Application Programming Interface Design and Rate Limiting Questions

Focuses on designing scalable application programming interfaces that handle high request volumes while protecting infrastructure and preserving developer experience. Topics include API surface design and versioning, idempotency and retry semantics, authentication and authorization impacts, consistency and backward compatibility, and choosing protocols and payload formats. For scaling and protection discuss rate limiting and quota strategies such as token bucket, fixed window, sliding window, leaky bucket, per API key and per user limits, and hierarchical quotas. Cover backpressure, graceful degradation, circuit breakers, throttling responses and headers that communicate limits to clients, retry guidance, and strategies to avoid thundering herd effects. Also include operational concerns: monitoring and observability for request and error rates, metrics for usage and latency, metering and billing implications for usage based pricing, developer platform experience, documentation and developer tooling, testing at scale, and trade offs between strict protection and usability.

HardTechnical

0 practiced

Design a developer portal and documentation features to help API consumers understand and handle limits. Include real-time usage dashboards, examples for handling 429, SDKs with built-in retries, a quota simulator, changelogs, and support pathways. Explain how these tools reduce support load and improve onboarding.

EasyTechnical

0 practiced

Describe the token bucket rate limiting algorithm: explain the key parameters (capacity and refill rate), how it enforces throughput while allowing bursts, and how it differs behaviorally from leaky bucket and fixed-window approaches. Provide a short example of when token bucket is preferable.

EasyTechnical

0 practiced

Compare REST and GraphQL for public API design. Describe differences in API surface, typical use cases, how each affects caching, versioning, and rate limiting. For a mobile-first public API with many low-bandwidth clients, explain which you'd choose and why. Discuss trade-offs including overfetching, underfetching, toolchain, client complexity, and developer experience.

MediumSystem Design

0 practiced

You are designing versioning strategy for a public API with thousands of clients and multiple language SDKs. Describe options (URL versioning, header-based, media type/versioning, GraphQL evolution), deprecation policy, migration windows, backward compatibility guarantees, automated compatibility tests, and SDK release coordination. Provide a sample timeline for a breaking change.

MediumTechnical

0 practiced

Implement a single-process token bucket rate limiter in Python. Requirements: class TokenBucket(capacity, refill_rate_per_second) with method try_consume(n=1) -> bool. It must be thread-safe for concurrent callers, use O(1) time per call, and handle fractional refill using timestamps. Provide code and explain thread-safety and edge cases.

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