The article describes how Haodf's SRE team replaced Elasticsearch with ClickHouse to handle massive microservice logs, achieve low‑latency queries, reduce storage costs, and support real‑time monitoring, tracing, and metric analysis through columnar OLAP features, sharding, TTL, and materialized views.
The article presents a comprehensive case study of Haodf.com's transition to a micro‑service architecture, detailing the challenges of service stability and observability, the design of a unified governance platform with log‑holographic analysis, real‑time alerts, application profiling, SLO/SLA definition, and future roadmap for capacity and reliability improvements.
This article explains how latency is used as a key indicator for application risk identification, defines slow interfaces, describes why percentile‑based thresholds are preferred over averages, and outlines the architecture, task workflow, and practical optimization strategies for a full‑chain monitoring system in a microservice environment.
The article describes the motivation, requirements, and architectural design of a highly available, scalable, low‑cost distributed time‑series database built on Mimir, detailing write and read paths, multi‑tenant isolation, compaction, and the performance and cost improvements achieved after deployment.
This article details Liulishuo's customized Prometheus monitoring architecture, including data backup to Aliyun SLS, ECS service discovery, advanced alerting with PagerDuty and Goalert, GitOps-driven config management, cloud resource exporters, SLA monitoring, and future plans for storage and alert pipelines.
Joining a new team and project as a fresh graduate at FlowingTalk, I describe the supportive environment, codebase initialization, various HTTP testing strategies using Go and Gin, the adoption of OpenCensus, Prometheus, and Sentry for monitoring, and how iterative development accelerates my growth as a backend engineer.
This article details Snowball's front‑end team's end‑to‑end cross‑platform architecture, covering high‑availability monitoring, performance measurement, bundle hot‑update and splitting strategies, Hermes engine migration, stability fixes, and a systematic migration plan for RN/H5 pages, while outlining future roadmap and lessons learned.
This article explains how Prometheus can be used to monitor custom thread‑pool metrics in a Spring Boot backend, detailing configuration, dynamic parameter updates via Apollo, code examples for metric registration, and visualization and alerting with Grafana.
This article explains how to determine whether Redis is truly experiencing latency issues, outlines benchmark testing methods, identifies common causes such as network problems, high‑complexity commands, big keys, slow logs, memory limits, fork overhead, AOF configuration, swap usage, fragmentation, and provides practical troubleshooting and optimization steps.
This article explains common frontend exceptions such as UI glitches, script errors, and network failures, classifies JavaScript error types, demonstrates handling techniques using try‑catch, finally, window.onerror, event listeners, Promise rejection handling, and framework‑specific solutions like React error boundaries, Vue errorHandler, and Axios interceptors.
This article explains the challenges of distributed locking, compares existing lock services, and details the design, implementation, and monitoring features of the ylock framework, which provides reentrant read‑write locks over Redis and Zookeeper with unified APIs and Spring Boot integration.
The document outlines the background, vision, current status, technical research, value, product and technical architecture, and functional design of a cloud‑native monitoring platform that integrates SkyWalking and Prometheus to provide comprehensive APM, resource utilization, alerting, and rapid fault localization for business and technical middle‑platform services.
This article shares practical Java performance‑optimization techniques, covering UI and non‑UI latency metrics, baseline data collection, bottleneck discovery with tools like Arthas, chronic issue handling, and a comprehensive set of governance measures ranging from network‑level caching to code‑level refactoring, asynchronous processing, and service splitting to achieve stable sub‑200 ms response times.
The article chronicles the design, evolution, and lessons learned of JD Digital Technology’s self‑built host monitoring platform “DiTing”, detailing its initial requirements, V1 architecture, subsequent V2 and V3 redesigns, encountered challenges, and future directions toward intelligent operations.
This article explores why Java applications experience high CPU usage, examines common misconceptions about loops, GC, thread counts, and blocked states, explains how CPU usage is calculated, and provides practical troubleshooting steps with real‑world examples and monitoring tips.
This article provides a comprehensive, operations‑focused overview of Elasticsearch monitoring, covering tool selection, key metrics for black‑box and white‑box monitoring, common issues discovered through alerts, and practical optimization recommendations to ensure high availability of ES clusters.
This article shares practical guidance on designing, protecting, and continuously improving high‑concurrency backend services—covering baseline capacity, rate limiting, data‑structure optimization, stateless architecture, and horizontal scaling—to help engineers evolve small systems into robust, production‑grade platforms.
This article describes how JD's open gateway handles billions of requests during major sales events by employing a multi‑layer architecture, Nginx + Lua unified access, NIO asynchronous processing, service isolation, dynamic routing, degradation, rate‑limiting, circuit‑breaking, fast‑fail mechanisms, and comprehensive monitoring to ensure high performance and reliability.
This article shares the design and implementation of a lightweight, robust, and low‑intrusion monitoring management system for microservice traffic, detailing data collection via client filters, Redis‑based structured storage, alerting, rate‑limiting, degradation, and authorization mechanisms, and discusses performance optimizations and future improvements.
CallGraph is JD.com's internally developed distributed tracing and service governance platform that addresses the challenges of monitoring complex microservice architectures by providing low‑intrusion, low‑latency tracing, real‑time analytics, configurable sampling, and integration with JMQ, Storm, Spark, HBase, and JimDB for both operational insight and performance optimization.