2 Replies Latest reply: Aug 9, 2017 8:58 AM by Michalina Kuczynska RSS

    QV Performance in AWS

    Rob Wunderlich

      Has anyone been able to achieve acceptable performance running production QVS in AWS?



        • Re: QV Performance in AWS
          Graeme Smith

          Very interesting question Rob.  We are still hosting QV internally on physicals (DL580's + SSD).  For a QS POC using geo analytics I'm using internally hosted VM's, and performance has been ok, but the data volumes and user base is minuscule compared to volumes on our QV deployment (i.e. 10's vs 1000's of users).  Will be very interested to see responses on this thread, as where practical, we're looking to move in that direction (although I think it will be difficult for larger deployments unless the majority of your data is hosted or at least cached in the same location as the QV servers).  Would have used QS cloud for this latest POC, but it doesn't support geo analytics plug ins, so I had to use internal VM's + QS Enterprise.

          • Re: QV Performance in AWS
            Michalina Kuczynska



            Yes, very interesting thread. I will agree with Graeme that difference in performance is significantly lower for QS setup in AWS comparing to what we had with (internally hosted) VM QV environment. To compare - 2 node VM env with each 4xCPU, 48GB ram supporting 500 users in QlikView (and intraday job reloads) without bigger issues VS 32cpu 244GB ram AWS setup for testing only for 10 users (no admin jobs running on this host) and user experience isn't the greatest (up to 30 sec to render the charts (simple calculations, no set analysis), file size 5GB).


            To be honest - that's the first time I'm working with a single node QS setup on AWS so can't really confirm if this is more likely related to AWS performance, QS performance, data volumes or our code. I do know that for the PoC we were still using locally hosted VM and we had much less concerns about the performance during the development.


            Would love to know what the rest of you thinks,