dlFindDuplicates Performance Tuning: Improve Speed and Accuracy
Overview
dlFindDuplicates identifies duplicate records in datasets. Performance tuning focuses on reducing runtime, minimizing memory use, and improving match accuracy.
Key Strategies
- Indexing & Pre-filtering
- Filter early: Remove irrelevant records (nulls, out-of-scope ranges) before duplicate checks.
- Create indexes on the fields most frequently compared to speed lookups.
- Choose the Right Matching Keys
- Primary keys first: Use high-discrimination fields (IDs, normalized emails) to quickly rule out non-matches.
- Composite keys: Combine multiple fields when single fields have low uniqueness.
- Normalize and Clean Data
- Standardize formats (lowercase, trim whitespace, uniform date formats).
- Remove punctuation and normalize diacritics for name/address comparisons.
- Tokenize long text fields and compare tokens instead of raw strings.
- Adjust Matching Thresholds
- Loosen/tighten thresholds based on acceptable false positive/negative rates.
- Multi-stage matching: Use a strict pass to find exact/near-exact matches, then a relaxed fuzzy stage for remaining candidates.
- Use Blocking / Partitioning
- Blocking keys: Partition data into blocks (e.g., zip code, first letter of last name) and compare only within blocks.
- Canopy clustering or phonetic grouping (Soundex, Metaphone) reduce pairwise comparisons drastically.
- Optimize Similarity Metrics
- Select efficient algorithms: Jaro-Winkler for short names, token-based Jaccard for multi-word fields.
- Precompute hashes or signatures (MinHash, shingling) for expensive similarity measures.
- Parallelization & Batch Processing
- Process blocks in parallel across CPU cores or worker nodes.
- Stream processing: Run duplicates detection in batches for very large datasets to limit memory.
- Memory & Data Structures
- Use compact structures (arrays, primitive types) where possible.
- Avoid full pairwise matrices; compute similarities on demand.
- Cache intermediate results (normalized values, hashes) to avoid recomputation.
- Incremental / Real-time Handling
- Delta processing: Only compare new/changed records against existing index instead of reprocessing all data.
- Maintain