Bigquery 中新安装用户的 Firebase 事件发生

Question

鉴于用户的安装日期，我想获取 Firebase (1) Event Occurrences 和 (2) Event Distinct Users' Count 对于我们在第 0 天到第 30 天的所有 200 多个 Firebase 事件。我在屏幕截图中模拟了下面（对于 D0-D30）的输出 table，但代码仅适用于 Day0-Day7。

(1) 事件发生

SELECT
  event.name as event_name,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170802' THEN event_count END) AS D0_USERS,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170802' AND _TABLE_SUFFIX < '20170803' THEN event_count END) AS D1_USERS,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170803' AND _TABLE_SUFFIX < '20170804' THEN event_count END) AS D2_USERS,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170804' AND _TABLE_SUFFIX < '20170805' THEN event_count END) AS D3_USERS,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170805' AND _TABLE_SUFFIX < '20170806' THEN event_count END) AS D4_USERS,
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170806' AND _TABLE_SUFFIX < '20170807' THEN event_count END) AS D5_USERS,  
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170807' AND _TABLE_SUFFIX < '20170808' THEN event_count END) AS D6_USERS,  
  COUNT(CASE WHEN _TABLE_SUFFIX >= '20170808' AND _TABLE_SUFFIX < '20170809' THEN event_count END) AS D7_USERS    
FROM `<<project-id>>.app_events_*`, UNNEST(event_dim) AS event
WHERE
  _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170809' AND
  user_dim.first_open_timestamp_micros BETWEEN 1501545600000000 AND 1501632000000000;

和

(2) 事件独立用户计数

SELECT
  event.name as event_name,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170802' THEN user_dim.app_info.app_instance_id END) AS D0_USERS,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170802' AND _TABLE_SUFFIX < '20170803' THEN user_dim.app_info.app_instance_id END) AS D1_USERS,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170803' AND _TABLE_SUFFIX < '20170804' THEN user_dim.app_info.app_instance_id END) AS D2_USERS,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170804' AND _TABLE_SUFFIX < '20170805' THEN user_dim.app_info.app_instance_id END) AS D3_USERS,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170805' AND _TABLE_SUFFIX < '20170806' THEN user_dim.app_info.app_instance_id END) AS D4_USERS,
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170806' AND _TABLE_SUFFIX < '20170807' THEN user_dim.app_info.app_instance_id END) AS D5_USERS,  
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170807' AND _TABLE_SUFFIX < '20170808' THEN user_dim.app_info.app_instance_id END) AS D6_USERS,  
  COUNT(DISTINCT CASE WHEN _TABLE_SUFFIX >= '20170808' AND _TABLE_SUFFIX < '20170809' THEN user_dim.app_info.app_instance_id END) AS D7_USERS    
FROM `<<project-id>>.app_events_*`, UNNEST(event_dim) AS event
WHERE
  _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170809'
  AND user_dim.first_open_timestamp_micros BETWEEN 1501545600000000 AND 1501632000000000
GROUP BY 1;

问题：

有没有更优化的写法？对于少量的列，它是有意义的 (D0-D7)，但对于 D0-D30，我认为可能有更好的方法。非常感谢任何建议！

Mikhail 反馈后的最终答案：

我将两个查询组合在一个查询中，然后创建了一个数据透视表 table。请记住在执行前在 BigQuery 编辑器中 select "Standard SQL"。

SELECT
  event.name AS event_name,
  _TABLE_SUFFIX as day,
  COUNT(1) as event_occurances,
  COUNT(DISTINCT user_dim.app_info.app_instance_id) as event_unique_users
FROM `<<project-id>>.app_events_*`, UNNEST(event_dim) AS event
WHERE
  _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170901' AND
  user_dim.first_open_timestamp_micros BETWEEN 1501545600000000 AND 1501632000000000
GROUP BY event_name, day
ORDER BY event_name;

附录注释：

2017 年 8 月 1 日的时间戳转换

纪元时间戳：1501545600
以毫秒为单位的时间戳：1501545600000

2017 年 8 月 2 日的时间戳转换

纪元时间戳：1501632000
以毫秒为单位的时间戳：1501632000000

Answer 1

Is there a more optimised way to write this?

1。优化它的一种方法是在下面重写

COUNT(CASE WHEN _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170802' THEN event_count END) AS D0_USERS

到这个

COUNTIF(_TABLE_SUFFIX = '20170801') AS D0_USERS

:o( 对于 D0-D30 的情况，你仍然需要将这一行写 31 次，但至少它不那么重了

2。另一种（正确的）方法是遵循最佳实践并将数据检索与数据可视化分开

所以你可以像下面这样做来检索所需的数据

#standardSQL
SELECT
  event.name AS event_name,
  _TABLE_SUFFIX as day,
  COUNT(1) as users
FROM `<<project-id>>.app_events_*`, UNNEST(event_dim) AS event
WHERE
  _TABLE_SUFFIX >= '20170801' AND _TABLE_SUFFIX < '20170809' AND
  user_dim.first_open_timestamp_micros BETWEEN 1501545600000000 AND 1501632000000000
GROUP BY event_name, day

然后您可以使用您喜欢的任何工具来调整结果

例如，在不离开 UI 的情况下使用 BigQuery Mate，您可以获得如下所示的枢轴

快速披露 - 我是 BigQuery Mate Chrome Extension

的作者

请注意：我没有调整或更改您的查询逻辑 - 我只是回答了您的具体问题 - 是否有更优化的方式来编写此内容？

Bigquery 中新安装用户的 Firebase 事件发生

Firebase Event Occurrences for New Installed Users in Bigquery

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