The tie-breaking part is what tripped me up a bit.
Start by clarifying the problem: define 'distinct words', confirm whether k can exceed the number of distinct words, and explicitly state your tie-breaking rule (e.g., lexicographical order). Then outline an efficient algorithm using a frequency map and a heap or sorting, and discuss time/space complexity and trade-offs.
Pro tip: Explicitly state your tie-breaking rule upfront and justify it; interviewers value candidates who proactively handle ambiguity and consider edge cases like k larger than the number of distinct words.
Ask clarifying questions: Are words case-sensitive? What if k exceeds the number of distinct words? How should ties be broken? Confirm the expected output format.
Build a frequency map (hash map) of word counts. For top k frequent, use a min-heap of size k; for least k frequent, use a max-heap of size k. Alternatively, sort the distinct words by frequency and then by tie-breaker.
Decide on a deterministic tie-breaker, such as lexicographical order. Apply it consistently when frequencies are equal, both in heap comparisons and final sorting.
Write clean code, handle edge cases (empty array, k=0, k > distinct count), and walk through a small example to verify correctness.
State time and space complexity (e.g., O(n + m log k) where m is distinct words). Discuss alternative approaches (e.g., full sort O(m log m)) and when each is preferable.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: unbounded stream, top-k and bottom-k queries, and whether exact or approximate results are acceptable. Then propose a hash map for exact frequency counts combined with a min-heap for top-k and a max-heap for bottom-k, discussing the tradeoffs of each component and alternatives like count-min sketch for space efficiency.
Pro tip: Demonstrate awareness that in an unbounded stream, exact top-k with a heap requires O(k) space but O(log k) update time, while approximate methods like count-min sketch with a heap can reduce space at the cost of accuracy. Also mention that bottom-k can be derived from top-k by negating counts or using a separate structure.
Ask about the expected stream size, memory limits, required accuracy (exact vs approximate), and whether top-k and bottom-k need to be queried simultaneously or separately.
Use a hash map to store word frequencies, and maintain a min-heap of size k for top-k and a max-heap of size k for bottom-k. Explain that updating the heap on each word insertion takes O(log k) time.
Discuss that the hash map uses O(n) space for n distinct words, which is infeasible for unbounded streams. The heaps use O(k) space. Time per word is O(1) for hash map update plus O(log k) for heap update.
Mention count-min sketch or lossy counting to reduce space to sublinear, with probabilistic guarantees. Combine with a heap for top-k, but note that bottom-k may require a separate sketch or inverted counts.
Conclude that for exact results with moderate distinct words, the hash map + heaps is fine; for truly unbounded streams, approximate methods are necessary. Highlight that bottom-k can be obtained by tracking least frequent items with a max-heap or by using a sketch that supports both.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the stream processing model and constraints, then propose a data structure that maintains insertion order and supports O(1) frequency updates and O(k) retrieval of non-repeating words. Combine a doubly linked list of unique words with a hash map for frequencies and a hash map from word to list node, and handle edge cases like k larger than the number of unique words.
Pro tip: Emphasize that you would discuss trade-offs between time and space, and mention that for very large k or high-throughput streams, you might need to consider approximate or windowed solutions.
Ask about the definition of 'non-repeating' (frequency exactly 1), the expected size of k, whether the stream is infinite, and if updates are needed in real-time or batch.
Propose a doubly linked list to maintain the order of unique words, a hash map for word frequencies, and a hash map from word to its node in the list for O(1) access.
For each incoming word, update its frequency; if it becomes 1, append to the list; if it becomes 2, remove from the list; if it was 2 and becomes 3, do nothing.
Traverse the list from head or tail to collect up to k words, handling cases where fewer than k unique words exist.
State that updates are O(1) and retrieval is O(k), and discuss edge cases like k=0, k > unique count, and memory constraints.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.