This guide explains why Linux is essential for software testers, outlines how to build test environments, use key commands like tail, grep, top, and vmstat for log analysis and performance monitoring, and offers tips for deeper Linux learning and interview preparation.
This article explains why front‑end error logging is essential, how BadJS (JavaScript runtime errors) are captured, enriched, and reported, and provides practical strategies for analyzing logs, handling Script errors, dealing with hybrid WebView environments, and scaling the system with big‑data pipelines.
This article examines why traditional network‑centric NPMD tools, agent‑based APM solutions, and their combination fall short of true end‑to‑end business performance monitoring, and argues that Business Performance Monitoring (BPM) using passive traffic mirroring offers the most complete, non‑intrusive full‑link visibility for application operations.
This article explains how to retrieve and interpret key MySQL performance metrics—including TPS, QPS, thread status, InnoDB I/O, buffer pool health, MyISAM statistics, temporary tables, response time, and replication lag—using mysqladmin, SHOW GLOBAL STATUS, and Percona tools, with practical calculation formulas and command examples.
When you log into a Linux server for performance troubleshooting, Netflix’s engineering team shows that running ten standard command‑line tools within the first minute gives a comprehensive view of system load, resource saturation, errors, and bottlenecks, enabling rapid root‑cause analysis.
The article defines MySQL response time as total request‑to‑reply latency, illustrates it with a travel analogy, and then details two monitoring solutions—Percona’s tcprstat utility that captures TCP‑level timings and the Query Response Time audit plugin that records latency distribution in INFORMATION_SCHEMA tables for analysis and Grafana visualization.
This guide explains why APM tools are essential for microservice architectures, introduces SkyWalking’s features and architecture, and provides step‑by‑step instructions for installing, configuring, and using SkyWalking with both Java and .NET applications, including multi‑service tracing visualizations.
This article explains the basics of Java agents, demonstrates how to use premain and agentmain to modify bytecode at load time or during execution, and provides practical examples with ASM to monitor method execution time and capture method parameters and return values in running Java processes.
This guide walks through dozens of Linux performance‑monitoring utilities—including top, perf, sar/ksar, mpstat, uptime, vmstat, pidstat, htop, atop, glances, nmon, pcp‑gui, collectl, and colplot—explaining what each tool measures, how to invoke it, key command‑line options, sample outputs, and where to find further documentation.
This guide explains how to use a variety of Linux performance tools—including top, perf, sar/ksar, mpstat, uptime, vmstat, pidstat, time, cpustat, htop, atop, glances, nmon, pcp‑gui, collectl and colplot—to monitor CPU, memory, disk, network and process activity with concrete command examples and visualisations.
This article explains the three main performance‑monitoring approaches—log‑based, agent‑based, and network‑traffic analysis—detailing their data‑collection methods, comparing deployment time, data completeness, impact, risk and scalability, and illustrating their differences with a real‑world failure case.
This article explains how the open‑source Java diagnostic tool Arthas can monitor method execution with the monitor command, describes each monitoring metric, shows how to configure the sampling interval, and demonstrates the impact on response time using a concrete code example.
This article compiles Arthas video tutorials and explains the four basic commands—JVM, sysprop, sysenv, and vmoption—detailing what they display, their differences from built‑in JVM tools, and where to find further resources for deeper JVM parameter tuning.
This article explains how the Arthas thread command can reveal detailed JVM thread information—including states, CPU usage, stack traces, and lock status—and provides a practical code demo to help Java developers monitor and diagnose performance issues effectively.
This article introduces Arthas, an open‑source Java diagnostic tool, explains its key features, demonstrates four practical troubleshooting scenarios with command examples, and shows how to start using it via an IDE plugin or direct download.
This article explains CPU fundamentals, distinguishes physical and logical cores, shows how to retrieve CPU details on Linux, defines CPU usage and load average, offers practical thresholds, and provides step‑by‑step troubleshooting techniques for high user‑mode CPU consumption.
This article explains how to instrument legacy Node.js back‑end projects with IstanbulJS, collect coverage data via memory sharing or file storage, and generate detailed reports, providing a practical solution for assessing system functionality without writing new unit tests.
