Warmup question, basically just pick a random index.
Clarify the function signature and constraints, then propose a solution using random index selection. Discuss uniform distribution, edge cases, and potential optimizations for large lists.
Pro tip: Mention that for very large lists, you can avoid copying by using random.choice which internally uses indexing, and discuss thread safety if needed.
Ask about input size, whether the list can be empty, and if the function should handle IPv4/IPv6. Confirm that 'uniformly random' means each IP has equal probability.
Select a random index uniformly from 0 to n-1 and return the IP at that index. This ensures each IP is equally likely.
Write code using a random number generator (e.g., random.randint in Python) to pick an index. Handle edge cases like empty list by raising an exception or returning None.
State that time complexity is O(1) and space complexity is O(1) beyond the input list. Discuss that no additional memory is needed.
Suggest testing with a small list to verify distribution, and consider edge cases like single-element list or duplicate IPs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify requirements and constraints, then propose a solution using prefix sums and binary search for O(log n) sampling after O(n) preprocessing. Discuss trade-offs between preprocessing time, space, and query time, and mention alternatives like the alias method for O(1) sampling.
Pro tip: Mention the alias method as an O(1) alternative and discuss when it's preferable, showing you understand trade-offs beyond the basic solution. Also, handle edge cases like zero weights and floating-point precision.
Ask about input size, update frequency, and precision requirements to determine the best approach.
Explain building a prefix sum array and using binary search on a random number to select an IP in O(log n) time.
State preprocessing O(n) time and space, query O(log n) time, and compare with naive O(n) selection.
Mention the alias method for O(1) query time with O(n) preprocessing, and when it might be preferred.
Address zero weights, floating-point precision, and potential updates to weights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what QPS, how frequent updates, and acceptable consistency trade-offs. Then propose a lock-free, read-optimized data structure (e.g., copy-on-write or RCU) with atomic pointer swaps for dynamic updates, and discuss how to achieve sub-millisecond latency via caching and minimal contention. Finally, address thread safety through immutability and atomic operations, and mention monitoring and fallback strategies.
Pro tip: Emphasize that you would measure and profile before optimizing, and that you'd consider using a proven library or pattern (like RCU in the Linux kernel) rather than reinventing the wheel. This shows pragmatism and depth.
Ask about expected QPS, update frequency, consistency requirements (e.g., can stale reads be tolerated?), and latency SLA. This ensures you design for the right scale and trade-offs.
Propose a read-optimized approach: immutable data structures with atomic reference swapping (copy-on-write) or RCU. Explain how writers create a new version and atomically publish it, while readers access the current version without locks.
Discuss techniques: precomputed data structures (e.g., arrays for weighted round-robin), lock-free reads, CPU cache-friendly layouts, and avoiding dynamic memory allocation on the read path. Mention using per-thread caches if needed.
Describe how updates are batched or applied asynchronously, with versioning to ensure consistency. Address how to avoid reader stalls during updates (e.g., epoch-based reclamation or garbage collection).
Cover failure scenarios: what if an update fails? How to roll back? Mention metrics (latency, QPS, update success rate) and alerting. Also discuss testing under load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.