I went with the log approach first since it's more accurate, basically storing a sorted list of timestamps per client and evicting anything outside the window.
Start by clarifying requirements (e.g., exact vs. approximate, memory constraints, distributed setting) and then present both sliding-window-log and sliding-window-counter approaches. Walk through the data structures, algorithms, and trade-offs, and finally provide a concrete implementation for the chosen approach, justifying your choice based on the constraints.
Pro tip: Mention that sliding-window-log can be optimized by storing timestamps in a circular buffer or using a Redis sorted set with TTL, and that sliding-window-counter is often preferred in distributed systems due to lower memory and simpler atomic operations.
Ask about the expected request rate, memory limits, accuracy requirements, and whether the solution should be distributed. This shows you consider real-world factors before diving into algorithms.
Describe storing each request timestamp in a log (e.g., list or sorted set) per client. To check, remove timestamps older than W seconds and compare the count to N. Discuss O(N) memory per client and exact accuracy.
Describe dividing time into fixed windows (e.g., W seconds) and maintaining counters for the current and previous windows. Estimate the request count using a weighted sum based on the current position in the window. Discuss O(1) memory per client and approximate accuracy.
Contrast memory usage (O(N) vs O(1)), accuracy (exact vs approximate), and implementation complexity. Mention that sliding-window-counter can allow bursts at window boundaries but is more memory-efficient.
Provide a clean implementation for one approach (likely sliding-window-log for simplicity or sliding-window-counter for efficiency) and explain why it fits the given constraints. Discuss potential optimizations like using Redis for distributed rate limiting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started sweating a little.
Start by generalizing the rate limiter's key from a single dimension to a composite key (e.g., tuple of tier, endpoint, user) and explain how the underlying data structure (e.g., hash map of token buckets) naturally extends. Then address memory concerns by discussing lazy allocation, eviction policies (like LRU or TTL), and probabilistic data structures for rare combinations.
Pro tip: Emphasize that the choice of eviction policy should align with business priorities—e.g., evicting inactive users first to preserve fairness—and mention that you'd monitor cardinality to avoid memory blowups.
Explain that the rate limiter's key becomes a composite key (e.g., (tier, endpoint, user_id)) and the data structure becomes a map from composite key to bucket state.
Discuss using a hash map (or concurrent hash map) for O(1) access, and consider nested maps or a single map with tuple keys for flexibility.
Only create bucket entries when a request for that combination occurs, avoiding pre-allocation for all possible combinations.
Use an LRU cache or TTL-based expiration to remove stale entries, and discuss trade-offs between memory and accuracy.
For extremely high cardinality, mention using approximate data structures like count-min sketch or cuckoo filters to bound memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.