This article introduces DingTalk’s self‑built ANRCanary, explains the complete Android ANR lifecycle, details how it captures and aggregates main‑thread tasks, filters false positives, and provides concrete code‑level insights to help mobile developers pinpoint and resolve ANR issues efficiently.
To cut Baidu APP Android’s live‑stream startup from over three seconds to about 1.3 seconds, engineers introduced fine‑grained monitoring, switched first‑entry to a lightweight level‑1 plugin, pre‑started the player, scattered initialization tasks, prioritized first‑frame delivery, and balanced plugin pre‑download with full‑load performance.
The article proposes a full‑stack H5 containerization framework for mobile apps that boosts user experience and development efficiency by pre‑heating WebViews, using offline packages and parallel data loading to cut load times, adding comprehensive performance monitoring, a modular native‑plugin system, and a configuration‑driven shell‑container factory that frees frontend teams from mobile‑team dependence.
This article introduces the open‑source Nginx‑GUI tool, explains its configuration‑management and performance‑monitoring features, and provides a step‑by‑step tutorial—including download, file edits, Linux‑specific renaming, and server deployment—complete with code snippets and screenshots for users.
This article from Netflix's performance engineering team outlines ten standard Linux command‑line tools and the USE method to quickly assess system health, focusing on error and saturation metrics before utilization, enabling rapid diagnosis of CPU, memory, disk, or network bottlenecks within the first minute.
This article introduces the open‑source Pinpoint APM tool for Java micro‑services, explains its architecture and data model, demonstrates deployment options for Tomcat and SpringBoot, and provides a step‑by‑step guide to installing and configuring the Pinpoint PHP agent for end‑to‑end performance monitoring.
This article provides a comprehensive guide to Linux performance monitoring, covering essential tools such as vmstat, iostat, dstat, iotop, pidstat, top/htop, mpstat, netstat, ps, strace, perf and sar, with usage examples, command syntax, and explanations of the metrics they report.
This article presents a complete solution for routing Sentry error and performance data to the correct micro‑frontend projects in a Qiankun architecture by intercepting transport, redistributing URLs, and distinguishing transaction types, with detailed code examples for both Sentry 6.x and 7.x versions.
This article analyzes full‑link monitoring in micro‑service architectures, outlines the goals and functional modules of tracing systems, explains core concepts such as Span, Trace, and Annotation, and then compares Zipkin, SkyWalking, and Pinpoint across performance impact, scalability, data analysis depth, developer transparency, and topology visualization.
This article explains how to monitor Linux system performance by covering CPU metrics with top and vmstat, memory usage via top columns and cache details, I/O health using iostat and zero‑copy techniques, as well as network statistics with sar, providing practical commands and interpretation guidance.
This article examines the challenges of full‑link monitoring in microservice architectures, outlines the goals for an effective tracing system, describes the four core functional modules, compares three popular APM solutions—Zipkin, Pinpoint, and SkyWalking—across performance, scalability, data analysis, developer transparency, and topology features, and clarifies the distinction between tracing and general monitoring.
This article explains why manual timestamp logging is cumbersome, introduces Spring's org.springframework.util.StopWatch and Apache Commons Lang's StopWatch, provides Maven dependencies, detailed usage examples, key methods, and important cautions for accurate performance measurement in Java applications.
This article explains how ByteDance measures website performance, introduces key front‑end performance metrics such as FP, FCP, LCP, TTI, TBT, FID, and MPFID, describes the APIs and code needed to collect them, and discusses how to evaluate and improve overall site performance using these indicators.
This article walks through setting up real‑time performance monitoring by sending JMeter metrics to InfluxDB via Backend Listener, visualizing them in Grafana, and extending the approach to system metrics with node_exporter, Prometheus, and Grafana, covering configuration steps, code snippets, and query examples.
This article explains how to assess Linux system performance by examining CPU usage with top, interpreting load averages, using vmstat for detailed metrics, monitoring memory consumption via top, understanding cache behavior, evaluating I/O performance with iostat and sar, and provides practical commands and visual examples for each component.
