Hive_Hive ROLLUP, GROUPING SETS, CUBE 聚合函数与 GROUPING 函数

在使用Hive 的时候，我们常常进行聚合统计运算。

聚合统计的运算函数有很多，比如我们最常用的 GROUP BY 函数。但是常常我们需要多维度统计数据，这个时候我们就会用到Hive 的聚合统计函数

这里我们讲解下 ROLLUP, GROUPING SETS, CUBE 的含义以及用法。

我们结合案例讲解下这几个函数的使用规则

数据统计背景：

我们现在有多个公司，多个部门，多位员工的薪水情况。现在我们需要按照多种维度去统计薪水。

我们看下这几个函数的用法ROLLUP, GROUPING SETS, CUBE

我们从 GROUPING SETS , ROLLUP, CUBE 一点点讨论。

GROUPING SETS

GROUPING SETS作为GROUP BY的子句，允许开发人员在GROUP BY语句后面指定多个统计选项，可以简单理解为多条group by语句通过union all把查询结果聚合起来结合起来，下面是几个实例可以帮助我们了解.

首先我们学习下 GROUPING SETS

GROUPING SETS 用法如下：

SELECT

a,b...,f

FROM test_table

GROUP BY

a,b...,f

GROUPING SETS ((?,...,?),(xxx),(yyy))

GROUPING SETS 中间可以填写多个条件。

其中 (?,...,?) 可以为 a~f 中不重复的任意项

具体例子如下：

SELECT

a,b,SUM(c)

FROM test_table

GROUP BY

a,b

GROUPING SETS ((a),(a,b),())

等价于

SELECT

,NULL

,SUM(c)

FROM test_table

GROUP BY

UNION ALL

SELECT

,SUM(c)

FROM test_table

GROUP BY

UNION ALL

SELECT

NULL

,NULL

,SUM(c)

FROM test_table

;

实际案例，我们想按照公司，和整体去统计员工的薪水，但是我们想在一条语句中完成。

我们该如何编写SQL 呢？

SQL如下：

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

GROUPING SETS ((company_name), ())

ORDER BY

grouping__id

;

输出如下：

INFO : MapReduce Jobs Launched:

INFO : Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 4.13 sec HDFS Read: 11666 HDFS Write: 175 SUCCESS

INFO : Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 3.73 sec HDFS Read: 7060 HDFS Write: 188 SUCCESS

INFO : Total MapReduce CPU Time Spent: 7 seconds 860 msec

INFO : Completed executing command(queryId=hive_20200408032038_18e04047-b8c0-4d07-a5de-00ccbc7cb4cc); Time taken: 51.459 seconds

INFO : OK

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 1 | x.qx | NULL | NULL | 59500.00 |

| 1 | s.zh | NULL | NULL | 74000.00 |

+---------------+---------------+-----------+----------+---------------+

可以看到 grouping_id 为0，计算的是整体的薪水和,而grouping_id 为1, 计算的是分公司的薪水

我们刚才说过用 GROUPING SETS 和 GROUP BY + UNION ALL 是等价的，但是它们真的一样么。我们运行 EXPLAIN 检验一下。

下面给出两个运行结果等价的SQL 的 EXPLAIN 结果。

EXPLAIN

SELECT

FROM

(

SELECT

0 AS mark

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

UNION ALL

SELECT

1 AS mark

,company_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

) tmp

ORDER BY mark

;

SQL2

EXPLAIN

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

GROUPING SETS ((company_name), ())

ORDER BY

grouping__id

;

先贴出2个SQL的分别EXPLAIN 结果

UNION ALL 的SQL

注意： UNION ALL 中做聚合增加更多的SQL 会增加 JOB 数量。

STAGE DEPENDENCIES:

Stage-1 is a root stage

Stage-2 depends on stages: Stage-1, Stage-3

Stage-3 is a root stage

Stage-0 depends on stages: Stage-2

STAGE PLANS:

Stage: Stage-1

Map Reduce

Map Operator Tree:

TableScan

alias: datacube_salary_org

filterExpr: (pt = '20200407') (type: boolean)

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: salary (type: decimal(10,2))

outputColumnNames: salary

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Group By Operator

aggregations: sum(salary)

mode: hash

outputColumnNames: _col0

Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE

Reduce Output Operator

sort order:

Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE

value expressions: _col0 (type: decimal(20,2))

Reduce Operator Tree:

Group By Operator

aggregations: sum(VALUE._col0)

mode: mergepartial

outputColumnNames: _col0

Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: 0 (type: int), null (type: string), _col0 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col4

Statistics: Num rows: 1 Data size: 112 Basic stats: COMPLETE Column stats: NONE

File Output Operator

compressed: false

table:

input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

serde: org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe

Stage: Stage-2

Map Reduce

Map Operator Tree:

TableScan

Union

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: _col0 (type: int), _col1 (type: string), _col4 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col4