This article introduces BPF/eBPF fundamentals, explains the bcc toolkit, provides installation steps for Ubuntu and CentOS, and demonstrates how to use bcc's dbstat and dbslower utilities to trace MySQL query latency and slow queries on Linux systems.
This guide walks you through the essential daily maintenance tasks for GaussDB T clusters, covering ETCD startup, cluster health checks, host resource monitoring, tablespace usage, abnormal wait events, log inspection, and common error troubleshooting with concrete commands and SQL examples.
This article explains how to implement method‑execution time monitoring on iOS by hooking objc_msgSend using fishhook and custom assembly, detailing the concepts of hooks, the dyld symbol‑rebinding process, and providing complete code examples for building the QiLagMonitor tool.
This article explains how to monitor iOS main‑thread lag using RunLoop observers and the QiLagMonitor tool, detailing RunLoop modes, observer activities, and providing Objective‑C code for creating semaphores, observers, and a background monitoring loop that captures stack traces when the main thread stalls.
The article explains how Baidu App’s smoothness monitoring distinguishes jitter and stutter, sets 30 ms and 70 ms thresholds for jank detection, and adopts the per‑second and stage‑converted jank rate metrics—derived from jank time—to provide intuitive, bounded measurements while avoiding FPS limitations.
The article describes a full‑stack performance monitoring system for WeChat Mini Programs presented by Niu Tifa, covering Mini Program architecture fundamentals, a monitoring system architecture using a JS SDK, Druid, Elasticsearch, and practical applications like load timing, error handling, fallback strategies, with dashboards and alerts, emphasizing low request volume and non‑intrusive monitoring.
This guide shows how to programmatically generate a JVM heap dump using HotSpotDiagnosticMXBean, retrieve the binary file, and analyze it offline with the jhat tool, including code snippets, usage notes, and visualization of memory usage.
This guide outlines essential Linux performance evaluation areas—CPU, memory, disk I/O, network I/O, and overall system load—detailing the primary commands (vmstat, free, iostat, ifstat, top), interpreting their output fields, and offering practical thresholds for diagnosing bottlenecks.
The iQIYI API Gateway, built on Kong/Nginx, offers a high‑performance, extensible entry point that centralizes API configuration, routing, authentication, rate‑limiting, and disaster‑recovery across regions, managing over 4,000 APIs and 30 billion daily requests while reducing operational complexity for micro‑service governance.
This guide introduces essential JVM performance monitoring tools—including jps, jstack, jmap, jhat, jstat, and hprof—explains their command syntax, demonstrates step‑by‑step usage with real‑world examples, and shows how to interpret their output to diagnose memory leaks, thread issues, GC behavior, and CPU hotspots.
This article shares the evolution and practical application of Node.js at WeDoctor, detailing its rapid adoption for internal efficiency tools, Vue server‑side rendering, API services, full‑stack frameworks, and ecosystem enhancements such as tracing and performance monitoring, illustrating how front‑end engineers can leverage Node.js to boost productivity and reliability.
This article explains the concept of response time (RT) for MySQL queries, illustrates how network delays and server load affect RT, and provides two practical monitoring methods—using Percona's tcprstat tool and the MySQL Query Response Time plugin—along with installation commands, sample outputs, and visualization tips.
This guide explains Elastic APM’s architecture—including agents, server, Elasticsearch, and Kibana—provides step‑by‑step instructions for deploying the APM server with Docker, configuring it, installing agents for various languages, and visualizing performance data in Kibana, enabling developers to monitor and troubleshoot application latency and errors.
This article provides a comprehensive guide to Elasticsearch’s architecture, node roles, data organization, and the most important performance metrics—including search, indexing, memory, JVM garbage collection, host‑level system metrics, cluster health, and resource saturation—offering practical advice on monitoring and tuning the cluster for reliability and efficiency.
This article provides a comprehensive tutorial on MySQL 8.0's Clone Plugin, covering its concepts, local and remote cloning procedures, required permissions, configuration steps, monitoring queries, and a detailed comparison with XtraBackup for fast data replication and recovery.
