6.7.4 窗口函数
1．相关函数说明
OVER()：指定分析函数工作的数据窗口大小，这个数据窗口大小可能会随着行的变而变化
CURRENT ROW：当前行
n PRECEDING：往前n行数据
n FOLLOWING：往后n行数据
UNBOUNDED：起点，UNBOUNDED PRECEDING 表示从前面的起点， UNBOUNDED FOLLOWING表示到后面的终点
LAG(col,n)：往前第n行数据
LEAD(col,n)：往后第n行数据
NTILE(n)：把有序分区中的行分发到指定数据的组中，各个组有编号，编号从1开始，对于每一行，NTILE返回此行所属的组的编号。注意：n必须为int类型。
2．数据准备：name，orderdate，cost
jack,2017-01-01,10
tony,2017-01-02,15
jack,2017-02-03,23
tony,2017-01-04,29
jack,2017-01-05,46
jack,2017-04-06,42
tony,2017-01-07,50
jack,2017-01-08,55
mart,2017-04-08,62
mart,2017-04-09,68
neil,2017-05-10,12
mart,2017-04-11,75
neil,2017-06-12,80
mart,2017-04-13,94
3．需求
- 查询在2017年4月份购买过的顾客及总人数
- 查询顾客的购买明细及月购买总额
- 上述的场景,要将cost按照日期进行累加
- 查询顾客上次的购买时间
- 查询前20%时间的订单信息
4．创建本地business.txt，导入数据
[atguigu@hadoop102 datas]$ vi business.txt
5．创建hive表并导入数据
|
create table business( name string, orderdate string, cost int ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’;
load data local inpath “/opt/module/datas/business.txt” into table business; |
6．按需求查询数据
- 查询在2017年4月份购买过的顾客及总人数
|
select name,count(*) over () from business where substring(orderdate,1,7) = ‘2017-04’ group by name; |
- 查询顾客的购买明细及月购买总额
|
select name,orderdate,cost,sum(cost) over(partition by month(orderdate)) from business; |
- 上述的场景,要将cost按照日期进行累加
|
select name,orderdate,cost, sum(cost) over() as sample1,–所有行相加 sum(cost) over(partition by name) as sample2,–按name分组，组内数据相加 sum(cost) over(partition by name order by orderdate) as sample3,–按name分组，组内数据累加 sum(cost) over(partition by name order by orderdate rows between UNBOUNDED PRECEDING and current row ) as sample4 ,–和sample3一样,由起点到当前行的聚合 sum(cost) over(partition by name order by orderdate rows between 1 PRECEDING and current row) as sample5, –当前行和前面一行做聚合 sum(cost) over(partition by name order by orderdate rows between 1 PRECEDING AND 1 FOLLOWING ) as sample6,–当前行和前边一行及后面一行 sum(cost) over(partition by name order by orderdate rows between current row and UNBOUNDED FOLLOWING ) as sample7 –当前行及后面所有行 from business; |
- 查看顾客上次的购买时间
|
select name,orderdate,cost, lag(orderdate,1,’1900-01-01′) over(partition by name order by orderdate ) as time1, lag(orderdate,2) over (partition by name order by orderdate) as time2 from business; |
- 查询前20%时间的订单信息
|
select * from ( select name,orderdate,cost, ntile(5) over(order by orderdate) sorted from business ) t where sorted = 1; |
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