This article presents a systematic methodology and a curated set of Linux tools—including CPU, memory, disk I/O, network, load monitoring, and flame‑graph techniques—illustrated with a real‑world nginx case study to help engineers quickly locate and resolve performance issues.
This guide explains how to use Alibaba's open‑source Java diagnostic tool Arthas to non‑intrusively monitor, locate, and resolve CPU performance bottlenecks in production Java applications, covering installation, key commands, a step‑by‑step troubleshooting workflow, and a real‑world case study.
This article explains how to use Flame Graphs to visualize MySQL execution plans, discusses the limitations of traditional EXPLAIN output, introduces the EXPLAIN ANALYZE feature in MySQL 8.0, provides sample SQL and command‑line usage, and shows how the visual tool helps quickly identify performance bottlenecks.
The article examines Java thread states—WAITING and TIMED_WAITING—through monitoring screenshots, explains their definitions, trigger conditions, and recovery mechanisms, analyzes real‑world code involving concurrent tasks, and presents flame‑graph based performance optimizations for backend services.
This article explains Java thread run states, analyzes why threads appear in WAITING or TIMED_WAITING during concurrent tasks, shows flame‑graph diagnostics, and offers concrete code‑level optimizations to reduce CPU pressure and improve overall backend performance.
This article explains how to use eBPF and perf events to trace NFS read/write system calls, capture kernel and user stack traces, resolve them to source locations via DWARF, and generate flame‑graph data that is pushed to Pyroscope for performance analysis.
The article introduces Intel's AI Flame Graph, a low‑overhead profiling tool that visualizes AI accelerator and GPU workloads across the full software stack, explains its design, demonstrates SYCL matrix‑multiply benchmarks, discusses challenges of AI instruction analysis, and outlines future adoption and impact.
The article introduces Intel’s AI Flame Graph, a low‑overhead profiling tool that visualizes AI accelerator and GPU execution alongside the full software stack, explains its design, shows SYCL matrix‑multiply examples, discusses challenges of AI workload analysis, and outlines future adoption and impact on performance and energy savings.
This article explains how to profile WebAssembly code by inserting lightweight instrumentation hooks, optimizing the overhead with a tree‑based call‑stack, generating flame‑graph data, and demonstrates the whole workflow with a sample Fibonacci program compiled to WASM.
This article explains how to produce Lua‑level flame graphs for OpenResty by leveraging SystemTap’s lj‑lua‑stacks tool, demonstrates the underlying data structures and call‑stack extraction, and explores a possible eBPF‑based rewrite for safer, kernel‑level tracing.
This guide explains how to use Go's built‑in pprof tool and the go tool pprof command to profile CPU and memory usage, generate analysis files, interpret interactive console output, create flame graphs, and apply practical optimizations for faster, more efficient Go applications.
This article walks you through using the open‑source Java diagnostic tool Arthas to locate and analyze performance problems in Java applications, covering basic and advanced tracing, filtering, and flame‑graph visualization without modifying source code.
This article provides a comprehensive guide to Linux Perf, covering its architecture, counting and sampling modes, event classifications, the full suite of Perf tools, and how to generate and interpret CPU and Off‑CPU flame graphs for deep performance analysis.
Linux perf is a powerful, flexible profiling tool that lets developers and system administrators monitor hardware and software events, analyze CPU, memory, and I/O performance, generate flame graphs, and troubleshoot bottlenecks across single processes, containers, and multi‑core systems, with extensive commands and advanced techniques.
This article explores the evolution of continuous performance profiling, explains why traditional tracing falls short, and details a series of optimizations—including batch processing, object‑reference serialization, aggregation insertion, and multi‑chunk handling—to dramatically reduce memory usage and speed up Java Flight Recorder analysis using async‑profiler and flame graphs.
The article investigates why pure‑read QPS drops significantly after bulk updates in OceanBase, reproduces the issue with a sysbench workload, analyses flame‑graph and SQL audit data, explains the LSM‑Tree read‑amplification mechanism, and proposes practical mitigation steps such as major freeze, plan binding, index creation, and the queuing‑table feature.
The Tencent Docs SmartSheet view’s sluggish 20 FPS performance was resolved by a suite of optimizations—including incremental rendering of only visible widgets, disabling costly canvas hit‑testing, replacing node cloning with direct construction, caching text measurements, and employing offscreen canvas rendering—boosting frame rates to near 60 FPS.
Using lightweight dynamic‑tracing tools that record allocator calls and page‑fault events, the authors diagnose a production MySQL‑proxy memory leak in TDSQL, generate focused flame‑graphs with custom memstacks and pgfaultstacks, and demonstrate a fast, source‑independent alternative to gdb or Valgrind.
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 comprehensive guide explains Linux kernel performance profiling—both on‑CPU and off‑CPU—by stressing the need to target the critical 3 % of code, covering throughput, latency and power metrics, scalability laws, flame‑graph visualizations, perf and eBPF tools, lock‑contention analysis, and further reading recommendations.
The article describes an iOS lag‑monitoring system that detects main‑thread RunLoop blocks, samples stacks every 50 ms into a circular buffer, uses an annealing Fibonacci‑based interval to minimize overhead, visualizes hot paths with flame graphs, and streams data through a Flink‑APM pipeline with only ~2 % CPU and a few megabytes of memory impact.
The article describes an iOS lag‑monitoring system that uses RunLoop state checks to detect main‑thread stalls, captures and caches stack traces, employs a Fibonacci‑based annealing algorithm to limit overhead, symbolises addresses on a server, and visualises results with flame graphs while maintaining low CPU and memory impact.
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 article explains how flame graphs visualize call‑stack frequency and duration, shows how to generate them from Java jstack dumps using Perl tools, and demonstrates how to interpret their widths and flat tops to pinpoint performance hot spots in production systems.
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.
This guide explains how to use Linux performance analysis tools—including top, vmstat, perf, flame graphs, and differential flame graphs—to diagnose CPU, memory, I/O, and network bottlenecks, with step‑by‑step commands, methodology, and real‑world case studies.
This article introduces a systematic approach to diagnosing Linux performance issues, covering the 5W2H analysis framework, essential CPU, memory, disk I/O and network monitoring tools, practical command examples, flame‑graph generation, and a real‑world nginx case study with actionable insights.
This comprehensive guide explains how to diagnose Linux system performance issues using a structured 5W2H approach, covering CPU, memory, disk I/O, network, load, and flame‑graph analysis, with practical command examples and a real‑world nginx case study.
This article introduces three popular Linux performance profiling tools—Perf, gprof, and Valgrind—explaining their installation, basic command‑line usage, and how to visualize results with flame graphs, call graphs, or KCachegrind, while comparing their intrusiveness, startup methods, and output formats.
The article details how to identify and resolve memory leaks and performance bottlenecks in high‑concurrency Nginx workloads using tools such as Valgrind, AddressSanitizer, perf and flame‑graphs, while also sharing practical tips and personal reflections on debugging under pressure.
This article explains how to leverage the Linux perf tool to profile Oracle database workloads, covering basic commands, probe-based tracing of Oracle internal functions, generating various flame graphs, and practical Q&A for integrating perf into performance monitoring workflows.
