从Big Query滚动30天的数据

问题描述 投票:1回答:2

假设我有这个查询:

SELECT ga_channelGrouping, ga_sourceMedium,ga_campaign, SUM(ga_sessions) as sessions,
SUM(ga_sessionDuration)/SUM(ga_sessions) as avg_sessionDuration, 
SUM(ga_users)as Users, SUM(ga_newUsers)as New_Users, SUM(ga_bounces)/SUM(ga_sessions) 
AS ga_bounceRate, SUM(ga_pageviews)/SUM(ga_sessions)as pageViews_per_sessions, 
SUM( ga_transactions)/SUM(ga_sessions) AS ga_conversionRate 


FROM db.table 

group by ga_channelGrouping, ga_sourceMedium,ga_campaign

如何从Big Query中找到滚动30天的数据。我的DATE列值是这种格式:2018-06-19 11:00:00 UTC

google-bigquery
2个回答
2
投票

您可以使用DATE_ADDDATE_SUB函数来移动日期值和TIMESTAMP_ADDTIMESTAMP_SUB来移动时间戳值。

所以你可以尝试:

SELECT ga_channelGrouping, ga_sourceMedium,ga_campaign, SUM(ga_sessions) as sessions,
SUM(ga_sessionDuration)/SUM(ga_sessions) as avg_sessionDuration, 
SUM(ga_users)as Users, SUM(ga_newUsers)as New_Users, SUM(ga_bounces)/SUM(ga_sessions) 
AS ga_bounceRate, SUM(ga_pageviews)/SUM(ga_sessions)as pageViews_per_sessions, 
SUM( ga_transactions)/SUM(ga_sessions) AS ga_conversionRate 


FROM db.table 

WHERE your_date_column >= TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24*30 HOUR)

group by ga_channelGrouping, ga_sourceMedium,ga_campaign

TIMESTAMP_SUB没有把DAY作为间隔,所以在这里我们已经完成24*30小时回到30天。


编辑:如果您想要回滚30天而不管当天的时间,您可以执行以下操作:

WHERE your_date_column >= TIMESTAMP_TRUNC(TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 24*30 HOUR), DAY)

要么

WHERE CAST(your_date_column AS DATE) >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))

1
投票

如何从Big Query中找到滚动30天的数据。我的DATE列值的格式为:2018-06-19 11:00:00 UTC

首先,我想指出aggregating last 30 daysrolling 30 days完全不同 - 所以下面的回答实际上是关注rolling 30 daysjust last 30 days

下面是BigQuery Standard SQL,并假设您的日期列名为your_date_column,并且是TIMESTAMP数据类型

#standardSQL
SELECT 
  your_date_column, -- data type of TIMESTAMP with value like 2018-06-19 11:00:00 UTC
  ga_channelGrouping, 
  ga_sourceMedium,
  ga_campaign, 
  SUM(ga_sessions) OVER(win) AS sessions,
  (SUM(ga_sessionDuration) OVER(win))/(SUM(ga_sessions) OVER(win)) AS avg_sessionDuration, 
  SUM(ga_users) OVER(win) AS Users, 
  SUM(ga_newUsers) OVER(win) AS New_Users, 
  (SUM(ga_bounces) OVER(win))/(SUM(ga_sessions) OVER(win)) AS ga_bounceRate, 
  (SUM(ga_pageviews) OVER(win))/(SUM(ga_sessions) OVER(win)) AS pageViews_per_sessions, 
  (SUM(ga_transactions) OVER(win))/(SUM(ga_sessions) OVER(win)) AS ga_conversionRate 
FROM `project.dataset.table`
WINDOW win AS (
  PARTITION BY ga_channelGrouping, ga_sourceMedium, ga_campaign
  ORDER BY UNIX_DATE(DATE(your_date_column)) 
  RANGE BETWEEN 29 PRECEDING AND CURRENT ROW
)    

为了让你了解它是如何工作的 - 尝试使用下面的虚拟示例(为简单起见它会滚动3天)

#standardSQL
WITH `project.dataset.table` AS (
  SELECT 1 value, TIMESTAMP '2018-06-19 11:00:00 UTC' your_date_column UNION ALL
  SELECT 2, '2018-06-20 11:00:00 UTC' UNION ALL
  SELECT 3, '2018-06-21 11:00:00 UTC' UNION ALL
  SELECT 4, '2018-06-22 11:00:00 UTC' UNION ALL
  SELECT 5, '2018-06-23 11:00:00 UTC' UNION ALL
  SELECT 6, '2018-06-24 11:00:00 UTC' UNION ALL
  SELECT 7, '2018-06-25 11:00:00 UTC' UNION ALL
  SELECT 8, '2018-06-26 11:00:00 UTC' UNION ALL
  SELECT 9, '2018-06-27 11:00:00 UTC' UNION ALL
  SELECT 10, '2018-06-28 11:00:00 UTC' 
)
SELECT 
  your_date_column, 
  value, 
  SUM(value) OVER(win) rolling_value
FROM `project.dataset.table`
WINDOW win AS (ORDER BY UNIX_DATE(DATE(your_date_column)) RANGE BETWEEN 2 PRECEDING AND CURRENT ROW)
ORDER BY your_date_column   

结果是什么

Row your_date_column        value   rolling_value    
1   2018-06-19 11:00:00 UTC 1       1    
2   2018-06-20 11:00:00 UTC 2       3    
3   2018-06-21 11:00:00 UTC 3       6    
4   2018-06-22 11:00:00 UTC 4       9    
5   2018-06-23 11:00:00 UTC 5       12   
6   2018-06-24 11:00:00 UTC 6       15   
7   2018-06-25 11:00:00 UTC 7       18   
8   2018-06-26 11:00:00 UTC 8       21   
9   2018-06-27 11:00:00 UTC 9       24   
10  2018-06-28 11:00:00 UTC 10      27   
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