Advanced Search & Filtering

Full-text search uses PostgreSQL Full-Text Search or Elasticsearch to index and query course content, lessons, and metadata. Both engines tokenize text, rank results by relevance, and support phrase queries and boolean operators. PostgreSQL FTS is lightweight for smaller datasets; Elasticsearch scales for millions of documents and provides advanced analytics.

Faceted filtering aggregates counts of distinct values—category, level, price, rating—alongside search results, letting users narrow results interactively. A query builder processes filter selections, applies WHERE constraints, and regenerates facet counts. This enables users to explore data without rebuilding searches manually.

Autocomplete suggests completions and past searches as users type, powered by prefix matching or fuzzy matching algorithms. Storing search history and term frequency enables instant suggestions. Combined with full-text indexing, autocomplete reduces query time and improves user discovery of courses.

PostgreSQL Full-Text Search handles advanced search for small to medium datasets with built-in ranking and phrase support. Elasticsearch excels at scale, fuzzy matching, and analytics across millions of documents. Choose PostgreSQL for simplicity and lower operational overhead; pick Elasticsearch for high query volume, complex analytics, and large indexes.

Relevance ranking scores results by term frequency, proximity, and field weights using TF-IDF or BM25 algorithms in PostgreSQL FTS or Elasticsearch. Alternate sort modes include price, rating, duration, instructor, and recency. A query builder exposes sort selection, letting users toggle between relevance and domain-specific criteria.

Search analytics track query frequency, click-through rates, zero-result queries, and filter patterns. This data reveals popular courses, missing content, and user intent. Saved searches and search history enable personalization and identify trending topics, informing content strategy and UX improvements.