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You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction Shadower is a load testing tool that allows us to provide load testing as a service to any microservice at Uber. Shadower started as a command line application that allowed us to read a local file to load test a local application. At the time, Maps PEs were

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Painting the Picture Before 2021, Uber engineers would have to take quite a taxing journey to make a code change to the Go Monorepo. First, the engineer would make their changes on a local branch and put up a code revision to our internal code review system, Phabricator

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

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more At Uber, magical customer experiences depend on accurate arrival time predictions (ETAs). We use ETAs to calculate fares, estimate pickup times, match riders to drivers, plan deliveries, and more. Traditional routing engines compute ETAs by dividing up the road network

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

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction The Fulfillment Platform is a foundational Uber domain that enables the rapid scaling of new verticals. The platform handles billions of database transactions each day, ranging from user actions (e.g., a driver starting a trip) and system actions (e.g., cre

Uber delivers efficient and reliable transportation across the global marketplace, which is powered by hundreds of services, machine learning models, and tens of thousands of datasets. While growing rapidly, we’re also committed to maintaining data quality, as it can greatly impact business operations and decisions. Without data quality guarantees, downstream service computation or machine learnin

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Introduction As Uber’s business grew, we scaled our Apache Hadoop (referred to as ‘Hadoop’ in this article) deployment to 21000+ hosts in 5 years, to support the various analytical and machine learning use cases. We built a team with varied expertise to address the chal

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more API gateways are an integral part of microservices architecture in recent years. An API gateway provides a single point of entry for all our apps and provides an interface to access data, logic, or functionality from back-end microservices. It also provides a centralize

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

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more The App Size Problem Uber’s iOS mobile Apps for Rider, Driver, and Eats are large in size. The choice of Swift as our primary programming language, our fast-paced development environment and feature additions, layered software and its dependencies, and statically linked

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

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more At Uber, we use feature flags to customize our mobile app execution, serving different features to different sets of users. These flags allow us to, for example, localize the user’s experience in different regions where we operate and, more importantly, to gradually rol

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Data analytics play a critical part in Uber’s decision making, driving and shaping all aspects of the company, from improving our products to generating insights that inform our business. To ensure timely and accurate analytics, the aggregated, anonymous data that power

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more On the Uber Labs team, our mission is to leverage insights and methodologies from behavioral science to build programs and products that are intuitive and enjoyable for customers. Our group of scientists have PhDs in fields including psychology, marketing, and cognitive

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In traditional industries such as automobile or aerospace, engineers first design the products and the manufacturing facilities produce the cars or aircrafts according to the design. In software development, a build system is similar to the manufacturing facilities that

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Running queries on Uber’s data platform lets us make data-driven decisions at every level, from forecasting rider demand during high traffic events to identifying and addressing bottlenecks in the driver sign-up process. Our Apache Hadoop-based data platform ingests hun

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

Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing At Uber, we use robust data processing systems such as Apache Flink and Apache Spark to power the streaming applications that helps us calculate up-to-date pricing, enhance driver dispatching, and fight fraud on our platform. Such solutions can process data at a massive scale in real time with exactly-once semantics,

Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. As Uber’s operations became more complex and we offered additional features and services through our platform, we needed a way to generate more timely analytics on our aggregated marketplace data to better und

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more The Uber Eats app serves as a portal to more than 320,000 restaurant-partners in over 500 cities globally across 36 countries. In order to make the user experience more seamless and easy-to-navigate, we show users the dishes, restaurants, and cuisines they might like up

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more When a customer contacts Uber with a support issue, we want to quickly and seamlessly address their concerns. To make the customer support ticket resolution process as streamlined as possible, our Customer Obsession Engineering team designed and developed a new web appl

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more This article is the second in our series dedicated to highlighting causal inference methods and their industry applications. Previously, we published an article on mediation modeling, which is one of many methods within the broader category of causal inference. In futur

Mobile, EngineeringEmploying QUIC Protocol to Optimize Uber’s App PerformanceMay 14, 2019 / Global Uber operates on a global scale across more than 600 cities, with our apps relying entirely on wireless connectivity from over 4,500 mobile carriers. To deliver the real-time performance expected from Uber’s users, our mobile apps require low-latency and highly reliable network communication. Unfortu

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

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Data / MLDBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data LakeMarch 14, 2019 / Global Keeping the Uber platform reliable and real-time across our global markets is a 24/7 business. People may be going to sleep in San Franc

Optimizing M3: How Uber Halved Our Metrics Ingestion Latency by (Briefly) Forking the Go Compiler In Uber’s New York engineering office, our Observability team maintains a robust, scalable metrics and alerting pipeline responsible for detecting, mitigating, and notifying engineers of issues with their services as soon as they occur. Monitoring the health of our thousands of microservices helps us

You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more A Docker registry’s primary purpose is to store and distribute Docker images. This may seem like a relatively trivial task, but with a large-scale compute cluster like Uber’s, it can easily turn into a scaling bottleneck. In computing environments with multiple regions