Hive常用函数大全（二）（窗口函数、分析函数、增强group）
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窗口函数与分析函数
应用场景：
（1）用于分区排序 （2）动态Group By （3）Top N （4）累计计算 （5）层次查询窗口函数
FIRST_VALUE：取分组内排序后，截止到当前行，第一个值
LAST_VALUE： 取分组内排序后，截止到当前行，最后一个值 LEAD(col,n,DEFAULT) ：用于统计窗口内往下第n行值。第一个参数为列名，第二个参数为往下第n行（可选，默认为1），第三个参数为默认值（当往下第n行为NULL时候，取默认值，如不指定，则为NULL） LAG(col,n,DEFAULT) ：与lead相反，用于统计窗口内往上第n行值。第一个参数为列名，第二个参数为往上第n行（可选，默认为1），第三个参数为默认值（当往上第n行为NULL时候，取默认值，如不指定，则为NULL）OVER从句
1、使用标准的聚合函数COUNT、SUM、MIN、MAX、AVG
PARTITION BY语句，使用一个或者多个原始数据类型的列 3、使用PARTITION BY与ORDER BY语句，使用一个或者多个数据类型的分区或者排序列 4、使用窗口规范，窗口规范支持以下格式： (ROWS | RANGE) BETWEEN (UNBOUNDED | [num]) PRECEDING AND ([num] PRECEDING | CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)(ROWS | RANGE) BETWEEN CURRENT ROW AND (CURRENT ROW | (UNBOUNDED | [num]) FOLLOWING)(ROWS | RANGE) BETWEEN [num] FOLLOWING AND (UNBOUNDED | [num]) FOLLOWING
当ORDER BY后面缺少窗口从句条件，窗口规范默认是 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW.
当ORDER BY和窗口从句都缺失, 窗口规范默认是 ROW BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING.
OVER从句支持以下函数， 但是并不支持和窗口一起使用它们。
Rank, NTile, DenseRank, CumeDist, PercentRank. Lead 和 Lag 函数. 分析函数
ROW_NUMBER() 从1开始，按照顺序，生成分组内记录的序列,比如，按照pv降序排列，生成分组内每天的pv名次,ROW_NUMBER()的应用场景非常多，再比如，获取分组内排序第一的记录;获取一个session中的第一条refer等。
RANK() 生成数据项在分组中的排名，排名相等会在名次中留下空位 DENSE_RANK() 生成数据项在分组中的排名，排名相等会在名次中不会留下空位 CUME_DIST 小于等于当前值的行数/分组内总行数。比如，统计小于等于当前薪水的人数，所占总人数的比例 PERCENT_RANK 分组内当前行的RANK值-1/分组内总行数-1 NTILE(n) 用于将分组数据按照顺序切分成n片，返回当前切片值，如果切片不均匀，默认增加第一个切片的分布。NTILE不支持ROWS BETWEEN，比如 NTILE(2) OVER(PARTITION BY cookieid ORDER BY createtime ROWS BETWEEN 3 PRECEDING AND CURRENT ROW)。Hive2.1.0及以后支持Distinct
在聚合函数（SUM, COUNT and AVG）中，支持distinct，但是在ORDER BY 或者 窗口限制不支持。
COUNT(DISTINCT a) OVER (PARTITION BY c)
Hive 2.2.0中在使用ORDER BY和窗口限制时支持distinct
COUNT(DISTINCT a) OVER (PARTITION BY c ORDER BY d ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING)
Hive2.1.0及以后支持在OVER从句中支持聚合函数
SELECT rank() OVER (ORDER BY sum(b))FROM TGROUP BY a;
## COUNT、SUM、MIN、MAX、AVG
## COUNT、SUM、MIN、MAX、AVGselect user_id, user_type, sales, --默认为从起点到当前行 sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc) AS sales_1, --从起点到当前行，结果与sales_1不同。 sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS sales_2, --当前行+往前3行 sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN 3 PRECEDING AND CURRENT ROW) AS sales_3, --当前行+往前3行+往后1行 sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN 3 PRECEDING AND 1 FOLLOWING) AS sales_4, --当前行+往后所有行 sum(sales) OVER(PARTITION BY user_type ORDER BY sales asc ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS sales_5, --分组内所有行 SUM(sales) OVER(PARTITION BY user_type) AS sales_6 from order_detailorder by user_type, sales, user_id;
result
注意:
结果和ORDER BY相关,默认为升序
如果不指定ROWS BETWEEN,默认为从起点到当前行; 如果不指定ORDER BY，则将分组内所有值累加;关键是理解ROWS BETWEEN含义,也叫做WINDOW子句：
PRECEDING：往前FOLLOWING：往后CURRENT ROW：当前行UNBOUNDED：无界限（起点或终点）UNBOUNDED PRECEDING：表示从前面的起点 UNBOUNDED FOLLOWING：表示到后面的终点 其他COUNT、AVG，MIN，MAX，和SUM用法一样。
