Azure Cosmos DB is a schemaless, document database that provides high performance, availability, 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 driver offers to bridge the gap with relational SQL and a document database.
Working with Azure Cosmos DB Objects as Tables
The driver models the schemaless Azure Cosmos DB objects into relational tables and translates SQL queries into Azure Cosmos DB queries to get the requested data. See Query Mapping (Sql API) for more details on how various Azure Cosmos DB operations are represented as SQL.
Discovering Schemas Automatically
The Automatic Schema Discovery scheme automatically finds the data types in a Azure Cosmos DB object by scanning a configured number of rows of the object. You can use RowScanDepth, FlattenArrays, and FlattenObjects to control the relational representation of the collections in Azure Cosmos DB. You can also write Free-Form Queries not tied to the schema.
Optionally, you can use Custom Schema Definitions to project your chosen relational structure on top of a Azure Cosmos DB object. This allows you to define your chosen names of columns, their data types, and the location of their values in the collection.
Set GenerateSchemaFiles to save the detected schemas as simple configuration files that are easy to extend. You can persist schemas for all collections in the database or for the results of SELECT queries.
Limitations of the RawValue TypeDetectionScheme
If the TypeDetectionScheme is set to RawValue, the driver will push each document as single aggregate value on a column named JsonData, along with its resource identifier on the separate Primary Key column. The JSON documents are not processed, and as a result, the below functionalities are NOT supported with this configuration.