Databricks SDK for Python
In this article, you learn how to automate operations in Azure Databricks accounts, workspaces, and related resources with the Databricks SDK for Python. This article supplements the Databricks SDK for Python documentation on Read The Docs and the code examples in the Databricks SDK for Python repository in GitHub.
Note
This feature is in Beta and is okay to use in production.
During the Beta period, Databricks recommends that you pin a dependency on the specific minor version of the Databricks SDK for Python that your code depends on. For example, you can pin dependencies in files such as requirements.txt
for venv
, or pyproject.toml
and poetry.lock
for Poetry. For more information about pinning dependencies, see Virtual Environments and Packages for venv
, or Installing dependencies for Poetry.
Before you begin
You can use the Databricks SDK for Python from within an Azure Databricks notebook or from your local development machine.
- To use the Databricks SDK for Python from within an Azure Databricks notebook, skip ahead to Use the Databricks SDK for Python from an Azure Databricks notebook.
- To use the Databricks SDK for Python from your local development machine, complete the steps in this section.
Before you begin to use the Databricks SDK for Python, your development machine must have:
- Azure Databricks authentication configured.
- Python 3.8 or higher installed. For automating Azure Databricks compute resources, Databricks recommends that you have the major and minor versions of Python installed that match the one that is installed on your target Azure Databricks compute resource. This article's examples rely on automating clusters with Databricks Runtime 13.3 LTS, which has Python 3.10 installed. For the correct version, see Databricks Runtime release notes versions and compatibility for your cluster's Databricks Runtime version.
- Databricks recommends that you create and activate a Python virtual environment for each Python code project that you use with the Databricks SDK for Python. Python virtual environments help to make sure that your code project is using compatible versions of Python and Python packages (in this case, the Databricks SDK for Python package). This article explains how to use venv or Potetry for Python virtual environments.
Create a Python virtual environment with venv
From your terminal set to the root directory of your Python code project, run the following command. This command instructs
venv
to use Python 3.10 for the virtual environment, and then creates the virtual environment's supporting files in a hidden directory named.venv
within the root directory of your Python code project.# Linux and macOS python3.10 -m venv ./.venv # Windows python3.10 -m venv .\.venv
Use
venv
to activate the virtual environment. See the venv documentation for the correct command to use, based on your operating system and terminal type. For example, on macOS runningzsh
:source ./.venv/bin/activate
You will know that your virtual environment is activated when the virtual environment's name (for example,
.venv
) displays in parentheses just before your terminal prompt.To deactivate the virtual environment at any time, run the command
deactivate
.You will know that your virtual environment is deactivated when the virtual environment's name no longer displays in parentheses just before your terminal prompt.
Skip ahead to Get started with the Databricks SDK for Python.
Create a virtual environment with Poetry
Install Poetry, if you have not done so already.
From your terminal set to the root directory of your Python code project, run the following command to instruct
poetry
to initialize your Python code project for Poetry.poetry init
Poetry displays several prompts for you to complete. None of these prompts are specific to the Databricks SDK for Python. For information about these prompts, see init.
After you complete the prompts, Poetry adds a
pyproject.toml
file to your Python project. For information about thepyproject.toml
file, see The pyproject.toml file.With your terminal still set to the root directory of your Python code project, run the following command. This command instructs
poetry
to read thepyproject.toml
file, install and resolve dependencies, create apoetry.lock
file to lock the dependencies, and finally create a virtual environment.poetry install
From your terminal set to the root directory of your Python code project, run the following command to instruct
poetry
to activate the virtual environment and enter the shell.poetry shell
You will know that your virtual environment is activated and the shell is entered when the virtual environment's name displays in parentheses just before your terminal prompt.
To deactivate the virtual environment and exit the shell at any time, run the command
exit
.You will know that you have exited the shell when the virtual environment's name no longer displays in parentheses just before your terminal prompt.
For more information about creating and managing Poetry virtual environments, see Managing environments.
