Scaling Guide¶

Strategies and best practices for scaling Amebo to handle increased load and ensure high availability.

Scaling Strategies¶

Horizontal Scaling¶

Add more Amebo instances:

# Scale with Docker Compose
docker-compose up -d --scale amebo=5

# Kubernetes scaling
kubectl scale deployment amebo --replicas=5

Vertical Scaling¶

Increase resources per instance:

services:
  amebo:
    deploy:
      resources:
        limits:
          cpus: '4.0'
          memory: 4G

Database Scaling¶

Read replicas for query distribution
Connection pooling optimization
Database sharding strategies

Load Balancing¶

Nginx Configuration¶

upstream amebo_backend {
    least_conn;
    server amebo-1:3310 max_fails=3 fail_timeout=30s;
    server amebo-2:3310 max_fails=3 fail_timeout=30s;
    server amebo-3:3310 max_fails=3 fail_timeout=30s;
}

Health Checks¶

Ensure load balancer only routes to healthy instances:

location /health {
    proxy_pass http://amebo_backend/health;
    proxy_connect_timeout 5s;
    proxy_read_timeout 5s;
}

Performance Optimization¶

Configuration Tuning¶

{
  "AMEBO_ENVELOPE": 1024,
  "AMEBO_IDLES": 2,
  "AMEBO_REST_WHEN": 20
}

Database Optimization¶

-- Increase connection limits
ALTER SYSTEM SET max_connections = 500;

-- Optimize memory settings
ALTER SYSTEM SET shared_buffers = '512MB';
ALTER SYSTEM SET effective_cache_size = '2GB';

Monitoring at Scale¶

Key Metrics¶

Requests per second across all instances
Response time distribution
Error rates by instance
Resource utilization trends

Auto-scaling¶

# Kubernetes HPA
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: amebo-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: amebo
  minReplicas: 3
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70

Best Practices¶

Capacity Planning¶

Baseline performance testing
Load testing with realistic scenarios
Monitor growth trends
Plan for peak loads

Deployment Strategies¶

Blue-green deployments
Rolling updates
Canary releases
Circuit breaker patterns

Data Management¶

Event retention policies
Archive old events
Optimize database indexes
Regular maintenance tasks