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This is a SQL command reference for Databricks SQL and Databricks Runtime.
For information about using SQL with Lakeflow Declarative Pipelines, see Lakeflow Declarative Pipelines SQL language reference.
Note
Databricks SQL Serverless is not available in Azure China. Databricks SQL is not available in Azure Government regions.
General reference
This general reference describes data types, functions, identifiers, literals, and semantics:
- “Applies to” label
- How to read a syntax diagram
- How to add comments to SQL statements
- Configuration parameters
- Data types and literals
- Functions
- SQL data type rules
- Datetime patterns
- H3 geospatial functions
- Lambda functions
- Window functions
- Identifiers
- Names
- IDENTIFIER clause
- NULL semantics
- Expressions
- Parameter markers
- Variables
- Name resolution
- JSON path expressions
- Collation
- Partitions
- ANSI compliance in Databricks Runtime
- Apache Hive compatibility
- Principals
- Privileges and securable objects in Unity Catalog
- Privileges and securable objects in the Hive metastore
- Refresh Unity Catalog metadata
- External locations
- External tables
- Credentials
- Volumes
- SQL Scripting
- Delta Sharing
- Federated queries (Lakehouse Federation)
- Information schema
- Reserved words
DDL statements
You use data definition statements to create or modify the structure of database objects in a database:
- ALTER CATALOG
- ALTER CONNECTION
- ALTER CREDENTIAL
- ALTER DATABASE
- ALTER LOCATION
- ALTER MATERIALIZED VIEW
- ALTER PROVIDER
- ALTER RECIPIENT
- ALTER STREAMING TABLE
- ALTER TABLE
- ALTER SCHEMA
- ALTER SHARE
- ALTER VIEW
- ALTER VOLUME
- COMMENT ON
- CREATE BLOOMFILTER INDEX
- CREATE CATALOG
- CREATE CONNECTION
- CREATE DATABASE
- CREATE FUNCTION (SQL)
- CREATE FUNCTION (External)
- CREATE LOCATION
- CREATE MATERIALIZED VIEW
- CREATE PROCEDURE
- CREATE RECIPIENT
- CREATE SCHEMA
- CREATE SERVER
- CREATE SHARE
- CREATE STREAMING TABLE
- CREATE TABLE
- CREATE VIEW
- CREATE VOLUME
- DECLARE VARIABLE
- DROP BLOOMFILTER INDEX
- DROP CATALOG
- DROP CONNECTION
- DROP DATABASE
- DROP CREDENTIAL
- DROP FUNCTION
- DROP LOCATION
- DROP PROCEDURE
- DROP PROVIDER
- DROP RECIPIENT
- DROP SCHEMA
- DROP SHARE
- DROP TABLE
- DROP VARIABLE
- DROP VIEW
- DROP VOLUME
- MSCK REPAIR TABLE
- REFRESH FOREIGN (CATALOG, SCHEMA, or TABLE)
- REFRESH (MATERIALIZED VIEW or STREAMING TABLE)
- SET TAG
- SYNC
- TRUNCATE TABLE
- UNDROP TABLE
- UNSET TAG
DML statements
You use data manipulation statements to add, change, or delete data from a Delta Lake table:
- COPY INTO
- DELETE FROM
- INSERT INTO
- INSERT OVERWRITE DIRECTORY
- INSERT OVERWRITE DIRECTORY with Hive format
- LOAD DATA
- MERGE INTO
- UPDATE
Data retrieval statements
You use a query to retrieve rows from one or more tables according to the specified clauses. The full syntax
and brief description of supported clauses are explained in the Query article.
The related SQL statements SELECT and VALUES are also included in this section.
In addition to standard SQL queries in the style of SELECT FROM WHERE, Azure Databricks also supports SQL Pipeline Syntax which composes SQL as a series of chained operations such as FROM |> WHERE |> SELECT.
Databricks SQL also provides the ability to inspect the generated logical and physical plan for a query using the EXPLAIN statement.
Delta Lake statements
You use Delta Lake SQL statements to manage tables stored in Delta Lake format:
- CACHE SELECT
- CONVERT TO DELTA
- DESCRIBE HISTORY
- FSCK REPAIR TABLE
- GENERATE
- OPTIMIZE
- REORG TABLE
- RESTORE
- VACUUM
For details on using Delta Lake statements, see What is Delta Lake in Azure Databricks?.
SQL scripting statements
You use SQL scripting to execute procedural logic in SQL.
- CASE statement
- BEGIN END compound statement
- FOR statement
- GET DIAGNOSTICS statement
- IF THEN ELSE statement
- ITERATE statement
- LEAVE statement
- LOOP statement
- REPEAT statement
- RESIGNAL statement
- SIGNAL statement
- WHILE statement
Auxiliary statements
You use auxiliary statements to collect statistics, manage caching, explore metadata, set configurations, and manage resources:
- Analyze statement
- Apache Spark Cache statements
- Describe statements
- Show statements
- Configuration, variable management, and misc statements
- Resource management
Analyze statement
Apache Spark Cache statements
Applies to: 
Databricks Runtime
Describe statements
- DESCRIBE CATALOG
- DESCRIBE CONNECTION
- DESCRIBE CREDENTIAL
- DESCRIBE DATABASE
- DESCRIBE FUNCTION
- DESCRIBE LOCATION
- DESCRIBE PROCEDURE
- DESCRIBE PROVIDER
- DESCRIBE QUERY
- DESCRIBE RECIPIENT
- DESCRIBE SCHEMA
- DESCRIBE SHARE
- DESCRIBE TABLE
- DESCRIBE VOLUME
Show statements
- LIST
- SHOW ALL IN SHARE
- SHOW CATALOGS
- SHOW COLUMNS
- SHOW CONNECTIONS
- SHOW CREATE TABLE
- SHOW CREDENTIALS
- SHOW DATABASES
- SHOW FUNCTIONS
- SHOW GROUPS
- SHOW LOCATIONS
- SHOW PARTITIONS
- SHOW PROCEDURES
- SHOW PROVIDERS
- SHOW RECIPIENTS
- SHOW SCHEMAS
- SHOW SHARES
- SHOW SHARES IN PROVIDER
- SHOW TABLE
- SHOW TABLES
- SHOW TABLES DROPPED
- SHOW TBLPROPERTIES
- SHOW USERS
- SHOW VIEWS
- SHOW VOLUMES
Configuration, variable management, and misc statements
- CALL
- EXECUTE IMMEDIATE
- RESET
- SET
- SET RECIPIENT
- SET TIMEZONE
- SET VARIABLE
- USE CATALOG
- USE DATABASE
- USE SCHEMA
Resource management
Applies to: Databricks Runtime
Applies to: Databricks SQL Connector
Security statements
You use security SQL statements to manage access to data:
- ALTER GROUP
- CREATE GROUP
- DENY
- DROP GROUP
- GRANT
- GRANT SHARE
- REPAIR PRIVILEGES
- REVOKE
- REVOKE SHARE
- SHOW GRANTS
- SHOW GRANTS ON SHARE
- SHOW GRANTS TO RECIPIENT
For details about using these statements, see Hive metastore privileges and securable objects (legacy).