#Jprofiler 7 license key software#

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.

#Jprofiler 7 license key software#

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.

Pandas, py2neo and Bokeh on Jupyter* as documentation, execution and analysis environment.Neo4j graph database and Cypher graph query language for executing the graph analysis.jQAssistant static analysis tool for reading in call trees.JProfiler profiler for recording performance measures.JMeter load testing tool for executing some requests.Tomcat 8 installation as servlet container for the application (standalone, for easier integration of the profiling tool).Fork of the Spring sample project PetClinic as application to torture.I hope you’ll see that this is a very reasonable approach albeit the simplified use case that I show in this blog post/notebook.īefore we start, I want to briefly introduce you to the setup that I use for this analysis: Based on the results, we work out possible improvements for that specific hotspot, create a prototypical fix for it, measure the fix’s impact and, if the results are convincing, roll out the fix for that problem application wide (and work on the next performance bottleneck and so on). I use this analysis at work to determine the biggest performance bottleneck in a medium sized application (~700 kLOCs). This is very helpful to determine the most critical parts in the application and gives you a hint where you could start improving immediately. I achieve this by extracting various additional information like the web requests, application’s entry points and the triggers within the application causing the hotspots. With this approach, I’m not only able to show the hotspots that are involved in the performance issue (that show that some SQL statements take long to execute or are executed too often), but also the reasons behind these hotspots. The key idea is to use graph analysis to analyze call stacks that were created by a profiling tool. In this example, I show you how I determine the reasons behind a massive amount of executed SQL statements in an application step by step. In general, I use this approach to make the point that there are severe design errors that have a negative influence on the application’s overall performance. In this notebook, I’ll show you one of my approaches for mining performance problems based on application runtime performance analysis.