This article explains five common Java memory leak scenarios—long-lived collections, unclosed resources, ThreadLocal misuse, inner class references, and unregistered listeners—with code examples and recommends MAT and VisualVM for leak detection.
This comprehensive guide covers 10 hidden Java memory leak scenarios, a 6-step MAT-based investigation SOP, code-level fixes for ThreadLocal and static collections, emergency mitigation tactics, and long-term prevention standards to eliminate recurring OOM crashes.
This comprehensive guide covers four Java OOM types (heap, metaspace, stack, direct memory), details enterprise troubleshooting SOP using jmap and MAT analysis, identifies five common memory leak root causes, and provides a six-step production emergency response plus long-term prevention rules.
This article demonstrates using Arthas to diagnose a Spring Boot service with 100% CPU, memory leaks, and latency spikes, showing step-by-step commands like dashboard, thread, heapdump, trace, and watch to pinpoint dead loops, unbounded caches, and missing SQL indexes, plus emergency hot-patching with redefine.
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.
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.
This guide explains how to troubleshoot common online Java problems—including high CPU, OutOfMemoryError, disk I/O, and network latency—by using tools such as top, jps, jstack, jmap, MAT, df, and ping to pinpoint root causes like infinite loops, memory leaks, lock contention, and resource exhaustion.
By analyzing heap dumps with MAT and scrutinizing GC logs, the Vivo Internet Server Team identified thread‑local FutureAdapter and Jackson BufferRecycler objects and tuned promotion thresholds, cutting the Java game service’s Full GC occurrences from roughly 120 per day to about 30 and dramatically shortening pause times.
This article explains how to generate a heap dump with jmap, import it into Eclipse Memory Analyzer (MAT), and interpret the various reports such as Histogram, Dominator Tree, Top Consumers, and Leak Suspects to identify memory leaks or insufficient heap sizing in Java applications.
The article presents a detailed case study of an Android WebView memory‑leak bug that gradually slows page opening, explains how DDMS and MAT were used to pinpoint the leaking DynamicBgDrawable objects, and describes the fix of converting a strong view reference to a weak reference to eliminate the leak.
The article presents a detailed case study of an Android WebView memory leak that causes progressively slower page loads, explains how DDMS and MAT were used to pinpoint the leak in DynamicBgDrawable via a WeakHashMap reference cycle, and proposes converting the view reference to a weak reference to resolve the issue.
This guide explains practical techniques for spotting basic memory leaks, using the MAT tool to analyze heap dumps, comparing hprof files, and implementing daily leak monitoring in Android apps through AspectJ instrumentation, MLD utilities, and UIAutomator automation.
