The core idea clicked pretty fast: sort the intervals, then for each trip do a sweep over the relevant intervals and accumulate.
Clarify the input format and edge cases, then propose an efficient algorithm that sorts intervals and uses binary search or a sweep line to compute pay for each trip. Walk through a concrete example to demonstrate handling of gaps and multi-interval trips, and analyze time and space complexity.
Pro tip: Mention that you would preprocess the rate schedule into a sorted list of non-overlapping intervals with cumulative pay to enable O(log n) queries per trip, showing awareness of scalability for large datasets.
Ask about input format, whether intervals are sorted, how gaps are defined, and if trips can span multiple intervals. Confirm the flat bonus per trip and whether it applies to all trips.
Sort intervals by start time and merge overlaps if necessary. Precompute cumulative pay for each interval to allow quick calculation of pay over any sub-interval.
For each trip, find overlapping intervals using binary search. For each overlap, compute the duration within that interval and multiply by the rate, then sum and add the flat bonus.
If a trip spans a gap, treat the gap as zero pay. Ensure the algorithm correctly splits the trip across multiple intervals and sums the contributions.
State time complexity: O(n log n) preprocessing + O(m log n) for m trips. Space complexity: O(n). Walk through a small example to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then implement round-robin routing with add/remove server support using a simple list and index. Next, explain the limitations of round-robin for caching and introduce consistent hashing with a hash ring and virtual nodes to minimize remapping. Finally, discuss trade-offs and potential optimizations.
Pro tip: Mention that consistent hashing with virtual nodes is used in real systems like Cassandra and DynamoDB to balance load and minimize disruption, showing you understand production-grade design.
Ask about expected scale, concurrency, server addition/removal frequency, and whether requests have keys (e.g., user IDs) that need stickiness.
Use a circular list of servers and an index to pick the next server. For add/remove, update the list and adjust the index carefully to avoid skipping or repeating servers.
Explain that round-robin doesn't consider request keys, so adding/removing servers remaps many requests, which is bad for caching and stateful sessions.
Map servers and request keys to a hash ring (e.g., using a sorted array or balanced tree). Use virtual nodes per server to improve load distribution. For a request, hash its key and find the next server clockwise on the ring.
Discuss how consistent hashing reduces remapping to ~1/N of keys when a server is added/removed. Mention virtual node count tuning, replication for fault tolerance, and potential hotspots.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.