This guide explains how to use JDK command-line utilities such as jps, jstat, jinfo, jmap, jhat, and jstack to monitor JVM processes, inspect memory usage, generate heap dumps, and diagnose performance issues, providing command syntax, options, and practical examples.
This guide walks through using JDK's native command‑line and GUI utilities—such as jps, jinfo, jvisualvm, jstat, jstack, and jcmd—to monitor JVM performance, inspect GC behavior, analyze thread dumps, and troubleshoot memory configuration problems in Java programs.
This article introduces core JVM performance and monitoring utilities—including jps, jstack, jmap, jhat, jstat, and hprof—explains their command syntax, demonstrates practical steps to locate high‑CPU threads, analyze heap dumps, and interpret GC statistics, and provides concrete code examples for Java developers.
The article recounts a weekend on‑call incident where a Java monitoring service suffered network packet loss and a severe memory leak, leading to massive timeouts, high CPU usage, and frequent GC, and explains how the problem was diagnosed and resolved using tools such as top, jstat, jstack, jmap, and MAT.
This guide explains where Java's command‑line tools reside, why they are small wrappers around tools.jar, and provides detailed usage, options, and examples for the most common diagnostics utilities—including jps, jstat, jinfo, jmap, jhat, and jstack—helping developers monitor and troubleshoot JVM processes effectively.
This guide explains how to systematically investigate Java application problems across CPU, disk, memory, and network layers using Linux utilities and Java tools such as jstack, jstat, jmap, iostat, vmstat, and network commands, providing concrete commands and analysis steps to pinpoint root causes.
This article presents a systematic experiment that compares four different IntelliJ IDEA memory configurations on a MacBook Pro, measuring IDE startup time, project loading speed, and JVM garbage‑collection metrics with jstat to identify the most efficient setup for large Java monoliths and micro‑service projects.
This article details a systematic experiment that compares default, modest, balanced, and aggressive JVM memory configurations for IntelliJ IDEA, measuring startup time, project load speed, and garbage‑collection behavior on a large monolith and micro‑service projects, and concludes with practical tuning recommendations.
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.
This article explains common JVM problems such as OutOfMemoryError, memory leaks, and thread deadlocks, then introduces core monitoring tools—jps, jstack, jmap/jhat, jstat, and hprof—detailing their syntax, options, and practical examples to help Java developers diagnose and tune production applications.
This article reports a systematic experiment that compares four IntelliJ IDEA memory configurations on a large Java monolith and two micro‑services, measuring startup, project loading times and JVM garbage‑collection metrics with jstat to identify the most efficient setting.
This article reports a systematic experiment that compares four IntelliJ IDEA memory configurations on a MacBook Pro, measuring startup time, project loading speed, and garbage‑collection behavior with jstat, and concludes that allocating more heap (2‑4 GB) significantly improves IDE responsiveness for large codebases.
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 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 explains how to identify and resolve intermittent high CPU usage in Java/Tomcat services by analyzing thread stacks with jstack, examining GC behavior with Jstat, and using Jmap for memory inspection, providing step‑by‑step commands and practical tips for ops engineers.
