alerting-oncall

Alerting & On-Call

Configure effective alerting and on-call management for production systems.

Prerequisites

• Monitoring system (Prometheus, Datadog, etc.)
• On-call platform (PagerDuty, Opsgenie, Grafana OnCall)
• Communication channels (Slack, email)

Alerting Best Practices

Alert Categories

# Severity levels
critical:
  - Service completely down
  - Data loss imminent
  - Security breach
  response: Immediate page, wake people up

high:
  - Service degraded significantly
  - Error rate above SLO
  - Capacity near limit
  response: Page during business hours, notify after hours

medium:
  - Performance degradation
  - Non-critical component failure
  - Warning thresholds exceeded
  response: Notify via Slack, review next business day

low:
  - Informational alerts
  - Capacity planning triggers
  - Routine maintenance needed
  response: Email notification, weekly review

Alert Design Principles

# Good alert characteristics
alerts:
  actionable:
    - Every alert should require human action
    - Include runbook links
    - Clear remediation steps

  relevant:
    - Alert on symptoms, not causes
    - Focus on user impact
    - Avoid alerting on expected behavior

  timely:
    - Appropriate thresholds
    - Suitable evaluation windows
    - Account for normal variance

  unique:
    - No duplicate alerts
    - Proper alert grouping
    - Clear ownership

Prometheus Alerting

Alert Rules

# prometheus/rules/alerts.yml
groups:
  - name: service_alerts
    rules:
      # High-level service health
      - alert: ServiceDown
        expr: up{job="myapp"} == 0
        for: 1m
        labels:
          severity: critical
        annotations:
          summary: "Service {{ $labels.instance }} is down"
          description: "{{ $labels.job }} on {{ $labels.instance }} has been down for more than 1 minute."
          runbook_url: "https://wiki.example.com/runbooks/service-down"

      # Error rate alert
      - alert: HighErrorRate
        expr: |
          sum(rate(http_requests_total{status=~"5.."}[5m])) by (service)
          / sum(rate(http_requests_total[5m])) by (service) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High error rate for {{ $labels.service }}"
          description: "Error rate is {{ $value | humanizePercentage }} for the last 5 minutes"

      # Latency alert (SLO-based)
      - alert: HighLatency
        expr: |
          histogram_quantile(0.95, 
            sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service)
          ) > 0.5
        for: 5m
        labels:
          severity: high
        annotations:
          summary: "P95 latency above 500ms for {{ $labels.service }}"