Elasticsearch is a document-oriented database that provides high performance searching, flexibility, and scalability. These features are not necessarily incompatible with a standards-compliant query language like SQL-92. In this section we will show various schemes that the connector offers to bridge the gap with relational SQL and an Elasticsearch database.

The connector models Elasticsearch objects into relational tables and translates SQL queries into Elasticsearch queries to get the requested data. See Schema Mapping for more details on how Elasticsearch objects are mapped to tables to generate schemas. See Query Mapping for more details on how various Elasticsearch operations are represented as SQL.

The Automatic Schema Discovery scheme automatically finds the data types by retrieving the mapping for the Elasticsearch type. You can use RowScanDepth, FlattenArrays, and FlattenObjects to control the relational representation of the collections in Elasticsearch.

Optionally, you can use Custom Schema Definitions to project your chosen relational structure on top of a Elasticsearch object. This allows you choose your own column names, their data types, and the location of their values in the collection.

When GenerateSchemaFiles is set, you can persist schemas for all collections in the database or for the results of SELECT queries.