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

Severity	Impact	Response Time	Example
SEV1	Complete outage, data loss	15 min	Production down
SEV2	Major degradation	30 min	Critical feature broken
SEV3	Minor impact	2 hours	Non-critical bug
SEV4	Minimal impact	Next business day	Cosmetic issue

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

Runbook Templates

Template 1: Service Outage Runbook

# [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](https://grafana/d/payments)
- [Error Tracking](https://sentry.io/payments)
- [Dependency Status](https://status.stripe.com)

## Initial Triage (First 5 Minutes)

### 1. Assess Scope

```bash
# 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

Symptom	Likely Cause	Go To Section
All requests failing	Service down	Section 4.1
High latency	Database/dependency	Section 4.2
Partial failures	Code bug	Section 4.3
Spike in errors	Traffic surge	Section 4.4

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

Condition	Escalate To	Contact
> 15 min unresolved SEV1	Engineering Manager	@manager (Slack)
Data breach suspected	Security Team	#security-incidents
Financial impact > $10k	Finance + Legal	@finance-oncall
Customer communication needed	Support Lead	@support-lead

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

```markdown
# 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
```sql
-- 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

## 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:

```bash
# 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