In high-performance code, a single misplaced counter increment can cost more than the operation it’s measuring. That creates a paradox: instrument too much and you slow the system down; instrument too little and you miss the insights you need to continuously deliver.

This talk focuses on techniques for instrumenting latency-sensitive, high-throughput systems with minimal impact—approaches rooted in C and Rust, but with lessons that may apply more broadly. We’ll examine the true costs of metrics collection, the pitfalls of percentile reporting, and how to extend observability from application code down to the kernel using eBPF. Along the way, we’ll discuss cacheline-aware counter design, the trade-offs in struct and memory layout, and the value of a unified metrics framework for both application and infrastructure insights.

Attendees will gain practical, language-level strategies for building observability into performance-critical systems—without sacrificing the speed their users expect.