When a Linux server shows performance issues, the Netflix performance engineering team recommends a set of ten standard command‑line tools—uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar (network and TCP), and top—to quickly assess system load, errors, resource saturation, and utilization within the critical first minute of investigation.
This comprehensive guide walks you through the entire front‑end performance optimization lifecycle—from understanding why performance matters and setting measurable goals, to building metrics, implementing concrete strategies across caching, networking, rendering, and finally validating results with monitoring and retrospectives.
This article explains how to use Spring's org.springframework.util.StopWatch and Apache Commons Lang3 StopWatch to accurately measure and analyze Java code execution time, covering setup, basic usage, advanced features, thread‑safety considerations, and practical code examples for both libraries.
This guide walks you through the ten essential Linux command‑line tools that Netflix’s performance team uses to quickly assess system health, focusing on error and saturation metrics before utilization, so you can pinpoint and resolve server issues within the critical first minute.
This article explains why traditional pprof sampling often fails in production, introduces Go's built‑in profiling tools and runtime.pprof, defines practical rules for triggering automatic sampling based on resource spikes, and demonstrates using the open‑source Holmes library and Docker demo to collect self‑diagnostic profiles.
This article explains what eBPF is, why it was created, its core characteristics, common use cases such as network optimization, fault diagnosis, security control and performance monitoring, and provides practical step‑by‑step guidance, tooling commands, program types, and ecosystem resources for leveraging eBPF in cloud‑native environments.
This article explains how to leverage Spring's org.springframework.util.StopWatch and Apache Commons Lang3 StopWatch for precise Java performance measurement, covering setup, usage examples, source code insights, important considerations, and practical code snippets for both libraries.
This article explains the background, design, and core components of Havok—a full‑link load‑testing platform that replays production logs, supports traffic scaling, mock services, real‑time monitoring, isolation, and circuit‑breaker protection—to help enterprises evaluate capacity and improve reliability without polluting live data.
This article explains how iOS RunLoop can be leveraged for thread keep‑alive, controlled termination, lazy image loading, various lag‑detection techniques, crash protection strategies, and demonstrates its integration within the open‑source Matrix framework, providing concrete Swift and Objective‑C code examples.
eBPF is a kernel‑level sandbox technology that enables safe, high‑performance, programmable instrumentation for networking, security, and observability, and this article answers seven key questions covering its definition, applications, origins, usage steps, implementation details, best practices, and current ecosystem.
This guide introduces Sentry's open‑source error and performance monitoring platform, outlines how to set up and extend the service, and details its event‑processing architecture, including Relay, Redis caching, Symbolicator, and alert handling via Celery and Kafka.
This technical guide explores Android out‑of‑memory crashes by classifying OOM into thread‑count, file‑descriptor, and heap‑memory issues, then details non‑intrusive thread and thread‑pool optimizations, file‑descriptor and I/O monitoring, image compression strategies, and both Java and native memory‑leak detection techniques.
This article explains Linux’s storage I/O stack, defines the five core performance metrics—utilization, saturation, IOPS, throughput, and response time—and demonstrates how to monitor them using tools such as iostat, pidstat, and iotop, with practical command examples.
This article outlines the evolution of Meituan’s Database Autonomy Service (DAS), describing the growing scale‑vs‑operations imbalance, the strategic roadmap for self‑service and AI‑driven diagnostics, detailed architectural designs across data collection, compute/storage, and analysis layers, and the measurable outcomes and future plans for full database autonomy.
The Database Autonomy Service (DAS) is a platform that uses big‑data, machine‑learning, and expert knowledge to automatically collect, compress, and analyze MySQL metrics, providing self‑service fault detection, root‑cause diagnosis, and security management, thereby reducing manual effort, shortening MTTR, and supporting Meituan’s rapid database growth.
