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Sampler is a Linux visual monitoring tool that runs from a single binary, uses simple YAML files to define widgets such as sparklines and bar charts, and displays real‑time CPU, memory, network, Docker container statistics and other metrics, while being easily extensible to services like MySQL, MongoDB and Kafka.
This article introduces the Sampler visual monitoring tool, explains how to install it on Linux, and provides step‑by‑step YAML configuration examples for tracking CPU, memory, Docker containers, network activity, and system time, enabling quick, intuitive server status checks.
This article outlines key strategies and Python tools for server monitoring, including defining metrics, utilizing libraries like psutil and requests, log analysis, load testing with Locust and PyTest, and implementing automated alerts for high availability.
This article describes a Python‑based solution that monitors CPU, memory, disk usage and network traffic on a server and multiple client machines, stores alerts in MySQL, and sends notifications via email and an Enterprise WeChat robot when predefined thresholds are exceeded.
This article presents Python scripts for server‑side and client‑side resource monitoring, automatically checking CPU, memory, disk usage and network traffic, storing alerts in MySQL and optionally sending notifications via email or Enterprise WeChat, with deployment instructions and cron scheduling.
This guide compiles a comprehensive set of Linux command‑line techniques—using awk, grep, netstat, and other tools—to help you count unique IPs, track page visits, identify heavy‑traffic files, monitor connection states, and extract performance metrics from Apache and other web server logs.
This guide presents a comprehensive collection of Linux command‑line techniques—including awk, grep, and netstat—to help you analyze web server logs, identify unique visitors, track page popularity, monitor connection states, and detect performance bottlenecks in a systematic way.
To dramatically improve page load speed, first diagnose whether slowness stems from client network, DNS, or server bottlenecks, then apply solutions such as scaling servers, optimizing TCP settings, enabling HTTP/2/QUIC, compressing assets, and most cost-effectively integrating a CDN for global edge caching and compression.
This guide walks through customizing Nginx 404 error pages, enabling and reading the stub_status module, increasing worker processes and connections, adjusting kernel limits, expanding header buffers, and configuring browser caching for static assets to dramatically improve server performance and reliability.
This guide presents a curated set of practical Linux command-line techniques for analyzing web server logs, including counting unique IPs, identifying popular pages, measuring traffic volume, detecting slow or error responses, and monitoring connection states, enabling administrators to efficiently monitor performance and security.
This article explains how the Linux top command provides real‑time server monitoring, details its common options, interprets each line of its output—including CPU, memory, and process information—and shows useful interactive commands and a practical example for tracking a specific process.
This tutorial demonstrates how to create a full‑stack server‑monitoring web app by setting up a Vue‑based frontend with Element and ECharts, and a Python Flask backend that collects load and disk usage data via Paramiko, stores it in SQLite, and serves it through a JSON API.
This tutorial walks through creating a web‑based server monitoring tool that tracks CPU load and disk usage, using Vue.js with Element UI and Echarts for the frontend and a Python Flask backend with SQLite for data storage and API delivery.
This guide compiles a comprehensive set of Linux commands—using awk, grep, netstat, and more—to help you analyze web server logs, track traffic, identify top IPs, monitor connection states, and detect performance bottlenecks on an Alibaba Cloud ECS instance.
The article explains how to achieve precise, real‑time detection of physical server outages, reduce false alarms through heartbeat monitoring, network and special‑case interference filtering, and detailed analysis, ultimately improving detection accuracy and coverage for reliable operations.
This guide explains how to set up JMeter’s PerfMon plugins for both GUI and non-GUI server performance monitoring, including required plugin downloads, agent installation, JAR placement, test plan configuration, command-line execution, and result analysis.
This article describes how Netflix’s technical operations team built an automatic anomaly‑detection system based on the DBSCAN clustering algorithm to identify subtly failing servers from time‑series error‑rate data, evaluate its effectiveness, and discuss practical deployment considerations.