nw-sd-patterns

# Core Distributed Systems Patterns ## Load Balancing **Problem**: single server can't handle all traffic. **Approaches**: Round Robin (simple, ignores load) | Weighted Round Robin (accounts for capacity) | Least Connections (fewest active) | IP Hash (session affinity) | Layer 4/transport (IP/port, fast) | Layer 7/application (HTTP-aware, smarter) **Placement**: client-to-web | web-to-app | app-to-database **Trade-offs**: LB itself is SPOF -- use active-passive pair | session affinity complicates horizontal scaling -- prefer stateless servers | health checks critical ## Caching **Problem**: repeated DB reads are slow. **Strategies**: Cache-aside/lazy loading (app checks cache, fills on miss -- most common) | Write-through (write cache+DB simultaneously) | Write-behind (cache only, async to DB) | Read-through (cache fronts DB transparently) **Cache-aside pattern**: Read: `cache.get(key) -> hit? return : db.read -> cache.set -> return` | Write: `db.write -> cache.delete(key)` **Eviction**: LRU (most common) | LFU (skewed access) | TTL (time-based) **Problems**: thundering herd (many misses simultaneously -- use locking/coalescing) | cache penetration (non-existent keys -- Bloom filter or cache null) | cache avalanche (mass expiration -- jittered TTLs) | size cache based on working set, not total data ## Database Replication **Master-Slave**: all writes to master, reads to replicas | replication lag = eventual consistency | master fails: promote replica **Multi-Mas...