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

Reduce Output Operator

key expressions: _col0 (type: int)

sort order: +

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

value expressions: _col1 (type: string), _col4 (type: decimal(20,2))

TableScan

Union

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: _col0 (type: int), _col1 (type: string), _col4 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col4

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

Reduce Output Operator

key expressions: _col0 (type: int)

sort order: +

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

value expressions: _col1 (type: string), _col4 (type: decimal(20,2))

Reduce Operator Tree:

Select Operator

expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: string), null (type: void), null (type: void), VALUE._col3 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col2, _col3, _col4

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

File Output Operator

compressed: false

Statistics: Num rows: 4 Data size: 257 Basic stats: COMPLETE Column stats: NONE

table:

input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

Stage: Stage-3

Map Reduce

Map Operator Tree:

TableScan

alias: datacube_salary_org

filterExpr: (pt = '20200407') (type: boolean)

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: company_name (type: string), salary (type: decimal(10,2))

outputColumnNames: company_name, salary

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Group By Operator

aggregations: sum(salary)

keys: company_name (type: string)

mode: hash

outputColumnNames: _col0, _col1

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Reduce Output Operator

key expressions: _col0 (type: string)

sort order: +

Map-reduce partition columns: _col0 (type: string)

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

value expressions: _col1 (type: decimal(20,2))

Reduce Operator Tree:

Group By Operator

aggregations: sum(VALUE._col0)

keys: KEY._col0 (type: string)

mode: mergepartial

outputColumnNames: _col0, _col1

Statistics: Num rows: 3 Data size: 145 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: 1 (type: int), _col0 (type: string), _col1 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col4

Statistics: Num rows: 3 Data size: 145 Basic stats: COMPLETE Column stats: NONE

File Output Operator

compressed: false

table:

input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

serde: org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe

Stage: Stage-0

Fetch Operator

limit: -1

Processor Tree:

ListSink

GROUPING SETS 的SQL

STAGE DEPENDENCIES:

Stage-1 is a root stage

Stage-2 depends on stages: Stage-1

Stage-0 depends on stages: Stage-2

STAGE PLANS:

Stage: Stage-1

Map Reduce

Map Operator Tree:

TableScan

alias: datacube_salary_org

filterExpr: (pt = '20200407') (type: boolean)

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: company_name (type: string), dep_name (type: string), user_id (type: bigint), salary (type: decimal(10,2))

outputColumnNames: company_name, dep_name, user_id, salary

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Group By Operator

aggregations: sum(salary)

keys: company_name (type: string), dep_name (type: string), user_id (type: bigint), 0 (type: int)

mode: hash

outputColumnNames: _col0, _col1, _col2, _col3, _col4

Statistics: Num rows: 14 Data size: 680 Basic stats: COMPLETE Column stats: NONE

Reduce Output Operator

key expressions: _col0 (type: string), _col1 (type: string), _col2 (type: bigint), _col3 (type: int)

sort order: ++++

Map-reduce partition columns: _col0 (type: string), _col1 (type: string), _col2 (type: bigint), _col3 (type: int)

Statistics: Num rows: 14 Data size: 680 Basic stats: COMPLETE Column stats: NONE

value expressions: _col4 (type: decimal(20,2))

Reduce Operator Tree:

Group By Operator

aggregations: sum(VALUE._col0)

keys: KEY._col0 (type: string), KEY._col1 (type: string), KEY._col2 (type: bigint), KEY._col3 (type: int)

mode: mergepartial

outputColumnNames: _col0, _col1, _col2, _col3, _col4

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

Select Operator

expressions: _col3 (type: int), _col0 (type: string), _col1 (type: string), _col2 (type: bigint), _col4 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col2, _col3, _col4

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

File Output Operator

compressed: false

table:

input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

serde: org.apache.hadoop.hive.serde2.lazybinary.LazyBinarySerDe

Stage: Stage-2

Map Reduce

Map Operator Tree:

TableScan

Reduce Output Operator

key expressions: _col0 (type: int)

sort order: +

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

value expressions: _col1 (type: string), _col2 (type: string), _col3 (type: bigint), _col4 (type: decimal(20,2))

Reduce Operator Tree:

Select Operator

expressions: KEY.reducesinkkey0 (type: int), VALUE._col0 (type: string), VALUE._col1 (type: string), VALUE._col2 (type: bigint), VALUE._col3 (type: decimal(20,2))

outputColumnNames: _col0, _col1, _col2, _col3, _col4

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

File Output Operator

compressed: false

Statistics: Num rows: 7 Data size: 340 Basic stats: COMPLETE Column stats: NONE

table:

input format: org.apache.hadoop.mapred.SequenceFileInputFormat

output format: org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat

serde: org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe

Stage: Stage-0

Fetch Operator

limit: -1

Processor Tree:

