Monitoring
#9 10 pagesMonitoring & Observability
Metrics, logs, traces, and alerting — from Prometheus fundamentals to long-term storage and SLO-driven alerting.
Files
| File | Topics |
|---|---|
| prometheus.md | Architecture, data model (4 types + math), TSDB internals, PromQL, scrape config, recording rules, production alerts |
| alertmanager.md | Routing tree, grouping, inhibition, silences, complete config, debugging |
| grafana.md | Panel types, variables, USE/RED/SLO dashboards, provisioning as code |
| alerting-philosophy.md | Four Golden Signals, symptoms vs causes, alert fatigue, urgency tiers, runbook structure |
| opentelemetry.md | Three pillars (traces/metrics/logs), OTEL Collector, Go SDK, auto-instrumentation |
| loki.md | Architecture, labels vs content, LogQL, Promtail, trace correlation, Fluent Bit zero-loss pipeline |
| performance-debugging.md | USE method, RED method, 60-second checklist, Go pprof, bpftrace one-liners |
| slo-sli.md | SLI/SLO/Error Budget math, multi-window multi-burn-rate alerts, recording rules, decision framework |
| thanos-mimir.md | Thanos components, Mimir distributed TSDB, long-term S3 storage, deduplication, downsampling |
| monitoring-scenarios.md | 12 debugging scenarios with Prevention: target DOWN, missing metrics, Prometheus OOM, alert storms |
Database-specific monitoring (PostgreSQL, MySQL, Redis, MongoDB) lives in ../sre/db-monitoring.md.
Read Order
prometheus → data model, TSDB, PromQL foundation
alertmanager → routing, grouping, inhibition
grafana → dashboards (USE/RED/SLO)
alerting-philosophy → what to alert on, symptoms vs causes
opentelemetry → traces/metrics/logs, OTEL Collector
loki → log aggregation, LogQL
performance-debugging → USE/RED methods in practice
slo-sli → error budgets, burn-rate alerts
thanos-mimir → long-term/global metric storage
monitoring-scenarios → applied debugging runbooks