Infrastructure tutorials
Production-grade guides for Linux, servers, security and performance. Copy-paste commands, multi-distro support, written by engineers who run this in production.
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Linux
System administration, shell scripting, package management
Hosting & Servers
Web servers, reverse proxies, SSL, domains
Security
Firewalls, hardening, encryption, access control
Performance
Caching, optimization, profiling, load testing
Databases
MySQL, PostgreSQL, Redis, backups, replication
Networking
DNS, load balancing, VPN, TCP/IP, routing
DevOps
CI/CD, Docker, Kubernetes, automation
Monitoring
Logging, alerting, metrics, observability
Most viewed
Install and configure Deno for web development with systemd and reverse proxy
hostingInstall and configure Caddy web server with automatic HTTPS and reverse proxy
hostingInstall and configure Ollama for local AI models on Linux servers
devopsInstall and configure Uvicorn ASGI server with systemd and reverse proxy for FastAPI applications
hostingInstall and configure Uptime Kuma for website monitoring with SSL and email alerts
monitoringRecently published
Configure NTP monitoring with Grafana dashboards and Prometheus alerting
monitoringConfigure Cherokee caching and compression for improved performance
performanceConfigure advanced Jaeger sampling strategies for high-traffic environments
monitoringConfigure Jaeger distributed tracing on Kubernetes cluster with Helm charts and Elasticsearch backend
monitoringConfigure Spark on Kubernetes with cluster autoscaling for dynamic workloads
devopsConfigure Spark on Kubernetes with cluster autoscaling for dynamic workloads
Deploy Apache Spark 3.5 on Kubernetes with automatic cluster scaling, dynamic resource allocation, and comprehensive monitoring for production data processing workloads.
Implement Kubernetes cluster autoscaler for automatic node scaling
Configure Kubernetes cluster autoscaler to automatically add and remove worker nodes based on pod resource demands. This tutorial covers cloud provider integration, scaling policies, and monitoring for production-grade horizontal scaling.
Configure Kubernetes horizontal pod autoscaler for dynamic scaling based on resource metrics
Set up HPA with CPU and memory targets for automatic pod scaling. Configure metrics server and Prometheus adapter for custom metrics monitoring. Enable dynamic workload scaling based on resource utilization.
Implement Kubernetes cluster autoscaling with Helm charts and KEDA for dynamic workload scaling
Configure comprehensive Kubernetes autoscaling with cluster autoscaler for node management, KEDA for event-driven pod scaling, and vertical pod autoscaler for resource optimization. This tutorial covers production-grade deployment using Helm charts with monitoring and optimization strategies.
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