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SAP Analytics Cloud Backend Runtime Analysis and Statistics

Imagine your Colleague is asking: “How does the recently established SAP Analytics Cloud Live Connection affect my Backend System?” “Do you have any Statistics on Queries that are running against my Backend System?”. “Could you check the most expensive SAP Analytics Cloud Models for improvement potential?”.

Questions like these can now be answered faster with a new SAP Analytics Cloud Model and Sample Story.

2020.Q3 QRC and Wave 2020.14 introduce SAP Analytics Cloud Content in the System Files Directory without the need to deploy it from the Analytics Content Network. All out of the Box. This System Folder contains the Analytic Model SAC_STATISTICS_MDS_QUERY_PERF and the Story Backend Application Performance.

The purpose of the Story is to help you answer typical Performance related Questions as well as to give you an Idea what Information can be accessed via the Model if you want to customize the Sample Story.

Besides the Timeframe you are interested in, you will be able to filter for a specific Connection, Model, Story, or your most active Users.

We help you to identify active Users, most used Connections, and Models with high total Processing Time in the Backend.

A Model can be used in different Stories. Sometimes the Model itself is performing well, whereas the combination of Model and Story can cause an Performance Issue. We show the combinations with the most Impact in the Backend System.

And Charts to analyze the Query Response Times and the Execution Counts over time as well as the Runtime Distribution for selected Queries.

Some customers have already been testing this Model and Story, as a Proof of Concept. Now we are happy to release it to the full customer community! Stay tuned for further enhancements and updates.

Please also check SAP Analytics Cloud Performance Statistics and Analysis which contains Frontend related information, SAP Analytics Cloud Network Statistics for Network Statistics and SAP Analytics Cloud Performance Analysis Tool for analysis of single Story or Analytic Application runs.

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  • Hi Thomas,

    Great article!  Fyi I notice that the story name has changed slightly and is now Performance Statistics  and Analysis.




    • Hi Nivetha Jeyananthan ,

      The connection SAC means local connections from the SAC client to your SAC backend.

      This performance analysis is for the Analytical Backend Query Processing only. Therefore only live connections are tracked. File Server does not have Analytical Processing Queries and therefore it also doesn’t have a live connection. That is why there is no tracking for it available.

      I hope this answers your questions!

      Thanks and Best Regards,


      • Hi, Thanks for responding.

        we were in an assumption that if we select the connection name from the checkbox, it would show the models & stories associated with it and the user id's which are accessing the connection.

        Does this story provide the above information?



          • But it gives the data for live connections only. Can we get the same for each import data connections?

            Also I don't see the import data connections in the 'connection name' column. I can only see the connection name 'SAC' which is showing all the models & stories created using import data connections.





          • Hi Nivetha,

            as for now, we are working on tracking more SAP HANA components in the future before looking into these other type of connections. For now, analytical backend queries are tracked.

            Thanks and Best Regards,


  • Hi,

    I cannot find any documentation on the model measures. It would be great to understand the definition of Overall Runtime, Resultset size, Resultset Count, Thoughput Cells per second ect.

    Br. Peter

  • Hi Peter,

    We're currently working on providing a clean and precise documentation to the user. But until it's ready I respond to your specific question:

    Overall Runtime
    Full backend processing time of the request

    Two dimensional array with data which will be ultimately shown to the user in the form of a widget like table, chart, histogram, etc.

    Resultset size, Resultset count
    Number of rows times number of columns in resultset

    Throughput Cells per second
    Number of resultset cells processed in backend per second


    Best regards,


  • Hi Thomas ,


    very great blog and indeed very helpful for analysis.


    Wondering what are the user specific rights , needed to have this story running.

    i have admin access and i could run , and in some cases customer may not want to give admin access to production tenants to troubleshoot.


    Thanks in advance