When a production Java service throws a NullPointerException at 2 AM, this article shows how the Arthas diagnostic tool can attach to the running JVM, inspect threads, monitor method parameters, and pinpoint the faulty null value without restarting the service, all within a few minutes.
The article explains how to safely adjust JVM parameters for large‑scale e‑commerce events by first calculating the total container memory usage, using targeted G1GC tweaks, validating changes with load tests and metrics, and preparing a detailed gray‑release and rollback plan.
This article explains how to differentiate memory leaks from out‑of‑memory errors, identifies classic GC‑based leak signals, introduces a toolchain (jstat, jmap, MAT, Arthas, JProfiler), walks through a step‑by‑step investigation workflow, lists common leak patterns, presents a real‑world ThreadLocal leak case, and offers preventive measures such as monitoring, regular heap dumps, code review, and stress testing.
When a Java application suddenly hits 100% CPU and becomes unresponsive, this guide walks through a three-step investigation—identifying the hottest process, the most CPU-intensive thread, and the offending code—using tools like top, jstack, and Arthas, and presents common causes and preventive measures.
This article details the architecture, security mechanisms, and implementation of a unified Arthas online diagnosis platform that enables SSH‑free, audited access to Java applications across dozens of isolated Kubernetes clusters, covering control‑plane design, WebSocket tunneling, credential management, RBAC, and front‑end integration with Vue and xterm.js.
Arthas Eyes packages Alibaba’s Arthas into a browser‑based GUI, using a Python FastAPI backend and a Vue 3 front‑end, allowing developers to attach to local or remote Java processes, visualize memory, GC, threads, trace calls, view flame graphs, and perform diagnostics within five minutes without memorizing CLI commands.
When a monitoring alarm showed the /api/order/create endpoint’s P99 latency jump from under 50 ms to over 3 s, the team traced the root cause to a Full GC storm caused by an unbounded static HashMap cache, using Arthas, heap dumps, MAT analysis, and finally applied both quick‑fix and permanent code changes to restore performance.
The article explains how integrating Arthas with the Model Context Protocol (MCP) enables AI-driven, natural‑language troubleshooting of Java‑based online incidents, offering step‑by‑step diagnostics, concrete case studies, and a balanced view of its advantages and current limitations.
The article explains how the open‑source Java diagnostic tool Arthas now supports the Model Context Protocol (MCP), allowing AI assistants to invoke Arthas commands directly for CPU, latency, version, and Spring Bean issues, while covering installation, usage scenarios, and production safeguards.
The article walks through a 13‑hour out‑of‑memory incident on a Spring Boot 2.7 service running in Kubernetes, detailing how to preserve the crash dump, interpret GC logs, use MAT and Arthas to pinpoint a static HashMap leak, and apply both temporary and permanent fixes while hardening the system for future safety.
When a service’s P99 latency spikes to seconds and CPU hits 90% without logs, Arthas lets you inspect the JVM in real time without code changes, and its AI‑driven MCP extension automates command selection, enabling developers to diagnose, trace, decompile, and monitor issues through simple natural‑language prompts and Spring Boot integration.
The article explains how integrating Arthas with the Model Context Protocol (MCP) enables AI to automatically execute Java diagnostic commands, analyze results, and provide concrete remediation steps, dramatically simplifying online incident resolution for developers and operations teams.
The article explains how Arthas, now integrated with the Model Context Protocol (MCP), lets AI automatically execute and interpret diagnostic commands for Java applications, offering step‑by‑step case studies, a detailed workflow, and an analysis of its strengths and limitations.
This article explains how the Model Context Protocol (MCP) integrates with Arthas to let AI agents automatically diagnose Java production issues—such as high CPU, slow responses, memory leaks, and deadlocks—by invoking Arthas commands via natural‑language queries, eliminating manual command‑line steps.
After optimizing a Spring Boot channel service, a mysterious extra ~100 ms appeared per request; detailed tracing with Arthas revealed that embedded Tomcat repeatedly loads Swagger UI META‑INF resources, causing a 70 ms overhead, which can be eliminated by upgrading Tomcat or removing the Swagger dependencies.
The article explains the JVM's core components, execution workflow, garbage collection fundamentals—including GC roots and Stop-The-World pauses—and introduces basic tuning tools like VisualVM and Arthas, helping readers grasp where performance optimizations should focus.
