← Grammarly Interview Insights
The basic version I got pretty quickly, stack-based approach where you track characters and pop when there's a match.
Start by clarifying the problem and edge cases, then propose a stack-based solution for the base case (k=2) and extend it to a generalized stack that tracks character counts. Discuss time and space complexity, and consider alternative approaches like recursion or two-pointer for completeness.
Pro tip: Mention that the stack approach naturally handles the follow-up by storing (char, count) pairs, and that you can optimize by only pushing when count > 0. Also, note that the problem is similar to 'remove adjacent duplicates' and can be solved in O(n) time.
Ask if the removal should be done iteratively until no more pairs exist, and confirm that k is a positive integer. Discuss edge cases like empty string, no removals, and all characters removed.
Use a stack to process characters one by one: if the top of the stack equals the current character, pop; otherwise, push. This simulates the removal of adjacent equal pairs in O(n) time.
Modify the stack to store pairs of (character, consecutive count). When the current character matches the top's character, increment its count; if the count reaches k, pop the pair. Otherwise, push a new pair with count 1.
Explain that the algorithm runs in O(n) time and O(n) space in the worst case. Mention that the stack size is bounded by the number of distinct character groups, and that no extra passes are needed.
Walk through a few examples (e.g., 'abbaca' for k=2, 'deeedbbcccbdaa' for k=3) to verify correctness. Briefly mention alternative approaches like recursion or two-pointer, but highlight the stack's efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I knew binary search was the move here since timestamps are strictly increasing per key, so you can just store a sorted list of (timestamp, value) pairs per key in a hashmap.
Start by clarifying requirements and constraints, then propose a design using a hash map from key to a list of (timestamp, value) pairs, with binary search for get. Discuss trade-offs between time and space, and consider edge cases like multiple sets at the same timestamp.
Pro tip: Mention that timestamps are monotonically increasing per key, so you can append and binary search; also note that if the store is distributed, you'd need a different approach like a time-series database.
Ask about expected scale, read/write ratio, timestamp granularity, and whether timestamps are unique per key. Confirm that get should return the value at the largest timestamp <= given timestamp.
Use a hash map where each key maps to a list of (timestamp, value) pairs, sorted by timestamp. Since timestamps are increasing, appending maintains order.
For set, append the new (timestamp, value) to the list for the key. For get, perform binary search on the list to find the largest timestamp <= the query timestamp, then return the corresponding value.
Set is O(1) amortized, get is O(log n) where n is the number of timestamps for that key. Space is O(total number of set calls).
Handle cases where the key doesn't exist or no timestamp <= query. Discuss potential optimizations like using a balanced BST or skip list if timestamps are not monotonic, or if we need to delete old entries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.