昨天和小米的童鞋交流，听说的一个痛点。
也是很多给企业做BI分析的开发小伙伴，可能经常会遇到这样的痛苦，运营人员今天问你要这样的维度报表，明天换个维度又来"折腾"你。
对于开发的小伙伴，确实是非常痛苦的事情，那么有什么好的应对策略，而且对运营来说可能会显得比较高逼格呢？
多维分析派上用场，比如你的表有10个字段，允许运营人员以任意字段组合，产生报表。
很多商业数据库都带了这个功能，开源数据库带这个功能的不多。PostgreSQL真是业界良心啊~~~
例子
假设有4个业务字段，一个时间字段。
postgres=# create table tab5(c1 int, c2 int, c3 int, c4 int, crt_time timestamp);
CREATE TABLE生成一批测试数据
postgres=# insert into tab5 select
trunc(100*random()),
trunc(1000*random()),
trunc(10000*random()),
trunc(100000*random()),
clock_timestamp() + (trunc(10000*random())||' hour')::interval
from generate_series(1,1000000);
INSERT 0 1000000
postgres=# select * from tab5 limit 10;
c1 | c2 | c3 | c4 | crt_time
----+-----+------+-------+----------------------------
72 | 46 | 3479 | 20075 | 2017-02-02 14:56:36.854218
98 | 979 | 4491 | 83012 | 2017-06-13 08:56:36.854416
54 | 758 | 5838 | 45956 | 2016-09-18 02:56:36.854427
3 | 67 | 5148 | 74754 | 2017-01-01 01:56:36.854431
42 | 650 | 7681 | 36495 | 2017-06-20 15:56:36.854435
4 | 472 | 6454 | 19554 | 2016-06-18 19:56:36.854438
82 | 922 | 902 | 17435 | 2016-07-21 14:56:36.854441
68 | 156 | 1028 | 13275 | 2017-07-16 10:56:36.854444
0 | 674 | 7446 | 59386 | 2016-07-26 09:56:36.854447
0 | 629 | 2022 | 52285 | 2016-11-04 13:56:36.85445
(10 rows)创建一个统计结果表, 其中bitmap表示统计的字段组合, 用位置符0,1表示是否统计了该维度
create table stat_tab5 (c1 int, c2 int, c3 int, c4 int, time1 text, time2 text, time3 text, time4 text, cnt int8, bitmap text);生成业务字段任意维度组合+4组时间任选一组的组合统计
PS (如果业务字段有空的情况，建议统计时用coalesce转一下，确保不会统计到空的情况)
insert into stat_tab5
select c1,c2,c3,c4,t1,t2,t3,t4,cnt,
'' ||
case when c1 is null then 0 else 1 end ||
case when c2 is null then 0 else 1 end ||
case when c3 is null then 0 else 1 end ||
case when c4 is null then 0 else 1 end ||
case when t1 is null then 0 else 1 end ||
case when t2 is null then 0 else 1 end ||
case when t3 is null then 0 else 1 end ||
case when t4 is null then 0 else 1 end
from
(
select c1,c2,c3,c4,
to_char(crt_time, 'yyyy') t1,
to_char(crt_time, 'yyyy-mm') t2,
to_char(crt_time, 'yyyy-mm-dd') t3,
to_char(crt_time, 'yyyy-mm-dd hh24') t4,
count(*) cnt
from tab5
group by
cube(c1,c2,c3,c4),
grouping sets(to_char(crt_time, 'yyyy'), to_char(crt_time, 'yyyy-mm'), to_char(crt_time, 'yyyy-mm-dd'), to_char(crt_time, 'yyyy-mm-dd hh24'))
)
t;
INSERT 0 49570486
Time: 172373.714 ms在bitmap上创建索引方便取数据
create index idx_stat_tab5_bitmap on stat_tab5 (bitmap);用户勾选几个维度，取出数据
c1,c3,c4,t3 = bitmap(10110010)
postgres=# select c1,c3,c4,time3,cnt from stat_tab5 where bitmap='10110010' limit 10;
c1 | c3 | c4 | time3 | cnt
----+----+-------+------------+-----
41 | 0 | 30748 | 2016-06-04 | 1
69 | 0 | 87786 | 2016-06-04 | 1
70 | 0 | 38805 | 2016-06-04 | 1
79 | 0 | 65892 | 2016-06-08 | 1
51 | 0 | 13615 | 2016-06-11 | 1
47 | 0 | 42196 | 2016-06-28 | 1
45 | 0 | 54736 | 2016-07-01 | 1
50 | 0 | 21605 | 2016-07-02 | 1
46 | 0 | 40888 | 2016-07-16 | 1
41 | 0 | 90258 | 2016-07-17 | 1
(10 rows)
Time: 0.528 ms
postgres=# select * from stat_tab5 where bitmap='00001000' limit 10;
c1 | c2 | c3 | c4 | time1 | time2 | time3 | time4 | cnt | bitmap
----+----+----+----+-------+-------+-------+-------+--------+----------
| | | | 2016 | | | | 514580 | 00001000
| | | | 2017 | | | | 485420 | 00001000
(2 rows)
Time: 0.542 ms执行计划，可以看到优雅的sort，一次sort多次rolldown, 不是简单的union all哦。
QUERY PLAN
------------------------------------------------------------------------------------------------------------------
