key: bid  
                     ->  Sort  (cost=97272.79..99227.04 rows=781700 width=4)  
                           Sort Key: bid  
                           ->  Seq Scan on pgbench_accounts_102008 pgbench_accounts  (cost=0.00..20759.00 rows=781700 width=4)  
(12 rows)

4、对比是否使用HLL加速(少量唯一值，HLL没有性能提升，因为本身就不存在瓶颈)

4.1、未使用hll


newdb=# set citus.count_distinct_error_rate to ;  
newdb=# select count(distinct bid) from pgbench_accounts;  
 count   
-------  
  1000  
(1 row)  
  
Time: 423.364 ms  
  
postgres=# set citus.count_distinct_error_rate to ;  
postgres=# select count(distinct a) from test;  
 count   
-------  
    10  
(1 row)  
  
Time: 2392.709 ms (00:02.393)

4.2、使用hll


newdb=# set citus.count_distinct_error_rate to 0.005;  
newdb=# select count(distinct bid) from pgbench_accounts;  
 count   
-------  
  1000  
(1 row)  
  
Time: 444.287 ms  
  
postgres=# set citus.count_distinct_error_rate to 0.005;  
postgres=# select count(distinct a) from test;  
 count   
-------  
    10  
(1 row)  
  
Time: 2375.473 ms (00:02.375)

5、对比是否使用HLL加速(大量唯一值，HLL性能提升显著)

5.1、未使用hll


postgres=# set citus.count_distinct_error_rate to ;  
  
  count   
----------
 10000000
(1 row)
 
Time: 5826241.205 ms (01:37:06.241)

128个节点，每个节点最多发送10亿/128条数据给coordinator，慢是可以理解的。另一方面，coordinator可以边接收边去重(postgresql 11增加了parallel gather, merge sort等能力，citus coordinator可以借鉴)，没必要等所有数据都收完再去重。

5.2、使用hll


postgres=# set citus.count_distinct_error_rate to 0.005;  
postgres=# select count(distinct (a,c)) from test;  
  count    
---------  
 9999995  
(1 row)  
  
Time: 4468.749 ms (00:04.469)

6、设置不同的精度参数，性能对比


newdb=# set citus.count_distinct_error_rate to 0.1;  
newdb=#  select count(distinct (aid,bid)) from pgbench_accounts ;  
  count     
----------  
 94778491  
(1 row)  
Time: 545.301 ms  
  
newdb=# set citus.count_distinct_error_rate to 0.01;  
newdb=#  select count(distinct (aid,bid)) from pgbench_accounts ;  
   count     
-----------  
 100293937  
(1 row)  
Time: 554.333 ms  
  
-- 推荐设置0.005  
  
newdb=# set citus.count_distinct_error_rate to 0.005;  
newdb=#  select count(distinct (aid,bid)) from pgbench_accounts ;  
   count     
-----------  
 100136086  
(1 row)  
Time: 1053.070 ms (00:01.053)  
  
newdb=# set citus.count_distinct_error_rate to 0.001;  
newdb=#  select count(distinct (aid,bid)) from pgbench_accounts ;  
   count     
-----------  
 100422107  
(1 row)  
Time: 9287.934 ms (00:09.288)