The article explains Baidu App’s weak‑network optimization strategy, detailing metrics such as httprtt, tcprtt, throughput and signal strength, describing active probing and passive collection for detection, defining thresholds, and outlining best‑practice techniques like QUIC activation, pre‑connection and composite connections that markedly improve success rates and reduce latency.
This article introduces 360’s iOS mobile performance monitoring solution, QDAS‑APM, detailing its background, monitored metrics such as page rendering time, main‑thread stalls, network errors, FPS, CPU, memory, crash and startup duration, and explains implementation techniques using KVO, runtime class traversal, and code snippets.
This article introduces the WAPM performance monitoring SDK for Android, detailing its four‑layer architecture, memory‑leak detection via LeakCanary, UI thread jank analysis using Looper callbacks, network request monitoring through framework hooks, and page load timing via activity and view lifecycle instrumentation.
This article walks through key Linux performance monitoring tools—top, vmstat, pidstat, iostat, sar, netstat, and tcpdump—explaining their output fields, how to interpret CPU, memory, disk, and network metrics, and how to use them for effective system troubleshooting.
An InnoDB storage engine sits above the file system and below SQL tools, providing transactions with redo/undo logs, row and table locking, MVCC, B+‑tree and adaptive hash indexes, compression, encryption, checkpointing, multi‑threaded buffering, backup and replication mechanisms, and extensive performance‑monitoring commands.
The article introduces Aspect‑Oriented Programming, explains its core concepts and compile‑time versus runtime models, and demonstrates how Android developers can employ compile‑time AOP (using AspectJ) to implement method‑level hot‑fixes, performance monitoring, logging, and other cross‑cutting features while weighing benefits, overhead, and optimization strategies.
Bianque, TDSQL’s intelligent operation platform, automatically collects and indexes database metrics, applies a knowledge‑base‑driven analysis engine to diagnose availability, performance and reliability issues, issue risk warnings and optimization recommendations, dramatically cutting DBA effort and support tickets across Tencent’s cloud services.
This article explains how the Android Monkey tool can be used to perform automated stress testing on fragmented Android devices, covering its purpose, installation locations, key command‑line parameters, a complete example command, and practical steps for integrating Monkey into a mobile testing workflow.
This article introduces a curated selection of Alibaba’s open‑source and cloud‑based developer tools—including Arthas, Cloud Toolkit, ChaosBlade, ARMS, Docsite, Freeline, EasyExcel, Druid, Dragonwell and more—detailing their use cases, tutorials, and acquisition methods to help developers improve efficiency and code quality.
Apache SkyWalking, an open‑source observability platform that originated in 2015, has graduated to a top‑level Apache project, offering comprehensive APM features such as distributed tracing, metrics, service topology, root‑cause analysis, and flexible storage options for cloud‑native microservice environments.
This article explains Xianyu's comprehensive Flutter performance monitoring system—including frame‑rate tracking, page‑load timing, and exception rate metrics—detailing the underlying principles, implementation steps, and real‑world results that help maintain a smooth user experience at massive scale.
The article examines how traditional synchronous and semi‑synchronous API gateways can fail under high load by analyzing CPU utilization, disk I/O, and network latency, and offers practical monitoring metrics and mitigation strategies to prevent cascading failures in large‑scale systems.
Matrix for iOS/macOS is an open‑source performance probing tool from the WeChat team that monitors crashes, jank, and memory issues on iOS and macOS, offering plugins for detailed stack capture and out‑of‑memory reporting, with future plans for power‑consumption tracking and cloud integration.
Pfinder is a new tool that combines problem finder and performance finder functionalities to help users visualize call relationships and time consumption distributions for efficient problem localization in distributed systems.
This guide walks through the background, installation, configuration, and usage of the open‑source Slowquery tool—integrating Percona pt‑query‑digest, Soar, and email alerts—to automate MySQL slow‑query monitoring and performance recommendations.
The iQIYI PC Web NodeJS middleware aggregates multiple backend APIs behind a single endpoint, using an nginx‑Varnish‑Node stack and a Koa‑based framework to stitch, cache, and transform data, while comprehensive logging, alerting, and system monitoring ensure 99.99% stability and faster front‑end development.
