This article explains how Alibaba Cloud's ARMS uses configurable templates to provide tiered, low‑overhead JVM monitoring and full APM capabilities, enabling users to create, customize, and apply monitoring policies across applications and workspaces for cost‑effective observability.
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 tutorial explains how to use Docker to deploy DBLE, Prometheus, and Grafana, configure JMX Exporter for JVM metrics, and create a monitoring dashboard that visualizes CPU, memory pool, GC, and thread statistics of DBLE instances.
This guide explains how to use Java's java.lang.management APIs to retrieve real-time JVM CPU usage, system load averages, garbage collection details, and heap/non‑heap memory statistics, providing code snippets and practical usage scenarios for performance testing and resource allocation.
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.
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 article introduces VisualVM, a Java profiling tool bundled with the JDK, explains how to install and use its plugins for monitoring CPU, memory, threads, and garbage collection, and demonstrates step‑by‑step memory‑leak detection and remote Tomcat monitoring with code examples.
This article provides a step‑by‑step tutorial for installing, configuring, and running a JVM monitoring solution that integrates with Spring Boot, covering repository cloning, server configuration, Maven installation, application property setup, and accessing the monitor server UI.
Vivo’s vmonitor system replaces its legacy RabbitMQ‑based pipeline with an HTTP‑driven collector and gateway, stores minute‑level JVM, system, and business metrics in a customized OpenTSDB on HBase, adds precise floating‑point handling and null‑aware aggregation, buffers data in Redis, and provides multi‑dimensional alerts comparable to Zabbix, Open‑Falcon, and Prometheus.
This tutorial explains how to deploy a Spring Boot‑based monitoring solution that tracks multiple remote JVMs, covering repository cloning, configuration of server and client properties, Maven installation of the starter jar, and running the monitor‑server with example code snippets and screenshots.
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 guide explains how to use the jstat command-line tool to monitor Java Virtual Machine performance metrics such as garbage collection, memory usage, and compilation time, detailing its syntax, options, intervals, counts, and providing concrete examples with expected outputs.
JVisualVM, bundled with JDK, offers a visual interface to monitor JVM threads, memory, CPU usage, and perform heap dumps, enabling developers to detect memory leaks, analyze object references, and remotely monitor applications such as Tomcat, with step-by-step installation, plugin usage, and code examples.
This article introduces Alibaba’s open‑source Java diagnostic tool Arthas, explains how to install and use it for JVM monitoring, class loading inspection, and performing hot code updates via memory compilation and the redefine command, while noting its limitations.
During a weekend on‑call shift a Java detection service repeatedly timed out due to network packet loss, leading to massive CPU usage and full GC cycles caused by a memory leak in a Map that stored request results, which was finally diagnosed and resolved using JVM tools like jstat, jstack, and MAT.