Get started with the Databricks SDK for Python
This section describes how to get started with the Databricks SDK for Python from your local development machine. To use the Databricks SDK for Python from within an Azure Databricks notebook, skip ahead to Use the Databricks SDK for Python from an Azure Databricks notebook.
On your development machine with Azure Databricks authentication configured, Python already installed, and your Python virtual environment already activated, install the databricks-sdk package (and its dependencies) from the Python Package Index (PyPI), as follows:
Venv
Use
pip
to install thedatabricks-sdk
package. (On some systems, you might need to replacepip3
withpip
, here and throughout.)pip3 install databricks-sdk
Poetry
poetry add databricks-sdk
To install a specific version of the
databricks-sdk
package while the Databricks SDK for Python is in Beta, see the package's Release history. For example, to install version0.1.6
:Venv
pip3 install databricks-sdk==0.1.6
Poetry
poetry add databricks-sdk==0.1.6
Tip
To upgrade an existing installation of the Databricks SDK for Python package to the latest version, run the following command:
Venv
pip3 install --upgrade databricks-sdk
Poetry
poetry add databricks-sdk@latest
To show the Databricks SDK for Python package's current
Version
and other details, run the following command:Venv
pip3 show databricks-sdk
Poetry
poetry show databricks-sdk
In your Python virtual environment, create a Python code file that imports the Databricks SDK for Python. The following example, in a file named
main.py
with the following contents, simply lists all the clusters in your Azure Databricks workspace:from databricks.sdk import WorkspaceClient w = WorkspaceClient() for c in w.clusters.list(): print(c.cluster_name)
Run your Python code file, assuming a file named
main.py
, by running thepython
command:Venv
python3.10 main.py
Poetry
If you are in the virtual environment's shell:
python3.10 main.py
If you are not in the virtual environment's shell:
poetry run python3.10 main.py
Note
By not setting any arguments in the preceding call to
w = WorkspaceClient()
, the Databricks SDK for Python uses its default process for trying to perform Azure Databricks authentication. To override this default behavior, see the following authentication section.
Authenticate the Databricks SDK for Python with your Azure Databricks account or workspace
This section describes how to authenticate the Databricks SDK for Python from your local development machine over to your Azure Databricks account or workspace. To authenticate the Databricks SDK for Python from within an Azure Databricks notebook, skip ahead to Use the Databricks SDK for Python from an Azure Databricks notebook.
The Databricks SDK for Python implements the Databricks client unified authentication standard, a consolidated and consistent architectural and programmatic approach to authentication. This approach helps make setting up and automating authentication with Azure Databricks more centralized and predictable. It enables you to configure Databricks authentication once and then use that configuration across multiple Databricks tools and SDKs without further authentication configuration changes. For more information, including more complete code examples in Python, see Databricks client unified authentication.
Note
The Databricks SDK for Python has not yet implemented Azure managed identities authentication.
Some of the available coding patterns to initialize Databricks authentication with the Databricks SDK for Python include:
Use Databricks default authentication by doing one of the following:
- Create or identify a custom Databricks configuration profile with the required fields for the target Databricks authentication type. Then set the
DATABRICKS_CONFIG_PROFILE
environment variable to the name of the custom configuration profile. - Set the required environment variables for the target Databricks authentication type.
Then instantiate for example a
WorkspaceClient
object with Databricks default authentication as follows:from databricks.sdk import WorkspaceClient w = WorkspaceClient() # ...
- Create or identify a custom Databricks configuration profile with the required fields for the target Databricks authentication type. Then set the
Hard-coding the required fields is supported but not recommended, as it risks exposing sensitive information in your code, such as Azure Databricks personal access tokens. The following example hard-codes Azure Databricks host and access token values for Databricks token authentication:
from databricks.sdk import WorkspaceClient w = WorkspaceClient( host = 'https://...', token = '...' ) # ...
See also Authentication in the Databricks SDK for Python documentation.
