The logging part was fine, basically a map from test ID to an ordered list of events.
Clarify the requirements and constraints first, then propose a data structure that stores events per test ID in timestamp order. For each test ID, maintain a list of failure blocks and the shortest time from the start of each block to the next pass, updating efficiently as new events arrive.
Pro tip: Emphasize that timestamps are globally increasing, so you can process events in order and avoid sorting. Also, discuss how to handle edge cases like a failure block with no subsequent pass or multiple passes after a block.
Ask about query frequency, expected number of events, and whether updates and queries are interleaved. Confirm that timestamps are strictly increasing and that a 'failure block' is a contiguous sequence of failures for a given test ID.
Propose a hash map from test ID to a data structure that stores events in order. For each test ID, maintain a list of failure blocks, each with its start time and the time of the next pass (if any), and keep track of the minimum duration across all blocks.
When a new event arrives, update the per-test structure: if it's a failure, either start a new block or extend the current one; if it's a pass, close the current failure block (if any) and compute its duration, updating the minimum.
For a query on a test ID, return the precomputed minimum duration from the start of any failure block to the next pass. If no such block exists, return a sentinel value (e.g., infinity).
Discuss time and space complexity: O(1) per event update and O(1) per query, with O(n) space for n events. Mention that this is optimal given the constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model each test failure as a time interval [start, end) and use a sweep-line algorithm over sorted event points to track the number of distinct failing tests. Identify maximal contiguous segments where the count meets or exceeds the threshold, then return the longest such segment, ensuring the end timestamp is exclusive.
Pro tip: Clarify whether intervals are half-open [start, end) and handle edge cases like zero-length intervals or threshold=0, as these details often trip up candidates and show attention to correctness.
Confirm that test failures are represented as half-open intervals [start, end), the threshold is a positive integer, and the query should return the longest contiguous interval with count >= threshold. Ask about tie-breaking and whether intervals can be empty.
For each test failure interval, create two events: a start event (+1) at start time and an end event (-1) at end time. Sort all events by timestamp, processing end events before start events at the same timestamp to maintain half-open semantics.
Iterate through sorted events, maintaining a running count of distinct failing tests. When the count crosses the threshold, record the start of a candidate interval; when it drops below, close the interval and compare its length to the current best.
Account for cases where the count never reaches the threshold (return empty interval) or where multiple intervals tie for longest (return the earliest or as specified). Ensure the end timestamp is exclusive in the returned interval.
Explain that sorting events takes O(N log N) time and O(N) space, where N is the total number of intervals. Discuss potential optimizations like using a balanced BST for dynamic updates if the data structure must support insertions/deletions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.