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Architecture

Uber’s Big Data Platform: 100+ Petabytes with Minute Latency

Responsible for cleaning, storing, and serving over 100 petabytes of analytical data, Uber's Hadoop platform ensures data reliability, scalability, and ease-of-use with minimal latency.

Why Uber Engineering Switched from Postgres to MySQL

Uber Engineering explains the technical reasoning behind its switch in database technologies, from Postgres to MySQL.

Employing QUIC Protocol to Optimize Uber’s App Performance

Implementing QUIC protocol against TCP over cellular networks on our apps led to a reduction of 10-30 percent in tail-end latencies for HTTP traffic.

M3: Uber’s Open Source, Large-scale Metrics Platform for Prometheus

M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.

Designing Schemaless, Uber Engineering’s Scalable Datastore Using MySQL

The making of Schemaless, Uber Engineering’s custom designed datastore using MySQL, which has allowed us to scale from 2014 to beyond. This is part one of a three-part series on Schemaless.

Building Reliable Reprocessing and Dead Letter Queues with Apache Kafka

The Uber Insurance Engineering team extended Kafka’s role in our existing event-driven architecture by using non-blocking request reprocessing and dead letter queues (DLQ) to achieve decoupled, observable error-handling without disrupting real-time traffic.

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.

Open Sourcing Peloton, Uber’s Unified Resource Scheduler

First introduced by Uber in November 2018, Peloton manages resources across large-scale, distinct workloads, combining separate compute clusters.

Engineering the Architecture Behind Uber’s New Rider App

In November 2016 Uber unveiled a sleek new rider app. The app implements a new mobile architecture across both iOS and Android. In this article, Uber Engineering discusses why we felt the need to create a new architecture pattern, and how it helps us reach our goals.

Service-Oriented Architecture: Scaling the Uber Engineering Codebase As We Grow

Moving away from a monolithic codebase to a service-oriented architecture (SOA) has not been an easy task. Here's a brief glimpse of the scalability problems we've faced and the steps we've taken to solve them.

Evolving Distributed Tracing at Uber Engineering

This article is about developing Uber Engineering's open source distributed tracing system, Jaeger.

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop

Uber Engineering's data processing platform team recently built and open sourced Hudi, an incremental processing framework that supports our business critical data pipelines. In this article, we see how Hudi powers a rich data ecosystem where external sources can be ingested into Hadoop in near real-time.

JVM Profiler: An Open Source Tool for Tracing Distributed JVM Applications at Scale

Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.

Managing Uber’s Data Workflows at Scale

In this article, we discuss Uber's journey toward a unified, multi-tenant, and scalable data workflow management system.

Databook: Turning Big Data into Knowledge with Metadata at Uber

Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.

The Road to uChat: Building Uber’s Internal Chat Solution

Learn how Uber Engineering’s Employee Productivity Tools team built uChat, an internal chat solution capable of scaling to meet the needs of our growing global company.

Migrating Functionality Between Large-scale Production Systems Seamlessly

With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.

Observability at Scale: Building Uber’s Alerting Ecosystem

Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.

Rewriting Uber Engineering: The Opportunities Microservices Provide

To show how a microservice is implemented in Uber Engineering's ecosystem, we look at the development of Tincup, our currency and exchange rate service.

Introducing AthenaX, Uber Engineering’s Open Source Streaming Analytics Platform

Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.

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