ListSink

通过以上分析比较，我们不难发现 GROUPING SETS 的作业划分数量更少，

实际情况下 : GROUPING SETS 的运行效率也要高于 UNION ALL 的 GROUP BY 形式

下面是对比的执行时间

GROUPING SETS

INFO : MapReduce Jobs Launched:

INFO : Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.62 sec HDFS Read: 11666 HDFS Write: 175 SUCCESS

INFO : Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 3.51 sec HDFS Read: 7060 HDFS Write: 188 SUCCESS

INFO : Total MapReduce CPU Time Spent: 7 seconds 130 msec

INFO : Completed executing command(queryId=hive_20200408045412_4ab9e09f-436e-4433-9a1f-a03d5b32ef3e); Time taken: 49.676 seconds

INFO : OK

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 1 | x.qx | NULL | NULL | 59500.00 |

| 1 | s.zh | NULL | NULL | 74000.00 |

+---------------+---------------+-----------+----------+---------------+

UNION ALL 的 GROUP BY

INFO : MapReduce Jobs Launched:

INFO : Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 3.7 sec HDFS Read: 10541 HDFS Write: 119 SUCCESS

INFO : Stage-Stage-3: Map: 1 Reduce: 1 Cumulative CPU: 4.34 sec HDFS Read: 10919 HDFS Write: 152 SUCCESS

INFO : Stage-Stage-2: Map: 2 Reduce: 1 Cumulative CPU: 5.08 sec HDFS Read: 12932 HDFS Write: 188 SUCCESS

INFO : Total MapReduce CPU Time Spent: 13 seconds 120 msec

INFO : Completed executing command(queryId=hive_20200408045141_2033cbf6-a457-4bdb-aaec-65900b386972); Time taken: 84.365 seconds

INFO : OK

+-----------+----------+----------+----------+-------------------+

+-----------+----------+----------+----------+-------------------+

| 1 | x.qx | NULL | NULL | 59500.00 |

| 1 | s.zh | NULL | NULL | 74000.00 |

+-----------+----------+----------+----------+-------------------+

可以看到 GROUPING SETS 的执行效率更高

ROLLUP

rollup可以实现从右到左递减多级的统计，显示统计某一层次结构的聚合。（即指定字段组合的递减汇总，如with rollup a,b,c,d 那对应的二进制只能是1111，1110，1100，1000，0000）

即

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

WITH ROLLUP

ORDER BY

grouping__id

;

等价于

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

GROUPING SETS ((company_name, dep_name, user_id), (company_name, dep_name), (company_name),())

ORDER BY

grouping__id

;

等价于

SELECT

company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

UNION ALL

SELECT

company_name

,dep_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

UNION ALL

SELECT

company_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

UNION ALL

SELECT

NULL

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

;

运算结果

INFO : MapReduce Jobs Launched:

INFO : Stage-Stage-1: Map: 1 Reduce: 1 Cumulative CPU: 4.21 sec HDFS Read: 11674 HDFS Write: 563 SUCCESS

INFO : Stage-Stage-2: Map: 1 Reduce: 1 Cumulative CPU: 3.91 sec HDFS Read: 7448 HDFS Write: 602 SUCCESS

INFO : Total MapReduce CPU Time Spent: 8 seconds 120 msec

INFO : Completed executing command(queryId=hive_20200408052638_740f42b9-6f08-49a6-8123-9a77aedc6b19); Time taken: 50.563 seconds

INFO : OK

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 1 | s.zh | NULL | NULL | 74000.00 |

| 1 | x.qx | NULL | NULL | 59500.00 |

| 3 | s.zh | tester | NULL | 20000.00 |

| 3 | x.qx | kiccp | NULL | 8600.00 |

| 3 | x.qx | finance | NULL | 50900.00 |

| 3 | s.zh | enginer | NULL | 54000.00 |

| 7 | x.qx | kiccp | 7 | 8600.00 |

| 7 | x.qx | finance | 6 | 13000.00 |

| 7 | x.qx | finance | 5 | 24500.00 |

| 7 | x.qx | finance | 4 | 13400.00 |

| 7 | s.zh | enginer | 2 | 26000.00 |

| 7 | s.zh | enginer | 1 | 28000.00 |

| 7 | s.zh | tester | 3 | 20000.00 |

+---------------+---------------+-----------+----------+---------------+

这里我们简要的说一下 GROUPING__ID 的计算规则

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

WITH ROLLUP

ORDER BY

grouping__id

;

可以看到 GROUP BY 中有三个字段，依次是 company_name, dep_name, user_id，可以看作3位二进制（0/1),(0/1),(0/1) ，低位对应 company_name，高位对应 user_id ，如果这一位聚合了（GROUP BY 中存在该字段），则为0，GROUPING 函数对应也返回0 ，最后 GROUPING_ID 为对应2进制转换的 10进制数。

