Mistakes and Trade-offs when optimizing the hot-path (...)

"Mistakes and Trade-offs when optimizing the hot-path: when premature optimization becomes early optimization"

Having SLA’s data, we can build performance tests in a way that allows us to reason about the system before production deployment. An important observation related to many systems is that the code that brings most of the business value often occupies a small percentage of the whole codebase. This observation is based on the Pareto principle and was validated in many business domains, not only software engineering. Based on those observations, we can design a process that allows us to find the hot-path in our code. Hot-path is executed for almost all users’ requests. When we detect the hot-path in our code, we can optimize a small subset of our codebase, achieving substantial performance gains. Therefore, we can work more effectively. This presentation will focus on analyzing a system and applying a method based on the Pareto principle, SLA, and performance tests to find and optimize the hot path. Each step will be backed up with performance tests and data to verify that we are progressing with our optimizations in a good direction.