FREE LESSON · Networks & distributed systems · 4 OF 4
Load Balancing, Caching and Message Queues
Distributed mastery: shape and observe load — Load balancers, caches, queues, and graceful degradation
Scalability is controlled work placement under failure.
Load balancers choose healthy destinations and need a policy for uneven work. Caches trade freshness and invalidation complexity for lower latency and origin load. Queues decouple production from consumption and absorb bursts, but backlog is delayed work, not disappeared work. Bulkheads and degradation preserve critical paths when capacity is scarce.
Every buffer hides pressure temporarily; every replica creates a consistency question.
Cache invalidation is a consistency protocol
Cache-aside reads check the cache, load from origin on miss, then populate. On update, deleting the cached value avoids serving a known old copy but races and failures still matter. TTL bounds some staleness; versioned keys or event-driven invalidation strengthen particular workflows. “Use a cache” is incomplete without a freshness contract.
Name the maximum acceptable staleness and the source of truth before choosing invalidation.