Data Race Patterns in Go
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber has adopted Golang (Go for short) as a primary programming language for developing microservices. Our Go monorepo consists of about 50 million lines of code (and growing) and contains approximately 2,100 unique Go services (and growing). Go makes concurrency a firs
How We Saved 70K Cores Across 30 Mission-Critical Services (Large-Scale, Semi-Automated Go GC Tuning @Uber)
How We Saved 70K Cores Across 30 Mission-Critical Services (Large-Scale, Semi-Automated Go GC Tuning @Uber) Introduction 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
Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com
Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com At Uber Eats, we want ordering the food you crave at the touch of a button to be as easy as possible, whether on desktop or mobile. That’s why our engineering team spends a lot of time thinking about, building, and maintaining web applications for restaurants and customers. Uber Eats relies heavily on web-b
Measuring Kotlin Build Performance at Uber
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more This article was written in collaboration with the Kotlin team at JetBrains. At Uber, we strive to maintain a modern tech stack in all our applications. A natural progression in the Android space was to start adopting Kotlin, a modern multi-platform programming language
Manifold: A Model-Agnostic Visual Debugging Tool for Machine Learning
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Machine learning (ML) is widely used across the Uber platform to support intelligent decision making and forecasting for features such as ETA prediction and fraud detection. For optimal results, we invest a lot of resources in developing accurate predictive ML models. I
Introducing Nanoscope: An Extremely Accurate Method Tracing Tool for Android
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Last October, Uber’s Mobile Engineering team kicked off an effort to improve app performance, and we’ve made great progress so far with speedups of well over 50 percent for some of our key transitions. Early on, we learned that certain classes of performance issues are
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