Databricks Connect searches for configuration properties in the following order until it finds them.

See Cluster URL and ID.Īny other properties that are necessary for the Databricks authentication type that you want to use.Ĭonfigure the connection within your code. You can obtain the cluster ID from the URL. This is the same as the Server Hostname value for your cluster see Get connection details for a cluster. For more information about Spark Connect, see Introducing Spark Connect.ĭatabricks Connect for Databricks Runtime 13.0 supports only Databricks personal access token authentication for authentication.Ĭollect the following configuration properties. Spark Connect can be embedded everywhere to connect to Databricks: in IDEs, notebooks, and applications, allowing individual users and partners alike to build new (interactive) user experiences based on the Databricks Lakehouse. With this “V2” architecture based on Spark Connect, Databricks Connect becomes a thin client that is simple and easy to use. Spark Connect introduces a decoupled client-server architecture for Apache Spark that allows remote connectivity to Spark clusters using the DataFrame API and unresolved logical plans as the protocol.

Because the client application is decoupled from the cluster, it is unaffected by cluster restarts or upgrades, which would normally cause you to lose all the variables, RDDs, and DataFrame objects defined in a notebook.įor Databricks Runtime 13.0 and higher, Databricks Connect is now built on open-source Spark Connect. Shut down idle clusters without losing work. You do not need to restart the cluster after changing Python library dependencies in Databricks Connect, because each client session is isolated from each other in the cluster. Iterate quickly when developing libraries. Step through and debug code in your IDE even when working with a remote cluster. Databricks Connect for Databricks Runtime 13.0 and higher currently supports running only Python applications.

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