## first_value与last_value
## first_value与last_valueselect user_id, user_type, ROW_NUMBER() OVER(PARTITION BY user_type ORDER BY sales) AS row_num, first_value(user_id) over (partition by user_type order by sales desc) as max_sales_user, first_value(user_id) over (partition by user_type order by sales asc) as min_sales_user, last_value(user_id) over (partition by user_type order by sales desc) as curr_last_min_user, last_value(user_id) over (partition by user_type order by sales asc) as curr_last_max_userfrom order_detail;
result
## lead与lag
## lead与lagselect user_id,device_id, lead(device_id) over (order by sales) as default_after_one_line, lag(device_id) over (order by sales) as default_before_one_line, lead(device_id,2) over (order by sales) as after_two_line, lag(device_id,2,'abc') over (order by sales) as before_two_linefrom order_detail;
result
## RANK、ROW_NUMBER、DENSE_RANK
## RANK、ROW_NUMBER、DENSE_RANKselect user_id,user_type,sales, RANK() over (partition by user_type order by sales desc) as r, ROW_NUMBER() over (partition by user_type order by sales desc) as rn, DENSE_RANK() over (partition by user_type order by sales desc) as drfrom order_detail;
result
## NTILE
## NTILEselect user_type,sales, --分组内将数据分成2片 NTILE(2) OVER(PARTITION BY user_type ORDER BY sales) AS nt2, --分组内将数据分成3片 NTILE(3) OVER(PARTITION BY user_type ORDER BY sales) AS nt3, --分组内将数据分成4片 NTILE(4) OVER(PARTITION BY user_type ORDER BY sales) AS nt4, --将所有数据分成4片 NTILE(4) OVER(ORDER BY sales) AS all_nt4from order_detailorder by user_type, sales;
result
## 求取sale前20%的用户ID
# 求取sale前20%的用户IDselect user_idfrom( select user_id, NTILE(5) OVER(ORDER BY sales desc) AS nt from order_detail)Awhere nt=1;
## CUME_DIST、PERCENT_RANK
## CUME_DIST、PERCENT_RANK select user_id,user_type,sales,--没有partition,所有数据均为1组CUME_DIST() OVER(ORDER BY sales) AS cd1,--按照user_type进行分组CUME_DIST() OVER(PARTITION BY user_type ORDER BY sales) AS cd2 from order_detail;
result
select user_type,sales--分组内总行数 SUM(1) OVER(PARTITION BY user_type) AS s, --RANK值 RANK() OVER(ORDER BY sales) AS r, PERCENT_RANK() OVER(ORDER BY sales) AS pr,--分组内 PERCENT_RANK() OVER(PARTITION BY user_type ORDER BY sales) AS prg from order_detail;
增强的聚合 Cube和Grouping 和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。
## 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,
其中的GROUPING__ID，表示结果属于哪一个分组集合。select user_type, sales, count(user_id) as pv, GROUPING__ID from order_detailgroup by user_type,salesGROUPING SETS(user_type,sales) ORDER BY GROUPING__ID;
result
select user_type, sales, count(user_id) as pv, GROUPING__ID from order_detailgroup by user_type,salesGROUPING SETS(user_type,sales,(user_type,sales)) ORDER BY GROUPING__ID;
result
## CUBE
根据GROUP BY的维度的所有组合进行聚合。
select user_type, sales, count(user_id) as pv, GROUPING__ID from order_detailgroup by user_type,salesWITH CUBE ORDER BY GROUPING__ID;
result
## 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。
select user_type, sales, count(user_id) as pv, GROUPING__ID from order_detailgroup by user_type,salesWITH ROLLUP ORDER BY GROUPING__ID;
result
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