Stripe

6 design problems known to be asked at Stripe.

Easy2

Easylow level design design patterns oop

Adapter Pattern: Make Two Interfaces That Weren't Built to Talk, Talk

Your code expects one interface; the useful class only speaks another — and you can't edit either. Adapter is the thin impedance-matching layer that lets them work together without a rewrite.

Asked at Amazon, Stripe, Uber +2

Aug 8, 2026·16 min read

Easylow level design design patterns oop

Facade Pattern: One Door Into a Room Full of Machinery

When 'place an order' means orchestrating inventory, payment, shipping, and notifications — every caller re-learns the whole subsystem. A Facade gives them one door, and leaves the machinery open for anyone who still needs it.

Asked at Amazon, Google, Microsoft +3

Aug 8, 2026·14 min read

Medium3

Mediumhigh level design distributed systems microservices

The API Gateway: One Front Door for a Hundred Services

When a mobile screen fires a dozen calls to a dozen services, each doing its own auth and its own CORS, the answer isn't a smarter client — it's a single entry point that owns the cross-cutting concerns once.

Asked at Netflix, Amazon, Uber +2

Aug 8, 2026·15 min read

Mediumhigh level design distributed systems reliability

Idempotency: How to Retry a Payment Without Charging Twice

The network fails after your server does the work but before the answer gets home, so the client retries — and a naive charge endpoint bills the card twice. Idempotency is the discipline that makes every retry safe.

Asked at Stripe, Amazon, PayPal +3

Aug 8, 2026·15 min read

Mediumhigh level design distributed systems networking

Rate Limiting: How a Bucket of Tokens Keeps Your API Alive

One misbehaving client with a retry loop can take down a service faster than any DDoS. Rate limiting is the cheap, boring control that decides who gets served — here's how the five classic algorithms actually work, and how to make one correct across a fleet.

Asked at Stripe, Cloudflare, GitHub +2

Aug 8, 2026·16 min read

Hard1

Hardsystem design interview algorithms caching concurrency

Design a Distributed Rate Limiter

Picking token bucket vs sliding window is the easy part. The real interview is what happens when the limiter runs on fifty machines: where the counter lives, why 'local counters' quietly lets 50× your limit through, and how to enforce one global cap without a round-trip on every request.

Asked at Stripe, Cloudflare, GitHub +2

Aug 9, 2026·17 min read