通过滚动窗口分区计算不同客户的数量

我的问题类似于redshift：通过窗口分区计算不同客户的数量 https://stackoverflow.com/questions/47736584/redshift-count-distinct-customers-over-window-partition但我有一个滚动窗隔断。

我的查询看起来像这样，但在 COUNT in 中不同Redshift https://docs.aws.amazon.com/redshift/latest/dg/r_WF_COUNT.html不支持

select p_date, seconds_read, 
count(distinct customer_id) over (order by p_date rows between unbounded preceding and current row) as total_cumulative_customer
from table_x

我的目标是计算截至每个日期的唯一客户总数（因此是滚动窗口）。

我尝试使用dend_rank() 方法 https://stackoverflow.com/a/47736585/4046274但它会失败，因为我不能像这样使用窗口函数

select p_date, max(total_cumulative_customer) over ()
(select p_date, seconds_read, 
dense_rank() over (order by customer_id rows between unbounded preceding and current row) as total_cumulative_customer -- WILL FAIL HERE
from table_x

任何解决方法或不同的方法都会有所帮助！

EDIT:

输入数据样本

+------+----------+--------------+
| Cust |  p_date  | seconds_read |
+------+----------+--------------+
|    1 | 1-Jan-20 |           10 |
|    2 | 1-Jan-20 |           20 |
|    4 | 1-Jan-20 |           30 |
|    5 | 1-Jan-20 |           40 |
|    6 | 5-Jan-20 |           50 |
|    3 | 5-Jan-20 |           60 |
|    2 | 5-Jan-20 |           70 |
|    1 | 5-Jan-20 |           80 |
|    1 | 5-Jan-20 |           90 |
|    1 | 7-Jan-20 |          100 |
|    3 | 7-Jan-20 |          110 |
|    4 | 7-Jan-20 |          120 |
|    7 | 7-Jan-20 |          130 |
+------+----------+--------------+

预期输出

+----------+--------------------------+------------------+--------------------------------------------+
|  p_date  | total_distinct_cum_cust  | sum_seconds_read |                  Comment                   |
+----------+--------------------------+------------------+--------------------------------------------+
| 1-Jan-20 |                        4 |              100 | total distinct cust = 4 i.e. 1,2,4,5       |
| 5-Jan-20 |                        6 |              450 | total distinct cust = 6 i.e. 1,2,3,4,5,6   |
| 7-Jan-20 |                        7 |              910 | total distinct cust = 6 i.e. 1,2,3,4,5,6,7 |
+----------+--------------------------+------------------+--------------------------------------------+

对于此操作：

select p_date, seconds_read, 
       count(distinct customer_id) over (order by p_date rows between unbounded preceding and current row) as total_cumulative_customer
from table_x;

您可以通过两个级别的聚合来完成您想要的几乎所有操作：

select min_p_date,
       sum(count(*)) over (order by min_p_date rows between unbounded preceding and current row) as running_distinct_customers
from (select customer_id, min(p_date) as min_p_date
      from table_x
      group by customer_id
     ) c
group by min_p_date;

对读取的秒数求和也有点棘手，但您可以使用相同的想法：

select p_date,
       sum(sum(seconds_read)) over (order by p_date rows between unbounded preceding and current row) as seconds_read,
       sum(sum(case when seqnum = 1 then 1 else 0 end)) over (order by p_date rows between unbounded preceding and current row) as running_distinct_customers
from (select customer_id, p_date, seconds_read,
             row_number() over (partition by customer_id order by p_date) as seqnum
      from table_x
     ) c
group by min_p_date;