Event

<p>Reliable performance measurement remains an unsolved problem across most open source projects. Benchmarks are often an afterthought, and when they aren't they can be noisy, non-repeatable, and hard to act on.</p> <p>This talk shares lessons learned from building a large-scale benchmarking system at Datadog and shows how small fixes can make a big difference: controlling environmental noise, designing benchmarks, interpreting results with sound statistical methods, and more.</p> <p>Attendees should leave with practical principles they can apply in their own projects to make benchmarks trustworthy and actionable. We'll illustrate each principle with real data — for instance, environment tuning that cut variance by 100x, or design changes that turned a flaky benchmark into a reliable one.</p>