Incident Runbook Templates

Incident Runbook Templates

Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.

When to Use This Skill

• Creating incident response procedures
• Building service-specific runbooks
• Establishing escalation paths
• Documenting recovery procedures
• Responding to active incidents
• Onboarding on-call engineers

Core Concepts

1. Incident Severity Levels

2. Runbook Structure

1. Overview & Impact
2. Detection & Alerts
3. Initial Triage
4. Mitigation Steps
5. Root Cause Investigation
6. Resolution Procedures
7. Verification & Rollback
8. Communication Templates
9. Escalation Matrix

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

• Keep runbooks updated - Review after every incident
• Test runbooks regularly - Game days, chaos engineering
• Include rollback steps - Always have an escape hatch
• Document assumptions - What must be true for steps to work
• Link to dashboards - Quick access during stress

Don'ts

• Don't assume knowledge - Write for 3 AM brain
• Don't skip verification - Confirm each step worked
• Don't forget communication - Keep stakeholders informed
• Don't work alone - Escalate early
• Don't skip postmortems - Learn from every incident

Troubleshooting

Runbook steps work in staging but fail during a real incident

Steps often assume preconditions that are true in a healthy environment but not during an outage. For each command in your runbook, add a prerequisite check and a "what to do if this command fails" note:

# Step: Check pod status
kubectl get pods -n payments

# Prerequisites: kubectl configured, kubeconfig points to correct cluster
# If this fails: run `aws eks update-kubeconfig --name prod-cluster --region us-east-1`
# Expected output: pods in Running state

On-call engineer panics and skips steps out of order

Add a numbered checklist at the top of the runbook that mirrors the section numbers, so responders can track progress under stress without reading the full document:

## Quick Checklist
- [ ] 1. Declare incident severity and open war room
- [ ] 2. Check service health (Section 4.1)
- [ ] 3. Check recent deployments (Section 4.1)
- [ ] 4. Roll back if deploy is suspect (Section 4.1)
- [ ] 5. Post initial notification to #payments-incidents
- [ ] 6. Escalate if > 15 min unresolved

Runbook is outdated — commands reference old cluster names or endpoints

Runbooks rot because they're updated manually. Include a "Last Verified" date and owner at the top, and add a CI check that validates all curl endpoints and kubectl context names are still valid:

## Runbook Metadata
| Field | Value |
|---|---|
| Last verified | 2024-11-15 |
| Owner | @platform-team |
| Review cadence | After every SEV1/SEV2 |

Stakeholder communication is delayed while engineers are heads-down

Assign a dedicated incident communicator role (separate from the incident commander) whose only job is to post status updates. Add a standing agenda in the communication template:

Update every 15 minutes (even if no new information):
- Current status (Investigating / Mitigating / Monitoring)
- Impact (what is broken, who is affected, % of traffic)
- What we are doing right now
- Next update in: 15 minutes

Database runbook commands cause additional downtime when run incorrectly

Add explicit warnings before destructive SQL commands and require a dry-run output check before executing:

-- WARNING: This terminates active connections. Verify count first.
-- DRY RUN (check count before terminating):
SELECT count(*) FROM pg_stat_activity WHERE state = 'idle' AND query_start < now() - interval '10 minutes';

-- EXECUTE only after verifying count is reasonable (< 50):
SELECT pg_terminate_backend(pid) FROM pg_stat_activity
WHERE state = 'idle' AND query_start < now() - interval '10 minutes';

