Databricks Connect Client
Project description
Databricks Connect
Databricks Connect is a Python library to run PySpark DataFrame queries on a remote Spark cluster. Databricks Connect leverages the power of Spark Connect. An application using Databricks Connect runs locally, and when the results of a DataFrame query need to be evaluated, the query is run on a configured Databricks cluster.
The following is a simple Python code that uses Databricks Connect and prints out a number range. The number range query is executed on the Databricks cluster.
from databricks.connect import DatabricksSession
session = DatabricksSession.builder.getOrCreate()
df = session.range(1, 10)
df.show()
Specifying Connection Parameters
DatabricksSession offers a few ways to specify the Databricks workspace, cluster and user
credentials, collectively referred to in the rest of this document as connection parameters.
The specified credentials are used to execute the DataFrame queries on the cluster. This user must
have cluster access permissions and appropriate data access permissions.
NOTE: Currently, Databricks Connect only supports credentials based on Personal Access Token. Other authentication mechanisms are coming soon.
When DatabricksSession is initialized with no additional parameters as below, connection
parameters are picked up from the environment.
session = DatabricksSession.builder.getOrCreate()
First, the SPARK_REMOTE environment variable is used if it's configured.
If configured, the SPARK_REMOTE environment variable must contain the spark connect connection
string. Read more about spark connect connection string.
SPARK_REMOTE="sc://<databricks workspace url>:443/;token=<bearer token>;x-databricks-cluster-id=<cluster id>"
If this environment variable is not configured, Databricks Connect will now look for connection parameters using the Databricks SDK.
The Databricks Python SDK reads these values from two locations - first from environment variables
that may be configured. For parameters not configured via environment variables, the 'DEFAULT'
profile, if set up, from the configuration file .databrickscfg.
The details on the environment variable and configuration file can be found in the Databricks SDK.
Similar to the authentication environment variables, the Databricks SDK reads the cluster identifier from the environment variable
DATABRICKS_CLUSTER_IDor from thecluster_identry in the config file.
When the defaults should not be used, the Databricks Connect session can be initialized explicitly
with a Config object from the Databricks SDK.
In the below example, we are configuring Databricks Session to use the foo-user profile from the
configuration file.
Read more on profiles in configuration files in the Databricks SDK.
from databricks.sdk.core import Config
from databricks.connect import DatabricksSession
config = Config(
profile="foo-user",
# ...
)
session = DatabricksSession.builder.sdkConfig(config).getOrCreate()
Connection parameters can also be specified directly in code.
session = DatabricksSession.builder.remote(
host="<databricks workspace url>",
cluster_id="<databricks cluster id>",
token="<bearer token>"
).getOrCreate()
The spark connect connection string can also be specified directly in code.
session = DatabricksSession.builder\
.remote("sc://<databricks workspace url>:443/;token=<bearer token>;x-databricks-cluster-id=<cluster id>")\
.getOrCreate()
In summary, connection parameters are collected in the following order. When all connection parameters are available, evaluation is stopped.
- Specified directly using
remote(), either as a connection string or as keyword arguments. - Specified via the Databricks SDK using
sdkConfig(). - Specified in the
SPARK_REMOTEenvironment variable. - Specified via the Databricks SDK's default authentication.
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