This tutorial walks through the end‑to‑end setup of JMeter performance testing data collection using Backend Listener, sending metrics to InfluxDB, and visualizing real‑time TPS, response time, and error rates in Grafana, as well as monitoring OS metrics with node_exporter, Prometheus, and Grafana.
This article walks through the most common performance‑monitoring stack—JMeter, node_exporter, Prometheus, InfluxDB, and Grafana—explaining how to configure backend listeners, send metrics, store them, and build real‑time dashboards while highlighting code snippets and query examples.
MetricKit, introduced in iOS 13 and expanded in iOS 14, provides a low‑overhead, system‑driven way to collect app performance, power, and crash diagnostics over the last 24 hours, enabling developers to upload, analyze, and optimize their iOS applications without third‑party SDKs.
This article explains how to use Flink's Web UI, Kafka metrics, and YARN monitoring to observe performance, diagnose backpressure, and set alert thresholds, providing code examples and practical tips for reliable stream processing in production environments.
The article describes how a three‑day, cross‑team investigation of a mini‑program image‑upload failure led to the design and implementation of an SRE‑driven, metrics‑driven observability platform that quantifies SLIs, automates tracing, and provides dashboards for real‑time and long‑term analysis, ultimately reducing MTTR.
The author explains how they distinguished test automation from automated testing, identified monitoring pain points, built a custom scraper‑driven alert system with three escalation levels, tackled common pitfalls, and achieved faster, more reliable performance testing alerts.
The DeWu Tech Salon, co‑hosted by DeWu App Quality Platform and TesterHome, brought senior engineers from Alibaba Cloud, ByteDance, Lagou and DeWu together to share practical QA insights on end‑side monitoring, traffic replay, full‑link stress testing, and industry‑scale chaos engineering, while announcing a PPT collection, a testing‑expert recruitment drive, and a preview of the next wireless‑technology salon.
This article provides a comprehensive reference of essential Linux commands and procedures, covering system shutdown and reboot, hardware and environment inspection, file and directory management, user and permission handling, networking utilities, performance monitoring tools, and common text processing techniques for administrators and developers.
This article compiles a comprehensive set of Linux command‑line shortcuts covering system shutdown, hardware inspection, user management, networking, file handling, compression, performance monitoring, and many other routine administration tasks, providing a handy reference for developers and sysadmins alike.
This guide explains how to monitor Linux server performance using built‑in tools such as top, vmstat, pidstat, iostat, netstat, sar and tcpdump, detailing the metrics each provides and how to interpret them for CPU, memory, disk I/O and network troubleshooting.
This guide explains how to measure the performance impact of the Sentry SDK on web servers by using the open‑source Sentry SDK Benchmark suite, which runs baseline and instrumented versions of TechEmpower framework tests inside Docker Compose and reports latency and resource usage.
bpftrace is a Linux eBPF‑based tracing tool that compiles scripts to BPF bytecode, offering kernel and user‑space probes; the guide covers its purpose, installation on major distros and Docker, and demonstrates built‑in example scripts for command logging, I/O latency, disk size, CPU usage, and TCP connection tracing.
This guide explains how to use core Linux tools such as top, vmstat, pidstat, iostat, sar, netstat, and tcpdump to monitor CPU, memory, disk I/O, and network performance, interpret their metrics, and troubleshoot common bottlenecks on servers.
This guide explains how to monitor Linux server performance using built‑in tools such as top, vmstat, pidstat, iostat, netstat, sar and tcpdump, interpreting their output to diagnose CPU, memory, disk I/O and network issues quickly and effectively.
This article documents a system crash triggered by a memory‑leak in a network‑chip SDK, explains how low free memory and the kswapd process lead to deadlock, and shows how adjusting the kernel parameter min_free_kbytes can prevent the freeze while highlighting the importance of resource monitoring and tuning.
This article explains Linux process states, how the kernel calculates load average, the relationship between load and CPU utilization, and provides a systematic approach with tools and commands to identify resource bottlenecks, differentiate high‑load/high‑CPU scenarios from high‑load/low‑CPU cases, and pinpoint problematic processes and threads.
