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[Data Product] Dashboard summariser - Cumulative

[Data Product] Dashboard summariser - Cumulative

Dec 13, 2018

Summary:

  • Type - Dashboard cumulative summariser
  • Granularity - DAY
  • Computation Level - Level 3
  • Frequency - Runs Daily

Purpose:

The Dashboard summariser is used to compute the

  1. Unique Devices - The total number of unique devices that have ever accessed Diksha (across portal and app)

  2. Learning Experiences - The total number of content play sessions across portal and app to current date.

  3. Hours of interaction - The total time spent on Diksha (i.e. total session time, inclusive of but not limited to content play sessions)


Inputs:

         Derived Event : ME_WORKFLOW_SUMMARY


Output

{
  "eid": "ME_PORTAL_CUMULATIVE_METRICS",
  "ets": 1544670495619,
  "syncts": 1544670495619,
  "metrics_summary": {
    "noOfUniqueDevices": 1323,
    "totalContentPlayTime": 29.24,
    "totalTimeSpent": 35577.22,
    "totalContentPublished": 4105
  }
}

Algorithm


FieldDescriptionComputation
1noOfUniqueDevicesThis metric tracks the total number of unique devices that have accessed Diksha(Portal and App)

filter by with below fields and compute the total distinct count 

filter((d_period = 0 ) and (d_tag=="all") and (d_content_id=='all')and (d_user_id=='all') and (d_deviceId!='all') and distinctCount(d_device_id)
2
totalDigitalContentPublished 
Number of contents been published

composite search API:

/composite/v3/search

3totalContentPlaySessionsThis metric tracks the total number of content play sessions (Portal and App).

filter with below fields and  aggregating the edata.eks.time_spent field

filter((d_period = 0) and (d_tag=="all") and (d_content_id=='all')and (d_user_id=='all') and (d_mode=='play') and (d_type=='content') and  aggregate(m_total_sessions)
4totalTimeSpentThis metric track the total time spent on Diksha(Portal and App)

filter by with below fields and  aggregating the edata.eks.time_spent field

filter((d_period = 0) and (d_tag=="all") and (d_content_id=='all')and (d_user_id=='all') and   (d_type=='app' ||'session') and (d_deviceId=='all') aggregate(m_total_ts)



Conclusion: