3. Design a Distributed Cache
Partition cached data, survive node loss and keep cache misses from overwhelming the source of truth.
Pick a system. Work through the problem. Compare your approach.
Company tags are community-reported. Counts on cards show how many people reported that design.
Partition cached data, survive node loss and keep cache misses from overwhelming the source of truth.
A service commits to its database and then publishes an event. If it dies in between, the row exists and no one hears about it. Publishing first has the mirror problem: an event for a write that never happened. There is no ordering of two independent systems that makes this safe.
Stacks of nearby people in under 300 ms, 2 billion swipes a day, and a match never missed even when both like at once.
An S3-inspired object store: bytes before metadata, a partitioned namespace with cache coherence, then repair, heat, lifecycle and safe change. Three interview boards with explicit assumptions and AWS source boundaries.