This article explains how Java memory leaks can occur despite automatic garbage collection, describes JVM GC‑Root analysis, outlines practical monitoring with Spring Boot Actuator, Prometheus, and Grafana, and provides step‑by‑step debugging commands and code adjustments to locate and fix the leak.
This article introduces the fundamentals of time‑series databases, explains why InfluxDB was chosen, describes the TSM storage engine and shard concepts, outlines the internal 360 InfluxDB‑HA architecture, compares its performance with a single node, and provides integration and future‑development guidelines.
This article presents a comprehensive guide to building and applying a unified API automation testing tool, covering its background, framework design, core features, data and configuration management, public functions, test case handling, logging, execution workflow, CI integration, and monitoring in a search service environment.
This article shares practical methodologies for designing, deploying, and maintaining systems that can reliably operate for ten years, covering goal setting, holistic design considerations, carrier and data‑center choices, active‑active architecture, server and platform selection, monitoring, and continuous personal improvement.
The article explains how to proactively monitor Elasticsearch by covering key performance areas such as query and indexing latency, JVM heap and garbage‑collection behavior, and host‑level system metrics, providing practical guidance and visual diagrams for effective operations management.
This document outlines a comprehensive SRE‑driven operational framework for ensuring stable, high‑availability online education services during peak summer and winter periods, detailing pre‑, during‑, and post‑maintenance phases, architectural principles, load testing, monitoring, capacity management, safety hardening, chaos engineering, incident response, and post‑mortem practices.
This article outlines the challenges of operating petabyte‑scale big‑data clusters and presents a comprehensive monitoring framework—including basic and upgraded monitoring layers, metric collection, alerting pipelines, and strategies for alarm convergence and grading—to ensure reliable, proactive SRE operations.
This article presents a comprehensive case study of how Qunar Travel identified and resolved Kafka production bottlenecks—through metric monitoring, thread and flush parameter tuning, and Filebeat batch adjustments—resulting in a 2000‑core CPU reduction, higher network idle rates, and lower resource consumption across three clusters.
This article describes the architecture, challenges, and performance optimizations of Qunar.com's Watcher monitoring platform, covering massive metric collection, master‑worker redesign, Graphite/Whisper storage enhancements, and future migration to Go‑based cloud‑native solutions.
The article shares the author’s journey from early database development at DM6/DM7 through MySQL operations at Qunar, offering practical advice on demand‑driven implementation, data‑driven management, intelligent alerting, full‑log analysis, slow‑query risk modeling, high‑availability, and automation to transform traditional DBA work into a proactive, efficient New DBA role.
This article presents a lightweight gray release approach for complex flight ticket services, comparing traditional hardware and soft‑routing isolation methods, describing the authors' traffic‑based gray identification, business‑focused monitoring, implementation details, and automated safeguards to enable safe incremental deployments.
This article outlines essential container operation best practices—including native logging, JSON log formatting, sidecar aggregators, stateless and immutable design, avoiding privileged containers, effective monitoring, health checks, non‑root execution, and careful image tagging—to help developers build secure, maintainable, and observable workloads on Kubernetes.
This article presents seven practical techniques—including using EXPLAIN, creating proper indexes, adjusting default settings, loading data into memory, leveraging SSD storage, scaling horizontally, and improving observability—to keep MySQL databases fast, stable, and responsive as workloads grow.
This article explains Qunar's transition from MySQL, Redis, and HBase to TiDB, detailing the background of distributed databases, TiDB's architecture, hardware selection, deployment automation, monitoring setup, and real‑world usage scenarios to address scalability and high‑availability challenges.
The article recounts Ele.me's rapid growth and the resulting operational challenges, describing how the company progressed from ad‑hoc 1.0 practices to a standardized, automated 2.0 infrastructure built on ZStack private cloud, fine‑grained operations, and data‑driven management to improve quality, efficiency, and cost.
This article details Gome Group’s transition to cloud computing and operations automation, describing its corporate background, new operational strategies, the establishment of Gome Cloud, IAAS product architecture, monitoring solutions, automation standards, and deployment practices such as gray releases and Docker integration.
The article presents a comprehensive approach to solving crash and performance issues in large‑scale mobile applications by reconstructing user interaction traces through a no‑track analytics platform, compile‑time AOP instrumentation, and unified data aggregation, ultimately improving debugging efficiency and reducing operational overhead.
The article details Ctrip's rapid business growth driving the need for elastic scaling, the adoption of container technology to achieve second‑level provisioning, the design of their container cloud platform—including deployment principles, network choices, orchestration evaluations, monitoring solutions, and the CDOS overview—providing practical insights for large‑scale cloud‑native operations.
This article presents Ctrip's deployment of a virtual cloud desktop system for its call center, detailing the OpenStack‑based architecture, advantages over traditional PCs, challenges encountered, the evolution to a decoupled design, resource over‑commit strategies, networking issues, and the operational tools and automated testing that ensure stability.
This article explains the benefits and drawbacks of performance optimization, distinguishes between single‑application and structural optimization approaches, outlines common steps, tools, and techniques for each, and presents case studies illustrating architectural evolution for improved scalability and stability.