Started with a hashmap for counts, then a heap for the top-k part.
Start by clarifying the problem: confirm whether the input is a list of words (with duplicates) or a stream, and whether we need top-k or bottom-k, most or least frequent. Then propose a solution using a hash map to count frequencies, followed by a heap or bucket sort to extract the k words, discussing trade-offs between time and space.
Pro tip: Mention that for large datasets, a distributed approach like MapReduce can be used, and for streaming data, a space-saving algorithm like Misra-Gries or Count-Min Sketch can approximate frequencies. This shows you think beyond the basic algorithm and consider scalability.
Ask about input size, whether words are case-sensitive, if ties need a specific order, and if k can be larger than the number of unique words. Also confirm if the list is static or streaming.
Use a hash map to count occurrences of each word. This takes O(n) time and O(m) space, where m is the number of unique words.
Use a min-heap of size k for top-k most frequent (or max-heap for bottom-k) to achieve O(m log k) time. Alternatively, use bucket sort for O(m) time when frequencies are bounded by n.
If ties occur, decide on a secondary ordering (e.g., lexicographical) and implement it in the comparator. Mention that this can affect the choice of data structure.
Discuss time and space complexity, and consider edge cases like k=0, k > unique words, empty input, or all words having the same frequency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: stream processing, real-time updates, and whether exact or approximate counts are acceptable. Then propose a data structure like a hash map for counts combined with a min-heap or max-heap for top-k, and discuss trade-offs between exact and approximate methods (e.g., Count-Min Sketch). Finally, address scalability and latency concerns for high-throughput streams.
Pro tip: Mention that for very high-throughput streams, exact top-k may be infeasible, so approximate algorithms like Count-Min Sketch with a heap are often used in practice, and highlight the trade-off between accuracy and resource usage.
Ask about stream rate, memory constraints, exact vs approximate results, and whether the top-k needs to be updated continuously or on demand.
Propose a hash map to maintain word frequencies and a heap (min-heap for top-k, max-heap for bottom-k) to track the k most frequent words efficiently.
Describe how each incoming word updates the hash map and potentially the heap, ensuring O(1) average update time and O(log k) heap adjustments.
Discuss distributed processing (e.g., sharding by word), approximate algorithms (Count-Min Sketch, Space-Saving), and trade-offs between accuracy, memory, and latency.
Mention handling of word eviction (e.g., sliding window), concurrency, and potential use of a Trie for prefix-based queries if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Honestly the trickiest part of the whole thing.
Recognize that the problem shifts from frequency counting to distinct element tracking. Use a hash set to store seen words, and only emit a word the first time it is encountered. Discuss trade-offs between memory usage and correctness, and consider if order matters.
Pro tip: Mention that this is essentially a deduplication problem and that using a set is optimal for O(1) lookups, but be prepared to discuss memory constraints and alternatives like Bloom filters for large-scale streams.
Confirm that each word should be output exactly once, regardless of repetitions, and ask whether the output order matters (e.g., first occurrence order).
Select a hash set to track seen words because it provides average O(1) insertion and lookup, ensuring efficient deduplication.
For each incoming word, check if it exists in the set. If not, add it to the set and emit it; otherwise, skip it.
State that time complexity is O(n) for n words, and space complexity is O(u) where u is the number of unique words, which could be large.
Address potential memory issues with massive streams and suggest alternatives like approximate membership (Bloom filters) if exactness is not required, or external storage if it is.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.