![]() for CPU HotSpots, JProfiler sums up all the measurements for the method call leafs that take longer than 0.1% of all method calls. With such a view, you see which parts of your application call which classes and methods by drilling down the hierarchy by hand:īut there is more: You can also “turn around” the call tree and list all the so-called “HotSpots”. Below, you can see the call tree for the called methods with their measured CPU wall clock time (aka the real time that is spent in that method) and the number of invocations for a complete test run: A call tree shows you a tree of the called methods. What we usually need for performance analysis is a recorded runtime stack of all method calls as a call tree. The result is stored in a file as so-called “snapshot” (the use of a snapshot enables you to repeat your analysis over and over again with exactly the same performance measures). The recording of the measures starts before the execution of the performance test and stops after the test has finished successfully. It really worth the money because it gives you deep insights how your application performs under the hood.Īlso outside the advertisement block, I personally like JProfiler a lot because it does what it does very very good. ![]() JProfiler is a great commercial tool for profiling Java application and costs around 400 €. In my case, I run the Spring PetClinic performance test using JMeter.Īs profiling tool, I use JProfiler to record some performance measures while the test was running.Īt this point, I want to thank ej-technologies for providing me with a () for JProfiler that enables this blog post in exchange of mentioning their product: Additionally, we need something that uses or clicks through our application to get some numbers. Java Virtual Machine) of your application and measures diverse properties like method execution time, number of web service calls, executed SQL statements etc. A profiler will be integrated into the runtime environment (e. You can find that notebook on GitHub.Īs a prerequisite for this analysis, we need performance profiling data gathered by a profiler. * actually, what you see here, is the result of an executed Jupyter notebook, too. I’ll show you how all those tools fit together. jQAssistant, Neo4j and Pandas are my default environment for software analytics so far. The first ones are dependent on the environment and programming language you use.
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