π SRE Practice
13 min
Observability: The Three Pillars of Metrics, Logs, and Traces
Introduction Observability is the ability to understand the internal state of a system based on its external outputs. Unlike traditional monitoring, which tells you what is broken, observability helps you understand why itβs broken, even for issues youβve never encountered before.
Core Principle: βYou canβt fix what you canβt see. You canβt see what you donβt measure.β
The Three Pillars Overview βββββββββββββββββββββββββββββββββββββββββββ β OBSERVABILITY β βββββββββββββββ¬βββββββββββββββ¬βββββββββββββ€ β METRICS β LOGS β TRACES β βββββββββββββββΌβββββββββββββββΌβββββββββββββ€ β What/When β Why/Details β Where β β Aggregated β Individual β Causal β β Time-series β Events β Flows β β Dashboards β Search β Waterfall β βββββββββββββββ΄βββββββββββββββ΄βββββββββββββ When to Use Each:
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October 16, 2025 Β· 13 min Β· DevOps Engineer
π SRE Practice
23 min
Chaos Engineering: Building Resilient Systems Through Controlled Experiments
Introduction Chaos Engineering is the discipline of experimenting on a system to build confidence in the systemβs capability to withstand turbulent conditions in production. Rather than waiting for failures to happen, chaos engineering proactively injects failures to identify weaknesses before they impact users.
Why does this matter? In modern distributed systems (microservices, cloud infrastructure, containers), failures are inevitable. A network can partition, a server can crash, a database can slow down. Traditional testing canβt predict all the ways these components interact when things go wrong. Chaos engineering fills this gap by deliberately causing failures in a controlled way.
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October 16, 2025 Β· 23 min Β· DevOps Engineer
11 min
Linux Observability: Metrics, Logs, eBPF Tools, and 5-Minute Triage
Executive Summary Observability = see inside your systems: metrics (CPU, memory, I/O), logs (audit trail), traces (syscalls, latency).
This guide covers:
Metrics: node_exporter β Prometheus (system-level health) Logs: journald β rsyslog/Vector/Fluent Bit (aggregation) eBPF tools: 5 quick wins (trace syscalls, network, I/O) Triage: 5-minute flowchart to diagnose CPU, memory, I/O, network issues 1. Metrics: node_exporter & Prometheus What It Is node_exporter: Exposes OS metrics (CPU, memory, disk, network) as Prometheus scrape target Prometheus: Time-series database; collects metrics, queries, alerts Dashboard: Grafana visualizes Prometheus data Install node_exporter Ubuntu/Debian:
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October 16, 2025 Β· 11 min Β· 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 layersβand their interdependenciesβis critical for reliable, secure, performant infrastructure.
This guide covers:
Layered architecture (firmware β kernel β userspace β 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 Β· 9 min Β· DevOps Engineer
π οΈ 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 Donβt 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 Β· 21 min Β· DevOps Engineer
π SRE Practice
7 min
Understanding SLOs, SLIs, and SLAs: A Practical Guide
Introduction Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs) are fundamental concepts in Site Reliability Engineering. Understanding and implementing them correctly is crucial for maintaining reliable services.
Core Concepts SLI (Service Level Indicator) Definition: A quantitative measure of service reliability from the userβs perspective.
Common SLIs:
Availability: Percentage of successful requests Latency: Proportion of requests served faster than threshold Throughput: Requests processed per second Error Rate: Percentage of failed requests Example SLI Definitions:
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October 15, 2025 Β· 7 min Β· DevOps Engineer