This article analyzes the risks and ten typical problems of online applications, compares open‑source self‑built, hosted, and commercial tracing solutions, and explains which advanced diagnostics and dynamic features are essential for selecting the most suitable distributed tracing product.
This article chronicles the four‑year evolution of Baidu's Fengjing performance‑monitoring platform, detailing its data collection, processing pipelines, successive architectural versions (1.0‑4.0), challenges such as probe intrusion and massive data volume, and the engineering solutions that enabled large‑scale, low‑cost, cloud‑native observability for thousands of Java services.
This article explains what an APM system is, compares logs, traces and metrics, reviews popular tools, and then details the design and implementation of the in‑house Dog APM platform—including client data models, Kafka pipelines, processing pipelines, storage in ClickHouse/Cassandra, and UI visualizations.
The article reveals that while DevOps has exploded with trends like Hybrid Deployments, DataOps, and GitOps, the overlooked but critical shift toward non‑invasive production debugging—providing code‑level observability without disrupting services—will become essential for modern DevOps teams.
The article explores the rising DevOps trend of non‑invasive production debugging, explaining its advantages over traditional log‑based methods, detailing instrumentation techniques, showing code examples, and highlighting its impact on key DevOps metrics and industry adoption.
This article examines the challenges of monitoring complex micro‑service architectures, outlines the objectives of full‑link tracing, explains the Span/Trace data model, describes core functional modules, and provides a detailed performance and feature comparison of Google Dapper, Zipkin, Pinpoint, and SkyWalking.
The article discusses identifying and resolving ImageIO-related crash issues in iOS applications, particularly those occurring after iOS 14 updates, by analyzing crash logs and system behavior.
The article provides a comprehensive English overview of SkyWalking UI, detailing its dashboard metrics, topology visualization, trace analysis, performance profiling workflow, and alarm management, illustrating how the tool monitors microservice and cloud‑native environments with metrics such as throughput, latency, Apdex, and JVM statistics.
This guide walks through installing Apache SkyWalking, setting up an ElasticSearch storage cluster, configuring agents, launching services, and integrating DingTalk notifications for alarm handling, providing complete commands, configuration snippets, and troubleshooting tips.
From its 2016 debut to the present, Baidu’s Fengjing APM system has evolved through four major releases—moving from invasive jar‑based probes to non‑invasive bytecode agents, adding modular hot‑swap plugins, scaling to thousands of containers, handling billions of daily metrics via Kafka, Doris, and SIA TSDB, while solving probe upgrade downtime, data‑ingestion volume, and call‑graph query latency.
With micro‑service architectures generating complex call chains across thousands of servers, this article analyzes full‑link monitoring concepts, outlines essential requirements, details core components like spans and traces, and compares three major APM solutions—Zipkin, Pinpoint, and SkyWalking—evaluating probe impact, scalability, and data analysis capabilities.
The article describes a two‑part mobile app performance monitoring system that automatically instruments code to capture method execution times, ANR and frame stalls, then processes, cleans, aggregates, and visualizes the data on a backend platform to generate alerts, trend reports, and guide optimization across versions.
The article explains how Serverless is becoming the default cloud programming model, outlines the observability challenges it introduces, and details Alibaba Cloud Function Compute's evolution from basic logging to a comprehensive, open‑source‑compatible monitoring platform.
Facing challenges of distributed microservice architectures, Tencent’s Smart Retail team built a full‑link performance monitoring platform using Apache SkyWalking and the cloud‑native TSW service, detailing architecture, agent integration, deployment steps, and performance impact to guide similar observability implementations.
This article explains the fundamentals of full‑link (distributed) monitoring, describes its core components such as spans, traces and annotations, outlines typical system architecture, and provides a detailed performance and feature comparison of three popular APM solutions—Zipkin, SkyWalking, and Pinpoint.
The article explains the need for full‑link monitoring in micro‑service architectures, describes the core concepts of tracing such as spans and traces, outlines functional modules of APM systems, and provides a detailed comparison of three popular solutions—Zipkin, Pinpoint, and SkyWalking—covering performance impact, scalability, data analysis, developer transparency, and topology visualization.
