I jumped straight to hashing, which felt right, but I didn't think through the multi-file case fast enough.
Start by clarifying requirements and constraints, then propose a hash-based deduplication approach for a single file, and finally discuss scaling strategies such as partitioning, distributed processing, and external sorting. Emphasize trade-offs between memory usage, speed, and accuracy.
Pro tip: Mention that you would first check if approximate deduplication (e.g., MinHash) is acceptable, as it can drastically reduce memory and compute for large-scale data. Also, highlight the importance of normalizing text (e.g., lowercasing, trimming whitespace) before hashing to catch near-duplicates.
Ask about file size, memory limits, required accuracy, and whether near-duplicates should be considered. This determines the choice of algorithm and infrastructure.
Propose using a hash set to store hashes of paragraphs. If memory is insufficient, use external sorting or a Bloom filter with a second pass for exact verification.
Partition files across workers (e.g., by file or by hash range) and use a distributed framework like MapReduce or Spark. Each worker deduplicates its partition, then results are merged.
If duplicates across files matter, use a global hash table or shuffle by hash to ensure identical paragraphs land on the same reducer for deduplication.
Discuss optimizations like parallel processing, memory tuning, and using approximate algorithms. Validate with sampling and metrics like false positive rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.