HIVE - 基于日期计算具有窗口的分区的统计信息

Question

我已经看到了与我类似的问题的解决方案，但没有一个对我很有效。我也相信应该有办法让它发挥作用。

给定一张桌子

ID	Date	target
1	2020-01-01	1
1	2020-01-02	1
1	2020-01-03	0
1	2020-01-04	1
1	2020-01-04	0
1	2020-06-01	1
1	2020-06-02	1
1	2020-06-03	0
1	2020-06-04	1
1	2020-06-04	0
2	2020-01-01	1

ID 是 BIGINT，目标是 Int，Date 是 DATE

我想为每个 ID/Date 计算日期前 3 个月和 12 个月（含）内相同 ID 的总和和行数。输出示例：

ID	Date	Sum_3	Count_3	Sum_12	Count_12
1	2020-01-01	1	1	1	1
1	2020-01-02	2	2	2	2
1	2020-01-03	2	3	2	3
1	2020-01-04	3	5	3	5
1	2020-06-01	1	1	4	6
1	2020-06-02	2	2	5	7
1	2020-06-03	2	3	6	8
1	2020-06-04	3	5	7	10
2	2020-01-01	1	1	1	1

如何在 HIVE 中获得这段时间的结果？ 我不确定是否应该使用分析函数（以及如何使用）、分组依据等...？

Answer 1

如果您可以将几个月的近似值视为天数，那么您可以在 Hive 中使用窗口函数：

select id, date, 
    count(*) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 90 preceding -- 90 days
    ) as count_3,
    sum(target) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 90 preceding
    ) as sum_3,
    count(*) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 360 preceding -- 360 days
    ) as count_12,
    sum(target) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 360 preceding
    ) as sum_12
from mytable

您可以在同一个查询中聚合：

select id, date, 
    sum(count(*)) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 90 preceding -- 90 days
    ) as count_3,
    sum(sum(target)) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 90 preceding
    ) as sum_3,
    sum(count(*)) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 360 preceding -- 360 days
    ) as count_12,
    sum(sum(target)) over(
        partition by id 
        order by unix_timestamp(date)
        range 60 * 60 * 24 * 360 preceding
    ) as sum_12
from mytable
group by id, date, unix_timestamp(date)

Answer 2

如果您可以估计间隔（1 个月 = 30 天）：（GMB 的答案的改进）

with t as (
    select ID, Date,
        sum(target) target,
        count(target) c_target
    from table
    group by ID, Date
)
select ID, Date,
    sum(target) over(
        partition by ID
        order by unix_timestamp(Date, 'yyyy-MM-dd')
        range 60 * 60 * 24 * 90 preceding
    ) sum_3,
    sum(c_target) over(
        partition by ID
        order by unix_timestamp(Date, 'yyyy-MM-dd')
        range 60 * 60 * 24 * 90 preceding
    ) count_3,
    sum(target) over(
        partition by ID
        order by unix_timestamp(Date, 'yyyy-MM-dd')
        range 60 * 60 * 24 * 360 preceding
    ) sum_12,
    sum(c_target) over(
        partition by ID
        order by unix_timestamp(Date, 'yyyy-MM-dd')
        range 60 * 60 * 24 * 360 preceding
    ) count_12
from t

或者，如果您想要精确的间隔，您可以进行自连接（但成本高）：

with t as (
    select ID, Date,
        sum(target) target,
        count(target) c_target
    from table
    group by ID, Date
)
select
    t_3month.ID, 
    t_3month.Date, 
    t_3month.sum_3, 
    t_3month.count_3, 
    sum(t3.target) sum_12, 
    sum(t3.c_target) count_12
from (
    select 
        t1.ID, 
        t1.Date,
        sum(t2.target) sum_3,
        sum(t2.c_target) count_3
    from t t1
    left join t t2
    on t2.Date > t1.Date - interval 3 month and
       t2.Date <= t1.Date and
       t1.ID = t2.ID
    group by t1.ID, t1.Date
) t_3month
left join t t3
on t3.Date > t_3month.Date - interval 12 month and
   t3.Date <= t_3month.Date and
   t_3month.ID = t3.ID
group by t_3month.ID, t_3month.Date, t_3month.sum_3, t_3month.count_3
order by ID, Date;