Related Skills

• postmortem-writing - After resolving an incident, use postmortem templates to capture root cause and preventive actions
• on-call-handoff-patterns - Structure shift handoffs so the incoming responder has full context on active incidents

incident-runbook-templates — detailed patterns and worked examples

Runbook Templates

Template 1: Service Outage Runbook

````markdown

[Service Name] Outage Runbook

Overview

Service: Payment Processing Service Owner: Platform Team Slack: #payments-incidents PagerDuty: payments-oncall

Impact Assessment

• [ ] Which customers are affected?
• [ ] What percentage of traffic is impacted?
• [ ] Are there financial implications?
• [ ] What's the blast radius?

Detection

Alerts

• payment_error_rate > 5% (PagerDuty)
• payment_latency_p99 > 2s (Slack)
• payment_success_rate < 95% (PagerDuty)

Dashboards

• Payment Service Dashboard
• Error Tracking
• Dependency Status

Initial Triage (First 5 Minutes)

1. Assess Scope

# Check service health
kubectl get pods -n payments -l app=payment-service

# Check recent deployments
kubectl rollout history deployment/payment-service -n payments

# Check error rates
curl -s "http://prometheus:9090/api/v1/query?query=sum(rate(http_requests_total{status=~'5..'}[5m]))"

````

2. Quick Health Checks

• [ ] Can you reach the service? curl -I https://api.company.com/payments/health
• [ ] Database connectivity? Check connection pool metrics
• [ ] External dependencies? Check Stripe, bank API status
• [ ] Recent changes? Check deploy history

3. Initial Classification

Mitigation Procedures

4.1 Service Completely Down

# Step 1: Check pod status
kubectl get pods -n payments

# Step 2: If pods are crash-looping, check logs
kubectl logs -n payments -l app=payment-service --tail=100

# Step 3: Check recent deployments
kubectl rollout history deployment/payment-service -n payments

# Step 4: ROLLBACK if recent deploy is suspect
kubectl rollout undo deployment/payment-service -n payments

# Step 5: Scale up if resource constrained
kubectl scale deployment/payment-service -n payments --replicas=10

# Step 6: Verify recovery
kubectl rollout status deployment/payment-service -n payments

4.2 High Latency

# Step 1: Check database connections
kubectl exec -n payments deploy/payment-service -- \
  curl localhost:8080/metrics | grep db_pool

# Step 2: Check slow queries (if DB issue)
psql -h $DB_HOST -U $DB_USER -c "
  SELECT pid, now() - query_start AS duration, query
  FROM pg_stat_activity
  WHERE state = 'active' AND duration > interval '5 seconds'
  ORDER BY duration DESC;"

# Step 3: Kill long-running queries if needed
psql -h $DB_HOST -U $DB_USER -c "SELECT pg_terminate_backend(pid);"

# Step 4: Check external dependency latency
curl -w "@curl-format.txt" -o /dev/null -s https://api.stripe.com/v1/health

# Step 5: Enable circuit breaker if dependency is slow
kubectl set env deployment/payment-service \
  STRIPE_CIRCUIT_BREAKER_ENABLED=true -n payments

4.3 Partial Failures (Specific Errors)

# Step 1: Identify error pattern
kubectl logs -n payments -l app=payment-service --tail=500 | \
  grep -i error | sort | uniq -c | sort -rn | head -20

# Step 2: Check error tracking
# Go to Sentry: https://sentry.io/payments

# Step 3: If specific endpoint, enable feature flag to disable
curl -X POST https://api.company.com/internal/feature-flags \
  -d '{"flag": "DISABLE_PROBLEMATIC_FEATURE", "enabled": true}'

# Step 4: If data issue, check recent data changes
psql -h $DB_HOST -c "
  SELECT * FROM audit_log
  WHERE table_name = 'payment_methods'
  AND created_at > now() - interval '1 hour';"

4.4 Traffic Surge

# Step 1: Check current request rate
kubectl top pods -n payments

# Step 2: Scale horizontally
kubectl scale deployment/payment-service -n payments --replicas=20

# Step 3: Enable rate limiting
kubectl set env deployment/payment-service \
  RATE_LIMIT_ENABLED=true \
  RATE_LIMIT_RPS=1000 -n payments

# Step 4: If attack, block suspicious IPs
kubectl apply -f - <<EOF
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: block-suspicious
  namespace: payments
spec:
  podSelector:
    matchLabels:
      app: payment-service
  ingress:
  - from:
    - ipBlock:
        cidr: 0.0.0.0/0
        except:
        - 192.168.1.0/24  # Suspicious range
EOF