The article presents Alibaba's experience in large‑scale data‑center performance monitoring, describing the challenges of software and hardware upgrades, the SPEED platform’s estimation‑evaluation‑decision workflow, the RUE metric, and practical insights such as hyper‑threading effects, hardware heterogeneity, and Simpson’s paradox.
This article explores how Alibaba tackles the challenges of performance monitoring and analysis in massive data centers, introducing the SPEED platform’s Estimation‑Evaluation‑Decision‑Validation workflow, the RUE metric, hardware heterogeneity issues, and practical lessons such as hyper‑threading pitfalls and Simpson’s paradox.
The article describes the architecture, core functions, and workflow of a MySQL slow‑query analysis system built for the CloudDB platform, covering both daily report generation and real‑time slow‑SQL monitoring using tools such as pt‑query‑digest, ELK, and Kafka.
The article summarizes the 58 Group technical salon where experts from Tujia.com, Kingsoft Cloud, and 58 Group shared their experiences on building block‑based database automation systems, cloud database architectures, high‑availability designs, self‑service platforms, and intelligent operation practices for large‑scale database services.
The article explains how the Server‑Timing HTTP header can be used to transmit arbitrary server‑side metrics to the client, shows how to send multiple timing values, demonstrates Chrome DevTools support, warns about exposing sensitive data, and introduces a small hapi plugin that simplifies adding the header.
IOCanary is a non‑intrusive Android tool that hooks file I/O APIs and StrictMode to collect operation details, detect main‑thread blocking, small buffers, duplicate reads, and closeable leaks, then reports them for performance analysis and debugging.
This article explains what APM (Application Performance Management) is, its core concepts, how Java agents collect performance data, the process of integrating APM into distributed systems, and how it helps locate slow interfaces and improve overall application reliability.
This article explains why the traditional %CPU metric can be deceptive, describes how CPU stalls and memory bottlenecks affect perceived utilization, and offers practical guidance on using IPC and performance counters to obtain a more accurate view of system performance.
Matrix is Tencent’s open‑source Android APM that unifies performance monitoring, offering tools like APK Checker, SQLite Lint, Trace Canary, Resource Canary, and IO Canary to detect issues such as leaks, slow methods, and I/O problems, helping developers build higher‑quality apps.
This article details the architectural design and engineering practices behind building a comprehensive Application Performance Management dashboard for Android applications, covering real-time monitoring, version comparison, development cycle tracking, automated data collection, and integrated test coverage analysis to ensure sustainable software quality and delivery efficiency.
The article summarizes the fourth 58 Group technical salon held on November 28, 2018, where experts from the Listening Cloud AI & Cloud Platform and 58 TEG Wireless Architecture shared practical experiences on mobile APM performance monitoring, startup optimization, SDK quality assurance, logging, and related code implementations.
With micro‑service architectures causing requests to span dozens of services across multiple teams and data centers, this article explains APM fundamentals, details Google’s Dapper tracing model, and compares three popular APM solutions—Zipkin, Pinpoint, and SkyWalking—highlighting performance impact, scalability, data analysis depth, developer transparency, topology visualization, and community support.
The Lingjing system, built by Alibaba Infrastructure, provides an end‑to‑end hardware‑software performance diagnosis platform that collects fine‑grained metrics, visualizes data, automatically detects anomalies, and helps optimize resource utilization across complex data‑center stacks.
This article describes how a video SDK team implements automated UI testing, collects CPU and memory usage data, stores it in a database, provides backend APIs, and visualizes the performance trends on a front‑end dashboard to assess SDK stability.
This article presents a comprehensive catalog of mobile testing tools and techniques, covering Android and iOS platforms, including UI automation frameworks, unit testing libraries, performance monitoring utilities, network simulation tools, static analysis solutions, code coverage options, cloud testing services, and continuous integration pipelines.
This talk explains how to diagnose MySQL performance issues by combining traditional MySQL slow‑query analysis with system‑level observation tools, introducing eBPF, bcc scripts, and practical examples to pinpoint bottlenecks across the database, OS, and hardware layers.
This article explains how to use the Tampermonkey browser extension to interact with web pages, retrieve performance data, manipulate DOM elements, and automate testing tasks such as sign‑in assistance, event inspection, and cross‑origin requests, thereby improving testing efficiency and coverage.
