🔗 A deep (and fuzzy) dive into search

How does fuzzy search transform how we match terms in data? This article by Andrey Abramov unveils the inner workings of fuzzy search algorithms like Levenshtein automata and n-gram similarity. Initially launched in SereneDB six years ago, the article revisits the foundational algorithms that enable approximate matching in a new SQL-focused environment. It highlights the need for fuzzy search in handling unstructured text across fields from linguistics to bioinformatics, emphasizing its importance in deduplication scenarios. The author clearly explains how the Levenshtein distance defines similarity measures, and describes the powerful automaton trick that optimizes searching for close matches. The journey through theory and implementation offers insights into the complexity of scaling fuzzy search algorithms effectively.