Hive分析窗口函数(五) GROUPING SETS,GROUPING__ID,CUBE,ROLLUP

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GROUPING SETS,GROUPING__ID,CUBE,ROLLUP

这几个分析函数通常用于OLAP中，不能累加，而且需要根据不同维度上钻和下钻的指标统计，比如，分小时、天、月的UV数。

Hive版本为 apache-hive-0.13.1

数据准备：

    2015-03,2015-03-10,cookie1    2015-03,2015-03-10,cookie5    2015-03,2015-03-12,cookie7    2015-04,2015-04-12,cookie3    2015-04,2015-04-13,cookie2    2015-04,2015-04-13,cookie4    2015-04,2015-04-16,cookie4    2015-03,2015-03-10,cookie2    2015-03,2015-03-10,cookie3    2015-04,2015-04-12,cookie5    2015-04,2015-04-13,cookie6    2015-04,2015-04-15,cookie3    2015-04,2015-04-15,cookie2    2015-04,2015-04-16,cookie1         CREATE EXTERNAL TABLE lxw1234 (    month STRING,    day STRING,     cookieid STRING     ) ROW FORMAT DELIMITED     FIELDS TERMINATED BY ','     stored as textfile location '/tmp/lxw11/';              hive> select * from lxw1234;    OK    2015-03 2015-03-10      cookie1    2015-03 2015-03-10      cookie5    2015-03 2015-03-12      cookie7    2015-04 2015-04-12      cookie3    2015-04 2015-04-13      cookie2    2015-04 2015-04-13      cookie4    2015-04 2015-04-16      cookie4    2015-03 2015-03-10      cookie2    2015-03 2015-03-10      cookie3    2015-04 2015-04-12      cookie5    2015-04 2015-04-13      cookie6    2015-04 2015-04-15      cookie3    2015-04 2015-04-15      cookie2    2015-04 2015-04-16      cookie1

GROUPING SETS

在一个GROUP BY查询中，根据不同的维度组合进行聚合，等价于将不同维度的GROUP BY结果集进行UNION ALL

    SELECT     month,    day,    COUNT(DISTINCT cookieid) AS uv,    GROUPING__ID     FROM lxw1234     GROUP BY month,day     GROUPING SETS (month,day)     ORDER BY GROUPING__ID;         month      day            uv      GROUPING__ID    ------------------------------------------------    2015-03    NULL            5       1    2015-04    NULL            6       1    NULL       2015-03-10      4       2    NULL       2015-03-12      1       2    NULL       2015-04-12      2       2    NULL       2015-04-13      3       2    NULL       2015-04-15      2       2    NULL       2015-04-16      2       2              等价于     SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month     UNION ALL     SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day

再如：

    SELECT     month,    day,    COUNT(DISTINCT cookieid) AS uv,    GROUPING__ID     FROM lxw1234     GROUP BY month,day     GROUPING SETS (month,day,(month,day))     ORDER BY GROUPING__ID;         month         day             uv      GROUPING__ID    ------------------------------------------------    2015-03       NULL            5       1    2015-04       NULL            6       1    NULL          2015-03-10      4       2    NULL          2015-03-12      1       2    NULL          2015-04-12      2       2    NULL          2015-04-13      3       2    NULL          2015-04-15      2       2    NULL          2015-04-16      2       2    2015-03       2015-03-10      4       3    2015-03       2015-03-12      1       3    2015-04       2015-04-12      2       3    2015-04       2015-04-13      3       3    2015-04       2015-04-15      2       3    2015-04       2015-04-16      2       3              等价于    SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month     UNION ALL     SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day    UNION ALL     SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM lxw1234 GROUP BY month,day

其中的 GROUPING__ID，表示结果属于哪一个分组集合。

CUBE

根据GROUP BY的维度的所有组合进行聚合。

    SELECT     month,    day,    COUNT(DISTINCT cookieid) AS uv,    GROUPING__ID     FROM lxw1234     GROUP BY month,day     WITH CUBE     ORDER BY GROUPING__ID;              month      day             uv     GROUPING__ID    --------------------------------------------    NULL            NULL            7       0    2015-03         NULL            5       1    2015-04         NULL            6       1    NULL            2015-04-12      2       2    NULL            2015-04-13      3       2    NULL            2015-04-15      2       2    NULL            2015-04-16      2       2    NULL            2015-03-10      4       2    NULL            2015-03-12      1       2    2015-03         2015-03-10      4       3    2015-03         2015-03-12      1       3    2015-04         2015-04-16      2       3    2015-04         2015-04-12      2       3    2015-04         2015-04-13      3       3    2015-04         2015-04-15      2       3                   等价于    SELECT NULL,NULL,COUNT(DISTINCT cookieid) AS uv,0 AS GROUPING__ID FROM lxw1234    UNION ALL     SELECT month,NULL,COUNT(DISTINCT cookieid) AS uv,1 AS GROUPING__ID FROM lxw1234 GROUP BY month     UNION ALL     SELECT NULL,day,COUNT(DISTINCT cookieid) AS uv,2 AS GROUPING__ID FROM lxw1234 GROUP BY day    UNION ALL     SELECT month,day,COUNT(DISTINCT cookieid) AS uv,3 AS GROUPING__ID FROM lxw1234 GROUP BY month,day

ROLLUP

是CUBE的子集，以最左侧的维度为主，从该维度进行层级聚合。

    比如，以month维度进行层级聚合：    SELECT     month,    day,    COUNT(DISTINCT cookieid) AS uv,    GROUPING__ID      FROM lxw1234     GROUP BY month,day    WITH ROLLUP     ORDER BY GROUPING__ID;         month      day             uv     GROUPING__ID    ---------------------------------------------------    NULL             NULL            7       0    2015-03          NULL            5       1    2015-04          NULL            6       1    2015-03          2015-03-10      4       3    2015-03          2015-03-12      1       3    2015-04          2015-04-12      2       3    2015-04          2015-04-13      3       3    2015-04          2015-04-15      2       3    2015-04          2015-04-16      2       3         可以实现这样的上钻过程：    月天的UV->月的UV->总UV

    --把month和day调换顺序，则以day维度进行层级聚合：         SELECT     day,    month,    COUNT(DISTINCT cookieid) AS uv,    GROUPING__ID      FROM lxw1234     GROUP BY day,month     WITH ROLLUP     ORDER BY GROUPING__ID;              day        month              uv     GROUPING__ID    -------------------------------------------------------    NULL            NULL               7       0    2015-04-13      NULL               3       1    2015-03-12      NULL               1       1    2015-04-15      NULL               2       1    2015-03-10      NULL               4       1    2015-04-16      NULL               2       1    2015-04-12      NULL               2       1    2015-04-12      2015-04            2       3    2015-03-10      2015-03            4       3    2015-03-12      2015-03            1       3    2015-04-13      2015-04            3       3    2015-04-15      2015-04            2       3    2015-04-16      2015-04            2       3         可以实现这样的上钻过程：    天月的UV->天的UV->总UV    （这里，根据天和月进行聚合，和根据天聚合结果一样，因为有父子关系，如果是其他维度组合的话，就会不一样）

这种函数，需要结合实际场景和数据去使用和研究，只看说明的话，很难理解。

官网的介绍： https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup

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