1) ROLLUP 的全分组的子集

SELECT

company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

对应的结果

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 7 | x.qx | kiccp | 7 | 8600.00 |

| 7 | x.qx | finance | 6 | 13000.00 |

| 7 | x.qx | finance | 5 | 24500.00 |

| 7 | x.qx | finance | 4 | 13400.00 |

| 7 | s.zh | enginer | 2 | 26000.00 |

| 7 | s.zh | enginer | 1 | 28000.00 |

| 7 | s.zh | tester | 3 | 20000.00 |

+---------------+---------------+-----------+----------+---------------+

因为 GROUP BY 这三个字段都有，则为 111 ，所以 GROUPING__ID 为7

2) ROLLUP company_name, dep_name 分组的子集

SELECT

company_name

,dep_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

对应的子集

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 3 | s.zh | tester | NULL | 20000.00 |

| 3 | x.qx | kiccp | NULL | 8600.00 |

| 3 | x.qx | finance | NULL | 50900.00 |

| 3 | s.zh | enginer | NULL | 54000.00 |

+---------------+---------------+-----------+----------+---------------+

CUBE

cube简称数据魔方，可以实现hive多个任意维度的查询，cube(a,b,c)则首先会对(a,b,c)进行group by，然后依次是(a,b),(a,c),(a),(b,c),(b),(c),最后在对全表进行group by，它会统计所选列中值的所有组合的聚合（即所有字段的组合）

SELECT

grouping__id

,company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

WITH CUBE

ORDER BY

grouping__id

;

等价于

(company_name,dep_name,user_id)

SELECT

company_name

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

,user_id

UNION ALL

SELECT

company_name

,dep_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,dep_name

UNION ALL

SELECT

company_name

,NULL

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

,user_id

UNION ALL

SELECT

company_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

company_name

UNION ALL

SELECT

NULL

,dep_name

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

dep_name

,user_id

UNION ALL

SELECT

NULL

,dep_name

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

dep_name

UNION ALL

SELECT

NULL

,NULL

,user_id

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

GROUP BY

user_id

UNION ALL

SELECT

NULL

,NULL

,SUM(salary) AS total_salary

FROM datacube_salary_org

WHERE pt = '20200407'

;

结果如下：

+---------------+---------------+-----------+----------+---------------+

+---------------+---------------+-----------+----------+---------------+

| 1 | s.zh | NULL | NULL | 74000.00 |

| 1 | x.qx | NULL | NULL | 59500.00 |

| 3 | s.zh | tester | NULL | 20000.00 |

| 3 | s.zh | enginer | NULL | 54000.00 |

| 3 | x.qx | kiccp | NULL | 8600.00 |

| 3 | x.qx | finance | NULL | 50900.00 |

| 4 | NULL | NULL | 7 | 8600.00 |

| 4 | NULL | NULL | 5 | 24500.00 |

| 4 | NULL | NULL | 4 | 13400.00 |

| 4 | NULL | NULL | 3 | 20000.00 |

| 4 | NULL | NULL | 2 | 26000.00 |

| 4 | NULL | NULL | 1 | 28000.00 |

| 4 | NULL | NULL | 6 | 13000.00 |

| 5 | s.zh | NULL | 2 | 26000.00 |

| 5 | s.zh | NULL | 3 | 20000.00 |

| 5 | x.qx | NULL | 5 | 24500.00 |

| 5 | x.qx | NULL | 6 | 13000.00 |

| 5 | s.zh | NULL | 1 | 28000.00 |

| 5 | x.qx | NULL | 7 | 8600.00 |

| 5 | x.qx | NULL | 4 | 13400.00 |

| 6 | NULL | enginer | 1 | 28000.00 |

| 6 | NULL | finance | 4 | 13400.00 |

| 6 | NULL | tester | 3 | 20000.00 |

| 6 | NULL | finance | 5 | 24500.00 |

| 6 | NULL | kiccp | 7 | 8600.00 |

| 6 | NULL | enginer | 2 | 26000.00 |

| 6 | NULL | finance | 6 | 13000.00 |

| 7 | x.qx | finance | 5 | 24500.00 |

| 7 | x.qx | finance | 4 | 13400.00 |

| 7 | x.qx | kiccp | 7 | 8600.00 |

| 7 | s.zh | tester | 3 | 20000.00 |

| 7 | s.zh | enginer | 2 | 26000.00 |

| 7 | s.zh | enginer | 1 | 28000.00 |

| 7 | x.qx | finance | 6 | 13000.00 |

+---------------+---------------+-----------+----------+---------------+

总结：

grouping sets：指定字段的组合来做group by

with rollup：指定字段的组合依次从右向左来递减做group by

with cube：指定字段的全组合

————————————————

原文链接：https://blog.csdn.net/u010003835/article/details/105353510

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