Question

哪里可以找到平均Firebase分析中的会话时长指标？
如何提取平均Bigquery的会话时长指标数据？

平均会话持续时间指标是Firebase分析仪表板中以前可用的。但是现在，它在Firebase分析仪表板中不可用。现在，我们仅看到“每个用户的参与度”。每位用户的参与度和平均会话时长都一样吗？如何提取平均Fiebase分析的会话持续时间？如何在Bigquery中查询以提取平均费用。 Firebase的会话持续时间指标。 enter image description here

Answer 1

每位用户的参与度与平均水平不同。会话时长。每个用户的参与度是用户每天在应用中花费的时间，而不是会话中的花费。

您可以找到平均最新版本下的Firebase Analytics中的会话时长。
这里是用于计算平均值的查询。 BigQuery中的会话长度：

with timeline as
(
  select 
    user_pseudo_id
    , event_timestamp
    , lag(event_timestamp, 1) over (partition by user_pseudo_id order by event_timestamp) as prev_event_timestamp
  from 
    `YYYYY.analytics_XXXXX.events_*`
  where
    -- at first - a sliding period - how many days in the past we are looking into:
    _table_suffix
           between format_date("%Y%m%d", date_sub(current_date, interval 10 day))
           and     format_date("%Y%m%d", date_sub(current_date, interval 1 day))
) 
, session_timeline as 
(
  select 
    user_pseudo_id
    , event_timestamp
    , case 
        when 
           -- half a hour period - a threshold for a new 'session'
           event_timestamp - prev_event_timestamp >= (30*60*1000*1000)
             or
           prev_event_timestamp is null 
          then 1
          else 0 
      end as is_new_session_flag
  from 
    timeline
)
, marked_sessions as
(
  select 
    user_pseudo_id
    , event_timestamp
    , sum(is_new_session_flag) over (partition by user_pseudo_id order by event_timestamp) AS user_session_id
  from session_timeline
)
, measured_sessions as
(
  select
    user_pseudo_id
    , user_session_id
    -- session duration in seconds with 2 digits after the point
    , round((max(event_timestamp) - min(event_timestamp))/ (1000 * 1000), 2) as session_duration
  from 
    marked_sessions
  group by
    user_pseudo_id
    , user_session_id
  having 
    -- let's count only sessions longer than 10 seconds
    session_duration >= 10
)
select 
  count(1)                          as number_of_sessions
  , round(avg(session_duration), 2) as average_session_duration_in_sec
from 
  measured_sessions

关于如何获取event_date和app_info.id的其他问题，请参见以下查询：

with timeline as
(
  select 
     event_date,app_info.id,user_pseudo_id
    , event_timestamp
    , lag(event_timestamp, 1) over (partition by user_pseudo_id order by event_timestamp) as prev_event_timestamp
  from 
    `<table>_*`
  where
    -- at first - a sliding period - how many days in the past we are looking into:
    _table_suffix
          between format_date("%Y%m%d", date_sub(current_date, interval 10 day))
          and     format_date("%Y%m%d", date_sub(current_date, interval 1 day))
) 
, session_timeline as 
(
  select 
    event_date,id,
    user_pseudo_id
    , event_timestamp
    , case 
        when 
           -- half a hour period - a threshold for a new 'session'
           event_timestamp - prev_event_timestamp >= (30*60*1000*1000)
             or
           prev_event_timestamp is null 
          then 1
          else 0 
      end as is_new_session_flag
  from 
    timeline
)
, marked_sessions as
(
  select 
     event_date,id, user_pseudo_id
    , event_timestamp
    , sum(is_new_session_flag) over (partition by user_pseudo_id order by event_timestamp) AS user_session_id
  from session_timeline
)
, measured_sessions as
(
  select
     event_date,id, user_pseudo_id
    , user_session_id
    -- session duration in seconds with 2 digits after the point
    , round((max(event_timestamp) - min(event_timestamp))/ (1000 * 1000), 2) as session_duration
  from 
    marked_sessions
  group by
     event_date, id, user_pseudo_id
    , user_session_id
  having 
    -- let's count only sessions longer than 10 seconds
    session_duration >= 10
)
select 
   event_date, id, count(1)                          as number_of_sessions
  , round(avg(session_duration), 2) as average_session_duration_in_sec
from 
  measured_sessions
  group by event_date, id

Answer 2

每个会话（自2019年12月以来在这里定义：https://firebase.googleblog.com/2018/12/new-changes-sessions-user-engagement.html）都有一个session_id（除其他参数外）。我认为计算平均会话持续时间最安全，最可靠的方法是将数据提取到BigQuery，然后按会话计算第一个时间戳和最后一个时间戳之间的平均差。为此，您需要展平event_params的数组。例如，这是在AWS Athena中完成的方式：

WITH arrays_flattened AS 
    (SELECT params.key AS key,
         params.value.int_value AS id,
         event_timestamp,
         event_date
    FROM your_database
    CROSS JOIN UNNEST(event_params) AS t(params)
    WHERE params.key = 'ga_session_id'), duration AS 
    (SELECT MAX(event_timestamp)-MIN(event_timestamp) AS duration
    FROM arrays_flattened
    WHERE key = 'ga_session_id'
    GROUP BY  id)
SELECT AVG(duration)
FROM duration

在哪里可以找到Firebase Analytics中的平均会话持续时间。如何通过Bigquery提取此指标

2 个答案: