Databricks Workspace Client: Python SDK Guide

Nov 8, 2025 by SLV Team 46 views

Hey guys! Today, we're diving deep into the Databricks Workspace Client using the Python SDK. If you're looking to automate and manage your Databricks workspace programmatically, you've come to the right place. We'll explore everything from setting up the SDK to performing common operations like creating clusters, managing jobs, and handling notebooks. Buckle up, and let's get started!

Setting Up the Databricks Python SDK

Before we can start leveraging the Databricks Workspace Client, we need to set up the Databricks Python SDK. This involves installing the SDK and configuring it to authenticate with your Databricks workspace. Here’s how you can do it step by step.

Installation

First things first, let's install the Databricks SDK. Open your terminal or command prompt and run the following command:

pip install databricks-sdk

This command uses pip, the Python package installer, to download and install the databricks-sdk package along with its dependencies. Make sure you have Python and pip installed on your system before running this command. Once the installation is complete, you can verify it by checking the installed version:

pip show databricks-sdk

Authentication

After installing the SDK, you need to configure it to authenticate with your Databricks workspace. The Databricks SDK supports several authentication methods, including Databricks personal access tokens, Azure Active Directory (Azure AD) tokens, and more. For simplicity, we’ll focus on using a Databricks personal access token. Here’s how to set it up:

Generate a Personal Access Token:
- Log in to your Databricks workspace.
- Go to User Settings > Access Tokens.
- Click on "Generate New Token".
- Enter a description and set the lifetime for the token. It’s best practice to set an expiration date for security reasons.
- Click "Generate".
- Copy the generated token. Important: Treat this token like a password and keep it secure.
Configure the SDK:

You can configure the SDK using environment variables or by creating a Databricks configuration file.
- Using Environment Variables:
  
  Set the following environment variables:
```
export DATABRICKS_HOST=<your-databricks-workspace-url>
export DATABRICKS_TOKEN=<your-personal-access-token>
```
  Replace <your-databricks-workspace-url> with the URL of your Databricks workspace (e.g., https://adb-<xxxxxxxxxxxxxxxxx>.azuredatabricks.net) and <your-personal-access-token> with the token you generated.
- Using a Configuration File:
  
  Create a file named .databrickscfg in your home directory (e.g., /Users/yourusername/.databrickscfg on macOS/Linux or C:\Users\YourUsername\.databrickscfg on Windows). Add the following content:
```
[DEFAULT]
host  = <your-databricks-workspace-url>
token = <your-personal-access-token>
```
  Again, replace the placeholders with your Databricks workspace URL and personal access token.

Verify Authentication:

To verify that the SDK is correctly configured, you can run a simple test in Python:

from databricks.sdk import WorkspaceClient

try:
    w = WorkspaceClient()
    me = w.current_user.me()
    print(f"Successfully authenticated as {me.user_name}")
except Exception as e:
    print(f"Authentication failed: {e}")

If everything is set up correctly, you should see a message indicating that you have successfully authenticated.

By following these steps, you'll have the Databricks Python SDK up and running, ready to interact with your Databricks workspace. Now, let's move on to exploring some common operations.

Common Operations with the Workspace Client

Now that we have the Databricks Python SDK set up, let’s explore some common operations you can perform using the Workspace Client. These operations include managing clusters, running jobs, and interacting with Databricks notebooks.

Managing Clusters

Clusters are the heart of any Databricks environment. They provide the computational resources needed to run your data processing and analytics workloads. The Databricks Workspace Client allows you to create, manage, and monitor clusters programmatically. Here’s how:

Creating a Cluster:

You can create a new cluster using the clusters.create method. You’ll need to provide a cluster configuration, which specifies the cluster’s settings, such as the Databricks runtime version, node type, and number of workers. Here’s an example:
```
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

cluster_config = {
    "cluster_name": "my-new-cluster",
    "spark_version": "13.3.x-scala2.12",
    "node_type_id": "Standard_DS3_v2",
    "autoscale": {
        "min_workers": 1,
        "max_workers": 3
    }
}

try:
    cluster = w.clusters.create(**cluster_config)
    print(f"Cluster created with ID: {cluster.cluster_id}")
except Exception as e:
    print(f"Failed to create cluster: {e}")
```
In this example, we define a cluster configuration with a name, Spark version, node type, and autoscaling settings. We then use the clusters.create method to create the cluster. The method returns a ClusterInfo object, which contains information about the newly created cluster, including its ID.

Starting and Stopping a Cluster:

You can start and stop a cluster using the clusters.start and clusters.stop methods, respectively. You’ll need to provide the cluster ID as an argument. Here’s how:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
cluster_id = "1234-567890-abcdefg1"

try:
    w.clusters.start(cluster_id)
    print(f"Cluster {cluster_id} started")
except Exception as e:
    print(f"Failed to start cluster {cluster_id}: {e}")

try:
    w.clusters.stop(cluster_id)
    print(f"Cluster {cluster_id} stopped")
except Exception as e:
    print(f"Failed to stop cluster {cluster_id}: {e}")

These methods allow you to programmatically control the lifecycle of your clusters, which can be useful for automating tasks such as starting clusters before running jobs and stopping them afterward to save costs.

Listing Clusters:

You can list all clusters in your workspace using the clusters.list method. This method returns a list of ClusterInfo objects, each representing a cluster. Here’s an example:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

try:
    clusters = list(w.clusters.list())
    for cluster in clusters:
        print(f"Cluster ID: {cluster.cluster_id}, Name: {cluster.cluster_name}, State: {cluster.state}")
except Exception as e:
    print(f"Failed to list clusters: {e}")

This method is useful for monitoring the status of your clusters and ensuring that they are running as expected.

Managing Jobs

Databricks Jobs allow you to automate the execution of notebooks, JAR files, and Python scripts. The Databricks Workspace Client provides methods for creating, running, and managing jobs. Let's see how to leverage these features.

Creating a Job:

You can create a new job using the jobs.create method. You’ll need to provide a job configuration, which specifies the job’s settings, such as the task to be executed, the cluster to run the job on, and any dependencies. Here’s an example:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

job_config = {
    "name": "my-new-job",
    "tasks": [
        {
            "task_key": "my-notebook-task",
            "notebook_task": {
                "notebook_path": "/Users/your-email@example.com/my-notebook"
            },
            "existing_cluster_id": "1234-567890-abcdefg1"
        }
    ]
}

try:
    job = w.jobs.create(**job_config)
    print(f"Job created with ID: {job.job_id}")
except Exception as e:
    print(f"Failed to create job: {e}")

In this example, we define a job configuration with a name and a single task that executes a Databricks notebook. We then use the jobs.create method to create the job. The method returns a Job object, which contains information about the newly created job, including its ID.

Running a Job:

You can run an existing job using the jobs.run_now method. You’ll need to provide the job ID as an argument. Here’s how:
```
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
job_id = "12345"

try:
    run = w.jobs.run_now(job_id=job_id)
    print(f"Job {job_id} run started with ID: {run.run_id}")
except Exception as e:
    print(f"Failed to run job {job_id}: {e}")
```
This method starts a new run of the specified job and returns a Run object, which contains information about the run, including its ID.
Listing Jobs:

You can list all jobs in your workspace using the jobs.list method. This method returns a list of Job objects, each representing a job. Here’s an example:
```
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

try:
    jobs = list(w.jobs.list())
    for job in jobs:
        print(f"Job ID: {job.job_id}, Name: {job.settings.name}")
except Exception as e:
    print(f"Failed to list jobs: {e}")
```
This method is useful for monitoring the status of your jobs and ensuring that they are running as expected.

Interacting with Databricks Notebooks

Databricks notebooks are interactive environments for writing and running code, visualizing data, and collaborating with others. The Databricks Workspace Client allows you to manage notebooks programmatically. Let’s see how.

Creating a Notebook:

While the SDK doesn't directly create notebook content, you can use it to manage the workspace and then populate the notebook via the Databricks UI or other means. Creating the folder structure is something the SDK excels at.

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
notebook_path = "/Users/your-email@example.com/my-new-notebook.ipynb"

try:
    # Ensure parent directory exists.  This example doesn't create a notebook *file*
    # just manages workspace directories.
    w.workspace.mkdirs(path=os.path.dirname(notebook_path))
    print(f"Directory created for notebook at: {notebook_path}")
except Exception as e:
    print(f"Failed to create directory: {e}")

Exporting a Notebook:

You can export a notebook to various formats, such as source code, HTML, or DBC (Databricks Archive), using the workspace.export method. You’ll need to provide the path to the notebook and the format to export it to. Here’s an example:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
notebook_path = "/Users/your-email@example.com/my-notebook"
export_format = "SOURCE"
local_path = "my-notebook.py"

try:
    content = w.workspace.export(path=notebook_path, format=export_format)
    with open(local_path, "wb") as f:
        f.write(content)
    print(f"Notebook exported to {local_path}")
except Exception as e:
    print(f"Failed to export notebook: {e}")

In this example, we export the notebook to a Python source file. The workspace.export method returns the content of the notebook, which we then write to a local file.

Importing a Notebook:

You can import a notebook from a local file or a remote URL using the workspace.import method. You’ll need to provide the path to import the notebook to and the content of the notebook. Here’s an example:

import os
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
notebook_path = "/Users/your-email@example.com/my-imported-notebook.ipynb"
local_path = "my-notebook.py"
import_format = "SOURCE"

try:
    with open(local_path, "r") as f:
        content = f.read().encode("utf-8")
    w.workspace.import_(path=notebook_path, format=import_format, content=content, overwrite=True)
    print(f"Notebook imported to {notebook_path}")
except Exception as e:
    print(f"Failed to import notebook: {e}")

In this example, we import a notebook from a local Python source file. The workspace.import method imports the notebook to the specified path in the Databricks workspace.

Best Practices and Tips

When working with the Databricks Python SDK Workspace Client, there are several best practices and tips that can help you write more efficient and maintainable code. Let’s explore some of them.

Use Environment Variables for Secrets:

Avoid hardcoding sensitive information, such as personal access tokens, in your code. Instead, use environment variables to store these secrets and retrieve them at runtime. This makes your code more secure and easier to manage.
Handle Exceptions:

Always wrap your API calls in try...except blocks to handle exceptions. This prevents your code from crashing when an error occurs and allows you to gracefully handle the error.
Use Logging:

Use the logging module to log important events and errors. This makes it easier to debug your code and monitor its behavior.
Modularize Your Code:

Break your code into smaller, reusable functions and classes. This makes your code more modular, easier to test, and easier to maintain.
Use Configuration Files:

Use configuration files to store settings that can change over time. This makes it easier to update your code without having to modify the code itself.

By following these best practices and tips, you can write more efficient, maintainable, and secure code that leverages the Databricks Python SDK Workspace Client to its full potential.

Conclusion

Alright, guys, we've covered a lot today! From setting up the Databricks Python SDK to managing clusters, jobs, and notebooks, you now have a solid foundation for automating your Databricks workspace. Remember to practice these operations and explore the Databricks SDK documentation for even more advanced features. Happy coding, and see you in the next one!