I started with the naive approach: keep a sorted list, binary search for the time window, then walk to the percentile index.
Start by clarifying requirements (time window, percentile accuracy, read/write ratio, concurrency needs) and then propose a design that balances accuracy, memory, and performance. Use a bucketed time-series structure (e.g., ring buffer of time buckets) with lock-free or fine-grained locking for concurrency, and compute percentiles on demand from the buckets. Discuss trade-offs between exact vs. approximate percentiles and how to handle out-of-order or late-arriving samples.
Pro tip: Netflix values pragmatic solutions that scale: mention that exact percentiles over a sliding window are memory-intensive, so you'd likely use approximate algorithms like t-digest or HDR histograms, and explain how you'd validate accuracy against real traffic.
Ask about the expected write rate, read rate, acceptable error margin for percentiles, and whether the time window is fixed or sliding. Also confirm concurrency expectations (e.g., number of readers/writers).
Propose a ring buffer of time buckets (e.g., 1-second buckets covering the window) where each bucket stores a histogram or digest of latencies. This allows efficient eviction of old data and bounded memory.
Use per-bucket locks or atomic operations for writes, and allow readers to snapshot buckets without blocking writers. Alternatively, use a read-write lock or lock-free structures like concurrent ring buffers.
For each query, merge the histograms/digests from all buckets in the window and compute the requested percentile. Discuss using approximate algorithms (t-digest, HDR histogram) to keep memory and CPU low.
Compare exact vs. approximate percentiles, memory vs. accuracy, and locking strategies. Mention potential optimizations like caching percentiles or using a background thread to pre-aggregate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.