The article chronicles the design, implementation, and iterative evolution of JD Digital Technology’s in‑house host monitoring platform Diting, detailing its V1, V2, and V3 architectures, the challenges encountered at each stage, and future directions toward intelligent, automated operations.
The article analyzes the instability of a company's Kubernetes clusters, identifies root causes such as unstable release processes, lack of monitoring, logging, and documentation, and proposes comprehensive solutions including a Kubernetes‑centric CI/CD pipeline, federated Prometheus monitoring, Elasticsearch logging, centralized documentation, and integrated traffic management with Kong and Istio.
This article explores the fundamental principles of system architecture, illustrating how large‑scale services such as Uber and Weibo handle massive traffic through strategic abstraction, modularization, performance optimization, multi‑level caching, distributed tracing, and operational best practices to achieve scalability and reliability.
This article presents a comprehensive guide to Redis monitoring and alerting, covering metric classification, threshold settings, client traffic collection, host resource usage, instance health checks, cluster failover diagnostics, and detailed explanations of Redis INFO sections with practical code examples.
This article explains RocketMQ’s core features, the operational challenges of using its console, and how the MQCloud platform was designed to separate business and admin roles, provide comprehensive monitoring, automated deployment, security hardening, and a customized client, ultimately turning operational pain into a scalable, open‑source solution.
This article answers a developer's question about Node.js performance optimization by outlining major optimization areas, listing practical techniques such as using streams, clustering, and load balancing, and describing typical bottlenecks and essential performance metrics to monitor.
This article details Ctrip's migration from MySQL to TiDB, describing the multi‑data‑center architecture, real‑world use cases such as the international CDP platform and hotel settlement, performance tuning measures, comprehensive monitoring and alerting, auxiliary tools, and future roadmap for the distributed NewSQL database.
This article shares Ctrip International Ticketing’s cloud‑native migration experience, covering infrastructure‑as‑code with Terraform, managed Kubernetes, centralized logging and monitoring using Elastic Search, Prometheus, Grafana and Thanos, and practical cost‑optimization techniques such as auto‑scaling, spot instances, storage tiering and network proxying.
This article details how Ctrip builds, deploys, tests, releases, and operates Node.js applications—including engineering processes, core middleware, Docker-based deployment, multi‑process communication, monitoring, and full‑link tracing—while sharing practical lessons learned from real‑world production use.
This article describes how Ctrip introduced a customized Dubbo framework called CDubbo, covering the motivations for adopting Dubbo, the initial implementation of service governance and monitoring, and subsequent extensions such as callback enhancement, serialization support, circuit‑breaking, testing tools, and a bastion testing gateway.
The article details Ctrip’s Node.js technology stack, covering its deployment pipeline, version management, build principles, Docker‑based operations and monitoring, logging models, public services such as SOA, storage, cache, disaster recovery, and real‑world application scenarios like data aggregation and server‑side rendering.
This article details the architectural evolution of Ctrip's Hickwall monitoring and alerting platform, describing the transition from an Elasticsearch‑based first generation to an InfluxDB‑driven second generation, the design of the Incluster storage layer, data aggregation strategies, and the implementation of high‑performance stream‑based alerting.
This article describes the design and implementation of Ctrip's hotel intelligent monitoring platform, detailing its architecture, key components such as Smart, Mdata, Artemis, and Clog monitoring, the challenges of massive log data, and the achieved improvements in real‑time alerting and testing efficiency.
The article describes a production traffic replay system that records real user traffic, creates scalable pressure sources, supports both 4‑layer and 7‑layer protocols, and provides automated fail‑over and monitoring features to enable realistic functional and performance testing at large scale.
This article presents a comprehensive overview of Vipshop's message gateway redesign, covering its architectural positioning, internal modules, technical stack, monitoring, degradation strategies, and practical lessons learned to handle massive messaging traffic in a large‑scale e‑commerce environment.
This article presents the design, implementation, and ongoing improvements of Qunar's network device operations platform, detailing its background, optimization strategies, permission model, automated tasks, monitoring capabilities, and how it enhances operational efficiency while reducing risk.
This article details Ctrip's deployment of a large‑scale virtual cloud desktop solution for its call center, covering the motivations, original OpenStack architecture, its limitations, the redesigned decoupled architecture, and the operational practices such as resource over‑commit, network tuning, monitoring, and automated testing that ensure stability and scalability.
This article details the design, deployment, configuration, operation, monitoring, and backup of a PostgreSQL high‑availability cluster built on Patroni, Etcd, and LVS at 360, covering hardware layout, software versions, installation steps, parameter tuning, fail‑over testing, and future outlook.
This article introduces Qbus, a cloud‑based queue service built on Kafka, covering its architecture, core components such as log collection, SDKs, HDFS persistence, monitoring with Prometheus, business integration methods, use‑case scenarios, and future development directions.
This article provides a comprehensive overview of using Prometheus and Grafana for metric collection, storage, querying with PromQL, visualization, and alerting, including exporter integration, metric types, high‑availability setups, and practical examples for modern microservice architectures.