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Operations Jun 16, 2022 Qunar Tech Salon

Practical Chaos Engineering Practices at Qunar Travel: Architecture, Scenarios, and Automation

This article details Qunar Travel's mature chaos engineering platform built on chaosblade, covering value analysis, system architecture, shutdown and dependency drills, automated closed‑loop testing, attack‑defense exercises, and the measurable reliability improvements achieved across thousands of services.

distributed systemsautomationoperationschaos engineeringsystem reliabilityfault injection

Cloud Native Dec 2, 2021 GrowingIO Tech Team

Mastering Chaos Mesh: A Hands‑On Guide to Cloud‑Native Chaos Engineering

Chaos Mesh is an open‑source cloud‑native chaos engineering platform that lets you experiment with fault injection across Kubernetes environments, offering visual dashboards, extensive fault types, and step‑by‑step installation and experiment creation guides to help teams uncover system weaknesses and improve resilience.

Cloud NativeObservabilityKubernetesChaos EngineeringFault InjectionChaos Mesh

Operations Jun 1, 2021 Efficient Ops

Mastering System Stability: Building a Chaos‑Driven Platform for Financial Ops

This article details how a major securities firm analyzed business stability, built a comprehensive stability engineering platform using chaos engineering, practiced extensive fault‑injection drills, and outlines future directions such as random‑scenario exercises, red‑blue battles, and AI‑driven risk detection.

Operationsplatformchaos engineeringincident responsestability engineeringfinancial systems

Big Data Feb 20, 2019 Qunar Tech Salon

Building Real-Time User Behavior Engineering with Apache Flink: Architecture, Features, and Implementation

This article introduces the design and implementation of a real‑time user behavior engineering platform at Qunar using Apache Flink, covering Flink's core characteristics, distributed runtime, DataStream programming model, fault‑tolerance, back‑pressure handling, event‑time processing, windowing, watermarks, and practical code examples for filtering, splitting, joining, and state management.

Real-time processingFlinkWatermarkDataStreamCheckpointEventTime

Frontend Development Apr 27, 2018 JD Tech

Redesigning JD.com Homepage: Architecture, Performance, and Frontend Engineering with Nerv and Athena

This article recounts the four‑month redesign of JD.com’s homepage, detailing the migration from jQuery + SeaJS to the Nerv framework, the introduction of the Athena engineering platform, performance and experience optimizations such as code splitting, lazy loading, IE8 compatibility, and the monitoring and automation practices that ensure stability and scalability.

frontendperformancewebpackcode-splittingathenaie8nerv

Backend Development Jun 14, 2023 JD Retail Technology

Reducing MTTR in a High‑Availability SaaS Platform through Chaos Engineering and Middleware Resilience

This article explains how a SaaS platform for employee incentives reduces mean time to recovery (MTTR) during large‑scale promotions by applying chaos‑engineering drills, automating fault detection, and leveraging JSF middleware features such as timeout‑retry, adaptive load balancing, and circuit breaking to improve overall system stability.

Chaos EngineeringMTTRCircuit BreakingBackend ResilienceTimeout Retry

Artificial Intelligence Dec 24, 2020 TAL Education Technology

AI Engineering Efficiency Platform: Architecture, Practices, and Case Studies

The presentation outlines the AI engineering efficiency platform covering algorithm metric and evaluation, micro‑service performance testing, and dataset management architectures, detailing business pain points, platform‑wide improvements, technical designs, real‑world demos, and future directions to achieve accurate, fast, and stable AI services.

MicroservicesAIAutomationMetricsPerformance TestingPlatformData Management

Artificial Intelligence Mar 8, 2019 JD Tech

Integrated Engineering & Algorithm Platform for AI Visual Applications

This article describes a comprehensive, end‑to‑end AI visual algorithm platform that unifies data collection, annotation, model training, deployment, testing, quality evaluation, and service gateways, illustrating how such integration improves transparency, efficiency, and quality across use cases like background removal, face swapping, and clothing recommendation.

Computer VisionAIModel DeploymentAlgorithm PlatformData AnnotationClothing Recommendation

Artificial Intelligence Aug 31, 2022 Baidu Geek Talk

Baidu Intelligent Cloud Launches Cloud-native AI 2.0 to Accelerate AI Engineering

Baidu Intelligent Cloud’s new Cloud‑native AI 2.0 platform tackles AI engineering bottlenecks by offering hybrid‑parallel large‑model training, flexible GPU virtualization, and an AI Accelerate Kit that boosts training efficiency over 50 % and cuts inference latency up to 63 %, raising GPU utilization from ~13 % to about 50 %.

cloud nativeAIlarge modelsAI accelerationGPU virtualizationworkflow orchestration

Backend Development Dec 24, 2024 JD Tech

Stability Challenges and Engineering Solutions for an Inventory Platform

The article analyzes the stability problems faced by an e‑commerce inventory platform—including complex workflows, data accuracy, database hotspots, and high‑frequency calculations—and details a series of backend engineering solutions such as traffic splitting, gray‑release links, Redis caching, consistency checks, async rate limiting, and comprehensive monitoring to improve reliability and performance.

BackendPerformanceDatabaseinventoryrediscachingstability

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