This article continues the Elasticsearch performance monitoring series by detailing host‑level system and network metrics, cluster health and node availability, resource saturation, and related errors, providing practical guidance on disk space, I/O, CPU, network throughput, file descriptors, HTTP connections, thread pools, caches, pending tasks, and failed GET requests.
This article compiles and explains a set of Linux command‑line utilities—including uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar and top—showing how to interpret their output to quickly identify CPU, memory, I/O, and network performance issues, with practical examples and key columns to monitor.
This article explains why monitoring page performance is crucial for user experience and SEO, outlines the design of a three‑part monitoring system—including front‑end data reporting via Navigation Timing, server‑side log collection with Nginx, data aggregation, sampling, storage, and visual dashboards—providing a complete end‑to‑end solution.
This article explains why manufacturing firms need cross‑level real‑time performance metrics, outlines key enterprise roles, presents reference architectures for system integration, MES functions, event‑driven mechanisms, communication protocols, and data collection and aggregation components.
The article introduces a Java‑based log instrumentation tool that leverages AOP and the Javassist library to transparently capture interface input, output, and performance metrics, enabling richer test case generation, mock creation, and long‑term performance monitoring for backend services.
This article walks through a practical Linux performance investigation, showing how to use the top command to pinpoint high‑CPU processes, examine thread details, convert thread IDs, analyze thread dumps for lock contention, and interpret key top output fields for effective troubleshooting.
This article explains the jstat command syntax, options, and detailed parameter descriptions for monitoring JVM class loading, memory usage, garbage collection, JIT compilation, and other runtime statistics, providing examples and visual tables to help developers analyze Java process performance.
This article describes Qunar's approach to quantifying, automatically detecting, and optimizing performance issues in React Native mobile apps by defining measurable metrics, real‑time data sampling, automated analysis rules, and visual feedback to enable proactive APM during development and testing.
This comprehensive guide explains how to monitor and tune service performance by examining CPU load, system and JVM memory usage, buffer/cache concepts, key performance metrics such as response time, throughput, QPS, and provides essential Linux tools and commands for effective operations management.
The article highlights Brendan Gregg’s updated Linux performance talk, presenting three concise visual guides that categorize the most reliable, lightweight tools for monitoring, testing, and optimizing various Linux subsystems, helping practitioners quickly select the right utilities for effective system performance management.
The article explains the concept, functions, design goals, data models, log collection, and deployment considerations of distributed tracing systems, and compares several open‑source and proprietary solutions such as Dapper, Zipkin, Pinpoint, Alibaba Eagle Eye, and JD Hydra to guide the selection of an appropriate tracing platform.
The article describes the background, architecture, and implementation details of a non‑intrusive Android SDK that monitors UI‑thread stalls, collects performance data, aggregates it on the server, and automatically generates work orders to help developers pinpoint and resolve lag issues efficiently.
The article describes the evolution of Ctrip's wireless APM platform from the early UBT-based monitoring to a globally‑oriented, metric‑rich system that processes over 100 billion data points daily using Storm and Elasticsearch, detailing its design, key performance dimensions, data‑volume trade‑offs, and implementation choices.
When troubleshooting a Linux server, this guide shows the ten essential command‑line tools—uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar, and top—to quickly assess CPU, memory, disk, and network health within the first sixty seconds, helping you identify saturation and bottlenecks.
This article expands on Elasticsearch performance monitoring by detailing host‑level system and network metrics, cluster health and node availability, resource saturation, thread‑pool behavior, cache usage, and common error indicators, offering practical guidance for maintaining a stable and efficient search cluster.
This article outlines fifteen key DevOps metrics—ranging from deployment frequency and lead time to MTTR and availability—that help organizations assess and improve their continuous delivery speed, quality, and overall system performance over time.
This article describes the design, challenges, and operational practices of a cloud‑native elastic computing platform that reuses idle resources from production servers to support massive image compression, video transcoding, AI inference, and log processing while ensuring online services maintain performance, latency, and reliability.
This article explains how 360 Mobile Guard, a massive Android security app built on the RePlugin framework, implements a comprehensive performance monitoring system—including offline code checks, Argus APM tools, online crash and metric collection, diagnostic plugins, and future AI-driven analysis—to identify and resolve performance issues and improve user experience.
