Uber Engineering Blog
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Cost Efficiency @ Scale in Big Data File Format
Capacity Recommendation Engine: Throughput and Utilization Based Predictive Scaling
The New Version of Orbit (v1.1) is Released: The Improvements, Design Changes, and Exciting Collaborations
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 effective, low-risk, large-scale, semi-automated Go GC tuning mechanism.
Uber’s tech stack is composed of...
Cadence Multi-Tenant Task Processing
CRISP: Critical Path Analysis for Microservice Architectures
How Uber Migrated Financial Data from DynamoDB to Docstore
Introduction Each day, Uber moves millions of people around the world and delivers tens of millions of food and grocery orders. This generates a large number of financial transactions that need to be stored with provable completeness, consistency, and compliance.
LedgerStore is an immutable, ledger-style database storing business transactions. LedgerStore provides signing/sealing of data to guarantee data completeness/correctness, strongly consistent indexes,...
Introducing uGroup: Uber’s Consumer Management Framework
Improving HDFS I/O Utilization for Efficiency
Building Uber’s Fulfillment Platform for Planet-Scale using Google Cloud Spanner
Real-Time Exactly-Once Ad Event Processing with Apache Flink, Kafka, and Pinot
YAML Generator for Funnel YAML Files: Streamlining the Mobile Data Workflow Process
At Uber, real-time mobile analytics events—generated by button taps, page views, and more—form the backbone of the mobile data workflow process.
To process these events, our Mobile Data Platform Team designed and developed the Fontana library, which converts the nearly-one-million-QPS (queries per second) volume of events into easily digestible and useful analytics for Uber engineers. As part of this process,...
Jellyfish: Cost-Effective Data Tiering for Uber’s Largest Storage System
Problem Uber deploys a few storage technologies to store business data based on their application model. One such technology is called Schemaless, which enables the modeling of related entries in one single row of multiple columns, as well as versioning per column.
Schemaless has been around for a couple of years, amassing Uber’s data. While Uber is consolidating all the use...
Streaming Real-Time Analytics with Redis, AWS Fargate, and Dash Framework
Enabling Seamless Kafka Async Queuing with Consumer Proxy
How Data Shapes the Uber Rider App
Building Scalable Streaming Pipelines for Near Real-Time Features
Background Uber is committed to providing reliable services to customers across our global markets. To achieve this, we heavily rely on machine learning (ML) to make informed decisions like forecasting and surge. As a result, real-time streaming pipelines, which are used to generate the data and features for ML, have become more popular and important.
At Uber, we leverage Apache Flink...
Eats Safety Team On-Call Overview
Introduction Our engineers have the responsibility of ensuring a consistent and positive experience for our riders, drivers, eaters, and delivery/restaurant partners.
Ensuring such an experience requires reliable systems: our apps have to work when anyone needs them. A major component of reliability is having engineers on call to deal with problems immediately as they arise. We set up our on-call engineers...
Unifying Support Content to Enable More Empathetic and Personalized Customer Support Experiences
Efficiently Managing the Supply and Demand on Uber’s Big Data Platform
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