Use the Databricks SDK for Python from an Azure Databricks notebook
You can call Databricks SDK for Python functionality from an Azure Databricks notebook that has an attached Azure Databricks cluster with the Databricks SDK for Python installed. The Databricks SDK for Python is already installed on all Azure Databricks clusters that use Databricks Runtime 13.3 LTS or above. For Azure Databricks clusters that use Databricks Runtime 12.2 LTS and below, you must install the Databricks SDK for Python first. See Step 1: Install or upgrade the Databricks SDK for Python.
To see the version number of the Databricks SDK for Python that is installed by default for a specific version of the Databricks Runtime, see the "Installed Python libraries" section of the Databricks Runtime release notes for that Databricks Runtime version.
Databricks SDK for Python 0.6.0 and above uses default Azure Databricks notebook authentication. Default Azure Databricks notebook authentication relies on a temporary Azure Databricks personal access token that Azure Databricks automatically generates in the background for its own use. Azure Databricks deletes this temporary token after the notebook stops running.
Important
Default Azure Databricks notebook authentication works only on the cluster's driver node and not on any of the cluster's worker or executor nodes.
Databricks Runtime 13.3 LTS and above supports default Azure Databricks notebook authentication with Databricks SDK for Python 0.1.7 or above installed. Databricks Runtime 10.4 LTS and above supports default Azure Databricks notebook authentication with Databricks SDK for Python 0.1.10 or above installed. However, Databricks recommends that you install or upgrade to Databricks SDK for Python 0.6.0 or above for maximum compatibility with default Azure Databricks notebook authentication, regardless of Databricks Runtime version.
You must install or upgrade the Databricks SDK for Python on the Azure Databricks cluster if you want to call Azure Databricks account-level APIs or if you want to use an Azure Databricks authentication type other than default Azure Databricks notebook authentication, as follows:
Authentication type | Databricks SDK for Python versions |
---|---|
Microsoft Entra ID service principal authentication | All versions |
Azure CLI authentication | All versions |
Databricks personal access token authentication | All versions |
Azure managed identities authentication is not yet supported.
Azure Databricks notebook authentication does not work with Azure Databricks configuration profiles.
Step 1: Install or upgrade the Databricks SDK for Python
Azure Databricks Python notebooks can use the Databricks SDK for Python just like any other Python library. To install or upgrade the Databricks SDK for Python library on the attached Azure Databricks cluster, run the
%pip
magic command from a notebook cell as follows:%pip install databricks-sdk --upgrade
After you run the
%pip
magic command, you must restart Python to make the installed or upgraded library available to the notebook. To do this, run the following command from a notebook cell immediately after the cell with the%pip
magic command:dbutils.library.restartPython()
To display the installed version of the Databricks SDK for Python, run the following command from a notebook cell:
%pip show databricks-sdk | grep -oP '(?<=Version: )\S+'
Step 2: Run your code
In your notebook cells, create Python code that imports and then calls the Databricks SDK for Python. The following example uses default Azure Databricks notebook authentication to list all the clusters in your Azure Databricks workspace:
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
for c in w.clusters.list():
print(c.cluster_name)
When you run this cell, a list of the names of all of the available clusters in your Azure Databricks workspace appears.
To use a different Azure Databricks authentication type, see Azure Databricks authentication methods and click the corresponding link for additional technical details.
Use Databricks Utilities
You can call Databricks Utilities (dbutils) reference from Databricks SDK for Python code running on your local development machine or from within an Azure Databricks notebook.
- From your local development machine, Databricks Utilities has access only to the
dbutils.fs
,dbutils.secrets
,dbutils.widgets
, anddbutils.jobs
command groups. - From an Azure Databricks notebook that is attached to an Azure Databricks cluster, Databricks Utilities has access to all of the available Databricks Utilities command groups, not just
dbutils.fs
,dbutils.secrets
, anddbutils.widgets
. Additionally, thedbutils.notebook
command group is limited to two levels of commands only, for exampledbutils.notebook.run
ordbutils.notebook.exit
.
