Design a distributed cache like Redis or Memcached that serves millions of reads per second.
A cache is easy until you spread it across many nodes. Then come consistent hashing, eviction policy, the thundering herd on a cache miss, hot keys, and how much staleness you can tolerate. Here is the design that holds up under real traffic.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
A cache is easy until you spread it across many nodes. Then come consistent hashing, eviction policy, the thundering herd on a cache miss, hot keys, and how much staleness you can tolerate. Here is the design that holds up under real traffic.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
