PostgreSQL Full-Text Search: Fast When Done Right (Debunking the Slow Myth) (blog.vectorchord.ai)

Article summary

The article discusses the performance of PostgreSQL's full-text search (FTS) feature, debunking the myth that it is slow. It highlights two major mistakes that can lead to poor performance: calculating tsvector on-the-fly and ignoring GIN index fastupdate. By applying standard optimizations, the article demonstrates a 50x speed improvement for standard PostgreSQL FTS. The article also introduces VectorChord-BM25, a PostgreSQL extension for fast, relevance-based ranking.

Main themes

• PostgreSQL Full-Text Search
• Performance Optimization
• Vector Search
• Database Indexing
• Search Engine Comparison

What commenters say

• The original benchmark likely misrepresented PostgreSQL FTS performance due to incorrect configuration, which can be optimized for better results.
• Storing tsvector in a separate column and indexing it can improve query performance, but may increase disk space usage.
• Some argue that virtual generated columns are not necessary for indexing expressions in PostgreSQL, as the query planner can identify candidate indexes.
• The trend of putting everything into PostgreSQL is driven by its flexibility, ease of use, and active community, but may not always be the best approach.
• Maintaining a separate search index can be challenging, and using the built-in search engine in PostgreSQL can simplify this problem.
• There is a cargo cult developing around PostgreSQL, with some developers choosing it without fully understanding its features or limitations.
• Optimizing PostgreSQL for search queries can be more efficient than using an external search index, especially for smaller-scale applications.
• The choice of database and search engine depends on the specific use case and requirements, and there is no one-size-fits-all solution.