Start by clarifying the definition of 'exact duplicate' and the scale of the stream, then propose a hash-based approach using a composite key of all fields to identify duplicates. Discuss trade-offs between memory and accuracy, and outline how to handle the stream in real-time or batch mode.
Pro tip: Mention that exact duplicates are rare in practice and that you'd likely need fuzzy matching for real-world deduplication, but for this question, focus on exact matches and highlight the importance of a stable hash function to avoid collisions.
Ask about the volume of records, whether duplicates are exact matches on all fields, and if the stream is bounded or unbounded. Confirm if removal should happen in real-time or in batches.
Propose using a hash set or Bloom filter to track seen records. For exact duplicates, compute a hash of the concatenated fields (e.g., merchant_id, name, address, phone) and check for existence.
If memory is limited, discuss using a Bloom filter for probabilistic detection or partitioning the stream by a key (e.g., merchant_id) to process in chunks. Mention trade-offs between false positives and memory usage.
For each record, compute its hash; if not seen, add to the set and emit the record; if seen, skip it. For batch processing, sort or group by hash and deduplicate.
Discuss distributed processing (e.g., using Kafka and a distributed cache) for high throughput, and handle hash collisions by storing full records or using a secondary check.
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Spent a minute actually thinking about what 'canonicalization' means in practice.
Start by normalizing records with a canonicalization pipeline (lowercasing, stripping punctuation/whitespace, standardizing phone numbers), then apply fuzzy matching algorithms like Levenshtein or Jaro-Winkler with configurable thresholds. Discuss trade-offs between precision and recall, and propose a scalable architecture using blocking and clustering to handle large datasets.
Pro tip: Emphasize that fuzzy matching should be tunable and monitored—use a feedback loop with human review to adjust thresholds and avoid false positives, especially in a payments context where incorrect merges can be costly.
Apply consistent transformations to all records: lowercase, remove punctuation, collapse whitespace, and format phone numbers to E.164. This reduces trivial variations before comparison.
Select appropriate algorithms (e.g., edit distance, token-based, phonetic) based on merchant name characteristics. Combine multiple signals (name, phone, address) with weighted scoring.
Use blocking or canopy clustering to avoid O(n^2) comparisons. Group records by keys like first few characters of normalized name or phone prefix, then compare within blocks.
Apply clustering algorithms (e.g., connected components, hierarchical) to group near-duplicates. Define a canonical record per cluster, possibly using survivorship rules.
Measure precision/recall with labeled data, set thresholds, and implement a feedback loop for manual review. Monitor performance and adjust as data evolves.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: data size, similarity threshold, and whether exact or approximate clustering is acceptable. Then describe a two-step approach: first, use blocking or locality-sensitive hashing (LSH) to reduce candidate pairs, then apply Jaccard similarity on n-grams to those pairs and cluster using union-find or connected components. Finally, discuss the time complexity (e.g., O(N^2) for naive pairwise comparison) and how to scale using distributed computing, indexing, or approximate methods.
Pro tip: Mention that Jaccard similarity on n-grams is a set-based measure, so you can optimize by using MinHash to estimate it efficiently, and that clustering is essentially finding connected components in a similarity graph—this shows you understand both the algorithm and its practical implementation.
Ask about data volume, acceptable false positives/negatives, threshold value, and whether real-time or batch processing is needed. This determines the choice of exact vs. approximate methods.
Normalize names and addresses (lowercase, remove punctuation), then generate character or token n-grams. Represent each record as a set of n-grams for Jaccard computation.
Use blocking (e.g., by zip code or first letter) or locality-sensitive hashing (MinHash + LSH) to avoid comparing all pairs. This reduces complexity from O(N^2) to near-linear for many datasets.
For candidate pairs, compute Jaccard similarity (or estimate via MinHash). If similarity exceeds threshold, add an edge between records. Then find connected components using union-find or graph traversal to form clusters.
Naive pairwise comparison is O(N^2 * L) where L is n-gram set size. With LSH, it's roughly O(N * cost of hashing). Scale by distributing MinHash/LSH across nodes (e.g., MapReduce), using approximate clustering, or incremental clustering for streaming data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.