This article explains how APM provides frame‑rate data for Android, discusses the challenges of inaccurate FPS, introduces metrics such as scroll FPS, frozen‑frame ratio, scrollHitchRate and frame‑cause analysis, details the rendering pipeline, code implementations, optimization techniques, and integration with AB testing for performance improvement.
This article presents a comprehensive framework for identifying, classifying, and resolving iOS stability issues—covering crash types, governance methodology, deep-dive attribution techniques, real-world case studies, and practical tools such as Zombie monitoring, Coredump, MemoryGraph, and MetricKit—to dramatically improve app reliability.
This guide traces the evolution of Sentry’s performance‑monitoring SDK, explains how transactions and spans were added, and details the identified issues—such as scope propagation, span‑ingestion modeling, nested transactions, and serialization challenges—along with the lessons learned for future improvements.
This guide explains how to enable distributed performance tracing in the Sentry SDK by configuring the tracesSampleRate or tracesSampler options, details the sampling decision process, describes the required SDK API changes, span and transaction handling, and the propagation of trace and sentry‑trace headers across services.
This article presents a comprehensive guide on systematically managing iOS stability problems, covering issue classification, a governance methodology, detailed root‑cause analysis for crashes, watchdogs, OOM, CPU and disk I/O anomalies, and practical tools and case studies from ByteDance’s APM platform.
This comprehensive guide walks you through Linux performance monitoring tools—top, free, and vmstat—detailing their syntax, key options, real‑world output examples, and how to interpret CPU, memory, swap, and process statistics for effective system administration.
This guide explains how to identify CPU, memory, storage, USB, network, and GPU bottlenecks on a Linux PC using only open‑source graphical tools such as GNOME System Monitor, HardInfo, atop and nethogs, providing clear visual cues and step‑by‑step instructions.
This article explains how to set up end‑to‑end performance monitoring by sending JMeter metrics to InfluxDB via Backend Listener, visualizing them in Grafana, and similarly collecting system metrics with node_exporter and Prometheus, covering configuration, data storage, query examples, and practical visualization techniques.
This article introduces a lightweight, cross‑platform SDK for mini‑programs that unifies log collection—including exception, normal, and performance data—by intercepting App, Page and platform‑specific APIs, handling multi‑environment differences, and providing flexible usage via npm or single‑file integration.
This article explains step‑by‑step how to collect JMeter test metrics via Backend Listener, store them in InfluxDB, and display real‑time performance charts—including TPS, response time, and error rates—in Grafana, while also covering node_exporter integration with Prometheus for system‑level monitoring.
This article walks through practical iOS startup optimization, covering monitoring strategies, non‑intrusive instrumentation, development‑stage testing, essential tools like Time Profiler and System Trace, and a comprehensive set of best‑practice tactics to shrink launch time and maintain performance.
The MemCorruption tool, developed by ByteDance's AppHealth team, provides an online, low‑overhead solution for detecting Use‑After‑Free, Double‑Free, and Heap‑Buffer‑Overflow problems in Android applications by hooking memory allocation functions, sampling allocations, and performing invisible SIGSEGV‑based detection.
This article provides a comprehensive collection of essential Linux command-line instructions covering system shutdown and reboot, hardware and process inspection, file manipulation, networking utilities, performance monitoring, and common administrative tasks, all organized for quick reference by developers and system administrators.
This article explains the necessity, design principles, and concrete implementation of a full‑chain tracing system for Node.js applications, covering architecture choices, industry solutions, core Async Hooks concepts, the zone‑context framework, invoke‑tree construction, garbage collection, and practical demo code.
This article explains how resource overflow such as memory out‑of‑memory (OOM) and file‑descriptor (FD) exhaustion occurs in Android applications, demonstrates how to identify the root causes using MARS‑APM (APMPlus), and provides practical steps to resolve these crashes.
