How Uber Uses JSON

Ceilometer is Uber's adaptive benchmarking framework designed to enhance the performance and reliability of their infrastructure by automating benchmarking processes and providing actionable insigh...

The article discusses how Uber AI Solutions enhances data labeling quality through a framework called Requirement Adherence, which utilizes Large Language Models (LLMs) for real-time validation and...

The article discusses the open-source release of Starlark Worker, a tool that integrates Cadence workflow orchestration with the Starlark scripting language.

The article discusses the evolution of Uber's Search Platform, highlighting its transition from Elasticsearch to an in-house solution called Sia, and ultimately to the adoption of OpenSearch.

This article details Uber's migration from Apache Hive to Apache Spark SQL for ETL workloads, highlighting the motivations behind the transition, the architecture involved, and the challenges faced...

The article discusses Uber's migration from Apache Mesos to Kubernetes, detailing the motivations, challenges, and solutions encountered during the transition.

The article discusses Profile-Guided Optimization (PGO) in Go programming, detailing its implementation at Uber to enhance performance through runtime profiling data.

The article discusses Uber's upgrade of its MySQL fleet from version 5. 7 to 8. 0, detailing the motivations, challenges, and solutions implemented during the process.

The article discusses Uber's GenAI Gateway, a unified platform designed to streamline the integration of Large Language Models (LLMs) across various teams within the company.

This article discusses the modernization of Uber's logging infrastructure using CLP, focusing on the development of an end-to-end system for managing unstructured logs.

The article discusses Uber's experience in adopting SPIFFE/SPIRE at scale, detailing the challenges and solutions encountered in implementing a Zero Trust security model across a complex microservi...

The article discusses how Uber utilizes Apache Pinot for real-time analytics of mobile app crashes, enhancing their ability to detect and resolve issues quickly.

The article discusses Uber's Automated Audit Framework designed to manage and audit financial transactions at internet scale.

The article discusses how Uber measures app performance, particularly focusing on iOS app startup latency.

This article discusses how Uber unified its configuration distribution across various systems through the Unified Configuration Distribution Platform (UCDP).

The article discusses the Unified Action Platform (uAct) developed by Uber, aimed at consolidating various internal communication systems into a single interface for managing requests and notificat...

The article discusses Uber's implementation of Alluxio local caching to enhance the performance of Presto, a data analytics engine.

The article introduces WorkflowGuard, a workflow governance and observability system developed by Uber that manages over 120,000 data workflows.

The article discusses how Uber reduced its logging costs significantly by integrating the Compressed Log Processor (CLP) into its logging architecture.

This article discusses Uber's migration from MySQL to MyRocks, a storage engine that integrates with RocksDB, to address disk space bottlenecks and improve operational efficiency.

The article discusses Uber's transition from a Server-Sent Events (SSE) architecture to a gRPC-based push platform, detailing the motivations, implementation challenges, and outcomes of this migrat...

Shadower is a minimalistic load testing tool developed by Uber that enables load testing as a service for microservices.

The article introduces Ballast, an adaptive load testing framework developed by Uber to efficiently test microservices under peak loads.

The article discusses CRISP, a tool developed by Uber for critical path analysis in microservice architectures.

The article discusses Jellyfish, a cost-effective data tiering solution developed by Uber for their largest storage system, Schemaless.

The article discusses Uber's initiatives to enhance the efficiency of its Big Data platform, focusing on cost reduction through optimizations in file formats, HDFS erasure coding, YARN scheduling i...

The article discusses Uber's comprehensive re-architecture of its Fulfillment Platform, aimed at enhancing its Go/Get strategy.

The article discusses Uber's Customer Support Automation Platform, focusing on the development and implementation of the Policy Engine (PE) to enhance customer service efficiency.

The article discusses the integration of Elastic Distributed Training with XGBoost on Ray, highlighting how this approach addresses challenges in distributed machine learning at scale.

The article discusses the scaling of Uber's API Gateway, focusing on the critical components of configuration management and runtime.

The article discusses the architecture of Uber's API gateway, detailing its evolution, components, and functionalities.

The article discusses Flipr, a dynamic configuration management tool developed by Uber to facilitate rapid and safe changes across their extensive software systems.

The article discusses how Uber addresses the challenge of large iOS app sizes, particularly for its Rider, Driver, and Eats applications.

Uber has developed a centralized, schema-agnostic log analytics platform that enhances logging efficiency and reliability.

The article discusses Uber's Gairos platform, a real-time data processing and querying system designed to enhance scalability and reliability.

Uber's Real-Time Push Platform focuses on enhancing user experiences by transitioning from polling to a gRPC-based bi-directional streaming protocol.

The article discusses Uber's development of uWorc, a no-code workflow orchestrator designed to simplify the creation of batch and streaming data pipelines.

The article discusses Uber's Databook, an in-house platform designed to manage and surface metadata related to various data entities.

Ludwig version 0. 3 introduces significant enhancements, including hyperparameter optimization, support for Transformers, and integration with TensorFlow 2.

The article discusses the evolution of Uber's API gateway, Edge Gateway, which serves as a self-serve platform for configuring, managing, and monitoring APIs across various business domains.

The article introduces the Domain-Oriented Microservice Architecture (DOMA) developed by Uber to address the complexities associated with traditional microservice architectures.

The article introduces Menu Maker, a new web-based tool by Uber Eats designed for restaurant partners to manage their menus efficiently. It discusses the evolution from Menu 1. 0 to Menu 2.

The article introduces Cyborg, an open-source library developed by Uber that allows iOS developers to use Android's VectorDrawable format for vector graphics.

The article discusses how Uber integrated GraphQL into its Customer Care Platform to enhance data hydration, streamline developer workflows, and improve error handling.

Ludwig v0. 2 introduces significant enhancements to its deep learning toolbox, including new features such as Comet.

The article discusses how Uber has implemented the Uber Spark Compute Service (uSCS) to simplify the use of Apache Spark across its extensive infrastructure.

The article introduces the Plato Research Dialogue System, a flexible conversational AI platform developed by Uber AI.

This article discusses the evaluation of Kotlin build performance at Uber, focusing on the transition from Java to Kotlin in their Android applications.

This article discusses Uber's evolution of its ticket routing system, emphasizing the transition to a workflow-based architecture using Cadence.

This article discusses Uber's approach to selecting the appropriate HDFS file formats for processing large volumes of imagery and metadata using Apache Spark.