How to analyze and visualize performance benchmarks in a way that is easy to both understand and interpret? This is a question that many developers and engineers ask themselves when they are trying to analyze the performance of their applications. Let's discuss some of the approaches that can be used for visualizing performance benchmarks and provide some examples of how you can use these techniques to improve your own performance analysis.
In a previous question titled "Continuous Profiling?" I shared an approach to continuous profiling that can be used to monitor the performance of application production work loads over time. That approach involved collecting performance data at regular intervals with pprof and analyzing the data using pyroscope to identify performance bottlenecks and other issues that may be affecting the performance of your applications in a production environment.
This time I'd like to focus on performing such analysis in a development environment prior to code being pushed to a repository. What are some of the best practices for visualizing performance benchmarks in a development environment? How do you use benchmarks to identify performance issues early in the development process and ensure that your applications are optimized for performance before the code is merged and deployed to production?
