Start by clarifying requirements (file size, memory limit, definition of a word, top-k value) and then propose a streaming solution using a hash map for counts and a min-heap for top-k. Discuss tokenization/normalization, memory management via chunking, and compare data structures and algorithms for the edge case where the file barely fits in memory.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that you would first check if the file fits in memory; if not, use external sorting or a distributed approach, and always consider the trade-off between exact and approximate counts (e.g., Count-Min Sketch) for extremely large files.
Ask about file size, available memory, definition of a word (e.g., delimiters, case sensitivity), and whether top-k needs to be exact. This ensures the solution aligns with the interviewer's expectations.
Define rules for splitting text into words (e.g., by whitespace and punctuation) and normalizing them (e.g., lowercasing, stemming). Mention handling of edge cases like hyphenated words and Unicode.
Read the file line-by-line or in chunks, update a hash map of word frequencies, and maintain a min-heap of size k for top-k words. This uses O(unique words) memory and O(n log k) time.
Explain that time is O(n) for tokenization and counting plus O(u log k) for heap operations, where u is unique words. Space is O(u + k). Discuss how this scales with file size and memory limits.
Discuss hash maps (fast but memory-heavy), tries (memory-efficient for shared prefixes but slower), and external sorting (scales to disk but slower). For files barely fitting in memory, consider chunking with intermediate disk storage or approximate algorithms.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.