使用 Over Partition By 运行 Count Distinct答案

【问题标题】：Running Count Distinct using Over Partition By使用 Over Partition By 运行 Count Distinct
【发布时间】：2019-10-22 11:33:02
【问题描述】：

我有一个数据集，其中包含随着时间的推移进行购买的用户 ID。我想显示按州和国家划分的 YTD 不同数量的已购买用户。输出将有 4 列：Country、State、Year、Month、YTD Count of Distinct Users with purchase activity。

有没有办法做到这一点？当我从视图中排除月份并进行不同计数时，以下代码有效：

Select Year, Country, State,
   COUNT(DISTINCT (CASE WHEN ActiveUserFlag > 0 THEN MBR_ID END)) AS YTD_Active_Member_Count
From MemberActivity
Where Month <= 5
Group By 1,2,3;

当用户在多个月内购买时会出现此问题，因为我无法按月汇总然后求和，因为它重复了用户计数。

出于趋势目的，我需要查看一年中每个月的年初至今计数。

【问题讨论】：

标签： sql teradata

【解决方案1】：

每个会员在购买的第一个月只退货一次，按月计算，然后应用累计金额：

select Year, Country, State, month,
   sum(cnt)
   over (partition by Year, Country, State
         order by month
         rows unbounded preceding) AS YTD_Active_Member_Count
from
  (
    Select Year, Country, State, month,
       COUNT(*) as cnt -- 1st purchses per month
    From 
     ( -- this assumes there's at least one new active member per year/month/country
       -- otherwise there would be mising rows 
       Select *
       from MemberActivity
       where ActiveUserFlag > 0 -- only active members
         and Month <= 5
         -- and year = 2019 -- seems to be for this year only
       qualify row_number() -- only first purchase per member/year
               over (partition by MBR_ID, year
                     order by month --? probably there's a purchase_date) = 1
     ) as dt
    group by 1,2,3,4
 ) as dt
;

【讨论】：

这完全有效！不能感谢你。我整个星期都在为此苦苦挣扎！我不熟悉限定语法，所以肯定需要阅读它以添加到我的知识库中......
QUALIFY 过滤 OLAP 函数的结果，类似于 WHERE 和 HAVING。它是专有的 Teradata 语法，在标准 SQL 中，您必须使用派生表（如 Gordon 的查询）嵌套它：select * from (select OLAP-function AS xx) where xx <= 5

【解决方案2】：

统计第一个月出现的用户数：

select Country, State, year, month,
       sum(case when ActiveUserFlag > 0 and seqnum = 1 then 1 else 0 end) as YTD_Active_Member_Count
from (select ma.*,
             row_number() over (partition by year order by month) as seqnum
      from MemberActivity ma
     ) ma
where Month <= 5
group by Country, State, year, month;

【讨论】：