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Alerting Best Practices

18. 06. 2022 1 min read intermediate

DevOps Intermediate

Alerting Best Practices

AlertingMonitoringSREObservability 6 min read

Effective alerting for production systems. Alert design, routing, grouping and noise reduction.

Alert Design Principles

  • Symptom-based: alert on impact (error rate), not cause (CPU high)
  • Actionable: every alert = someone must do something
  • Runbook link: every alert links to runbook
  • Appropriate severity: P1 = page, P3 = ticket
  • Tuned thresholds: minimize false positives

Routing and Grouping

# Alertmanager config
route:
  group_by: [alertname, namespace, service]
  group_wait: 30s
  group_interval: 5m
  repeat_interval: 4h
  receiver: default
  routes:
    - match:
        severity: critical
      receiver: pagerduty
      group_wait: 10s
      repeat_interval: 1h
    - match:
        severity: warning
      receiver: slack
    - match:
        severity: info
      receiver: email

inhibit_rules:
  - source_match:
      severity: critical
    target_match:
      severity: warning
    equal: [namespace, service]

Noise Reduction

  • Inhibition: critical suppresses warning for same service
  • Silences: temporary silencing during maintenance
  • Deduplication: grouping related alerts
  • Alerting on SLO burn rate instead of individual metrics

Alert Quality Metrics

  • False positive rate: < 5% (target)
  • Alert-to-incident ratio: how many alerts lead to action?
  • MTTA (Mean Time to Acknowledge): < 5 min for P1
  • Alerts per on-call shift: < 5 (target)

Summary

Quality alerting = symptom-based, actionable, with runbook link. Measure alert quality and continuously reduce noise.

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Stavíme core systémy a AI agenty, které drží provoz. 15 let zkušeností s enterprise IT.