Big Data Monitoring
Now, how do we monitor Big Data in the real world?
End to end flow – Track all of the app transactions across application tiers and analyze their response times. It’s easy to identify exactly which components are experiencing problems with this information.
Code level details – Once you’ve identified a performance problem in your big data application the next step is to understand what portion of the code is responsible for the problem. AppDynamics provides deep code diagnostics and is capable of showing you the call stack of your problematic transactions.
Back end processing – Tracing transactions from the end user, through the application tier, and into the backend repository is required to properly identify and isolate performance problems.
Big data metrics – Every company has it’s own set of relevant KPIs but the important part is to understand what is normal behavior and good performance, once you have this information you can identify when KPIs are deviating from normal behaviors. This, combined with the end to end transaction tracking, will tell you if there is a problem, where the problem is and the root cause.
Big data deep dive – Sometimes KPIs aren’t enough to help solve your big data performance issues. That’s when you need to pull out the big guns and use a deep dive tool to assist with troubleshooting. Deep dive tools will be very detailed and very specific to the big data repository type that you are using/monitoring.
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