Schema SQL Grammar

Schema support the listed SQL grammar for DDL workloads.

AI Data Platform supports all standard Spark SQL data types. For more information, see Apache Spark Documentation - Supported Data Types.

Table 22-2 List of Schema SQL Grammar

Operation	Grammar
Create Schema	CREATE SCHEMA [IF NOT EXISTS] catalog_name.schema_name Response <<SQL Command>> was successfully executed Error Error: <<SQL Command>> failed due to <<reason>>
Alter Schema	Alter Schema Description ALTER SCHEMA <schema-name> set dbproperties (DESCRIPTION=<property-value>) Response <<SQL Command>> was successfully executed Error Error: <<SQL Command>> failed due to <<reason>>
Delete Schema	DROP SCHEMA [ IF EXISTS ] <<schema_name>> By default during DROP schema, all child objects will also get deleted
List Schemas	SHOW SCHEMAS [ { FROM | IN } catalog_name ] [ [ LIKE ] regex_pattern ] Examples: SHOW SCHEMAS FROM defaultcatalog1 LIKE 'd*' SHOW SCHEMAS IN defaultcatalog1 LIKE 'd*' Response:   Schema 1 <<schema_1>> 2 <<schema_2>> 2 <<schema_3>> Error Error: <<SQL Command>> failed due to <<reason>>
Describe Schema (get details)	DESCRIBE SCHEMA <<catalog_name>>.<<schema_name>> DESCRIBE SCHEMA <<schema_name>> DESCRIBE SCHEMA <<schema_name>> in Catalog <<catalog_name>> Attribute Value Catalog name Catalog name Schema Schema name Description Schema description Created by User that created the catalog Created on Date and time created Updated by User that last updated the catalog Updated on Date and time last updated Location Location in the catalog
Drop Schema	drop schema [ IF EXISTS ] <<schema_name>> cascade