Query MySQL with Databricks

important

The legacy query federation documentation has been retired and might not be updated. The products, services, or technologies mentioned in this content are not officially endorsed or tested by Databricks. See What is Lakehouse Federation? instead.

This example queries MySQL using its JDBC driver. For more details on reading, writing, configuring parallelism, and query pushdown, see Query databases using JDBC.

Experimental

The configurations described in this article are Experimental. Experimental features are provided as-is and are not supported by Databricks through customer technical support. To get full query federation support, you should instead use Lakehouse Federation, which enables your Databricks users to take advantage of Unity Catalog syntax and data governance tools.

Using JDBC

Python
Scala

Python
driver = "com.mysql.cj.jdbc.Driver"

database_host = "<database-host-url>"
database_port = "3306" # update if you use a non-default port
database_name = "<database-name>"
table = "<table-name>"
user = "<username>"
password = "<password>"

url = f"jdbc:mysql://{database_host}:{database_port}/{database_name}"

remote_table = (spark.read
  .format("jdbc")
  .option("driver", driver)
  .option("url", url)
  .option("dbtable", table)
  .option("user", user)
  .option("password", password)
  .load()
)

Scala
val driver = "com.mysql.cj.jdbc.Driver"

val database_host = "<database-host-url>"
val database_port = "3306" # update if you use a non-default port
val database_name = "<database-name>"
val table = "<table-name>"
val user = "<username>"
val password = "<password>"

val url = s"jdbc:mysql://${database_host}:${database_port}/${database_name}"

val remote_table = spark.read
  .format("jdbc")
  .option("driver", driver)
  .option("url", url)
  .option("dbtable", table)
  .option("user", user)
  .option("password", password)
  .load()

Using the MySQL connector in Databricks Runtime

Using Databricks Runtime 11.3 LTS and above, you can use the named connector to query MySQL. See the following examples:

Python
SQL
Scala

Python
remote_table = (spark.read
  .format("mysql")
  .option("dbtable", "table_name")
  .option("host", "database_hostname")
  .option("port", "3306") # Optional - will use default port 3306 if not specified.
  .option("database", "database_name")
  .option("user", "username")
  .option("password", "password")
  .load()
)

SQL
DROP TABLE IF EXISTS mysql_table;
CREATE TABLE mysql_table
USING mysql
OPTIONS (
  dbtable '<table-name>',
  host '<database-host-url>',
  port '3306', /* Optional - will use default port 3306 if not specified. */
  database '<database-name>',
  user '<username>',
  password '<password>'
);
SELECT * from mysql_table;

Scala
val remote_table = spark.read
  .format("mysql")
  .option("dbtable", "table_name")
  .option("host", "database_hostname")
  .option("port", "3306") # Optional - will use default port 3306 if not specified.
  .option("database", "database_name")
  .option("user", "username")
  .option("password", "password")
  .load()

Using JDBC​

Using the MySQL connector in Databricks Runtime​

Using JDBC

Using the MySQL connector in Databricks Runtime