|By Derek Abing||
|June 4, 2014 03:32 PM EDT||
In production support it is often hard to correlate what might be happening on local servers with what users are reportedly experiencing. In April, the developers for a Java application that handles electronic distribution of scanned mail and electronic faxes were receiving reports that their application was running slowly from remote offices and came to our Performance Availability and Capacity Management (PaCMan) team for help in determining the cause of this issue. From vanilla server-side dynaTrace, everything was looking fine. No particular transaction was taking a substantial amount of time. This led us to believe that the problem may lie more on the client side. Coincidentally, we were in the middle of a Proof of Concept for dynaTrace User Experience Management (UEM) so we decided to apply these efforts towards this application to help identify the issue.
Getting insight into the actual click paths of end users and the load behavior of pages on certain browsers allowed us to improve Client Rendering Time by 47%, Overall Page Load Time by 29% as well as implementing a new feature in Struts that prevents users from impatiently clicking Save too many times causing problems on our server-side implementation.
Finding #2: "Impatient" trigger save action
UEM also gives you the ability to directly correlate user actions at the client with what happens on the server. Using this ability, we were able to identify that some users were exacerbating their slowdowns by repeatedly clicking buttons and hitting refresh while they were experiencing an issue. The developers plan to fix this issue by implementing an Apache Struts feature to allow only the first press of a button to cause an action. Figure 2 shows a user that was experiencing slowdowns due to network latency (viewable in UEM), but was increasing their slowdowns by repeatedly clicking the "Save to XXXX" button and the refresh button before the page had finished loading.
Figure 2: Getting insight into end users' actions reveled problems with users impatiently clicking Save several times
For findings 3 & 4, and for further insight, click here for the full article.
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