Code executed at scale must perform well. But how do we know if our performance optimizations actually make a difference?

Taking a step back, how do we even know what we need to optimize? First, we might need to discover hidden assumptions in the code, or figure out how to isolate the performance-critical bits. Even once we know what must be optimized, it's challenging to create reliable before-and-after benchmarks. We can only tell if our changes helped by profiling, improving, measuring, and profiling again. Without these steps, we might make things slower without realizing it.

In this talk, you'll learn how to:
* Identify the areas with an effort-to-value ratio that make them worth improving
* Isolate code to make its performance measurable without excessive refactoring
* Apply the "performance loop" to ensure performance actually improves and nothing breaks
* Become more “performance-aware” without getting bogged down in performance theater