pprof++: A Go Profiler with Hardware Performance Monitoring
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Motivation for a Better Go Profiler Golang is the lifeblood of thousands of Uber’s back-end services, running on millions of CPU cores. Understanding our CPU bottlenecks is critical, both for reducing service latencies and also for making our compute fleet efficient. Th
Introducing Domain-Oriented Microservice Architecture
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction Recently there has been substantial discussion around the downsides of service oriented architectures and microservice architectures in particular. While only a few years ago, many people readily adopted microservice architectures due to the numerous benefi
JVM Profiler: An Open Source Tool for Tracing Distributed JVM Applications at Scale
JVM Profiler: An Open Source Tool for Tracing Distributed JVM Applications at Scale Computing frameworks like Apache Spark have been widely adopted to build large-scale data applications. For Uber, data is at the heart of strategic decision-making and product development. To help us better leverage this data, we manage massive deployments of Spark across our global engineering offices. While Spark
The Road to uChat: Building Uber’s Internal Chat Solution
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Two years ago, Uber’s previous chat application began showing signs that it would not be able to adapt to our growth. There were app crashes, performance hiccups, and outages that crippled our company’s ability to effectively communicate online. With user satisfaction a
Peloton: Uber's Unified Resource Scheduler for Diverse Cluster Workloads
EngineeringPeloton: Uber’s Unified Resource Scheduler for Diverse Cluster WorkloadsOctober 30, 2018 / Global Cluster management, a common software infrastructure among technology companies, aggregates compute resources from a collection of physical hosts into a shared resource pool, amplifying compute power and allowing for the flexible use of data center hardware. At Uber, cluster management prov
Meet Michelangelo: Uber’s Machine Learning Platform
Data / MLMeet Michelangelo: Uber’s Machine Learning PlatformSeptember 5, 2017 / Global Uber Engineering is committed to developing technologies that create seamless, impactful experiences for our customers. We are increasingly investing in artificial intelligence (AI) and machine learning (ML) to fulfill this vision. At Uber, our contribution to this space is Michelangelo, an internal ML-as-a-serv
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
Dockerizing MySQL at Uber Engineering
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber Engineering’s Schemaless storage system powers some of the biggest services at Uber, such as Mezzanine. Schemaless is a scalable and highly available datastore on top of MySQL¹ clusters. Managing these clusters was fairly easy when we had 16 clusters. These days, w
Uber Engineering's Ringpop - The Uber Engineering Blog
EngineeringHow Ringpop from Uber Engineering Helps Distribute Your ApplicationFebruary 4, 2016 / Global Remember the candy jewel rings kids used to wear? Slide the one-size-fits-all ring on any finger and you could immediately become a fashion icon. Ringpop, an open source library developed at Uber to make our applications cooperative and scalable, is just as sweet as its lollipop counterpart. It
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Scaling Machine Learning at Uber with Michelangelo
Horovod Adds Support for PySpark and Apache MXNet and Additional Features for Faster Training