Uber Riderアプリでのデータ収集、標準化、大規模な利用Spring BootによるAPIバックエンド構築実践ガイド 第2版 何千人もの開発者が、InfoQのミニブック「Practical Guide to Building an API Back End with Spring Boot」から、Spring Bootを使ったREST API構築の基礎を学んだ。この本では、出版時に新しくリリースされたバージョンである Spring Boot 2 を使用している。しかし、Spring Boot3が最近リリースされ、重要な変...
Uber Engineering’s Incremental Processing Framework on Hadoop
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more With the evolution of storage formats like Apache Parquet and Apache ORC and query engines like Presto and Apache Impala, the Hadoop ecosystem has the potential to become a general-purpose, unified serving layer for workloads that can tolerate latencies of a few minutes
Uber's Data Platform with Zhenxiao Luo Holiday Repeat - Software Engineering Daily
Originally published May 24, 2018 When a user takes a ride on Uber, the app on the user’s phone is communicating with Uber’s backend infrastructure, which is writing to a database that maintains the state of that user’s activity. This database is known as a transactional database or “OLTP” (online transaction processing). Every active user and driver and UberEATS restaurant is writing data to the
Using Causal Inference to Improve the Uber User Experience
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
Introducing QALM, Uber’s QoS Load Management Framework
EngineeringIntroducing QALM, Uber’s QoS Load Management FrameworkMarch 22, 2018 / Global Much of Uber’s business involves connecting people with people, making the reliability of our customer platform crucial to our success. The customer platform supports everything from ridesharing and Uber Eats, to Uber Freight and Uber for Business. Our platform team owns four services with thousands of hosts,
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop Connecting users worldwide on our platform all day, every day requires an enormous amount of data management. When you consider the hundreds of operations and data science teams analyzing large sets of anonymous, aggregated data, using a variety of different tools to better understand and maintain
DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber's Apache Hadoop Data Lake
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
Code Migration in Production: Rewriting the Sharding Layer of Uber’s Schemaless Datastore
Code Migration in Production: Rewriting the Sharding Layer of Uber’s Schemaless Datastore In 2014, Uber Engineering built Schemaless, our fault-tolerant and scalable datastore, to facilitate the rapid growth of our company. For context, we deployed more than 40 Schemaless instances and many thousands of storage nodes in 2016 alone. As our business grew, so did our resource utilization and latencie
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
Engineering Uber’s On-Call Dashboard
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more Uber’s on-call engineers facilitate seamless experiences for riders and drivers worldwide by maintaining the 24/7 reliability of our apps. To execute on this reliability, however, we need to ensure that our on-call teams are set up for success. Until January 2016, our o
Google and Uber’s Best Practices for Deep Learning
There is more to building a sustainable Deep Learning solution than what is provided by Deep Learning frameworks like TensorFlow and PyTorch. These frameworks are good enough for research, but they don’t take into account the problems that crop up with production deployment. I’ve written previously about technical debt and the need from more adaptive biological like architectures. To support a via
Uber Engineering’s Micro Deploy: Deploying Daily with Confidence
You’re seeing information for Japan . To see local features and services for another location, select a different city. Show more In 2014, Uber began expanding ever rapidly. Our platform grew from about 60 cities to 100 in the spring, and then to 200 in the fall. Meanwhile, our fastest growing cities were among our oldest. As the number of additional platform engineers grew, so did the disorganiza
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Efficiently Managing the Supply and Demand on Uber’s Big Data Platform