Design a System for Data De-Duplication

To design a de-duplication system for a platform like LinkedIn, we first acknowledge that comparing every record against every other is impossible due to O(N^2) complexity. We must start with Blocking. We partition records into buckets based on shared attributes, such as the first letter of the last name or domain suffix of an email. This reduces comparisons from billions to manageable subsets. Next, within each bucket, we apply hashing techniques. For exact matches, standard hashes work well. However, for fuzzy matching where users might have typos or different formatting, we use Locality Sensitive Hashing (LSH) or SimHash. These algorithms ensure that similar vectors map to the same hash buckets with high probability. For instance, 'John Smith' and 'Jon Smyth' would generate similar signatures. Once potential pairs are identified via these hashes, we run a more expensive similarity function, like Jaccard similarity on token sets, to confirm duplicates. Finally, we implement a merge service that consolidates records while preserving the most recent or authoritative metadata. To handle scale, this pipeline runs on a distributed framework like Spark, processing data in parallel shards. This approach ensures we maintain data integrity while keeping latency low enough for near-real-time updates.