When MySQL CPU usage spikes to 500%, this guide walks you through a four‑step emergency process—quickly stopping the overload, diagnosing the root cause, applying targeted SQL and configuration optimizations, and setting up monitoring to prevent future spikes—ensuring service stability and performance.
The article explores how Dubbo’s ChannelEventRunnable code was optimized by separating the frequently‑taken ChannelState.RECEIVED case into its own if statement, compares the runtime efficiency of pure if‑else, mixed if‑switch, and pure switch structures, and explains the underlying CPU branch‑prediction and instruction‑pipeline mechanisms that affect these choices.
This comprehensive guide explains how to use Go's built‑in pprof tool to profile CPU, memory, and goroutine behavior, covering integration methods, command‑line analysis, visualization techniques, and step‑by‑step solutions for common performance bottlenecks in production Go services.
This article explains common interview questions on frequent Full GC and high CPU usage in Java backend development, detailing root causes, JVM parameter adjustments, memory leak prevention, and code optimization techniques, along with practical code examples and tool recommendations for effective performance tuning.
This article explains common CPU and JVM memory bottlenecks in Java applications, shows how to detect abnormal usage with monitoring and alerts, and provides step‑by‑step methods—including top, jstack, flame‑graph analysis, and APM tools—to pinpoint and resolve performance hotspots efficiently.
Learn how to leverage the Arthas flamegraph tool to profile Java applications, interpret CPU usage visualizations, and apply practical optimization techniques illustrated through real-world case studies that reduced CPU consumption by up to 6% and improved system stability during high‑traffic events.
The article details MNN’s CPU backend dynamic quantization for Transformer‑type models, describing runtime int8 conversion, block‑wise matrix‑multiply optimizations using ARM SMMLA/SDOT and AVX‑512 VNNI, weight‑group and batch‑wise quantization techniques, and reports up to three‑fold speed‑ups on Snapdragon 8 Gen 3.
This guide explains why MySQL and Java processes can exceed 900% CPU usage in production, and provides step‑by‑step methods—using top, show processlist, jstack, index tuning, caching, and code adjustments—to diagnose, mitigate, and prevent such spikes.
This article provides a comprehensive, step‑by‑step guide to tuning Kunpeng‑based servers, covering hardware characteristics, matrix‑multiplication benchmarks, NUMA‑aware scheduling, compiler and JDK optimizations, acceleration libraries, disk and NIC tuning, and a practical MariaDB performance‑tuning workflow.
Learn how to identify and resolve extreme CPU usage spikes—up to 900%—in MySQL and Java applications by using top, show processlist, indexing, caching, thread analysis, and code adjustments, with real‑world examples and step‑by‑step command guides.
This article explains how to evaluate device overheating using metrics such as CPU/GPU usage and temperature, collect and visualize Trace data with SmartPerf or DevEco Studio, analyze high‑load threads and frequency scaling, and apply targeted code changes—disabling sensors, releasing Lottie resources, and simplifying components—to reduce heat and restore frame‑rate stability.
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.
PP-LCNet is a lightweight convolutional neural network designed for Intel CPUs that leverages MKLDNN acceleration, H‑Swish activation, selective SE modules, larger kernels, and expanded fully‑connected layers to achieve higher accuracy without increasing inference latency across image classification, detection, and segmentation tasks.
This article explains how to diagnose and resolve severe CPU usage spikes—up to 900%—in MySQL and Java processes, detailing step‑by‑step identification, common causes such as missing indexes or excessive garbage collection, and practical remediation techniques including query optimization, indexing, caching, and thread analysis.
This guide explains how to identify and fix extreme CPU usage—up to 900%—in MySQL and Java processes, covering diagnostic commands, SQL indexing, caching strategies, thread analysis, and code adjustments to stabilize production systems.
This guide explains how high concurrency and fast response depend on throughput and latency, defines key performance metrics, shows how to interpret average load, CPU context switches, and memory usage, and provides practical Linux tools and command‑line examples for diagnosing and tuning system performance.
The article explains how to boost Android app performance by programmatically raising CPU frequency using Qualcomm’s BoostFramework, setting thread‑core affinity with native sched_setaffinity calls, and increasing execution priority via Process.setThreadPriority, providing Kotlin and C examples that demonstrate measurable speed gains.
This article explains the design of a compact RPC service that aggregates product benefit information, detailing how Sirector thread scheduling reduces CPU consumption and how abstract handlers and Spring prototype beans improve code maintainability and extensibility.
This comprehensive guide explains what performance testing (stress testing) is, why it matters, various test types, popular tools like ApacheBench, JMeter, LoadRunner and PTS, and provides detailed step-by-step methodologies and real-world case analyses for diagnosing memory, CPU, and latency issues in cloud‑native Java applications.
The article explains that CPU spikes during Java deployments are caused mainly by HotSpot C2 JIT compiler threads, and demonstrates that tiered compilation, CodeCache sizing, JWarmUp pre‑warming, gradual traffic ramp‑up, and higher JIT compile thresholds together effectively smooth startup CPU usage.
This comprehensive guide explains Linux performance optimization by covering core metrics such as throughput and latency, the meaning of average load, CPU context switching, memory management, common bottlenecks, and a suite of diagnostic tools like vmstat, pidstat, perf, and strace, plus practical tuning techniques for both applications and the operating system.
This article explains how hardware architecture, Linux OS version, kernel configuration, OVS integration, memory management, NUMA awareness, and CPU micro‑architecture affect DPDK application performance and provides concrete tuning steps such as CPU isolation, service disabling, huge‑page setup, and optimized memory allocation.