Arthas Agent adds an AI layer to the popular Java diagnostic tool Arthas, enabling natural‑language requests to be translated into secure, pre‑validated Arthas commands, with safety mechanisms, skill‑first design, and evidence‑based analysis for fast JVM troubleshooting.
A Spring Boot channel service showed an unexpected 100 ms latency; by systematically checking network, using curl, and employing Arthas to trace Spring MVC and Tomcat internals, the author discovered a Tomcat‑embed bug that repeatedly loads Swagger‑UI JAR resources, which is resolved by upgrading Tomcat.
This guide explains how to build, run, and deploy the open‑source Arthas Tunnel tool—including Maven compilation, Docker and Helm deployment, UI startup, and integration with Spring Boot via the eden‑architect framework—while providing configuration examples and repository links for quick setup.
This guide explains how to embed the Arthas online monitoring tool into a Spring Boot application using the Arthas Spring Boot Starter, configure telnet and HTTP ports, access the console via terminal or web, and extend debugging across multiple services with Arthas Tunnel while addressing security considerations.
This guide explains how to set up the open‑source Arthas Tunnel Server—covering its core features, Maven build steps, IDE and command‑line startup, Docker and Helm deployment options, and integration into Spring Boot applications using the eden‑architect framework.
This guide introduces Meteor-Console, a customized, non‑intrusive Java diagnostic tool built on Arthas, explains its architecture, provides step‑by‑step installation commands, lists key features such as JVM class query and method monitoring, and shares the open‑source repository link.
This article demonstrates how to use Java Agent and the java.lang.instrument API to non‑intrusively measure method execution time, dynamically modify bytecode with ASM, leverage Attach for runtime instrumentation, and explore related tools like Arthas and Bytekit for tracing and debugging Java applications.
A Java Spring application repeatedly stalled due to Redis connection pool deadlocks, and the investigation walks through using system tools, JVM thread dumps, and Arthas to pinpoint the issue, then shows how proper pool configuration and safe Redis access prevent the API from freezing.
During a Double‑11 promotion, a massive request timeout caused order success rates to plunge, but by using Arthas, jstack, and targeted code analysis, the team identified a non‑thread‑safe HashBiMap in a global cache, halted the outage, and implemented fixes to prevent future failures.
This guide explains how to use Alibaba's open‑source Java diagnostic tool Arthas to non‑intrusively monitor, locate, and resolve CPU performance bottlenecks in production Java applications, covering installation, key commands, a step‑by‑step troubleshooting workflow, and a real‑world case study.
This article explains why inserting manual timing code is invasive, introduces the java.lang.instrument API, shows how to write a tiny Java Agent with premain and agentmain methods, demonstrates dynamic class redefinition via the Attach API, and explores how tools like Arthas and Bytekit leverage these mechanisms for runtime tracing and bytecode enhancement.
Meteor, built on Alibaba's Arthas, is a non‑intrusive Java diagnostic console that lets developers monitor running applications, locate performance bottlenecks, memory leaks, and thread deadlocks without restarting services, offering a SpringBoot‑based architecture, quick start commands, and features such as class inspection, live code editing, method monitoring, thread management, and dashboards.
This article explains how Java developers can locate and resolve performance issues by using Skywalking and Arthas together, covering class loading mechanisms, Java Agent instrumentation, bytecode manipulation techniques, and practical command examples for monitoring, tracing, and hot‑spot analysis.
This guide demonstrates how Arthas enables rapid, non‑intrusive diagnosis of Java production problems by addressing common online challenges such as slow interfaces, thread blocking, memory leaks, hot‑fixes, and data inconsistencies, offering concrete commands, code examples, and best‑practice tips for reliable operations.
This article analyzes the CPU spikes that occur within minutes after a Java service restart, explains how excessive Runnable threads, frequent context switches, and JIT compiler activity cause the overload, and presents step‑by‑step diagnostics and mitigation strategies such as traffic greying, cache pre‑warming, request‑parameter cleanup, and JVM warm‑up.
This article explains why traditional monitoring tools often fail in production, introduces Arthas as a lightweight, non‑intrusive Java diagnostic solution, and walks through five real‑world scenarios—slow interfaces, thread blockage, memory leaks, hot‑fixes, and data inconsistency—showing exact commands, code snippets, and visualizations to quickly locate and resolve root causes.