Insert on stat_tab5 (cost=208059.84..142986926.23 rows=1536000000 width=184)
-> Subquery Scan on t (cost=208059.84..142986926.23 rows=1536000000 width=184)
-> GroupAggregate (cost=208059.84..35466926.23 rows=1536000000 width=152)
Group Key: (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c4, tab5.c2, tab5.c1
Group Key: (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c4, tab5.c2
Group Key: (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Group Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Group Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Sort Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Group Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy'::text))
Group Key: tab5.c3, tab5.c4, (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Sort Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1
Group Key: tab5.c2, tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: tab5.c1, tab5.c4, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, tab5.c4, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, tab5.c4, (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c2, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c3, tab5.c4
Group Key: tab5.c2, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c3, tab5.c4
Group Key: tab5.c2, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c3
Group Key: tab5.c2, (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Sort Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Sort Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c1, tab5.c4
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c1
Group Key: tab5.c3, (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c2, tab5.c3
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c2
Group Key: tab5.c1, (to_char(tab5.crt_time, 'yyyy'::text))
Sort Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c1
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c1
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
Sort Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1, tab5.c2, tab5.c3
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c1
Group Key: tab5.c4, (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2, tab5.c3
Group Key: (to_char(tab5.crt_time, 'yyyy-mm'::text)), tab5.c2
Group Key: (to_char(tab5.crt_time, 'yyyy-mm'::text))
Sort Key: (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2, tab5.c3
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text)), tab5.c2
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd hh24'::text))
Sort Key: (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2, tab5.c3, tab5.c4
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2, tab5.c3
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd'::text)), tab5.c2
Group Key: (to_char(tab5.crt_time, 'yyyy-mm-dd'::text))
-> Sort (cost=208059.84..210559.84 rows=1000000 width=144)
Sort Key: (to_char(tab5.crt_time, 'yyyy'::text)), tab5.c4, tab5.c2, tab5.c1
-> Seq Scan on tab5 (cost=0.00..26370.00 rows=1000000 width=144)
(93 rows)后续的优化手段
.1. 分区表
因为统计维度多，所以统计结果是非常庞大的。
数据分区可以帮组用户解决查询效率的问题。
例如按bitmap分区之后，在每个分区表再按时间维度分区。
.2. 流式计算
使用pipelinedb结合cube和grouping sets，把以上的统计改成流式统计，可以提升用户体验，快速得到报表。
.3. 使用MPP产品来提升数据的存储量和计算能力，例如Greenplum。
.4. 使用PostgreSQL 9.6 的CPU并行技术，性能可以有明显的提升。
.5. 使用GPU加速，也可以有非常巨大的提升。
.6. 更大数据量可以使用Greenplum，支持MPP。 实现PB级别多维分析。
参考
https://www.postgresql.org/docs/9.6/static/queries-table-expressions.html#QUERIES-GROUPING-SETS
