site-reliability

# Site Reliability Engineering ## What I Do I bridge the gap between development and operations by applying software engineering principles to operations problems. I focus on reliability, scalability, and efficiency through automation, measurement, and continuous improvement. ## When to Use Me - Building reliable production systems - Managing production infrastructure - Implementing SLOs and error budgets - Automating operational tasks - Incident response and post-mortems - Capacity planning and forecasting - Balancing reliability vs. feature velocity ## Core Concepts - **SRE (Site Reliability Engineering)**: Disciplined approach to operations - **SLI (Service Level Indicator)**: Metric measuring service behavior - **SLO (Service Level Objective)**: Target reliability level - **Error Budget**: Allowed unreliability before slowing feature work - **Toil Reduction**: Automating manual operational tasks - **Post-Mortem**: Learning from incidents without blame - **Service Catalog**: Inventory of services with ownership - **Operational Level Agreements**: Internal SLAs between teams - **CAPEX vs OPEX**: Capital and operational expenditure planning - **Reliability Engineering**: Building reliability into systems ## Code Examples **SLO Definition (YAML):** ```yaml apiVersion: reliability/v1 kind: ServiceLevelObjective metadata: name: api-slo namespace: production spec: service: payment-api description: "Payment processing API reliability" indicators: - name: availability description: "API responds successfully" threshold: 99.9 window: 30d - name: latency_p99 description: "99th percentile response time" threshold: "500ms" window: 7d - name: error_rate description: "Rate of 5xx errors" threshold: 0.1 window: 1d - name: throughput description: "Successful requests per second" threshold: 1000 window: 5m type: minimum compliance: rolling alert_budgets: - threshold: 0.5 severity: warning - threshold: 0.2 severity: critical --- apiVersion: reliability/v1 kind: ErrorBudgetPolicy metadata: name: error-budget-config spec: service: payment-api burn_rate_thresholds: - name: fast_burn threshold: 10 # 10x normal burn rate duration: 1m action: page_on_call - name: slow_burn threshold: 2 # 2x normal burn rate duration: 1h action: create_ticket recovery_actions: - name: feature_freeze when_budget_depleted: true - name: incident_declaration when_budget_depleted: true ``` **Incident Response Runbook:** ```markdown # Payment API Incident Runbook ## Severity Levels - **SEV1**: Complete outage, >50% users affected - **SEV2**: Degraded performance, >20% users affected - **SEV3**: Minor impact, <5% users affected - **SEV4**: Potential issue, no user impact ## Initial Response Checklist - [ ] Acknowledge alert within 15 minutes - [ ] Determine severity level - [ ] Declare incident in status page - [ ] Create incident channel in Slack - [ ] Notify on-call engineers ## Common Issues ### Database Connection Pool Exhaustion **Symptoms**: 503 errors, connection timeouts **Diagnosis**: ```bash kubectl exec -it postgres-0 -- psql -c "SELECT count(*) FROM pg_stat_activity;" ``` **Remediation**: 1. Increase connection pool limit in config 2. Restart affected pods: `kubectl rollout restart deployment/payment-api` 3. If persistent, scale up database ### High Latency on Payment Processing **Symptoms**: P99 > 2s, increased error rates **Diagnosis**: ```bash # Check recent traces jaeger-query --service=payment-api --operation=processPayment --lookback=1h ``` **Remediation**: 1. Check downstream dependencies 2. Enable circuit breakers 3. Scale horizontally if needed ## Rollback Procedures ```bash # Rollback to previous version kubectl rollout undo deployment/payment-api # Or rollback to specific image kubectl set image deployment/payment-api payment-api=registry.io/payment:v1.2.3 ``` ``` **Toil Reduction Automation (Python):** ```python #!/usr/bin/env python3 """ Automated operational tasks to reduce toil """ import boto3 import subprocess from datetime import datetime, timedelta class OperationalAutomation: def __init__(self): self.ec2 = boto3.client('ec2') self.s3 = boto3.client('s3') self.k8s = subprocess.run( ['kubectl', 'config', 'view', '-o', 'json'], capture_output=True, text=True ) def cleanup_old_log_files(self): """Delete log files older than 30 days""" cutoff = datetime.now() - timedelta(days=30) result = subprocess.run( ['find', '/var/log', '-name', '*.log', '-mtime', '+30', '-delete'], capture_output=True ) return f"Cleaned logs older than {cutoff}" def rotate_kubernetes_secrets(self): """Rotate service account tokens that are expiring""" result = subprocess.run( ['kubectl', 'get', 'secrets', '-A', '-o', 'json'], capture_output=True, text=True ) # Check for secrets older than 90 days and rotate return "Secret rotation complete" def scale_down_non_production(self): """Scale non-production workloads during off-hours""" clusters = self.ec2.describe_instances( Filters=[ {'Name': 'tag:Environment', 'Values': ['staging', 'development']}, {'Name': 'instance-state-name', 'Values': ['running']} ] ) for instance in clusters['Reservations']: # Implement scale-down logic pass return "Non-production environments scaled down" def generate_capacity_report(self): """Generate weekly capacity utilization report""" metrics = { 'cpu_utilization': [], 'memory_utilization': [], 'storage_iops': [], 'network_throughput': [] } # Collect metrics across all services return metrics def detect_anomalies(self): """Detect operational anomalies using statistical analysis""" # Implement anomaly detection return { 'anomalies_detected': 0, 'requires_attention': [] } def run_automated_remediation(self): """Run scheduled automated remediation tasks""" tasks = [ self.cleanup_old_log_files, self.rotate_kubernetes_secrets, self.scale_down_non_production, self.detect_anomalies ] results = [] for task in tasks: try: result = task() results.append({'task': task.__name__, 'status': 'success', 'result': result}) except Exception as e: results.append({'task': task.__name__, 'status': 'failed', 'error': str(e)}) return results if __name__ == "__main__": automation = OperationalAutomation() automation.run_automated_remediation() ``` ## Best Practices 1. **Define SLOs early** - Measure what matters to users 2. **Use error budgets** - Balance reliability and velocity 3. **Automate everything** - Eliminate manual toil where possible 4. **Write runbooks** - Document operational procedures 5. **Conduct regular game days** - Practice incident response 6. **Implement SRE rotation** - On-call with reasonable load 7. **Learn from incidents** - Blameless post-mortems 8. **Monitor SLO performance** - Alert on error budget burn rate 9. **Prioritize reliability work** - Balance features vs. stability 10. **Graduate to production carefully** - Progressive rollouts