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3.1.2.6. Implementing Auto Scaling, Load Balancing, Caching Solutions
3.1.2.6. Implementing Auto Scaling, Load Balancing, Caching Solutions
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Auto Scaling lifecycle hooks let you run custom actions during instance launch or termination. Use cases: pull configuration from S3 before entering service, drain connections before termination, register/deregister from external service discovery.
Caching architecture reduces load on backends and improves response time:
| Cache Layer | Service | Use Case |
|---|---|---|
| CDN (edge) | CloudFront | Static assets, API responses near users |
| API response | API Gateway cache | Repeated API calls with same parameters |
| Application | ElastiCache Redis/Memcached | Session data, computed results, database query cache |
| Database | DynamoDB DAX | Microsecond read latency for DynamoDB |
ElastiCache Redis vs. Memcached:
- Redis: Persistence, replication, pub/sub, complex data types, Multi-AZ failover. Choose for most use cases.
- Memcached: Simple key-value, multi-threaded, no persistence. Choose for simple caching with horizontal scaling.
Cache invalidation strategies:
- TTL-based: Set expiration time. Simple but may serve stale data.
- Write-through: Update cache when database is updated. Consistent but adds write latency.
- Cache-aside (lazy loading): Load into cache on first miss. Most common pattern.
Exam Trap: CloudFront's default TTL is 24 hours. If you deploy a new version of your application and users still see the old version, you need to create an invalidation (/*) or use versioned file names (cache busting). The exam often tests whether you understand CloudFront caching behavior during deployments.
Written byAlvin Varughese•Founder•15 professional certifications