How We Saved 70K Cores Across 30 Mission-Critical Services (Large-Scale, Semi-Automated Go GC Tuning @Uber) | Uber Blog

Explore how Uber employees from around the globe are helping us drive the world forward at work and beyond The technology behind Uber Engineering Uber news and updates in your country Product, how-to, and policy content—and more Share As part of Uber engineering’s wide efforts to reach profitability, recently our team was focused on reducing cost of compute capacity by improving efficiency. Some of the most impactful work was around GOGC optimization. In this blog we want to share our experience with a highly effective, low-risk, large-scale, semi-automated Go GC tuning mechanism. Uber’s tech stack is composed of thousands of microservices, backed by a cloud-native, scheduler-based infrastructure. Most of these services are written in Go. Our team, Maps Production Engineering, has previously played an instrumental role in significantly improving the efficiency of multiple Java services by tuning GC. At the beginning of 2021, we explored the possibilities of having a similar impact on G

Explore how Uber employees from around the globe are helping us drive the world forward at work and beyond The technology behind Uber Engineering Uber news and updates in your country Product, how-to, and policy content—and more Share As part of Uber engineering’s wide efforts to reach profitability, recently our team was focused on reducing cost of compute capacity by improving efficiency. Some of the most impactful work was around GOGC optimization. In this blog we want to share our experience with a highly effective, low-risk, large-scale, semi-automated Go GC tuning mechanism. Uber’s tech