Full-Text Search Explained

Go beyond exact matching — analyze, index, and rank natural language text to deliver relevant search results with fuzzy matching and faceted filtering.

Full-Text Search

Full-text search is a technique for searching natural language text within documents by analyzing, indexing, and ranking content based on relevance rather than exact string matching.

Explanation

Unlike SQL LIKE queries, full-text search understands language. It tokenizes text, applies stemming and stop-word removal, and builds an inverted index mapping terms to documents. Search queries are ranked by relevance using BM25 or TF-IDF algorithms. Advanced features include fuzzy matching, phrase matching, faceted search, and autocomplete. Elasticsearch is the dominant engine, with alternatives including Typesense, MeiliSearch, Algolia, and PostgreSQL built-in full-text search.

Bookuvai Implementation

Bookuvai implements full-text search using Elasticsearch for complex requirements and PostgreSQL tsvector for simpler use cases. Our implementations include fuzzy matching, faceted filtering, result highlighting, and autocomplete. We index content asynchronously to avoid impacting write performance.

Key Facts

  • Analyzes and indexes natural language text for relevance-based search
  • Inverted indexes map terms to documents for fast lookups
  • Ranking algorithms (BM25, TF-IDF) order results by relevance
  • Features: fuzzy matching, facets, highlighting, autocomplete
  • Elasticsearch, Typesense, MeiliSearch, and Algolia are popular engines

Related Terms

Frequently Asked Questions

Do I need Elasticsearch, or is PostgreSQL full-text search enough?
PostgreSQL handles basic search: stemming, ranking, and phrase matching. Choose Elasticsearch when you need fuzzy matching, complex faceted navigation, autocomplete, or search across millions of documents where performance is critical.
What is the difference between full-text search and semantic search?
Full-text search matches keywords and variations. Semantic search uses vector embeddings to understand meaning — finding documents about "automobiles" when searching for "cars." Semantic search requires a vector database; full-text search uses inverted indexes.
How do I keep the search index in sync with the database?
Use event-driven indexing: when data changes, publish an event that triggers re-indexing. Tools like Debezium capture database changes and stream them to Elasticsearch. Avoid synchronous dual-writes to prevent consistency issues.