The article explains the need for full‑link monitoring in microservice architectures, outlines its functional modules and design goals, details the core data structures of Google Dapper (Span, Trace, Annotation) with code examples, and compares three popular APM tools—Zipkin, Pinpoint and SkyWalking—across performance, scalability, analysis depth, transparency and topology features.
This article examines the challenges of monitoring distributed micro‑service architectures, outlines the requirements for a low‑overhead, non‑intrusive tracing system, describes the core concepts of Google Dapper (Span, Trace, Annotation), and provides a detailed functional and performance comparison of three popular APM tools—Zipkin, SkyWalking, and Pinpoint—covering probe impact, collector scalability, data analysis depth, developer transparency, topology visualization, and overall suitability.
This article explains how Youzan extended its APM framework with offline monitoring, built the AWACS visual tool, integrated Appium‑driven regression, instrumented method timing and network traffic via Gradle plugins, captured page rendering time, processed data in the backend, and created an issue‑management platform, outlining future enhancements.
This article examines the challenges of monitoring distributed micro‑service architectures, outlines the requirements for a full‑link tracing system, and provides a detailed comparison of three popular APM solutions—Zipkin, Pinpoint, and SkyWalking—covering performance impact, scalability, data analysis, developer transparency, and topology visualization.
This article explains the background of full‑link monitoring in micro‑service architectures, outlines the key requirements for tracing tools, describes core concepts such as spans, traces and annotations, compares Zipkin, Pinpoint and SkyWalking across performance, scalability, data analysis, transparency and topology features, and provides practical deployment guidance to help you select the most suitable solution.
This article examines the challenges of monitoring complex micro‑service architectures, outlines the goals and functional modules of full‑link tracing systems, explains Google Dapper’s core concepts such as Span, Trace and Annotation, and provides a detailed performance, scalability and feature comparison of three popular APM solutions—Zipkin, SkyWalking and Pinpoint.
This article outlines a comprehensive process for diagnosing and optimizing performance problems in production business systems, covering hardware, OS, database, middleware, JVM tuning, code inefficiencies, monitoring tools, and the limitations of pre‑release testing, with practical guidelines and visual references.
This article outlines a step‑by‑step approach for identifying root causes of performance bottlenecks in production business systems, covering common scenarios such as high concurrency, data growth, hardware limits, database and middleware tuning, code inefficiencies, and the role of monitoring and APM tools.
This article walks through the concepts, architecture, download, installation, agent setup, service startup, and alert configuration of Apache SkyWalking, an open‑source APM platform for cloud‑native microservices, including Elasticsearch integration and DingTalk notifications.
This tutorial walks through installing and configuring Apache SkyWalking, an open‑source APM system for micro‑services and cloud‑native environments, covering its architecture, Elasticsearch and InfluxDB storage setup, agent deployment, service startup, alarm integration, and essential documentation links.
This article details the background, system design, and successive optimizations of iQIYI's mobile Application Performance Monitoring (APM) network monitoring solution, covering SDK and backend architecture, DNS and weak‑network enhancements, gateway strategies, retry mechanisms, and the resulting significant reduction in error rates.
The iQIYI mobile APM system provides real‑time, user‑level network monitoring with classified error detection, cloud‑controlled SDK sampling, second‑level backend storage, and web dashboards, while employing DNS three‑layer caching, weak‑network grading, gateway multiplexing, super‑pipeline proxies and layered retry strategies, reducing Android error rates from 5.3 % to 0.48 % and iOS from 4.63 % to 0.35 %.
The article explains how newly founded development teams can implement inexpensive, real‑time exception monitoring using manual instrumentation, asynchronous alerting, unified log collection, and open‑source APM tools, providing practical code examples and guidance on essential alert information.
This article explains how to design and implement service‑chain (APM) monitoring and end‑to‑end process monitoring in distributed systems, covering concepts such as spans and traces, TRACE_ID generation, logging practices, visualisation techniques, and a practical expense‑report use case with code examples.
This article explains SkyWalking’s modular, protocol‑oriented and lightweight architecture, its core components, design principles, and advantages such as cross‑environment consistency, easy maintenance, high performance, and extensibility for both traditional and cloud‑native systems.
This article, excerpted from Wu Sheng’s "Apache SkyWalking实战", explains SkyWalking’s four‑core component architecture, its protocol‑oriented, modular, and lightweight design principles, and highlights its advantages such as seamless traditional and cloud‑native support, easy maintenance, high performance, and extensibility for secondary development.
