Caching
Caching is the single most effective performance and cost multiplier in distributed system design. Phase 7 explores caching physics from the hardware layer to global edge networks:
All Topics in Phase 7
0 of 12 completedExplore the physics of caching: RAM vs Disk latency differentials, database offloading, Pareto principle (80/20 rule), and egress cost reductions.
Master the most popular caching pattern: Application-orchestrated read-through, lazy population, TTL expiration, and cache invalidation.
Achieve tight cache-database consistency: Synchronous inline writes, cache as system-of-record, and read-latency optimization.
Maximize write throughput: In-memory write buffers, asynchronous database flushing, batching, and data loss trade-offs.
Prevent cache pollution: Direct-to-database writes, lazy read population, and large blob ingestion patterns.
Manage finite memory capacity: Least Recently Used (LRU), Least Frequently Used (LFU), First In First Out (FIFO), and Time-to-Live (TTL).
Conquer the hardest problem in computer science: Event-driven invalidation, TTL-based expiration, dual-writes, CDC cache syncing, and race conditions.
Scale caching horizontally: Redis Cluster (16,384 hash slots), Memcached multithreaded architecture, replication, and failover.
Prevent database meltdown on cache key expiration: Mutex locking, probabilistic early expiration (XFetch), and background refresh.
Evaluate architectural caching tiers: In-process heap caches (Guava, Caffeine) vs Remote distributed clusters (Redis, Memcached).
Design end-to-end caching across the stack: Edge CDN, API Gateway, Application Heap, Redis, and Database Buffer Pools.