This article explains why traditional heap dump and stack trace analysis are cumbersome, and shows how combining Spring Boot Actuator metrics with the Arthas diagnostic tool provides real‑time monitoring, rapid object inspection, and precise leak root‑cause identification, dramatically speeding up Java OOM troubleshooting.
This article explains what a deadlock is, the four necessary conditions that cause it, demonstrates classic synchronized‑based and Lock‑based deadlock examples in Java, shows how to detect deadlocks with tools such as Arthas, jstack, jvisualvm and JMC, and provides practical strategies—including lock ordering, timeout locks, and two‑phase locking—to break each condition and avoid deadlocks in production code.
This guide introduces JVM performance monitoring by explaining built‑in tools like JConsole and VisualVM, showcasing third‑party solutions such as Arthas, and providing step‑by‑step commands and screenshots to help developers quickly visualize and troubleshoot Java applications.
This guide explains why Java applications can hit 100% CPU in production, introduces the two primary diagnostic tools jstack and Arthas, and provides step‑by‑step commands and code examples to locate the exact source line responsible for the CPU spike.
This article explains how to replace invasive timing code with a Java Agent using the Instrumentation API, demonstrates both premain and attach approaches, shows ASM‑based bytecode transformation examples, and explores how Arthas trace leverages similar techniques for runtime monitoring.
This article explains how to replace invasive manual timing code with Java Instrumentation, demonstrating both premain and agentmain approaches, building and attaching agents, using ASM and Bytekit for bytecode enhancement, and leveraging Arthas for runtime tracing and debugging.
This article explains the six Java thread states defined in Thread.State, demonstrates how to use the Arthas diagnostic tool to identify blocked, waiting, or timed‑waiting threads, and provides practical strategies for diagnosing and optimizing thread‑blocking issues in Java applications.
This page compiles a comprehensive collection of tutorial links and video resources covering Chrome extension development, Java performance testing, interface testing, Groovy scripting, various utility videos, and the Arthas diagnostic tool, providing developers with organized references for learning and practice.
This guide introduces Alibaba's open‑source Arthas Java diagnostic tool, explains its installation, outlines common troubleshooting scenarios, and provides detailed examples of core commands such as stack, jad, sc, watch, trace, jobs, logger, dashboard, and redefine for live JVM debugging.
This guide introduces the open‑source Meteor Console, a non‑intrusive Java application diagnostic tool built on Arthas, covering its architecture, installation steps, core features like class querying, method monitoring, thread management, and links to the full microservice project and video tutorials.
The article details a step‑by‑step investigation of an unexpected ~100 ms latency in a Spring Boot‑based channel system, covering network checks, curl measurements, Arthas trace and watch commands, identification of TomcatJarInputStream’s repeated jar‑resource loading caused by Swagger dependencies, and the final fix by upgrading the embedded Tomcat version.
Meter-Console, a customized, non‑intrusive Java application diagnostic tool built on Alibaba’s open‑source Arthas, offers features such as JVM class inspection, online code editing, method monitoring, thread management, and a dashboard; the guide explains its architecture, installation steps, command usage, and provides the open‑source repository link.
This article details a step‑by‑step investigation of an OutOfMemoryError in a Spring Boot social app, revealing that frequent initialization of a script engine caused massive StringSequence instances via SPI loading, and shows how consolidating the engine eliminated the OOM issue.
Learn how to leverage the Arthas flamegraph tool to profile Java applications, interpret CPU usage visualizations, and apply practical optimization techniques illustrated through real-world case studies that reduced CPU consumption by up to 6% and improved system stability during high‑traffic events.
This article introduces the open‑source Java diagnostic tool Arthas, explains how to install and run it, showcases common use cases such as CPU spike detection, thread‑pool monitoring, deadlock analysis, and provides practical command examples and code snippets for effective production debugging.
This article introduces Arthas, an open‑source Java diagnostic tool, and demonstrates how to install it, use its command‑line and web console, and apply common commands such as dashboard, thread, watch, trace, and tt to quickly locate CPU spikes, deadlocks, memory leaks, and other production problems without redeploying code.
