Query MariaDB with Azure Databricks

Important

The legacy query federation documentation has been retired and might not be updated. The configurations mentioned in this content are not officially endorsed or tested by Databricks. If Lakehouse Federation supports your source database, Databricks recommends using that instead.

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

Create the JDBC URL

Python

driver = "org.mariadb.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:mariadb://{database_host}:{database_port}/{database_name}"

Scala

val driver = "org.mariadb.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:mariadb://${database_host}:${database_port}/${database_name}"

Query the remote table

Python

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

Scala

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