In this article we are going to see basic Performance Analysis approaches. I will be referring to Top-Down & Bottom-up approach. I will not compare them as both are used for analysis, I will only try to explain what are the basic steps and when to choose what type. This article mostly match for Java & DotNet application. You may use similar approaches for other platforms too.
Top-Down Analysis :
This is the most popular method. The idea is simple, performance monitoring application from top view. That means, client monitoring -> server monitoring in OS & Resource level, then app server-> then run time environment
And, when we get unusual behavior (or not expected or targeted), then profiling or instrument application. In this moment, we have found problems , and experiment with possible solution, choose the best one.
And then, we have to tune the system. Tune code with best solution, tune environment and tune run time.
Finally, we need to test for the impact. Typically, before starting an analysis, we should have some measurement data(or benchmarks) from performance test results. We need to retest those and compare with previous results. If that is significant, then it is done, if not, we need to get back to profiling and tuning application.
Here is a flow chart to show at a glance : Google drive link. Open with draw.io.
When we use?
-> Application causing issues, we need optimize whole or a part of application.
-> Optimize application for given resources(CPU/Disk/Memory/IO/network)
-> Tune the system & application for best performance.
-> We have access to code change, now need to tune application for specific goal.(throughput, response time, longer service etc).
-> We need Root Cause Analysis for unexpected Performance Issues(OOM, Slowness, crashing in different level or sub-systems, unwanted application behavior primary suspected for performance, etc)
Bottom-Up Analysis :
Here is a flow chart to show at a glance : Google drive link. Open with draw.io.
When we use?
-> When you need to increase performance but you cant change source code.
Top-Down Analysis :
This is the most popular method. The idea is simple, performance monitoring application from top view. That means, client monitoring -> server monitoring in OS & Resource level, then app server-> then run time environment
And, when we get unusual behavior (or not expected or targeted), then profiling or instrument application. In this moment, we have found problems , and experiment with possible solution, choose the best one.
And then, we have to tune the system. Tune code with best solution, tune environment and tune run time.
Finally, we need to test for the impact. Typically, before starting an analysis, we should have some measurement data(or benchmarks) from performance test results. We need to retest those and compare with previous results. If that is significant, then it is done, if not, we need to get back to profiling and tuning application.
Here is a flow chart to show at a glance : Google drive link. Open with draw.io.
When we use?
-> Application causing issues, we need optimize whole or a part of application.
-> Optimize application for given resources(CPU/Disk/Memory/IO/network)
-> Tune the system & application for best performance.
-> We have access to code change, now need to tune application for specific goal.(throughput, response time, longer service etc).
-> We need Root Cause Analysis for unexpected Performance Issues(OOM, Slowness, crashing in different level or sub-systems, unwanted application behavior primary suspected for performance, etc)
Bottom-Up Analysis :
This is another popular approach when we need to tune resource or platform (or hardware) for specific application. Let say, you have a java application , deployed. Now, bottom up analysis will allow you to analyze and find optimization scope for deployed system, hardware and resources. This is very common approach for application capacity planning, benchmarking for changed environments(migration). The key idea is, monitor application in specific environment and then tune environment(software+ hardware resources) that makes target application running at top performance.
Here is a flow chart to show at a glance : Google drive link. Open with draw.io.
When we use?
-> When you need to increase performance but you cant change source code.
-> You need to optimize resources & environment for specific application( deployed environment)
-> You need to have benchmark and get to know resource usages as well as find possible area for tuning.
-> You need to optimize run time (JVM/CLR) for your application. You can see resource usages and tune as your app needs.
-> When you need capacity planning for your hardware which must run application in optimal way.
-> When you have optimized your application from code and there is not visible area to tune, you can use this technique to achieve some more.
Please comment if you have any question.
Thanks.. :)
Please comment if you have any question.
Thanks.. :)