This article documents the step‑by‑step investigation of an unexpected ~100 ms response delay in a Spring Boot channel service, showing how network checks, curl tests, Arthas trace/stack commands, and source analysis revealed a Tomcat‑embed bug caused by Swagger‑ui resources and how upgrading Tomcat resolved the issue.
Java developers must master performance optimization by using tools like Skywalking and Arthas, which leverage Java agents, class-loading and bytecode manipulation (via ByteBuddy/ByteKit) to locate bottlenecks, filter data with OGNL, and fine-tune interface response times in real-time.
This article introduces Arthas, an open‑source Java diagnostic tool from Alibaba, explains its key features, shows how to install it via Maven, and provides step‑by‑step commands for entering the shell, monitoring JVM status, tracing method calls, observing system resources, dynamically redefining code, and connecting to applications using default or Telnet modes.
This guide explains how to use Arthas for fast problem location in Java services, demonstrates watch commands, shows one‑click installation, and introduces the ArthasHotSwap plugin for hot code deployment while warning against production misuse.
This article demonstrates how to quickly locate and resolve frequent full GC and CPU spikes in Java backend services by combining Arthas‑integrated async‑profiler flame graphs with VisualVM, GCEasy analysis, and practical step‑by‑step deployment procedures.
The article details how a high‑traffic Helios scoring service, originally taking several seconds to return a day’s worth of data, was profiled with Arthas and iteratively optimized through four code revisions, ultimately reducing latency to around 60 ms while exposing key performance pitfalls such as object creation, date formatting, and trivial list operations.
This article documents how the Helios scoring service, which originally took several seconds to retrieve a day's worth of data, was optimized from hundreds of milliseconds down to tens of milliseconds by analyzing Arthas trace data and applying multiple code and algorithm improvements.
A Java backend developer investigates why an enum‑based status field becomes an empty string during database insertion, tracing the issue through code inspection, Arthas watches, and ultimately discovering a mis‑implemented enum setter caused by a third‑party library.
This tutorial demonstrates how to employ the open‑source Java diagnostic tool Arthas to quickly locate and analyze a high‑CPU problem in a running JVM by leveraging commands such as dashboard, thread, jad, watch, and ognl, complete with code examples and step‑by‑step instructions.
This article shares a Java developer’s six‑year experience with Arthas, detailing how vmtool, hot‑swap, OGNL filtering and various Arthas commands can dramatically improve problem‑diagnosis, reduce log noise, and accelerate debugging in production environments.
This article details a step‑by‑step performance tuning of the Helios scoring API, showing how profiling with Arthas revealed costly date formatting and list operations, and how successive code refactors—changing data structures, eliminating redundant object creation, and optimizing loops—cut the response time from several seconds to just a few dozen milliseconds.
The article details a week‑long investigation of a sandbox environment where Spring Boot APIs became unresponsive due to Redis connection pool blocking, describing the use of system tools, JVM thread analysis, and code inspection to identify mis‑configured pool settings and recommending proper connection handling with Spring's RedisTemplate.
This article documents how the Helios scoring service, which processes hundreds of thousands of data points per day, was progressively optimized from several seconds to tens of milliseconds by analyzing Arthas trace data, refactoring loops, reducing object creation, and improving date handling, ultimately revealing that database access becomes the remaining bottleneck.
This article explains a real‑world Java backend issue where the first call to MD5 hashing caused a massive latency spike, how the problem was diagnosed with Arthas tracing, and why replacing the Hutool MD5 implementation with a lightweight alternative resolved the slowdown.
This article walks you through using the open‑source Java diagnostic tool Arthas to locate and analyze performance problems in Java applications, covering basic and advanced tracing, filtering, and flame‑graph visualization without modifying source code.
This article details how a hidden 100 ms delay in a Spring Boot channel system was traced to Tomcat‑embed’s handling of Swagger‑UI resources, diagnosed with Arthas, and resolved by upgrading the embedded Tomcat version.
This article details how the Helios scoring API, originally taking several seconds, was optimized to under 60 ms by analyzing Arthas traces, refactoring date handling, minimizing object creation, and improving list operations, ultimately revealing database access as the remaining bottleneck.
The article explains a real‑world issue where the first invocation of a MD5 hash in a Java backend took dozens of seconds, describes how the problem was traced using the Arthas diagnostic tool, reveals that the delay stemmed from BouncyCastle algorithm loading, and shows how replacing the library resolved the performance bottleneck.
