search

Full-text search with Elasticsearch indexes document content and executes multi-field queries to return ranked, relevant results. Create an index with text field mappings, configure custom analyzers for tokenization, then query using match or multi_match to retrieve documents by relevance score.

Indexing in Elasticsearch requires defining mappings that specify field types, analyzers, and tokenization rules before ingesting documents. Design mappings to optimize query performance and relevance by choosing appropriate field types like text for full-text search or keyword for exact matching.

Elasticsearch completion suggesters enable autocomplete by indexing prefix-optimized data structures. Configure a completion field type in your mapping, index documents with suggestion data, then query the suggest API to return matching prefixes as users type.

Faceted navigation using Elasticsearch aggregations groups search results by category, price ranges, or other dimensions without re-querying. Execute aggregation queries alongside search requests to compute counts and statistics, then display facets to users for drill-down filtering.

Zero-downtime reindexing in Elasticsearch uses aliases to switch between old and new indexes without client changes. Create a new index with updated mappings, reindex data from the old index, then atomically update the alias to point to the new index.

Full-text search with Elasticsearch provides relevance ranking, tokenization, and fuzzy matching across large document sets, outperforming simple substring matching or SQL LIKE queries. It's optimized for finding semantically relevant results rather than exact string matches.