consistency

Classic distributed-systems tradeoffs appear in ML infra with an ML twist: stale features and eventually-consistent reads carry model-accuracy consequences, not just correctness ones. Here is how to reason about it.

The distributed ordering problem that sits at the heart of Kafka, replicated logs, and consensus systems. Interviewers look for you to face the uncomfortable fact that physical clocks lie, then pick the right instruments: a single sequencer, consensus, or logical clocks. This walks that progression.

In payments, a duplicate charge or a lost credit is not a bug, it is a financial incident. The design turns on idempotency keys, double-entry bookkeeping, and an append-only ledger you can audit and reconcile. Here is how money systems stay correct.

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.