This version of the OpenSearch documentation is no longer maintained. For the latest version, see the current documentation. For information about OpenSearch version maintenance, see Release Schedule and Maintenance Policy.

Search

OpenSearch provides several features for customizing your search use cases and improving search relevance. In OpenSearch, you can:

• Use SQL and Piped Processing Language (PPL) as alternatives to query domain-specific language (DSL) for searching data.

• Run resource-intensive queries asynchronously with asynchronous search.

• Search for k-nearest neighbors with k-NN search.

• Abstract OpenSearch queries into search templates.

• Integrate machine learning (ML) language models into your search workloads with neural search.

• Compare search results to tune search relevance.

• Use a dataset that is fixed in time to paginate results with Point in Time.

• Paginate and sort search results, highlight search terms, and use the autocomplete and did-you-mean functionality.

• Rewrite queries with Querqy.

• Process search queries and search results with search pipelines.