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Advanced usage of Databricks Connect

note

This article covers Databricks Connect for Databricks Runtime 14.0 and above.

This article describes topics that go beyond the basic setup of Databricks Connect.

Configure the Spark Connect connection string​

In addition to connecting to your cluster using the options outlined in Configure a connection to a cluster, a more advanced option is connecting using the Spark Connect connection string. You can pass the string in the remote function or set the SPARK_REMOTE environment variable.

note

You can only use a Databricks personal access token authentication to connect using the Spark Connect connection string.

To set the connection string using the remote function:

Python
from databricks.connect import DatabricksSession

workspace_instance_name = retrieve_workspace_instance_name()
token                   = retrieve_token()
cluster_id              = retrieve_cluster_id()

spark = DatabricksSession.builder.remote(
   f"sc://{workspace_instance_name}:443/;token={token};x-databricks-cluster-id={cluster_id}"
).getOrCreate()

Alternatively, set the SPARK_REMOTE environment variable:

sc://<workspace-instance-name>:443/;token=<access-token-value>;x-databricks-cluster-id=<cluster-id>

Then initialize the DatabricksSession class:

Python
from databricks.connect import DatabricksSession

spark = DatabricksSession.builder.getOrCreate()

Use Spark Connect server with Databricks Connect​

You can optionally run Databricks Connect against an open source Spark Connect server.

important

Some features available in Databricks Runtime and Databricks Connect are exclusive to Databricks or not yet released in open source Apache Spark. If your code relies on these features, the following steps may fail with errors.

  1. Start a local Spark Connect server. See How to use Spark Connect

  2. Configure Databricks Connect. Set the environment variable SPARK_REMOTE to point to your local Spark Connect server. See Connecting to Spark Connect using Clients.

    export SPARK_REMOTE="sc://localhost"

  3. Initialize the Databricks session:

    Python
    from databricks.connect import DatabricksSession

    spark = DatabricksSession.builder.getOrCreate()

Additional HTTP headers​

Databricks Connect communicates with the Databricks Clusters via gRPC over HTTP/2.

To have better control over the requests coming from clients, advanced users may choose to install a proxy service between the client and the Databricks cluster. In some cases the proxies may require custom headers in the HTTP requests.

Use the header() method to add custom headers to HTTP requests:

Python
from databricks.connect import DatabricksSession

spark = DatabricksSession.builder.header('x-custom-header', 'value').getOrCreate()

Certificates​

If your cluster relies on a custom SSL/TLS certificate to resolve a Databricks workspace fully qualified domain name (FQDN), you must set the environment variable GRPC_DEFAULT_SSL_ROOTS_FILE_PATH on your local development machine. This environment variable must be set to the full path to the installed certificate on the cluster.

The following example sets this environment variable:

Python
import os

os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"] = "/etc/ssl/certs/ca-bundle.crt"

For other ways to set environment variables, see your operating system's documentation.

Logging and debug logs​

Databricks Connect for Python produces logs using standard Python logging.

Logs are emitted to the standard error stream (stderr) and by default they are turned off. Setting an environment variable SPARK_CONNECT_LOG_LEVEL=debug will modify this default and print all log messages at the DEBUG level and higher.

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