This guide explains step‑by‑step how to configure JMeter’s Backend Listener to send metrics to InfluxDB, set up Prometheus and node_exporter, and create Grafana dashboards for real‑time TPS, response time, and system resource monitoring.
Distributed tracing in Sentry captures interactions across services to provide a unified view of errors and performance, explaining the concepts of traces, transactions, spans, data sampling, consistency handling, and practical examples such as diagnosing slow page loads.
This guide walks you through installing, configuring, and running the open‑source Nginx‑GUI tool on a Linux server, covering download, property file edits, Linux‑specific renaming, permission setup, startup commands, and the features currently supported versus pending.
This article explains how to acquire full‑chain tracing information in Node.js applications by leveraging Async Hooks to build an invoke tree, manage context propagation, perform garbage collection, and attach custom metadata, enabling precise debugging of complex asynchronous call graphs.
The article introduces Tencent Cloud’s Real‑User‑Monitoring platform, explains key performance timing points and metric calculations, links RUM data to Google Web Vitals, and demonstrates a data‑driven optimization case that cut first‑screen load from 4.8 s to 3.2 s, halved resource load time, and dramatically improved CLS.
This article examines Youku's technical team's systematic approach to improving video playback by tackling slow start‑up, error handling, network stalls, audio‑video sync, and overall stability through metric‑driven optimization, adaptive streaming protocols, device capability databases, and forward‑looking features like low latency and high‑frame‑rate streaming.
The article details a full‑link tracing system for Node.js that leverages experimental async_hooks to monitor asynchronous resource lifecycles, builds an invoke‑tree to map parent‑child relationships, implements garbage collection, and provides a ZoneContext API for propagating custom tracing data across async call chains.
When a Linux server shows performance problems, the first 60 seconds are critical; using ten standard command‑line tools such as uptime, dmesg, vmstat, mpstat, pidstat, iostat, free, sar, and top lets you quickly assess load, memory, I/O, and network health to pinpoint the root cause.
As micro‑service architectures grow, tracing every request across thousands of services becomes essential; this article examines the need for full‑link monitoring, outlines core requirements and functional modules, explains Google Dapper’s Span/Trace model, and provides a detailed performance‑focused comparison of Zipkin, SkyWalking, and Pinpoint.
This article explains how Linux manages disks and file systems through inodes, directory entries, logical blocks, the Virtual File System layer, various I/O models, and practical performance monitoring commands, providing a comprehensive overview for developers and system engineers.
This article introduces a collection of Alibaba‑released open‑source tools—including Arthas, Cloud Toolkit, ChaosBlade, PTS, Druid, and more—detailing their usage scenarios, tutorials, and acquisition methods to help backend developers improve efficiency, debugging, monitoring, and reliability of their services.
This tutorial walks through the end‑to‑end monitoring pipeline—from JMeter’s Backend Listener sending metrics to InfluxDB, through storing and querying those metrics, to configuring Grafana dashboards for real‑time performance visualization, and also covers OS monitoring with node_exporter, Prometheus, and Grafana.
This article provides a comprehensive collection of Linux commands and techniques for identifying memory, CPU, network, disk, and application bottlenecks in Java backend services, including practical examples of free, vmstat, top, sar, iostat, iotop, and jstack usage.
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 the Android ANR workflow, analyzes the system's appNotResponding logic, and presents a robust monitoring strategy that captures SIGQUIT signals, validates true ANR events, and hooks trace writes to reliably detect and diagnose ANRs in mobile apps.
This article explains the relationship between UI stutter and ANR, critiques common monitoring tools, and presents three robust Android lag‑detection methods—WatchDog polling, Looper Printer replacement, and specialized handlers for IdleHandler, TouchEvent, and SyncBarrier leaks—complete with probability analysis and sample code.
