SQL 查询以对来自 Amazon AMS 的聚合数据进行分区

Question

我正在使用 Google Big Query 来存储来自 amazon ams 的数据。每天都会将一个 csv 文件加载到数据库中，其中包含生命周期支出、点击次数和展示次数。数据看起来像这样：

date_uploaded,campaign,spend,impressions,clicks
2017-11-01,product a,100,1000,50
2017-11-01,product b,50,500,20
2017-11-02,product a,175,1600,75
2017-11-02,product b,100,1000,50
2017-11-03,product a,250,2200,110
2017-11-03,product b,150,1500,80

我想转换此数据以显示每日支出（与前一天的差异），因此最终结果如下所示：

date_uploaded,campaign,spend,impressions,clicks
2017-11-02,product a,75,600,25
2017-11-02,product b,50,500,30
2017-11-03,product a,75,600,35
2017-11-03,product b,50,500,30

有没有办法通过这种方式查询BQ来分区数据？

Answer 1

您可以使用 lag 执行此操作。分区中第一行的差异将是 null 并且可以使用 where 子句过滤器排除它们。这假设每个日期一行，活动。如果有多行，则将给定日期的值相加并使用 lag.

select * from (
select date_uploaded,campaign,
spend-lag(spend) over(partition by campaign order by date_uploaded) as spend_diff,
impressions-lag(impressions) over(partition by campaign order by date_uploaded) as impressions_diff,
clicks-lag(clicks) over(partition by campaign order by date_uploaded) as clicks_diff
from tbl
) t
where spend_diff is not null and impressions_diff is not null and clicks_diff is not null

SQL 查询以对来自 Amazon AMS 的聚合数据进行分区

SQL Query to partition aggregate data from Amazon AMS

sql

amazon

google-bigquery