Verification Steps

# Verify service is healthy
curl -s https://api.company.com/payments/health | jq

# Verify error rate is back to normal
curl -s "http://prometheus:9090/api/v1/query?query=sum(rate(http_requests_total{status=~'5..'}[5m]))" | jq '.data.result[0].value[1]'

# Verify latency is acceptable
curl -s "http://prometheus:9090/api/v1/query?query=histogram_quantile(0.99,sum(rate(http_request_duration_seconds_bucket[5m]))by(le))" | jq

# Smoke test critical flows
./scripts/smoke-test-payments.sh

Rollback Procedures

# Rollback Kubernetes deployment
kubectl rollout undo deployment/payment-service -n payments

# Rollback database migration (if applicable)
./scripts/db-rollback.sh $MIGRATION_VERSION

# Rollback feature flag
curl -X POST https://api.company.com/internal/feature-flags \
  -d '{"flag": "NEW_PAYMENT_FLOW", "enabled": false}'

Escalation Matrix

Communication Templates

Initial Notification (Internal)

🚨 INCIDENT: Payment Service Degradation

Severity: SEV2
Status: Investigating
Impact: ~20% of payment requests failing
Start Time: [TIME]
Incident Commander: [NAME]

Current Actions:
- Investigating root cause
- Scaling up service
- Monitoring dashboards

Updates in #payments-incidents

Status Update

📊 UPDATE: Payment Service Incident

Status: Mitigating
Impact: Reduced to ~5% failure rate
Duration: 25 minutes

Actions Taken:
- Rolled back deployment v2.3.4 → v2.3.3
- Scaled service from 5 → 10 replicas

Next Steps:
- Continuing to monitor
- Root cause analysis in progress

ETA to Resolution: ~15 minutes

Resolution Notification

✅ RESOLVED: Payment Service Incident

Duration: 45 minutes
Impact: ~5,000 affected transactions
Root Cause: Memory leak in v2.3.4

Resolution:
- Rolled back to v2.3.3
- Transactions auto-retried successfully

Follow-up:
- Postmortem scheduled for [DATE]
- Bug fix in progress

````

Template 2: Database Incident Runbook

# Database Incident Runbook

## Quick Reference
| Issue | Command |
|-------|---------|
| Check connections | `SELECT count(*) FROM pg_stat_activity;` |
| Kill query | `SELECT pg_terminate_backend(pid);` |
| Check replication lag | `SELECT extract(epoch from (now() - pg_last_xact_replay_timestamp()));` |
| Check locks | `SELECT * FROM pg_locks WHERE NOT granted;` |

## Connection Pool Exhaustion

-- Check current connections SELECT datname, usename, state, count() FROM pg_stat_activity GROUP BY datname, usename, state ORDER BY count() DESC;

-- Identify long-running connections SELECT pid, usename, datname, state, query_start, query FROM pg_stat_activity WHERE state != 'idle' ORDER BY query_start;

-- Terminate idle connections SELECT pg_terminate_backend(pid) FROM pg_stat_activity WHERE state = 'idle' AND query_start < now() - interval '10 minutes'; ````

Replication Lag

-- Check lag on replica
SELECT
  CASE
    WHEN pg_last_wal_receive_lsn() = pg_last_wal_replay_lsn() THEN 0
    ELSE extract(epoch from now() - pg_last_xact_replay_timestamp())
  END AS lag_seconds;

-- If lag > 60s, consider:
-- 1. Check network between primary/replica
-- 2. Check replica disk I/O
-- 3. Consider failover if unrecoverable

Disk Space Critical

# Check disk usage
df -h /var/lib/postgresql/data

# Find large tables
psql -c "SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC
LIMIT 10;"

# VACUUM to reclaim space
psql -c "VACUUM FULL large_table;"

# If emergency, delete old data or expand disk