To call Databricks Utilities from either your local development machine or an Azure Databricks notebook, use dbutils
within WorkspaceClient
. This code example uses default Azure Databricks notebook authentication to call dbutils
within WorkspaceClient
to list the paths of all of the objects in the DBFS root of the workspace.
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
d = w.dbutils.fs.ls('/')
for f in d:
print(f.path)
Alternatively, you can call dbutils
directly. However, you are limited to using default Azure Databricks notebook authentication only. This code example calls dbutils
directly to list all of the objects in the DBFS root of the workspace.
from databricks.sdk.runtime import *
d = dbutils.fs.ls('/')
for f in d:
print(f.path)
To access Unity Catalog volumes, use files
within WorkspaceClient
. See Manage files in Unity Catalog volumes. You cannot use dbutils
by itself or within WorkspaceClient
to access volumes.
See also Interaction with dbutils.
Code examples
The following code examples demonstrate how to use the Databricks SDK for Python to create and delete clusters, run jobs, and list account-level groups. These code examples use default Azure Databricks notebook authentication. For details about default Azure Databricks notebook authentication, see Use the Databricks SDK for Python from an Azure Databricks notebook. For details about default authentication outside of notebooks, see Authenticate the Databricks SDK for Python with your Azure Databricks account or workspace.
For additional code examples, see the examples in the Databricks SDK for Python repository in GitHub. See also:
Create a cluster
This code example creates a cluster with the specified Databricks Runtime version and cluster node type. This cluster has one worker, and the cluster will automatically terminate after 15 minutes of idle time.
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
print("Attempting to create cluster. Please wait...")
c = w.clusters.create_and_wait(
cluster_name = 'my-cluster',
spark_version = '12.2.x-scala2.12',
node_type_id = 'Standard_DS3_v2',
autotermination_minutes = 15,
num_workers = 1
)
print(f"The cluster is now ready at " \
f"{w.config.host}#setting/clusters/{c.cluster_id}/configuration\n")
Permanently delete a cluster
This code example permanently deletes the cluster with the specified cluster ID from the workspace.
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
c_id = input('ID of cluster to delete (for example, 1234-567890-ab123cd4): ')
w.clusters.permanent_delete(cluster_id = c_id)
Create a job
This code example creates an Azure Databricks job that runs the specified notebook on the specified cluster. As the code runs, it gets the existing notebook's path, the existing cluster ID, and related job settings from the user at the terminal.
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import Task, NotebookTask, Source
w = WorkspaceClient()
job_name = input("Some short name for the job (for example, my-job): ")
description = input("Some short description for the job (for example, My job): ")
existing_cluster_id = input("ID of the existing cluster in the workspace to run the job on (for example, 1234-567890-ab123cd4): ")
notebook_path = input("Workspace path of the notebook to run (for example, /Users/someone@example.com/my-notebook): ")
task_key = input("Some key to apply to the job's tasks (for example, my-key): ")
print("Attempting to create the job. Please wait...\n")
j = w.jobs.create(
name = job_name,
tasks = [
Task(
description = description,
existing_cluster_id = existing_cluster_id,
notebook_task = NotebookTask(
base_parameters = dict(""),
notebook_path = notebook_path,
source = Source("WORKSPACE")
),
task_key = task_key
)
]
)
print(f"View the job at {w.config.host}/#job/{j.job_id}\n")
Manage files in Unity Catalog volumes
This code example demonstrates various calls to files
functionality within WorkspaceClient
to access a Unity Catalog volume.
