SRE - Site Reliability Engineering

Comprehensive guide for implementing SRE practices: SLI/SLO/SLA definitions, error budgets, incident management, on-call best practices, chaos engineering, observability, and reliability patterns for production systems.

Purpose

Enable teams to build and operate reliable, scalable systems by applying software engineering principles to infrastructure and operations challenges.

When to Use This Skill

Automatically activates when working on:

• Defining SLIs, SLOs, and SLAs
• Managing error budgets
• Incident response and management
• On-call rotation and procedures
• Observability and monitoring
• Chaos engineering and resilience testing
• Capacity planning and performance
• Disaster recovery planning

Quick Start Checklist

When implementing SRE practices:

• Define Service Level Indicators (SLIs)
• Set Service Level Objectives (SLOs) with stakeholders
• Implement error budget tracking
• Set up comprehensive monitoring and alerting
• Create incident response runbooks
• Establish on-call rotation
• Implement automated remediation
• Conduct post-incident reviews
• Perform chaos engineering experiments
• Document capacity planning process
• Set up disaster recovery procedures

Core Concepts

SLI / SLO / SLA Hierarchy

SLA (Service Level Agreement)
  ↓ Commits to customers
  ↓
SLO (Service Level Objective)
  ↓ Internal target (stricter than SLA)
  ↓
SLI (Service Level Indicator)
  ↓ Measurement that shows if you're meeting SLO
  ↓
Metrics (actual measurements)

Example:

SLA:  99.9% uptime guaranteed (3 customers)
SLO:  99.95% uptime target (internal)
SLI:  Percentage of successful HTTP requests
      (2xx, 3xx responses / total requests)

Error Budgets

Error Budget = 1 - SLO

Example:
SLO: 99.9% → Error Budget: 0.1%

Monthly calculations (30 days):
- Total time: 43,200 minutes
- Allowed downtime: 43.2 minutes

If error budget is:
- Available → Can take risks, deploy features
- Depleted → Focus on reliability, freeze features

The Four Golden Signals

1. Latency: How long requests take
   - Good: p50, p95, p99 latencies
   - Bad: Average latency (hides outliers)

2. Traffic: Demand on your system
   - Requests per second
   - Transactions per second
   - Bandwidth usage

3. Errors: Rate of failed requests
   - HTTP 5xx errors
   - Failed operations
   - Wrong results

4. Saturation: How "full" your service is
   - CPU usage
   - Memory usage
   - Disk I/O
   - Queue depth

Common Patterns

Pattern 1: SLO Definition

# slos/api-service.yaml
apiVersion: v1
kind: SLO
metadata:
  name: api-service-availability
  service: api-service
spec:
  description: "API service availability for customer requests"

  # SLI definition
  sli:
    name: availability
    description: "Percentage of successful requests"
    query: |
      sum(rate(http_requests_total{job="api-service",code=~"2.."}[5m]))
      /
      sum(rate(http_requests_total{job="api-service"}[5m]))

  # SLO target
  objectives:
  - target: 0.999  # 99.9%
    window: 30d    # Rolling 30 days

  # Alert thresholds
  alerting:
    - burn_rate: 14.4   # Alert if burning budget 14.4x faster
      short_window: 1h
      long_window: 5m
      severity: critical

    - burn_rate: 6
      short_window: 6h
      long_window: 30m
      severity: warning

Latency SLO:

apiVersion: v1
kind: SLO
metadata:
  name: api-service-latency
spec:
  sli:
    name: latency
    description: "95th percentile latency under 200ms"
    query: |
      histogram_quantile(0.95,
        sum(rate(http_request_duration_seconds_bucket{job="api-service"}[5m])) by (le)
      )

  objectives:
  - target: 0.200  # 200ms
    window: 7d

Pattern 2: Incident Response

Incident Severity Levels:

Severity 1 (Critical):
  - Complete service outage
  - Security breach
  - Data loss
  Response: Immediate, all hands
  Escalation: 15 minutes

Severity 2 (High):
  - Partial outage
  - Degraded performance
  - Feature broken
  Response: Within 30 minutes
  Escalation: 1 hour

Severity 3 (Medium):
  - Minor issues
  - Non-critical features affected
  Response: Within 4 hours
  Escalation: Next business day

Severity 4 (Low):
  - Cosmetic issues
  - Questions
  Response: Best effort

Incident Runbook Template:

# Incident: API Service High Latency

## Symptoms
- API p95 latency > 500ms
- Users reporting slow responses
- Alert: "api-service-latency-high" firing

## Impact
- Degraded user experience
- Potential timeout errors

## Investigation Steps

1. Check service status:
   \`\`\`bash
   kubectl get pods -n production -l app=api-service
   kubectl top pods -n production -l app=api-service
   \`\`\`

2. Check recent deployments:
   \`\`\`bash
   kubectl rollout history deployment/api-service -n production
   \`\`\`

3. Check database performance:
   - Query: `rate(pg_stat_database_tup_fetched[5m])`
   - Check slow query log

4. Check downstream dependencies:
   - External API status
   - Cache hit rate

## Mitigation

### Quick Fixes
- Scale up: `kubectl scale deployment api-service --replicas=10`
- Rollback: `kubectl rollout undo deployment/api-service`

