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Thread dumps provide a snapshot of all active JVM threads, including their state, stack trace, and metadata, enabling developers to diagnose performance issues such as slow responses, high CPU usage, deadlocks, and thread pool inefficiencies by examining key fields and applying practical analysis techniques.
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 article walks through a real‑world incident where a data‑platform server showed near‑100% CPU usage, explains step‑by‑step investigation using top, pwdx, and jstack, identifies a time‑conversion utility as the root cause, and presents a streamlined script‑based solution that reduced CPU load by thirtyfold.
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.
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Oracle GraalVM’s new release for JDK 17 and JDK 20 adds free‑to‑use native‑image features—including profile‑guided optimizations, G1 GC, object‑header compression, ML‑driven PGO, and SBOM support—delivering up to 46% faster startup, 2‑3× lower memory usage, and up to 1.6× higher peak throughput compared with traditional JIT, while also introducing new tooling such as native‑image bundles, build reports, enhanced AWT support, and experimental monitoring.
The article details how the author used async-profiler to analyze a Java dynamic QPS test case, identified a CPU hotspot in a time‑checking method, replaced it with a timestamp check, and achieved a modest 0.1% reduction in overall CPU usage, illustrated with flame‑graph images and code snippets.
This article provides a comprehensive overview of JVM memory layout, explains major garbage‑collection algorithms and collectors, details how different memory regions cooperate, and offers practical tuning steps and tool usage for diagnosing GC issues in Java applications.
The article guides readers from grasping hardware, OS, and software layers—including Intel Xeon, virtualization, VPC, and containers—to gathering performance data with tools like JProfiler and perf, then applying data‑driven optimization across business logic, architecture, code, caching, databases, runtime and hardware, illustrated by real‑world case studies and emphasizing continual learning.
This article explains how Shenandoah OpenJDK's new concurrent thread‑stack processing, introduced in JDK 17, dramatically reduces garbage‑collection pause times to sub‑millisecond levels by using stack watermarks, and provides practical configuration guidance and benchmark results.
This article explains the concept of false sharing in CPU caches, demonstrates its performance impact with Java code, analyzes the results, and shows how to prevent it using the @Contended annotation and appropriate JVM flags.
This guide walks you through a step‑by‑step process using Linux top, jstack, and hex conversion to pinpoint the exact Java thread causing CPU spikes, enabling effective service optimization and cost reduction without unnecessary scaling.
This article explains the design, phases, and practical implications of the C4 concurrent continuously compacting garbage collector, comparing it with G1 and IBM's Balanced GC, and provides guidance on when to choose C4 for enterprise Java workloads requiring low pause times and high scalability.