How to calculate Session and Session duration in Firebase Analytics raw data?

Question

How to calculate Session Duration in Firebase analytics raw data which is linked to BigQuery?

I have used the following blog to calculate the users

Answer 1

First you need to define a session - in the following query I'm going to break a session whenever a user is inactive for more than 20 minutes.

Now, to find all sessions with SQL you can use a trick described at https://blog.modeanalytics.com/finding-user-sessions-sql/.

The following query finds all sessions and their lengths:

#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

As you know, Google has changed the schema of BigQuery firebase databases: https://support.google.com/analytics/answer/7029846

Thanks to @Felipe answer, the new format will be changed as follow:

SELECT SUM(total_sessions) AS Total_Sessions, AVG(sess_length_seconds) AS Average_Session_Duration
FROM (
  SELECT user_pseudo_id, sess_id, MIN(min_time) sess_start, MAX(max_time) sess_end, COUNT(*) records, 
    MAX(sess_id) OVER(PARTITION BY user_pseudo_id) total_sessions,
    (ROUND((MAX(max_time)-MIN(min_time))/(1000*1000),1)) sess_length_seconds
  FROM (
    SELECT *, SUM(session_start) OVER(PARTITION BY user_pseudo_id ORDER BY min_time) sess_id
    FROM (
      SELECT *, IF(previous IS null OR (min_time-previous) > (20*60*1000*1000), 1, 0) session_start 
      FROM (
        SELECT *, LAG(max_time, 1) OVER(PARTITION BY user_pseudo_id ORDER BY max_time) previous
        FROM (SELECT user_pseudo_id, MIN(event_timestamp) AS min_time, MAX(event_timestamp) AS max_time
          FROM `dataset_name.table_name` GROUP BY user_pseudo_id)
      )
    )
  )
  GROUP BY 1, 2
  ORDER BY 1, 2
)

Note: change dataset_name and table_name based on your project info

Sample result:

Answer 3

With the new schema of Firebase in BigQuery, I found that the answer by @Maziar did not work for me, but I am not sure why. Instead I have used the following to calculate it, where a session is defined as a user engaging with your app for a minimum of 10 seconds and where the session stops if a user doesn't engage with the app for 30 minutes. It provides total number of sessions and the session length in minutes, and it is based on this query: 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)