The article shares practical insights on planning, designing, developing, testing, and rolling out a large‑scale backend system refactor, emphasizing resource limits, pain points of legacy code, unit‑test protection, layering, decoupling, monitoring, and staged deployment to ensure stability during high‑traffic events.
This article describes the design and implementation of a non‑intrusive Android SDK that monitors main‑thread UI lag by replacing the Looper printer, sampling stack traces, aggregating data on the server, and automatically generating work orders for precise performance optimization.
Suning’s 2017 O2O shopping festival achieved a “zero‑incident” goal by integrating real‑time browser performance monitoring, WEEX‑based WAP acceleration, comprehensive app data collection with cloud‑based analytics, precise DNS and HTTP2 optimizations, and a multi‑layer network and service monitoring system that enabled rapid fault detection and capacity planning.
This guide walks through key Linux commands—uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar, and top—explaining their outputs and how to interpret them for effective system performance analysis and troubleshooting.
This article shares practical methods for frontline engineers to quickly understand, assess, and resolve online system problems by categorizing system layers, evaluating impact, using essential Linux monitoring tools, and applying systematic troubleshooting and design‑for‑failure strategies to minimize downtime.
This article outlines a systematic methodology for understanding, evaluating, and quickly resolving production system incidents by categorizing system layers, assessing impact, employing Linux diagnostic tools, and designing fault‑tolerant mechanisms to minimize downtime and maintain core functionality.
This article presents a collection of straightforward, developer‑friendly techniques for enhancing Java API security and performance, covering API key protection, TLS adoption, Spring Boot web service creation, application monitoring, and safeguarding sensitive configuration files.
This article presents a systematic methodology for diagnosing and resolving online system issues, covering system understanding, impact assessment, rapid recovery techniques, detailed troubleshooting steps with Linux and Java tools, and design principles to mitigate future failures.
This guide explains how to use Linux performance monitoring utilities such as top, vmstat, pidstat, iostat, sar, netstat, and tcpdump, describing each tool's output fields and showing how to interpret them for effective troubleshooting of CPU, memory, disk, and network issues.
This article describes LinkedIn's network‑level MySQL query analyzer, explaining why it was needed, its three‑component architecture (agent, central server, UI), fingerprinting and hash‑map techniques, performance benchmarks, metric collection, security considerations, and the benefits it brings to database operations.
This article explains the role of APM in automated operations, outlines the challenges of manual thread diagnostics, and presents a one‑click thread analysis solution with a five‑stage architecture—capture, aggregation, storage, query, and deep analysis—illustrated through multiple real‑world scenarios.
The article introduces Wang Yaobo’s presentation on Ctrip’s "real‑time handheld testing" technology, explaining how to monitor key performance metrics, capture crashes and stalls in authentic environments, and pinpoint problematic code lines to improve Android app quality.
This article provides a comprehensive guide to Linux performance monitoring, covering essential tools such as vmstat, iostat, dstat, iotop, pidstat, top/htop, netstat, ps, strace, perf, and advanced tracing utilities, with usage examples and interpretation of their output.
This article details the end‑to‑end process of designing and deploying an operations‑focused monitoring framework, covering semantic, log, and performance monitoring, protobuf integration, implementation challenges, actual effects, and future optimization directions for large‑scale online services.
This article details the design and implementation of an iOS performance‑monitoring SDK that captures fundamental metrics such as CPU usage, memory consumption, frame rate, cold and hot startup times, and power draw, explains the underlying Mach APIs, provides sample Objective‑C code, and discusses practical considerations for accurate measurement and troubleshooting.
The article examines the challenges of selecting hardware and monitoring tools, comparing Intel CPUs on performance versus price, and outlines criteria for evaluating bypass‑monitoring products, emphasizing cost, reliability, scalability, and data‑decoding capabilities to guide informed operational decisions.
This article provides a comprehensive guide to Linux performance analysis, covering background concepts, detailed explanations of tools such as vmstat, iostat, dstat, iotop, pidstat, top/htop, mpstat, netstat, ps, strace, lsof, perf, and sar, along with practical command examples and usage tips.