In a mobile‑first world where limited device resources and unstable networks threaten user retention, Kuaishou’s performance team built an APM monitoring platform and applied systematic memory, startup, and jank optimizations that cut startup time by 40%, reduced package size by 23 MB, and significantly improved key product metrics.
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 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.
iQIYI boosted its mobile app’s network request success rate to 99.7% by implementing detailed monitoring, layered retry strategies, optimized timeouts, reduced concurrency, smarter compression, IPv4 preference, TLS 1.3, and connection‑racing techniques, and now targets 99.9% using multipath, QUIC and intelligent traffic scheduling.
This article introduces the background, core features, network and page performance modules, error monitoring, reporting, and optimization practices of a mobile App Performance Management (APM) system, highlighting its impact on monitoring, diagnostics, and performance improvement for large‑scale applications.
This article explains how cloud‑native applications can achieve full observability using the Elastic Stack by outlining the four essential steps—health checks, metrics, logs, and tracing—while discussing the underlying challenges, implementation patterns, and practical recommendations for reliable monitoring.
The talk explains how Elastic Stack can be used to achieve comprehensive observability for cloud‑native applications through a four‑step methodology—health checks, metrics, logging, and tracing—detailing the challenges, implementation details, and best practices for monitoring and debugging modern microservice systems.
To overcome native APM’s inability to monitor Flutter pages, the team created a high‑availability, open‑source Flutter SDK that records smoothness, load time, and exceptions through a four‑layer, publish‑subscribe architecture—API, Recorder, Processor, and Uploader—offering accurate, low‑overhead, extensible performance data for integration and future enhancements.
This guide surveys the most popular open‑source distributed tracing projects, classifying them by instrumentation, tracer, and analysis capabilities, and explains how they fit into modern microservice observability, helping newcomers understand each tool’s strengths, integrations, and the broader tracing ecosystem.
This article explains how the MaFengWo iOS app defined startup, measured key metrics such as launch duration and loss rate, and applied technical and user‑centered optimizations—including pre‑main and post‑main tweaks, fine‑grained interaction strategies, ad caching, and platform mechanisms—to cut launch time by half, lower loss rate by 30% and dramatically increase ad exposure.
This article introduces the Asynchronous Programming Model (APM) in C#, demonstrating how to use delegates and BeginInvoke/EndInvoke to run time‑consuming operations concurrently, includes sample code, explains delegate generation, and discusses practical considerations such as IAsyncResult handling and callback execution.
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.
MTHawkeye is Meitu’s iOS debugging and performance‑optimization toolkit that provides a three‑layer architecture—foundation, UI foundation, and plugin layers—to assist developers across development, testing, and production by offering memory leak detection, main‑thread profiling, CPU, network, graphics, storage monitoring, and integrated FLEX utilities.
This article provides a detailed, side‑by‑side comparison of SkyWalking 6.0.0‑GA and Pinpoint 1.8.2 across community activity, language support, protocols, storage options, UI, extensibility, alarm mechanisms, JVM and service monitoring, trace granularity, performance impact, release packaging and component compatibility, helping you choose the right APM solution for your needs.
This article lists and briefly describes the eleven most popular Java‑related GitHub repositories in December, covering learning guides, e‑commerce platforms, advanced Java topics, performance monitoring, high‑concurrency demos, diagnostic tools, Spring Boot, tutorials, messaging middleware, community software, and the Dubbo RPC framework.
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.
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 tutorial explains how to use Byte Buddy to build a JavaAgent for SkyWalking, debug and continuously integrate the agent, and develop custom SkyWalking plugins such as the kob scheduling framework, providing step‑by‑step code examples and configuration details for observability in Java backend services.
This article walks through a real‑world performance problem where an OpenAPI service sometimes responds in milliseconds and other times takes seconds, explains why adding logging is impractical, and demonstrates how to use Alibaba's TProfiler Java agent to profile the call chain, collect method timings, and analyze the results without modifying any source code.
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.
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.
Matrix‑ApkChecker, an in‑house APM tool used by WeChat, analyzes Android APKs to identify oversized files, redundant resources, unused assets, and other inefficiencies, providing detailed reports and enabling targeted optimizations such as removing duplicate files, converting PNGs to WebP, and stripping unused libraries, ultimately reducing app size by over 130 KB.
