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
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
12 min
Linux Reliability & Lifecycle: Time Sync, Logging, Shutdown, and Patching
Executive Summary Reliability means predictable, auditable behavior. This guide covers:
Time sync: Chrony for clock accuracy (critical for logging, security) Networking: Stable interface names & hostnames (infrastructure consistency) Logging: Persistent journald + logrotate (audit trail + disk management) Shutdown: Clean hooks to prevent data loss Patching: Kernel updates with livepatch (zero-downtime), tested rollback 1. Time Synchronization (chrony) Why Time Matters Critical for:
Logging: Accurate timestamps for debugging, compliance audits Security: TLS cert validation, Kerberos, API token expiry Distributed systems: Causality ordering (happens-before relationships) Monitoring: Alert timing, metric correlation Cost of poor time sync:
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October 16, 2025 Β· 12 min Β· DevOps Engineer
π οΈ Guide
10 min
ELK Stack Tuning: Elasticsearch Index Lifecycle and Logstash Pipelines
Introduction The ELK stack (Elasticsearch, Logstash, Kibana) is powerful for log aggregation and analysis, but requires proper tuning for production workloads. This guide covers Elasticsearch index lifecycle management, Logstash pipeline optimization, and performance best practices.
Elasticsearch Index Lifecycle Management (ILM) Understanding ILM ILM automates index management through lifecycle phases:
Phases:
Hot - Actively writing and querying Warm - No longer writing, still querying Cold - Rarely queried, compressed Frozen - Very rarely queried, minimal resources Delete - Removed from cluster Basic ILM Policy Create policy:
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October 15, 2025 Β· 10 min Β· DevOps Engineer