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This guide explains how to locate the cause of sudden CPU spikes in a Java program by using the top command to identify the offending process and thread, converting the thread ID to hexadecimal, and analyzing stack traces with jstack to pinpoint the problematic code line.
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 guide explains how to diagnose Linux performance bottlenecks—CPU, memory, disk I/O, network, and system load—using tools such as top, vmstat, perf, and flame graphs, and demonstrates a real‑world Nginx case study to pinpoint high‑CPU JSON parsing and upstream latency issues.
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.
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.
Using Go’s pprof, the team traced a gradual CPU spike in a high‑throughput kafka‑go consumer to a saturated commit queue and repeatedly nested context values, which forced costly lookups; eliminating the unnecessary trace‑id context injection (or recreating a fresh context each loop) resolved the issue and reduced CPU usage to under 2 %.
This article explains how to generate and interpret flame graphs for CPU performance profiling on Linux, covering the use of perf for sampling, the underlying kernel mechanisms, and the processing steps with Brendan Gregg's FlameGraph scripts to visualize hot functions.
This article explains how to verify and measure micro‑level CPU usage in client applications by selecting appropriate performance‑testing tools, demonstrating two validation methods—Process Explorer and the Windows Performance Toolkit (WPT)—and providing step‑by‑step installation and usage guidance.
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 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 presents a comprehensive guide to Linux performance analysis, covering background, methodology, tools, and step‑by‑step case studies for CPU, memory, disk I/O, network, system load, and flame‑graph techniques to quickly locate and resolve bottlenecks.
Using the open‑source Arthas tool, the author traced a Java server’s 99 % CPU usage to two runaway threads, inspected their stack traces, discovered a cyclic bucket in a HashBiMap caused by unsynchronized cache updates, and resolved the issue by adding a synchronized keyword to the cache‑sync method.
This article presents a comprehensive guide to Linux performance analysis, covering CPU, memory, disk I/O, network, system load, flame‑graph techniques, and a real‑world Nginx case study, enabling engineers to quickly locate and resolve bottlenecks.
The article examines common CPU performance pitfalls in C/C++ programs—such as excessive memset, inefficient string copying, improper container usage, lock contention, and heavy I/O logging—provides concrete code examples, compares profiling tools, and recommends best practices to reduce CPU consumption.