keyword-search

To build Elasticsearch keyword search and autocomplete, you define index mappings with text fields, keyword sub-fields, and completion fields to support full-text matching, exact filtering, and instant typing suggestions across structured catalogs.

Configuring Elasticsearch index mappings requires setting up text fields for full-text search alongside keyword sub-fields for exact filtering, plus completion fields to deliver autocomplete suggestions for structured product or article data.

The best way to implement typo-tolerant search and facets in Elasticsearch is by applying specific query patterns and ingestion guidance that support synonym matching, structured filtering, and relevance tuning to refine product catalog results.

Yes, Elasticsearch autocomplete works for product catalogs and article documents by utilizing completion fields within your index mapping to provide instant query suggestions during user typing sessions.

Irrelevant Elasticsearch keyword search results often stem from common ingestion pitfalls or unmapped text and keyword sub-fields, requiring relevance tuning and proper query pattern adjustments to improve text matching accuracy.

You need Elasticsearch keyword sub-fields when your search queries require exact filtering, structured data aggregation, or facets alongside standard full-text text matching for products, articles, or documents.