Web Cache - Trade-offs and Alternatives
Caching Strategies
Cache-Aside vs Write-Through
Cache-Aside (Lazy Loading): ✅ Only cache what's requested ✅ Resilient to cache failures ❌ Cache miss penalty ❌ Potential stale data
Write-Through: ✅ Always consistent ✅ No cache misses ❌ Higher write latency ❌ Wasted cache space
TTL vs Event-Based Invalidation
TTL-Based: ✅ Simple, automatic ✅ Predictable behavior ❌ May serve stale data ❌ May evict fresh data
Event-Based: ✅ Always fresh data ✅ Efficient invalidation ❌ Complex implementation ❌ Requires event infrastructure
Storage Trade-offs
Memory vs Disk
Memory Cache: ✅ Ultra-low latency (<1ms) ✅ High throughput ❌ Limited capacity ❌ Expensive per GB ❌ Volatile (lost on restart)
Disk Cache: ✅ Large capacity ✅ Persistent ✅ Cost-effective ❌ Higher latency (5-50ms) ❌ Lower throughput
Compression Trade-offs
Compressed Storage: ✅ 3-5x space savings ✅ Reduced bandwidth ❌ CPU overhead ❌ Decompression latency
Uncompressed: ✅ No CPU overhead ✅ Faster serving ❌ More storage needed ❌ Higher bandwidth
Consistency Trade-offs
Strong vs Eventual Consistency
Strong Consistency: ✅ Always fresh data ✅ No stale reads ❌ Higher latency ❌ Lower availability ❌ Complex coordination
Eventual Consistency: ✅ Low latency ✅ High availability ✅ Simple implementation ❌ Temporary stale data ❌ Invalidation lag
Alternative Architectures
Varnish vs Nginx vs Squid
Varnish:
- In-memory caching
- VCL configuration language
- Excellent for static content
- Limited disk caching
Nginx:
- Reverse proxy + cache
- Simple configuration
- Good all-around performance
- Limited cache management
Squid:
- Forward + reverse proxy
- Disk-based caching
- Complex configuration
- Good for large objects
CDN vs Self-Hosted Cache
CDN (CloudFlare, Akamai): ✅ Global edge network ✅ DDoS protection ✅ Managed service ❌ Vendor lock-in ❌ Higher cost at scale ❌ Less control
Self-Hosted: ✅ Full control ✅ Lower cost at scale ✅ Custom features ❌ Operational overhead ❌ Limited edge locations ❌ No DDoS protection
Eviction Policy Trade-offs
LRU vs LFU vs FIFO
LRU (Least Recently Used): ✅ Simple, effective ✅ Adapts to access patterns ❌ One-time access can evict useful data
LFU (Least Frequently Used): ✅ Better for skewed patterns ✅ Keeps popular content ❌ Slow to adapt ❌ Higher overhead
FIFO (First In First Out): ✅ Simplest implementation ✅ Predictable behavior ❌ Ignores access patterns ❌ Poor hit rate
This analysis helps in making informed decisions based on specific requirements and constraints.