What is a Search-Engine Database?

Search-engine databases help users quickly find the information they need in a high quality and cost-effective manner. They are primarily used for searching the data content. These databases are highly optimized for keyword queries and typically offer specialized methods such as full-text search, complex search expressions, and ranking of search results.

A sample search-engine datastore format

When to use a search-engine database

  • For applications that require powerful queries and aggregation.
  • For applications that need to support full-text searches.
  • For applications that need to support distributed search functionality for high scalability.
  • For applications that require secondary indexing.
  • For geo-spatial searches.
  • For ranking and grouping of search results.

Use cases for search-engine databases

  • Full-text search.
  • Navigational search.
  • Logging and analytics.
  • Time-series data such as metrics and application events.
  • Metric analysis.
  • Analyzing large volumes of data scraped from the web.


RediSearch is a real-time search engine that enables you to query your Redis data to answer a wide variety of complex questions. Use it as a secondary index for datasets hosted in Redis or search across data in other data stores, as a fast text-search or auto-complete engine, as well as an engine for light-speed aggregations and faceted queries. Rich with features, RediSearch supports capabilities for search and filtering such as geo-spatial queries, retrieving only IDs (instead of whole documents), and custom document scoring. Aggregations can combine map, filter, and reduce/groupby operations in custom pipelines that run across millions of elements in an instant. RediSearch also supports auto-completion with fuzzy prefix matching, and atomic real-time insertion of new documents to a search index.

Next section  ►  AI databases