32 min
Linux Core Subsystems: One-Page Reference Map
Overview This is a one-page cheat sheet for Linux kernel subsystems. Each subsystem controls a critical resource; understanding them is essential for troubleshooting, optimization, and security.
Why understanding subsystems matters:
Imagine your server is slow. Without subsystem knowledge, you’re guessing:
“Maybe add more RAM?” (might be CPU scheduler issue) “Maybe faster disk?” (might be memory cache problem) “Maybe more CPU?” (might be I/O scheduler misconfiguration) With subsystem knowledge, you diagnose systematically:
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October 16, 2025 · 32 min · DevOps Engineer
9 min
Linux Production Guide: Kernel Subsystems, Systemd, and Best Practices
Executive Summary Linux is a layered system: from firmware through kernel subsystems to containerized applications. Understanding these layers—and their interdependencies—is critical for reliable, secure, performant infrastructure.
This guide covers:
Layered architecture (firmware → kernel → userspace → containers) Core subsystems: process scheduling, memory, filesystems, networking systemd: unit management and service lifecycle Production best practices: security, reliability, performance, observability Note: For detailed boot flow and debugging, see the Linux Boot Flow & Debugging guide.
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October 16, 2025 · 9 min · DevOps Engineer
12 min
Linux Security Baseline for Production Servers
Executive Summary A security baseline is the foundation: OS-hardened, patched, with restricted access and audit trails. This guide covers minimal-install servers with hardened SSH, firewall (default-deny), LSM enforcement, least-privilege sudo, audit logging, and systemd hardening.
Goal: Reduce attack surface, detect breaches, and enforce privilege boundaries.
1. Minimal Install & Patching Minimal Install What it is:
Install only required packages (base + SSH + monitoring agent) No GUI, X11, unnecessary daemons Reduces vulnerabilities (fewer packages = fewer CVEs) Install steps (Ubuntu/Debian):
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October 16, 2025 · 12 min · DevOps Engineer
🛠️ Guide
21 min
Neo4j End-to-End Guide: Deployment, Operations & Best Practices
Executive Summary Neo4j is a native graph database that stores data as nodes (entities) connected by relationships (edges). Unlike relational databases that normalize data into tables, Neo4j excels at traversing relationships.
Quick decision:
Use Neo4j for: Knowledge graphs, authorization/identity, recommendations, fraud detection, network topology, impact analysis Don’t use for: Heavy OLAP analytics, simple key-value workloads, document storage Production deployment: Kubernetes + Helm (managed) or Docker Compose + Causal Cluster (self-managed)
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October 16, 2025 · 21 min · DevOps Engineer
🛠️ Guide
10 min
Docker Best Practices: Multi-stage Builds, Optimization, and Security
Introduction Building efficient and secure Docker images requires following best practices that reduce image size, improve build times, and minimize security vulnerabilities. This guide covers essential techniques for production-ready containers.
Multi-Stage Builds The Problem: Bloated Images Before (single-stage build):
FROM node:18 WORKDIR /app # Install dependencies COPY package*.json ./ RUN npm install # Includes devDependencies # Copy source COPY . . # Build RUN npm run build # Runtime includes build tools and dependencies CMD ["node", "dist/index.js"] Result: 1.2GB image with unnecessary build tools and dependencies.
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October 15, 2025 · 10 min · DevOps Engineer