My first instinct was a hash map for counts, then sort.
First, count the frequency of each distinct point using a hash map. Then, sort the distinct points with a custom comparator that orders by frequency descending, squared distance ascending, and finally x and y ascending. Finally, return the first K points.
Pro tip: Clarify edge cases upfront: what if K exceeds the number of distinct points? Also, discuss trade-offs between sorting and using a heap for large datasets.
Confirm input format, constraints, and expected output. Ask about K's range, duplicate handling, and tie-breaking rules.
Iterate through the array and use a hash map to count occurrences of each distinct point.
Extract unique points and sort them using a comparator that implements the specified ordering: frequency descending, squared distance ascending, then x and y ascending.
Take the first K elements from the sorted list. If K is larger than the number of distinct points, return all.
Discuss time and space complexity. Mention alternative approaches like using a heap for better performance when K is small.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Binary search question, pretty clear once you see it.
Start by clarifying the requirements: operations include set(key, value, timestamp) and get(key, timestamp). For each key, store a list of (timestamp, value) pairs sorted by timestamp, and use binary search to find the closest timestamp, handling ties by choosing the smaller timestamp. Discuss trade-offs between different data structures and consider concurrency if needed.
Pro tip: Mention that timestamps are monotonically increasing for set operations, so appending to a list maintains sorted order, enabling O(log n) binary search for get. Also, explicitly handle the tie-breaking rule by checking both neighbors after binary search.
Ask about expected operations, timestamp uniqueness, tie-breaking rules, and performance requirements. Confirm that set timestamps are strictly increasing per key.
Decide to use a hash map from key to a list of (timestamp, value) pairs, leveraging the monotonic timestamps to keep the list sorted. Alternatively, consider a balanced BST or skip list for more general cases.
For set, append the new (timestamp, value) to the list for the key. Since timestamps are increasing, the list remains sorted. This is O(1) amortized.
For get, perform binary search on the list to find the insertion point for the query timestamp. Then compare the timestamp at the insertion point and its predecessor to find the closest, applying tie-breaking (prefer smaller timestamp). Return the value or empty string if none.
Discuss time complexity: O(1) for set, O(log n) for get. Handle edge cases: query timestamp before all, after all, exact match, and multiple keys. Mention potential concurrency considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.