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Pretty standard as system design questions go.
Start by clarifying requirements: what are we rate limiting (API, user actions), what limits, and what behaviors on limit exceeded. Then propose a high-level design using a distributed rate limiter with algorithms like token bucket or sliding window, and discuss trade-offs, scalability, and failure modes.
Pro tip: At Pinterest scale, consider using a centralized rate limiting service with local caching to reduce latency and avoid a single point of failure. Also, discuss how to handle rate limiting for both authenticated and anonymous users.
Ask questions to understand the scope: what needs rate limiting (e.g., API endpoints, user actions), what are the limits (e.g., requests per second per user), and what should happen when limit is exceeded (e.g., return 429, queue, degrade).
Outline the components: a rate limiter service, a data store (e.g., Redis) for counters, and integration points (e.g., API gateway, middleware). Discuss whether to use a centralized or distributed approach.
Compare algorithms like token bucket, leaky bucket, fixed window, and sliding window. Explain which fits the requirements and why, considering factors like burst handling, accuracy, and memory usage.
Discuss how to scale the rate limiter (e.g., sharding by user ID, using Redis clusters), handle failures (e.g., fallback to local rate limiting), and ensure low latency (e.g., local caching).
Summarize trade-offs (e.g., accuracy vs. performance, centralized vs. decentralized). Mention extensions like dynamic rate limits, monitoring, and integration with other systems.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and assumptions, then propose a data structure that supports efficient nearest-neighbor queries and dynamic updates as elevators move. Discuss trade-offs between different approaches (e.g., sorted list, balanced BST, priority queue) and explain how to handle multiple requests over time.
Pro tip: Mention that elevator positions change over time, so the data structure must support updates; consider using a balanced BST or a segment tree to achieve O(log n) per operation. Also, discuss how to handle ties and multiple requests efficiently.
Ask about the number of elevators, request rate, and whether elevators can be assigned multiple requests. Confirm that 'nearest' means minimizing travel time based on current positions and directions.
Propose a balanced binary search tree (e.g., TreeSet in Java) or a segment tree to store elevator positions, enabling O(log n) insertion, deletion, and nearest-neighbor queries.
Explain how to update elevator positions as they move (e.g., after each second or upon request assignment) and maintain the data structure accordingly.
For each incoming request, query the data structure to find the nearest elevator, assign it, and update its position and the data structure.
Discuss time and space complexity, compare with alternatives (e.g., heap, sorted array), and mention potential optimizations like batching or using a priority queue for pending requests.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.