• Yevgeniy Miretskiy Sesh Nalla Arun Parthiban Alp Keles At Datadog, cost-aware engineering is more than a principle; itâs a performance challenge at scale. • Weâve published how we saved $17 million by rethinking our infrastructure, and weâve built Cloud Cost Management to help customers do the same. • But scaling deep, expert-level code optimization across a fast-moving engineering organization presents its own challenge. • Our journey didnât start with a grand AI design. • It began as a mission to trim CPU usage in several critical hot-path functions in our most expensive services. • For the hands-on performance engineer, weâll dig into the gritty work of optimizing Go code: eliminating compiler bounds checks, restructuring loops, and rewriting functions for maximum efficiency.
Article Summaries:
- Datadog’s latest post details how the company turned manual Go‑code optimization into an automated, self‑optimizing system called BitsEvolve. By first targeting high‑frequency, CPU‑intensive functions in its autoscaled services, Datadog engineers trimmed CPU usage in several critical hot‑paths, saving $17 million on infrastructure and powering its new Cloud Cost Management offering. The team identified three criteria for worthwhile micro‑optimizations-massive invocation counts, autoscaling, and measurable resource reduction-and applied techniques such as loop unrolling and bounds‑check elimination. Insights from these human‑driven tweaks informed the heuristics that drive BitsEvolve, enabling broader, scalable code optimization beyond a handful of experts.
Sources: