The article explains how Linux soft interrupts work, why they can become a CPU bottleneck under heavy network traffic, and provides detailed analysis, code examples, monitoring tools, and optimization strategies to diagnose and mitigate the issue.
This article explains which components (user processes, IO wait, system interrupts) consume CPU in MySQL, shows their impact on performance, and provides practical strategies to reduce CPU usage through IO optimization, query tuning, data modeling, caching, and hardware upgrades.
The article explains how IO and CPU bottlenecks increase active database connections, then details horizontal and vertical sharding techniques, practical tools, step‑by‑step implementation, and common pitfalls to help engineers relieve pressure on database resources.
The article analyses a real‑world Node.js service outage caused by sudden 504 timeouts, explains how the asynchronous I/O model creates time‑slice contention under high QPS, presents load‑testing code and results for both I/O‑ and CPU‑bound requests, and offers practical mitigation strategies such as clustering, caching and resource scaling.
The article examines why a 100 GB MySQL 5.6 transaction causes high CPU usage during lock_number_of_rows_locked calculation, compares the lock‑counting implementations of MySQL 5.6 and 5.7, and offers diagnostic steps and version‑upgrade recommendations to mitigate the bottleneck.
This article explains why high CPU usage on Linux servers isn’t always a problem, describes how to identify true CPU bottlenecks using tools like uptime, top, sar and vmstat, and offers practical tuning steps such as stopping unnecessary processes, adjusting priorities, using CPU affinity, and updating drivers.
This article explains core performance‑testing concepts such as TPS, response time, think time, and threading, then details how to identify CPU, memory, and disk bottlenecks on Linux using tools like top, vmstat, iostat, and pidstat, linking system resources to overall application performance.