This article demonstrates how to use Alibaba's open‑source Java diagnostic tool Arthas to quickly locate and fix high CPU usage in a Java application, covering installation, attaching to a JVM, using dashboard, thread, jad, watch, and ognl commands, and interpreting the results.
This article explains how Java objects store attributes and shared behavior, describes the JVM method area where method bytecode resides, and demonstrates runtime bytecode manipulation using java.lang.instrument.Instrumentation, BTrace scripts, and the Arthas diagnostic tool to solve real‑world debugging problems.
This guide shows how to use the open‑source Java diagnostic tool Arthas to attach to a running JVM, view real‑time metrics, and trace method execution without inserting manual timing code, enabling quick pinpointing and optimization of performance bottlenecks.
This guide explains how to use the open‑source Java diagnostic tool Arthas to attach to a running Spring Boot application, view real‑time metrics, and trace method execution in order to pinpoint and optimize performance‑critical code sections without modifying the source.
This article explains how to use the open‑source Java diagnostic tool Arthas to attach to a running JVM, view real‑time metrics, and employ the trace command to pinpoint and analyze time‑consuming methods without inserting manual timing code.
This guide explains how to use the Arthas monitoring tool to identify and troubleshoot Java thread deadlocks and excessive CPU usage, demonstrating commands like thread -b and thread -n, interpreting output, and analyzing thread dumps to pinpoint problematic code and optimize performance.
This tutorial walks through installing Alibaba Arthas, using it to monitor and analyze a Java Fibonacci application, and demonstrates key commands such as dashboard, thread, jad, monitor, watch, and profiler for effective performance debugging without code changes or restarts.
This article explains how to replace invasive manual timing code with a lightweight Java Agent that leverages the java.lang.instrument API, ASM bytecode manipulation, and the Attach API to measure method execution time, perform dynamic class redefinition, and integrate with tools like Arthas for runtime tracing.
A Java backend service suffered a massive first‑request latency because the MD5 hashing library loaded heavy cryptographic algorithms on first use, and the author traced, diagnosed, and solved the issue by replacing the library and sharing lessons on performance debugging.
The article recounts how a Java backend’s MD5‑based snapshot generation caused the first request to time out, details the investigation using Arthas to pinpoint the slow initialization of BouncyCastle algorithms, and explains the final fix by switching to a lightweight MD5 implementation.
This article walks through a real‑world investigation of a Metaspace Out‑Of‑Memory error in a Java service, detailing how JVM monitoring tools, class‑loader behavior, and hot‑deployment agents contributed to the issue and presenting practical fixes and preventive measures.
This article walks through the step‑by‑step investigation of an unexpected ~100 ms latency in a Spring Boot channel service, using network checks, curl timing, and the Arthas Java diagnostic tool to pinpoint a Tomcat‑embed bug caused by Swagger jars, and then shows how upgrading Tomcat or Spring Boot resolves the problem.
This article walks through the evolution of a system’s performance concerns, defines speed and pressure dimensions, explains how to calculate RT, QPS and concurrency, compares QPS with TPS, and provides step‑by‑step methods using tools like Arthas, JMeter and JVM diagnostics to identify and fix CPU, memory and pressure issues before applying layered optimization strategies.
This article explains common null pointer scenarios in Java, how to diagnose them using logs and Arthas, and presents defensive checks, early‑return patterns, strict validation, and Java 8 Optional usage, while also comparing Stream map and flatMap methods with practical code examples.
The article reproduces the AttachNotSupportedException when Java runs as PID 1 in a container, explains the JVM attach workflow and LinuxThread model, and provides practical workarounds such as creating .attach_pid files, enabling StartAttachListener, or using newer JDK images.
This article introduces Arthas, Alibaba's open‑source Java diagnostic tool, explains its installation, showcases common commands such as stack, jad, sc, watch, trace, jobs, logger, dashboard, and redefine, and demonstrates how to use them to monitor JVM status, trace method calls, inspect classes, and perform hot‑updates without restarting the application.
The article explains that CPU spikes during Java deployments are caused mainly by HotSpot C2 JIT compiler threads, and demonstrates that tiered compilation, CodeCache sizing, JWarmUp pre‑warming, gradual traffic ramp‑up, and higher JIT compile thresholds together effectively smooth startup CPU usage.