This article explores how to maximize Java application performance by defining measurable goals, using load‑testing tools like Gatling, monitoring with APM solutions such as Retrace, applying code‑level optimizations, and tuning JVM, database, and architectural settings for scalable, efficient systems.
This article compares probe‑based agents and wire‑data collection for application performance monitoring, detailing their architectures, advantages, drawbacks, and cost implications, and concludes which approach best supports modern, real‑time operational intelligence.
This article explains Application Performance Management (APM), its role in monitoring distributed and micro‑service systems, outlines the five‑dimensional APM model, details core monitoring functions, and reviews leading open‑source APM solutions such as PinPoint, Zipkin, SkyWalking, Prometheus, CAT and Hawkular.
This guide explains how to install, configure, and use the open‑source Pinpoint APM tool to monitor Java‑based Dubbo applications, covering environment preparation, downloading binaries, modifying configuration files, deploying collector and web components, installing agents, and adding startup parameters for both Tomcat and SpringBoot deployments.
This article explains how microservice architectures require a comprehensive monitoring system, covering data, resource, and code dimensions, and describes eight atomic monitoring scenarios such as URL, host, product, component, custom, resource, APM, and event monitoring to help engineers design effective observability solutions.
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.
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.
This article explains how to improve Java application performance by defining measurable metrics, using load‑testing tools like Gatling, monitoring with APM solutions such as Stackify Retrace, applying code‑level best practices, tuning JVM heap and garbage collection, optimizing JDBC access, and scaling the architecture.
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.
This article explains why traditional macro performance metrics are insufficient, introduces ten essential micro‑metrics covering memory, thread, and network aspects, and shows how to capture them using GC logs, thread dumps, and tools like netstat or open‑source APM solutions.
Pinpoint is a full‑stack, non‑intrusive tracing platform that visualizes service topology, active threads, request latency, and application health, and this article explains its architecture, data model, and step‑by‑step process for creating custom plugins—including ServiceLoader configuration, TraceMetadataProvider, and ProfilerPlugin implementations with code examples.
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.
This article shares practical insights from a senior network manager on why enterprises need Application Performance Management, how to visualize, automate, and intelligent‑ly monitor network infrastructure, define KPI metrics, select tools like Zabbix and ELK, and implement a custom APM platform to improve reliability and reduce operational friction.
Effective Node.js monitoring boosts competitiveness, user experience, and cost efficiency, and this guide outlines eleven key recommendations—from tracking downtime and response thresholds to linking performance with business metrics and leveraging third‑party APM tools—ensuring robust, noise‑free alerts and secure, scalable applications.
This article explores the challenges of locating performance bottlenecks in apps, browsers, networks, and servers, explains the evolution of Application Performance Management (APM) at a global DevOps conference, and introduces a full‑stack tracing solution that integrates client, mobile, and cloud monitoring.
The article explains how a layered application performance management solution—covering server resources, network links, application components, and business metrics—helps e‑commerce platforms detect and resolve high‑concurrency issues during peak shopping events such as Double‑11.
This article explains how Tencent's myAPM platform provides method‑level injection and monitoring for mobile apps, enabling automatic APK size analysis, lag detection, startup performance tracking, and core‑link analysis to help developers identify and eliminate unused code and performance bottlenecks.
This guide outlines the rapid evolution of cloud services and how developers can leverage cloud hosts, engines, data storage, APM, communication, security, and container platforms to boost efficiency, reduce operational costs, and seamlessly integrate these services into their projects.
Vipshop’s three‑tier monitoring system—covering system, application (Mercury), and business layers—collects and analyzes logs from distributed components, providing real‑time metrics, slow‑call detection, error tracing, and configurable alerts to help engineers quickly pinpoint and resolve performance issues.
After a major release, this article explains the most important Java performance metrics—response time, throughput, average load, error rate, GC pauses, business KPIs, uptime and log volume—and recommends tools to monitor them without relying on raw log files.
At GOPS 2015 Shanghai, over 800 attendees explored Operations 2.0 through keynote talks on lean, APM, DevOps, and intelligent operations, witnessed the launch of the Open Operations Alliance, and discussed future standards, best practices, and the human factor in automated infrastructure management.