The article investigates a Spark driver CPU overload caused by the compatiblePartitions method’s expensive permutation logic in window functions, explains the underlying O(n!) complexity, and presents a simplified implementation that eliminates the issue and has been merged into the official Spark codebase.
This article explains how CPU‑centric vectorization, especially SIMD, reduces instruction count and CPI, addresses the four major CPU bottlenecks, and how StarRocks systematically applies automatic and manual SIMD techniques, verification methods, and a suite of engineering optimizations to achieve multi‑fold query speedups.
This article demonstrates how adding two gevent monkey‑patch lines to a feapder spider reduces CPU usage from 121% to 99% while changing the effective thread count from 36 to 12, and discusses the underlying principle, performance trade‑offs, and future directions for coroutine support.
The article explains how replacing a sorting‑based Go cron scheduler with a min‑heap implementation reduced CPU consumption from about 50% to 10%, achieving a four‑to‑five‑fold performance boost while handling high‑throughput timed tasks.
This article explains which components—user processes, I/O wait, and system interrupts—consume most CPU in MySQL, describes the performance impact of high CPU, and provides concrete strategies such as reducing I/O, simplifying calculations, optimizing indexes, and judiciously scaling hardware to lower CPU usage.
This article shares practical techniques for improving Cocos Creator game performance by reducing black‑screen time, minimizing CPU draw‑calls, and managing memory through resource compression, atlasing, cache modes, node pooling, and texture compression, all illustrated with step‑by‑step examples and screenshots.
This article explains Linux's sophisticated scheduling system for multi‑core, SMP, and NUMA architectures, describes global, clustered, partitioned, and arbitrary schedulers, details scheduling domains and load‑balancing mechanisms, and provides practical performance‑tuning techniques using tools like perf, flame graphs, and various kernel optimizations.
The paper presents a unified, high‑performance frame‑extraction framework that dynamically selects CPU or GPU decoding, leverages multithreaded and CUDA‑accelerated pipelines, keeps frames in memory, and achieves up to ten‑fold latency reductions for diverse AI video‑inference tasks.
Understanding CPU usage in MySQL involves recognizing that user processes, IO waits, and system interrupts dominate consumption, and by reducing IO, optimizing queries, indexing, and minimizing costly calculations, you can lower CPU load, improve throughput, and avoid performance bottlenecks.
This article explains which components consume CPU on a MySQL server, analyzes the impact of user‑space processing and IO wait, and provides practical strategies—such as optimizing indexes, reducing unnecessary calculations, and improving IO handling—to lower CPU usage and improve overall database performance.
This article presents a comprehensive guide to Java performance optimization, covering common code pitfalls, CPU and memory analysis techniques, disk and network I/O troubleshooting, and a collection of essential Linux commands, enabling engineers to pinpoint and resolve critical bottlenecks efficiently.
The article demonstrates how Go’s built‑in pprof tools can be used for live performance profiling, walking through two real‑world cases—one where a malformed JSON request caused massive object allocation and CPU spikes, and another where per‑call self‑referencing structs leaked memory—while offering practical tips on input validation, allocation reduction, and GC monitoring.
This article explains which components (user processes, IO wait, interrupts) consume most CPU in MySQL workloads, the performance impacts of high CPU usage, and practical steps—reducing IO, optimizing queries, and hardware upgrades—to lower CPU consumption and improve throughput.
The article analyzes the growing demands on network I/O, explains Linux and x86 bottlenecks, introduces DPDK’s user‑space bypass architecture and its core optimizations such as hugepages, poll‑mode drivers, SIMD, and CPU‑specific tuning, and finally discusses the DPDK ecosystem and practical considerations for backend developers.
To resolve a MySQL single‑core CPU bottleneck, examine and tune slow queries and indexes, eliminate heavy lock contention, break large transactions into smaller ones, and employ tools like perf top to pinpoint performance hotspots, as recommended by Tencent Cloud Database expert Liu Di.
This article introduces Linux’s built‑in Perf tool, explains its architecture and hardware counters, walks through practical examples—including basic commands, statistical summaries, real‑time monitoring, and detailed call‑graph analysis—while providing C code samples and visual illustrations to help developers pinpoint and optimize performance bottlenecks.
This article explains how tuning the PHP‑FPM max_requests setting and modifying its source code can eliminate periodic CPU_IDLE and MEM_USED fluctuations in high‑traffic web services, resulting in smoother resource utilization and more reliable performance.
This article explains essential Spark concepts, illustrates common performance bottlenecks, and provides concrete tuning strategies for memory, CPU, serialization, data locality, file I/O, and shuffle reduction, backed by real‑world examples and visual metrics.
This guide walks through identifying the root cause of a SQL Server instance's 100% CPU usage using Profiler, analyzing the offending queries, and applying targeted index and query optimizations to dramatically reduce CPU consumption.
The article explains why modern web systems face ever‑increasing concurrent connections, analyzes how front‑end techniques and server‑side configurations such as Apache MPM modes, Nginx, sendfile, and epoll can reduce memory and CPU consumption, and offers practical recommendations for efficient backend architecture.
This article explains why modern web systems face exploding concurrent connections, how richer page interactions and higher browser limits increase server load, and presents front‑end caching, request merging, Apache/Nginx memory‑saving modes, sendfile, and epoll techniques to reduce both memory and CPU consumption.
The article examines the rise of high‑concurrency web connections, explains how richer front‑end interactions increase load, and presents a range of optimization techniques—from caching and request merging to server‑side memory and CPU reductions using Apache MPM modes, Nginx, and sendfile—to improve web service efficiency.