The interview with Kuaishou senior engineers Zhang and Fang discusses their ten‑year web development experience, recent Node.js infrastructure and front‑end performance projects, the technical highlights, implementation challenges, architectural considerations, and the topics they will present at the upcoming GIAC conference.
Understanding Linux load average and CPU utilization, this guide explains process states, how load is calculated, the difference between load and CPU usage, common bottlenecks, and step‑by‑step troubleshooting techniques using tools like top, vmstat, pidstat, iostat, and perf.
This article explains the difference between CPU utilization and CPU load, describes how to interpret these metrics, and provides practical Linux commands and step‑by‑step methods for diagnosing high load with low utilization or vice versa, including common I/O and indexing issues.
Lumos Hybrid Stack leverages Dart and Flutter to unify iOS and Android business logic, cutting duplicated development effort, accelerating debugging, and saving person‑days while providing a consistent, extensible architecture, performance monitoring, and a communication protocol that paves the way for future centralized state‑management across platforms.
Front‑end performance monitoring lets developers detect issues early, compare synthetic and real‑user approaches, track key metrics like LCP, FID, and CLS, and gather data via the web‑vitals library, Performance API, and error‑tracking hooks to systematically improve load speed, interactivity, and visual stability.
This article provides a detailed, step‑by‑step guide to Linux's built‑in performance monitoring utilities—top, free and vmstat—covering their purpose, essential options, output interpretation, interactive shortcuts, and practical examples for effective system administration.
This comprehensive article provides an in-depth analysis of how Linux kernel sends network packets, covering the complete process from user-space send() call through protocol stack processing to hardware transmission, with detailed source code examination and performance considerations.
This guide provides a comprehensive, step‑by‑step walkthrough of the Linux performance monitoring tools top, free, and vmstat, covering command syntax, useful options, real‑world examples, output interpretation, and practical tips for effective system tuning and troubleshooting.
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.
This article explains why front‑end performance is critical, outlines the most cost‑effective metrics such as FCP, TTS, and TSP, describes the SDK‑SLS‑Backend architecture, shares key technical decisions, and proposes future enhancements for a robust monitoring solution.
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.
This guide explains how to use Linux command‑line utilities such as free, vmstat, top, sar, iostat, iotop, netstat, and jstack to identify memory, CPU, network, and disk performance bottlenecks, and provides practical commands for monitoring and troubleshooting each resource.
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.
This article explains front‑end event tracking fundamentals, compares passive, code‑based, and SDK solutions, outlines common event attributes and types, details performance data collection via the Performance API, describes error capture methods, and presents reliable data reporting techniques using Beacon and image fallbacks.
This guide provides a comprehensive walkthrough of the Linux performance monitoring tools top, free, and vmstat, detailing their syntax, options, output interpretation, and practical tips for real‑time system analysis and optimization.
Alinode expands its Node.js performance diagnostics into a full‑stack observability and scheduling platform for serverless workloads, offering traffic monitoring, white‑screen logs, remote debugging, crash analysis, standardized metrics, and a cloud‑native runtime that balances cost and performance.
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 Xianyu Android Log Governance solution unifies local console logs, TLog and online logs, adds comprehensive logcat capture and AUS‑OSS retrieval, replaces BlockCanary with low‑overhead frame‑callback lag/ANR detection, and provides dashboards and a batch‑query platform, cutting technical‑opinion ratio from 10.5 % to 4.7 % and boosting upload success.
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 introduces five cutting‑edge solutions for modern web development: an Addon‑based Node.js performance monitoring and fault‑location tool, the full‑stack Midway Hooks framework for zero‑API backend development, the ultra‑fast AirpackCDN component delivery network, the visual‑oriented iMove JavaScript framework, and the versatile SSR framework 5.0 supporting both React and Vue.
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 the principles of distributed tracing, the OpenTracing standard, SkyWalking's architecture and sampling strategies, and shares a company's practical customizations—including forced sampling, fine‑grained group sampling, log4j traceId injection, and self‑developed plugins—to help pinpoint performance issues in microservice environments.
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.