### Root Cause Fixes
- Optimize slow queries
- Add caching layer
- Increase resource limits

## Communication

- Slack: #incidents channel
- Status page: Update customer-facing status
- Stakeholders: Notify via PagerDuty

## Post-Incident

- Create post-incident review
- Update runbook with learnings
- File tickets for improvements

Pattern 3: Chaos Engineering

# chaos-experiments/pod-failure.yaml
apiVersion: chaos-mesh.org/v1alpha1
kind: PodChaos
metadata:
  name: api-service-pod-failure
  namespace: chaos-testing
spec:
  action: pod-failure
  mode: one
  selector:
    namespaces:
      - production
    labelSelectors:
      app: api-service
  duration: "30s"
  scheduler:
    cron: "@every 2h"  # Run every 2 hours

---
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🎯 SKILL ACTIVATED: sre
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# Network chaos
apiVersion: chaos-mesh.org/v1alpha1
kind: NetworkChaos
metadata:
  name: api-network-delay
spec:
  action: delay
  mode: one
  selector:
    namespaces:
      - production
    labelSelectors:
      app: api-service
  delay:
    latency: "100ms"
    correlation: "100"
    jitter: "0ms"
  duration: "5m"

Resource Files

For detailed guidance on specific topics, see:

Core SRE Practices

• slo-sli-sla.md - Service level definitions, SLO implementation, error budgets, SLA management
• incident-management.md - Incident response procedures, runbooks, post-mortems, severity levels
• toil-reduction.md - Identifying toil, automation strategies, measuring toil reduction

Observability

• observability-stack.md - Metrics, logs, traces, Prometheus, Grafana, Jaeger, distributed tracing
• alerting-best-practices.md - Alert design, notification strategies, on-call alerting, alert fatigue
• performance-optimization.md - Profiling, performance tuning, optimization techniques, bottleneck analysis

Operations

• on-call-runbooks.md - On-call rotations, escalation procedures, runbook templates, burnout prevention
• capacity-planning.md - Load testing, capacity modeling, growth forecasting, resource planning
• disaster-recovery.md - Backup strategies, RPO/RTO, DR testing, failover procedures, BC planning

Reliability Patterns

• reliability-patterns.md - Circuit breakers, retries, timeouts, bulkheads, rate limiting, graceful degradation
• chaos-engineering.md - Fault injection, resilience testing, game days, chaos experiments

Best Practices

1. Start with User-Focused SLIs

Measure what matters to users, not infrastructure metrics.

# Good SLIs
- Request success rate
- Request latency
- Data freshness

# Poor SLIs
- CPU usage
- Memory usage
- Pod count

2. Set Realistic SLOs

Don't aim for 100% - it's:
- Impossible to achieve
- Extremely expensive
- Prevents risk-taking

Typical SLOs:
- User-facing: 99.9% - 99.99%
- Internal services: 99% - 99.9%
- Batch jobs: 95% - 99%

3. Embrace Error Budgets

Error budget available:
✅ Deploy new features
✅ Experiment
✅ Take calculated risks

Error budget depleted:
❌ Feature freeze
✅ Focus on reliability
✅ Fix technical debt

4. Automate Toil

Toil = Manual, repetitive, automatable work

Track toil:
- Measure time spent on toil
- Target: <50% of SRE time on toil
- Automate high-frequency tasks

5. Blameless Post-Mortems

# Post-Incident Review Template

## Incident Summary
- Date/Time
- Duration
- Severity
- Services affected

## Impact
- Users affected
- Revenue impact
- SLO impact

## Root Cause
Technical explanation (not who)

## Timeline
Detailed chronology

## Resolution
What fixed it

## Action Items
- [ ] Short-term fixes
- [ ] Long-term improvements
- [ ] Process changes

## Lessons Learned
What went well
What could be improved

6. Gradual Rollouts

Deployment stages:
1. Canary (1% traffic) - 15 mins
2. Small (10% traffic) - 30 mins
3. Medium (50% traffic) - 1 hour
4. Full (100% traffic)

Automated rollback on:
- Error rate increase >1%
- Latency increase >20%
- Failed health checks

Anti-Patterns to Avoid

❌ 100% availability target (unrealistic, expensive) ❌ Ignoring error budgets (defeats the purpose) ❌ Alert fatigue (too many noisy alerts) ❌ Manual toil (should be automated) ❌ Blame culture (prevents learning) ❌ No runbooks (tribal knowledge) ❌ Reactive only (should be proactive) ❌ Monitoring without alerting (data without action) ❌ No capacity planning (surprise outages) ❌ Skipping post-mortems (missing learning opportunities)

Integration Points

This skill integrates with:

• platform-engineering: Infrastructure reliability, Kubernetes operations
• devsecops: Security incident response, vulnerability management
• release-engineering: Safe deployments, rollback strategies
• cloud-engineering: Cloud monitoring, disaster recovery
• systems-engineering: Performance tuning, system monitoring

Triggers and Activation

This skill activates when you:

• Define or modify SLOs/SLIs
• Respond to incidents
• Set up monitoring and alerting
• Plan capacity or performance optimization
• Conduct post-incident reviews
• Implement chaos engineering

Focus: Reliability through engineering discipline Key Principle: Measure everything, improve continuously Maintained by: SRE team based on Google SRE practices and industry standards