Got through it in about 25 minutes which felt okay.
Clarify the problem and edge cases, then propose a DFS-based solution that computes the height of each node (where leaf height = 0) and groups nodes by their height. Explain that this approach runs in O(n) time and O(n) space, and discuss trade-offs with alternative methods like iterative removal.
Pro tip: Mention that the height of a node corresponds to the round in which it becomes a leaf, so grouping by height directly yields the answer. This insight simplifies the solution and demonstrates deep understanding.
Confirm that leaves are nodes with no children, and that after removing leaves, new leaves are those whose children were removed. Ask about input size, tree balance, and output format.
Propose a DFS that computes the height of each node, where leaf height is 0. Group nodes by height to form the result.
For each node, recursively compute the height of left and right subtrees. The node's height is 1 + max(leftHeight, rightHeight). Add the node's value to the list for its height.
State that the algorithm visits each node once, so time complexity is O(n). Space complexity is O(n) for the recursion stack and output storage.
Compare with an iterative approach that repeatedly removes leaves, which could be O(n^2) in skewed trees. Highlight the efficiency of the height-based method.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This came out of the coding question debrief and went on for 10+ minutes.
Start by framing the decision around business impact and user experience, then walk through a concrete example of how you'd set staleness thresholds and design background processing with idempotency and backpressure. Emphasize trade-offs between freshness, cost, and complexity, and how you'd validate your choices with metrics and testing.
Pro tip: Tie staleness tolerance to specific SLAs and user-facing features (e.g., 'ride ETA can be 30s stale, but pricing must be real-time'), and mention that you'd make the staleness window configurable per data type to avoid one-size-fits-all failures.
Ask about the specific data, its consumers, and the cost of stale vs. fresh data. Identify SLAs, consistency needs, and system constraints like throughput and latency.
Propose a staleness budget per data type based on business impact (e.g., real-time for pricing, minutes for analytics). Explain how you'd measure and monitor staleness.
Describe how you'd schedule and coordinate background threads, including idempotent operations, retries with backoff, and avoiding thundering herds. Mention using queues with visibility timeouts and dead-letter queues.
Explain strategies for queue management: prioritization, rate limiting, and graceful degradation. Discuss when to drop stale messages vs. process them, and how to signal backpressure to producers.
Outline how you'd test the system (e.g., chaos engineering, load tests) and use metrics (queue depth, processing latency, staleness) to tune parameters over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.