The Problem: Dirty Input vs Clean Data If you’ve ever built a search feature, you know the pain. Your database has a beautifully curated catalog of albums: Abbey Road The Dark Side of the Moon OK Computer But users type things like: beatles abbey rd dark side moon pink floyd ok computer radiohead 1997 How do you match these? A simple WHERE name = ? won’t cut it. You need something smarter. I recently worked on a classification system that needed to match messy invoice line items to a product catalog. The patterns I learned there apply perfectly to music, books, or any catalog matching problem. I ended up using two PostgreSQL extensions: pg_trgm for fuzzy text matching and pgvector for semantic similarity search. In this post, I’ll walk you through both approaches using a real dataset: 114,000 Spotify tracks from Hugging Face. You’ll be able to follow along with actual data. The Dataset: Spotify Tracks We’ll use the Spotify Tracks Dataset from Hugging Face. It contains 114,000+ tracks across 125 genres, with album names, artists, and popularity scores. It’s CC0 licensed (public domain), so you can use it freely. The dataset has real-world messiness: album names like “Abbey Road (Remastered)”, “The Dark Side of the Moon (2011 Remaster)”, and “OK Computer OKNOTOK 1997 2017”. Perfect for testing our matching approaches. Two Approaches, Two Extensions Approach Extension What it does Best for Fuzzy matching pg_trgm Compares character sequences (trigrams) Typos, abbreviations, word order Semantic search pgvector Compares meaning via embeddings Synonyms, paraphrasing, conceptual similarity pg_trgm breaks text into 3-character sequences and measures overlap. “Abbey Road” becomes {" ab", "abb", "bbe", "bey", "ey ", " ro", "roa", "oad", "ad "}. If two strings share many trigrams, they’re similar. pgvector stores vector embeddings—numerical representations of meaning generated by machine learning models. “Abbey Road” and “The Beatles final album” might have similar vectors even...
First seen: 2026-01-26 17:58
Last seen: 2026-01-26 19:59