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Incident Runbook Templates
试用Production-ready incident response runbook templates. Step-by-step procedures for detection, triage, mitigation, resolution, and communication. Includes esca...
它能做什么
Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.
技能文档
Incident Runbook Templates
Production-ready templates for incident response runbooks covering detection, triage, mitigation, resolution, and communication.
Do not use this skill when
- The task is unrelated to incident runbook templates
- You need a different domain or tool outside this scope
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Use this skill when
- 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 - < 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
# 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
## Resources
- [Google SRE Book - Incident Management](https://sre.google/sre-book/managing-incidents/)
- [PagerDuty Incident Response](https://response.pagerduty.com/)
- [Atlassian Incident Management](https://www.atlassian.com/incident-management)
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