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这篇文章主要介绍“在分布式数据库中怎么计算 count”，在日常操作中，相信很多人在在分布式数据库中怎么计算 count 问题上存在疑惑，丸趣 TV 小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”在分布式数据库中怎么计算 count”的疑惑有所帮助！接下来，请跟着丸趣 TV 小编一起来学习吧！
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=0 width=0) (actual time=31.748..31.749 rows=1 loops=1)
- Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0) (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 install2、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 , id2、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=0 width=0)
Group Key: remote_scan.worker_column_2
- Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0)
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 0;
newdb=# select count(distinct bid) from pgbench_accounts;
count
-------
1000
(1 row)
Time: 423.364 ms
postgres=# set citus.count_distinct_error_rate to 0;
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 0;
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)到此，关于“在分布式数据库中怎么计算 count”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注丸趣 TV 网站，丸趣 TV 小编会继续努力为大家带来更多实用的文章！
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