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
# Define volume, folder, and file details.
catalog = 'main'
schema = 'default'
volume = 'my-volume'
volume_path = f"/Volumes/{catalog}/{schema}/{volume}" # /Volumes/main/default/my-volume
volume_folder = 'my-folder'
volume_folder_path = f"{volume_path}/{volume_folder}" # /Volumes/main/default/my-volume/my-folder
volume_file = 'data.csv'
volume_file_path = f"{volume_folder_path}/{volume_file}" # /Volumes/main/default/my-volume/my-folder/data.csv
upload_file_path = './data.csv'
# Create an empty folder in a volume.
w.files.create_directory(volume_folder_path)
# Upload a file to a volume.
with open(upload_file_path, 'rb') as file:
file_bytes = file.read()
binary_data = io.BytesIO(file_bytes)
w.files.upload(volume_file_path, binary_data, overwrite = True)
# List the contents of a volume.
for item in w.files.list_directory_contents(volume_path):
print(item.path)
# List the contents of a folder in a volume.
for item in w.files.list_directory_contents(volume_folder_path):
print(item.path)
# Print the contents of a file in a volume.
resp = w.files.download(volume_file_path)
print(str(resp.contents.read(), encoding='utf-8'))
# Delete a file from a volume.
w.files.delete(volume_file_path)
# Delete a folder from a volume.
w.files.delete_directory(volume_folder_path)
List account-level groups
This code example lists the display names for all of the available groups within the Azure Databricks account.
from databricks.sdk import AccountClient
a = AccountClient()
for g in a.groups.list():
print(g.display_name)
Testing
To test your code, use Python test frameworks such as pytest. To test your code under simulated conditions without calling Azure Databricks REST API endpoints or changing the state of your Azure Databricks accounts or workspaces, use Python mocking libraries such as unittest.mock.
For example, given the following file named helpers.py
containing a create_cluster
function that returns information about the new cluster:
# helpers.py
from databricks.sdk import WorkspaceClient
from databricks.sdk.service.compute import ClusterDetails
def create_cluster(
w: WorkspaceClient,
cluster_name: str,
spark_version: str,
node_type_id: str,
autotermination_minutes: int,
num_workers: int
) -> ClusterDetails:
response = w.clusters.create(
cluster_name = cluster_name,
spark_version = spark_version,
node_type_id = node_type_id,
autotermination_minutes = autotermination_minutes,
num_workers = num_workers
)
return response
And given the following file named main.py
that calls the create_cluster
function:
# main.py
from databricks.sdk import WorkspaceClient
from helpers import *
w = WorkspaceClient()
# Replace <spark-version> with the target Spark version string.
# Replace <node-type-id> with the target node type string.
response = create_cluster(
w = w,
cluster_name = 'Test Cluster',
spark_version = '<spark-version>',
node_type_id = '<node-type-id>',
autotermination_minutes = 15,
num_workers = 1
)
print(response.cluster_id)
The following file named test_helpers.py
tests whether the create_cluster
function returns the expected response. Rather than creating a cluster in the target workspace, this test mocks a WorkspaceClient
object, defines the mocked object's settings, and then passes the mocked object to the create_cluster
function. The test then checks whether the function returns the new mocked cluster's expected ID.
# test_helpers.py
from databricks.sdk import WorkspaceClient
from helpers import *
from unittest.mock import create_autospec # Included with the Python standard library.
def test_create_cluster():
# Create a mock WorkspaceClient.
mock_workspace_client = create_autospec(WorkspaceClient)
# Set the mock WorkspaceClient's clusters.create().cluster_id value.
mock_workspace_client.clusters.create.return_value.cluster_id = '123abc'
# Call the actual function but with the mock WorkspaceClient.
# Replace <spark-version> with the target Spark version string.
# Replace <node-type-id> with the target node type string.
response = create_cluster(
w = mock_workspace_client,
cluster_name = 'Test Cluster',
spark_version = '<spark-version>',
node_type_id = '<node-type-id>',
autotermination_minutes = 15,
num_workers = 1
)
# Assert that the function returned the mocked cluster ID.
assert response.cluster_id == '123abc'
To run this test, run the pytest
command from the code project's root, which should produce test results similar to the following:
$ pytest
=================== test session starts ====================
platform darwin -- Python 3.12.2, pytest-8.1.1, pluggy-1.4.0
rootdir: <project-rootdir>
collected 1 item
test_helpers.py . [100%]
======================== 1 passed ==========================
Additional resources
For more information, see: