InfoQ Homepage Uber Content on InfoQ
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Uber Reduces Logging Costs by 169x Using Compressed Log Processor (CLP)
Uber recently published how it dramatically reduced its logging costs using Compressed Log Processor (CLP). CLP is a tool capable of losslessly compressing text logs and searching them without decompression. It achieved a 169x compression ratio on Uber's log data, saving storage, memory, and disk/network bandwidth.
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Multi-Factor Authentication Fatigue Key Factor in Uber Breach
Earlier this week, Uber disclosed that the recent breach it suffered was made possible through a multi-factor authentication (MFA) fatigue attack where the attacker disguised themselves as Uber IT.
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Uber Open-Sourced Its Highly Scalable and Reliable Shuffle as a Service for Apache Spark
Uber engineering has recently open-sourced its highly scalable and reliable shuffle as a service for Apache Spark. Spark is one of the most important tools and platforms in data engineering and analytics. It is shuffling data on local machines by default and causes challenges while the scale is getting very large. Shuffle as a service is a solution developed at Uber for this problem.
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Uber Introduces a Universal Signup and Login Stack
Uber recently introduced Unified Signup and Login (USL), an effort to consolidate signup and login experiences across all Uber apps and services. USL lowers the engineering complexity and maintenance overhead and allows faster rollout of security policies and fixes. Over the last two years, Uber rolled out USL and currently, more than 78% of Uber's traffic has adopted USL.
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Data Collection, Standardization and Usage at Scale in the Uber Rider App
Uber Engineering recently published how it collects, standardises and uses data from the Uber Rider app. Rider data comprises all the rider's interactions with the Uber app. This data accounts for billions of events from Uber's online systems every day. Uber uses this data to deal with top problem areas such as increasing funnel conversion, user engagement, etc.
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Uber Re-Architected Its Foundational Fulfilment Service
Uber recently shared how it re-architected its fulfilment service, one of Uber's foundational platform services. Following a two-year-long effort involving 30+ teams and hundreds of developers, Uber engineers "built a strong foundation for modelling various types of physical fulfilment categories in the new platform and migrated all existing transportation use cases."
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InfoQ Live July 20th: Software Supply Chain for DevOps & Reducing Feature Flag Debt
How can modern DevOps practices accelerate your software delivery without the quality issues? Learn how automation, continuous testing, and supply management techniques can improve software quality and speed of delivery. Get valuable insights from world-class domain experts at InfoQ Live on July 20th.
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Uber Implements Disaster Recovery for Multi-Region Kafka
In a recent blog post, Uber engineers highlight how they use a replication platform to implement disaster recovery at scale with a multi-region Kafka deployment. Uber has a large deployment of Apache Kafka, processing trillions of messages and multiple petabytes of data per day. Uber's engineers provided business resilience and continuity in the face of natural and human-made disasters.
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Safe and Fast Deploys at Planet Scale: QCon Plus Q&A
Uber has automated the deployment of services using a hybrid cloud model. All services are deployed using the same rollout techniques and workflows, ensuring safe deployment and mitigation of any issues. Abstracting away the differences between clouds supports engineers in building services that run on any platform.
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Metrics Collection at Scale: Learning from Uber's M3
In a recent InfoQ podcast, Rob Skillington, co-founder and CTO at Chronosphere, shared his experience and opinions on the topic of observability in modern distributed systems. Key topics covered: metrics collection at scale, multi-dimensional metrics and high-cardinality, the importance of the developer experience, and the value of open standards, such as OpenMetrics.
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Uber Open-Sources AI Abstraction Layer Neuropod
Uber open-sourced Neuropod, an abstraction layer for machine learning frameworks that allows researchers to build models in the framework of their choice while reducing the effort of integration, allowing the same production system to swap out models implemented in different frameworks. Neuropod currently supports several frameworks, including TensorFlow, PyTorch, Keras, and TorchScript.
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How Uber Deals with Unreachable Code Associated to Feature Flags in its Mobile Apps
Piranha is a newly open-sourced tool by Uber that can be used to remove stale code in mobile apps written in Java, Objective-C, or Swift for Android and iOS. The tool was born with the aim to pay technical debt ensuing from the process of implementing and eventually removing feature flags, says Uber.
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Uber's Synthetic Training Data Speeds Up Deep Learning by 9x
Uber AI Labs has developed an algorithm called Generative Teaching Networks (GTN) that produces synthetic training data for neural networks which allows the networks to be trained faster than when using real data. Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x.
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Uber Open-Sources Plug-and-Play Language Model for Controlling AI-Generated Text
Uber AI open-sourced the plug-and-play language model (PPLM) which can control the topic and sentiment of AI-generated text. The model's output is evaluated by human judges as achieving 36% better topic accuracy compared to the baseline GPT-2 model.
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Uber Releases Kraken: An Open Source P2P Docker Registry
Uber has released Kraken, an open source, peer-to-peer (P2P) Docker registry. Kraken is a highly available and scalable Docker registry tailored to meet the needs of enterprises and hybrid cloud environments.