如何计算Firebase Analytics原始数据中的会话和会话持续时间？

Question

如何计算链接到BigQuery的Firebase分析原始数据中的会话持续时间？

我使用以下博客通过对每个记录中嵌套的事件使用flatten命令来计算用户,但我想知道如何按国家和时间计算会话和会话持续时间.

(我配置了很多应用程序,但是如果你可以帮助我使用SQL查询来计算会话持续时间和会话,那将是非常有帮助的)

谷歌博客使用Firebase和大查询

Answer 1

首先,您需要定义一个会话 - 在以下查询中,每当用户处于非活动状态超过20分钟时,我将打破会话.

现在,要查找所有使用SQL的会话,您可以使用https://blog.modeanalytics.com/finding-user-sessions-sql/中描述的技巧.

以下查询查找所有会话及其长度:

#standardSQL

SELECT app_instance_id, sess_id, MIN(min_time) sess_start, MAX(max_time) sess_end, COUNT(*) records, MAX(sess_id) OVER(PARTITION BY app_instance_id) total_sessions,
   (ROUND((MAX(max_time)-MIN(min_time))/(1000*1000),1)) sess_length_seconds
FROM (
  SELECT *, SUM(session_start) OVER(PARTITION BY app_instance_id ORDER BY min_time) sess_id
  FROM (
    SELECT *, IF(
                previous IS null 
                OR (min_time-previous)>(20*60*1000*1000),  # sessions broken by this inactivity 
                1, 0) session_start 
                #https://blog.modeanalytics.com/finding-user-sessions-sql/
    FROM (
      SELECT *, LAG(max_time, 1) OVER(PARTITION BY app_instance_id ORDER BY max_time) previous
      FROM (
        SELECT user_dim.app_info.app_instance_id
          , (SELECT MIN(timestamp_micros) FROM UNNEST(event_dim)) min_time
          , (SELECT MAX(timestamp_micros) FROM UNNEST(event_dim)) max_time
        FROM `firebase-analytics-sample-data.ios_dataset.app_events_20160601`
      )
    )
  )
)
GROUP BY 1, 2
ORDER BY 1, 2

Answer 2

使用 BigQuery 中 Firebase 的新架构，我发现 @Maziar 的答案对我不起作用，但我不确定为什么。相反，我使用以下方法来计算它，其中会话被定义为用户与您的应用程序互动至少 10 秒，如果用户在 30 分钟内没有与应用程序互动，会话将停止。它提供会话总数和会话长度（以分钟为单位），它基于此查询：https : //modeanalytics.com/modeanalytics/reports/5e7d902f82de/queries/2cf4af47dba4

SELECT COUNT(*) AS sessions,
       AVG(length) AS average_session_length
  FROM (
  
SELECT global_session_id,
       (MAX(event_timestamp) - MIN(event_timestamp))/(60 * 1000 * 1000) AS length
  FROM (
SELECT user_pseudo_id,
       event_timestamp,
       SUM(is_new_session) OVER (ORDER BY user_pseudo_id, event_timestamp) AS global_session_id,
       SUM(is_new_session) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS user_session_id
  FROM (
       SELECT *,
              CASE WHEN event_timestamp - last_event >= (30*60*1000*1000) 
                     OR last_event IS NULL 
                   THEN 1 ELSE 0 END AS is_new_session
         FROM (
              SELECT user_pseudo_id,
                     event_timestamp,
                     LAG(event_timestamp,1) OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp) AS last_event
                FROM `dataset.events_2019*`
              ) last
       ) final
       ) session
 GROUP BY 1
       
       ) agg
WHERE length >= (10/60)