The basic rate limiter wasn't bad but the follow-ups stacked up fast.
Start by clarifying requirements (e.g., rate limit algorithm, time source, thread-safety needs) and then design a simple, correct solution using a token bucket or sliding window. Implement it with proper synchronization, then extend to multi-threading and abstract the time source to allow injection for testability. Discuss trade-offs and potential optimizations.
Pro tip: Emphasize the importance of testability and separation of concerns: by abstracting the time source, you make the rate limiter deterministic and easy to test. Also, mention that using a monotonic clock avoids issues with system time changes.
Ask questions to understand the expected rate limit (e.g., requests per second), the algorithm (token bucket, leaky bucket, fixed window, sliding window), and whether the limiter should be distributed or single-node. Confirm the need for thread-safety and the ability to inject time.
Choose an algorithm (e.g., token bucket) and outline the data structures and logic. Explain how the limiter would work if only one thread accessed it, and how time is used to refill tokens or reset windows.
Introduce synchronization mechanisms (e.g., mutex, atomic operations) to protect shared state. Discuss potential contention and how to minimize it (e.g., using lock striping or per-key locks if multiple keys are supported).
Replace direct calls to system time with an injectable clock interface. This allows for deterministic testing and flexibility (e.g., using a monotonic clock). Show how the rate limiter can be constructed with a clock instance.
Compare algorithms (e.g., token bucket vs. sliding window) in terms of accuracy, memory, and complexity. Mention distributed rate limiting considerations (e.g., using Redis) and how the design would change.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: confirm the input array is sorted, discuss handling duplicates, and define the return value. Then implement iterative binary search with careful mid calculation to avoid overflow, and test with edge cases like empty array, single element, and target not present.
Pro tip: Mention that you use `mid = left + (right - left) // 2` to prevent integer overflow, and discuss how binary search can be adapted for problems like finding the first/last occurrence or insertion point.
Ask if the array is sorted, if duplicates exist, and what to return if the target is not found. Consider empty array, single element, and target at boundaries.
Decide on an iterative approach for O(1) space and better performance, or recursive for simplicity. Explain your choice.
Write the code with correct loop condition (left <= right), mid calculation avoiding overflow, and proper updates to left and right.
Walk through test cases: target present, absent, empty array, duplicates, and large arrays to verify correctness and efficiency.
State time O(log n) and space O(1). Mention variations like finding first/last occurrence or using binary search on answer.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Interesting constraint specifying the doubly linked list.
Clarify requirements first (e.g., real-time updates, K size, data types). Then propose a hybrid structure: a hash map for O(1) access to nodes, a doubly linked list to maintain order for quick updates, and a min-heap or sorted list for top K retrieval. Explain how operations like insert, update, and getTopK work together, and discuss trade-offs.
Pro tip: At Uber, real-time data and scalability matter—mention how your design handles high-throughput updates and whether getTopK is called frequently. If so, consider maintaining the top K incrementally rather than recomputing.
Ask about data size, update frequency, K value, and whether getTopK needs to be real-time. This shapes the design and trade-offs.
Combine a hash map for O(1) access, a doubly linked list for order maintenance, and a min-heap or sorted list for top K. Explain how they interact.
Describe insert, update, delete, and getTopK. For getTopK, if using a heap, extract K elements; if maintaining a sorted list, return the first K.
State time and space complexity for each operation. For example, insert O(log n) with heap, getTopK O(K log n) or O(K) if pre-sorted.
Compare approaches (e.g., heap vs. sorted list) and suggest optimizations like lazy deletion or incremental top K maintenance for frequent calls.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was the most fun and also the most chaotic.
Start by clarifying requirements and constraints, then design a scalable system that merges multiple calendars to find common free slots. For follow-ups, explain how MapReduce can parallelize the computation and how to rank rooms using a weighted scoring model. Emphasize trade-offs between consistency, latency, and scalability.
Pro tip: Demonstrate awareness of real-world constraints like time zones, privacy, and partial availability, and proactively discuss how to handle them. Show that you can iterate from a simple solution to a distributed one, highlighting the evolution of your design.
Ask about scale (number of users, calendars), latency requirements, and whether the system is for internal (e.g., meeting rooms) or external use. Confirm assumptions about time zones, privacy, and recurrence.
Outline components: calendar service, scheduler service, database, and API. Describe how to fetch busy times from multiple calendars and compute free slots using interval merging.
Explain how to parallelize free slot computation: map each calendar to busy intervals, shuffle by time slot, and reduce to find slots free for all. Discuss partitioning and fault tolerance.
Define a scoring function for rooms based on factors like usage count and meeting duration. Describe how to compute and update scores, possibly using a priority queue or batch processing.
Discuss trade-offs: consistency vs. availability, precomputation vs. on-demand, and caching strategies. Mention potential bottlenecks and how to mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.