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Compute Top-K word frequencies under a path
Problem Description
Given a filesystem path, which may contain nested subdirectories and files, the task is to compute the top K most frequent words across all files within that path.
Requirements
- In-Memory Solution: Describe an in-memory solution for this problem, including its time and space complexity analysis.
- Scalable Approaches: When the entire corpus of files is too large to fit into memory, propose scalable approaches. Examples include external sorting/partitioning or MapReduce-style sharding and merging.
- Approximate Heavy-Hitters: Propose an approximate heavy-hitters approach (e.g., Count–Min Sketch with Space-Saving). For this approach, discuss the trade-offs between accuracy, latency, and storage.
AI interview prep
Core dilemma
Balancing the need to process massive datasets that exceed single-machine memory with the efficiency of finding top K elements, while navigating the trade-offs between exactness, resource consumption, and latency in distributed or approximate systems.
Mid-level signals3
Senior signals4
Red flags4
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Compute Top-K word frequencies under a path
Problem Description
Given a filesystem path, which may contain nested subdirectories and files, the task is to compute the top K most frequent words across all files within that path.
Requirements
- In-Memory Solution: Describe an in-memory solution for this problem, including its time and space complexity analysis.
- Scalable Approaches: When the entire corpus of files is too large to fit into memory, propose scalable approaches. Examples include external sorting/partitioning or MapReduce-style sharding and merging.
- Approximate Heavy-Hitters: Propose an approximate heavy-hitters approach (e.g., Count–Min Sketch with Space-Saving). For this approach, discuss the trade-offs between accuracy, latency, and storage.