site-reliability

name	site-reliability
description	Practices and principles for designing, building, and operating reliable systems at scale
category	devops

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):

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: 
  
      
       
      
       


 
 

   

   
  
  
      
         
       
       
    
      
         
       
       
  
  
      
       
      
       

Incident Response Runbook:

# 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:

# 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

# 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