This guide introduces Alibaba's open‑source Arthas tool for Java, explains when to use it, shows how to install it, and provides detailed examples of its most useful commands such as stack, jad, sc, watch, trace, jobs, logger, dashboard, and redefine for live JVM debugging and performance analysis.
This guide explains how to integrate the Arthas Java diagnostic tool with the Rainbond cloud‑native platform, covering plugin installation, tunnel setup, Web Console access, environment configuration, command usage, and flame‑graph generation for effective Java application troubleshooting.
This article investigates why DBLE repeatedly logs "no handler" messages by analyzing DBLE and MySQL logs, using Arthas to trace the call chain, reproducing the scenario with timeout settings, capturing network traffic, and explaining the MySQL 8.0.24 error‑packet behavior that leads to the observed logs.
This article details how a hidden 100 ms latency in a Spring Boot channel service was identified and resolved by tracing request flow, using the Arthas Java diagnostic tool, and ultimately fixing a Tomcat‑embed bug caused by Swagger‑ui resources.
This article introduces Arthas, Alibaba's open‑source Java diagnostic tool, explains its installation via wget and java‑jar, and provides detailed usage of common commands such as stack, jad, sc, watch, trace, jobs, logger, dashboard, and redefine for real‑time JVM monitoring and debugging.
Arthas, Alibaba’s open‑source Java diagnostic tool, enables dynamic code tracing, real‑time JVM monitoring, and on‑the‑fly debugging without stopping applications; this guide covers installation, common scenarios, and core commands such as stack, jad, sc, watch, trace, jobs, logger, dashboard, and redefine for effective troubleshooting.
A customer’s Spark card queries were consistently taking around 10 seconds, prompting a step‑by‑step investigation that revealed a misconfigured NAS mount option (lookupcache=none) as the root cause of the severe slowdown.
This tutorial demonstrates how to download, attach, and use the Arthas Java diagnostic tool—leveraging commands like dashboard, thread, jad, watch, and ognl—to quickly locate and fix high CPU problems caused by parallel stream code in a Java application.
This article walks through installing Arthas, selecting Java processes, and using core commands such as session, exit, dashboard, thread, jvm, trace, and monitor to diagnose, profile, and troubleshoot Java applications in production environments, complete with command examples and output snippets.
This article explains how Java object behavior can be altered at runtime using the JVM method area, java.lang.instrument.Instrumentation, bytecode frameworks like ASM, the BTrace tracing tool, and the Arthas diagnostic utility, providing practical code examples and usage guidelines.
The article details a week‑long investigation of unresponsive APIs in a sandbox environment, tracing the root cause to Redis connection pool blocking in a Spring Boot service, and presents step‑by‑step debugging, code analysis, configuration fixes, and best‑practice recommendations for proper Redis usage.
A Spring Boot channel service suffered an unexplained 100 ms extra latency, which was traced using Arthas to Tomcat’s handling of META‑INF resources from Swagger’s JAR, and the issue was resolved by upgrading the embedded Tomcat version.
This article shares practical Java performance‑optimization techniques, covering UI and non‑UI latency metrics, baseline data collection, bottleneck discovery with tools like Arthas, chronic issue handling, and a comprehensive set of governance measures ranging from network‑level caching to code‑level refactoring, asynchronous processing, and service splitting to achieve stable sub‑200 ms response times.
This article walks through a real‑world Java logging slowdown, explains how to benchmark the code with JMH, and shows step‑by‑step usage of Arthas commands like monitor, trace, and watch to pinpoint the exact cause of the performance regression.
This article introduces Arthas, a Java online diagnostic tool, explains its instrumentation‑based runtime principle, guides installation on various platforms, and provides a comprehensive command reference—including basic, system, class, and enhancement commands—for effective debugging, monitoring, and performance analysis of Java applications.
This article introduces Alibaba's open‑source Java diagnostic tool Arthas and demonstrates how to address eight common production issues—including locating class JARs, verifying code changes, online debugging without redeployment, monitoring JVM health, generating flame graphs, and finding class instances—through practical command‑line examples and code snippets.
This article describes how a hidden 100 ms latency in a Spring Boot channel service was traced with Arthas, revealing a Tomcat‑embed bug caused by repeated loading of Swagger UI resources, and explains how upgrading Tomcat or Spring Boot eliminates the delay.
