Explain the CAP theorem and what it means for distributed systems
Reported in Stripe USA engineering loops. Classic system design question asked at US big-tech and fintech on-site loops.
Interview scenario
Context for Stripe candidates:
Your team is choosing a datastore for a multi-region US deployment. The interviewer asks how CAP applies.
Model answer
Try answering aloud first
Cover trade-offs, structure, and a concrete example before revealing the baseline response.
How to frame this at Stripe: Connect your answer to measurable impact, clarity of thought, and trade-offs the team cares about. Below is a strong baseline response you can adapt with your own project examples.
CAP theorem: in the presence of a network partition, a distributed system must choose between Consistency (every read sees the latest write) and Availability (every request gets a response).
CP systems (e.g., strongly consistent metadata stores) may reject writes/reads during partition to preserve correctness—think financial ledger balances.
AP systems (e.g., Dynamo-style key-value) stay available but may return stale reads until convergence—acceptable for shopping carts or social feeds with repair paths.
Real systems tune per operation: quorum reads/writes, leader election, and conflict resolution (LWW, CRDTs). Mention that partition tolerance is non-optional at cloud scale—design for it explicitly.
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