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Most performance issues are solved the same way: add more CPU, more memory, or scale out. It works... but it’s often an expensive shortcut that hides the real problem.

In this session, we’ll explore a practical approach to performance engineering using open-source tools and reproducible methods. Instead of relying on intuition or single metrics like CPU usage, we’ll break down how to characterize workload behavior (CPU, memory, and I/O patterns), identify system limits through breaking-point testing, and measure efficiency using cost per unit of work.

Through multiple examples, we’ll analyze how different workload shapes behave under load, why average latency is misleading, and how to detect the “knee of the curve” where systems begin to fail. We’ll also dive into hands-on optimization techniques (including memory-efficient patterns, caching strategies, and SIMD vectorization) showing how code changes can outperform infrastructure scaling.

The goal is simple: give you a repeatable framework to understand where your performance bottlenecks really are, and how to fix them with evidence instead of guesswork.

You’ll leave with concrete techniques, open-source tooling, and a mental model to make better performance decisions...whether you’re working on bare metal, containers, or distributed systems.