The article outlines a comprehensive release management framework that emphasizes gray (canary) deployments, detailed verification steps, monitoring practices, and rollback procedures to mitigate risks and ensure system stability for production rollouts.
This article examines how test teams can enhance application observability and quality assurance by distinguishing observability from traditional monitoring, defining goals, outlining a monitoring foundation, and proposing module‑level and system‑level strategies for proactive fault detection, data analysis, and alerting.
This article describes how Beike built, refined, and scaled a mobile live‑streaming platform—detailing early challenges, architectural pain points of version 1.0, and the systematic improvements introduced in version 2.0 such as clear boundaries, functional aggregation, layered platform design, dynamic configuration, monitoring, and zero‑cost integration to support diverse business scenarios.
This article provides comprehensive Python scripts for system administration tasks including CPU monitoring, memory usage tracking, log analysis, file backup, system updates, network monitoring, service management, user administration, and system information collection.
This article explains how to monitor and tune Linux network packet reception using tools such as ethtool, ifconfig, and procfs, covering RingBuffer inspection, hardware and soft interrupt analysis, multi‑queue configuration, interrupt coalescing, and GRO settings to improve throughput and reduce packet loss.
This article details how Xigua Video analyzed and optimized the startup of multiple Android subprocesses—including push, mini‑app, sandboxed, and exec processes—by applying on‑demand loading, SDK integration, and monitoring techniques, resulting in measurable performance and quality improvements.
This article provides a detailed analysis of HikariCP’s initialization, core components, startup flow, connection acquisition logic, monitoring metrics, and key configuration parameters, illustrating how Spring Boot 2.x leverages this high‑performance JDBC connection pool and offering guidance for tuning and extending it.
This article details how JD Logistics guarantees the stability of its Double 11 supply‑chain dashboard by mapping the entire data‑flow, identifying risk points across ingestion, processing, storage, service, and monitoring layers, and applying targeted technical and organizational safeguards.
This article details how JDV, JD.com’s internal visual dashboard platform, tackled the massive data‑intensive 618 promotion by implementing real‑time updates, cross‑midnight count stops, request‑state control, heartbeat monitoring, proxy data sources, and a suite of developer tools to ensure stability, performance, and rapid feature delivery.
This article details the design and deployment of an intelligent alert attribution system for Ctrip Hotel's front‑end, describing the background challenges, the unified data pool, weighted alert rules, three attribution algorithms, achieved improvements in accuracy and troubleshooting speed, and future enhancement plans.
This article outlines the evolution of Ctrip’s performance testing approaches across three generations, analyzes their limitations, and presents the design, architecture, data construction, request tracing, monitoring, and operational considerations of the fourth-generation full‑link testing platform, including case studies and future outlook.
This article details the challenges, architectural evolution, key components, implementation experiences, and future directions of Ctrip's operations workflow platform, illustrating how a multi‑stage, layered design and standardized services have transformed manual IT operations into an automated, observable, and scalable system.
This article introduces a StackStorm‑driven ChatOps framework that consolidates monitoring alerts, applies rule‑based root‑cause analysis, and automatically executes self‑healing actions, outlining its architecture, components, workflow definitions, and practical deployment results within an enterprise operations environment.
This article details 360's comprehensive approach to improving Apache Kafka cluster stability through proactive operations, capacity assessment, parameter tuning, monitoring, version upgrades, and traffic control, offering concrete guidelines and best‑practice recommendations for large‑scale message‑queue deployments.
This article details how 58 Group’s cloud authentication service introduced an observability framework—optimizing logs, employing distributed tracing, defining SLO/SLA metrics, and implementing burn‑rate alerts—to improve fault detection, reduce false alarms, and achieve faster root‑cause analysis across the system.
In this interview, Li Yi, head of 58 Group’s Front‑End Technology Department, explains how the company built its large‑scale front‑end service system—including a Hybrid permission platform, a React Native hot‑update platform, and the Beidou monitoring system—while discussing cross‑platform frameworks, performance challenges, low‑code adoption, and advice for newcomers.
This article explains how to use MySQL's performance_schema to monitor and analyze per‑connection memory consumption, provides SQL queries to list memory instruments, shows Python scripts for sampling and visualizing memory usage over time, and demonstrates practical usage with example commands and output.
This article explains how to monitor MySQL full-text indexes by describing relevant InnoDB parameters, the metadata tables that expose index activity, and step‑by‑step examples that create a sample table, configure monitoring, observe cache behavior, and manage index maintenance operations.
This extensive article presents a systematic approach to front‑end stability, covering observability systems, full‑chain monitoring, high‑availability design, performance management, risk governance, process mechanisms, and engineering practices to ensure reliable user experiences and business continuity.
This article examines the rapid adoption of cloud‑native architectures and presents a comprehensive guide to identifying performance bottlenecks and applying architectural, resource‑management, caching, networking, and tooling techniques—such as Kubernetes, Prometheus, Grafana, and JMeter—to achieve high‑performance, scalable cloud‑native systems.