Databricks Python SDK: Workspace Client Guide

Nov 8, 2025 by Admin 46 views

Let's dive into the Databricks Python SDK, focusing on the workspace client. If you're looking to automate and manage your Databricks workspace using Python, you've come to the right place! This guide will walk you through everything you need to know, from setting up the SDK to performing common operations with the workspace client. So, grab your favorite beverage, and let's get started!

Setting Up the Databricks Python SDK

Before we can start using the workspace client, we need to set up the Databricks Python SDK. Here’s how you can do it:

Install the SDK:

First things first, you need to install the databricks-sdk package. Open your terminal or command prompt and run:
```
pip install databricks-sdk
```
This command will download and install the latest version of the Databricks SDK along with its dependencies. Make sure you have Python and pip installed on your system before running this command.
Configure Authentication:

Once the SDK is installed, you need to configure authentication so that your Python scripts can communicate with your Databricks workspace. The SDK supports multiple authentication methods, including Databricks personal access tokens, Azure Active Directory tokens, and more. For simplicity, let's use a Databricks personal access token.
- 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 an expiration date (or choose no expiration for testing purposes).
  - Click “Generate” and copy the token. Important: Treat this token like a password and keep it secure.
- Set Environment Variables:
  
  The easiest way to authenticate is by setting environment variables. Set the following variables in your environment:
```
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://dbc-xxxxxxxx.cloud.databricks.com) and <your_personal_access_token> with the token you generated.
Verify the Installation:

To verify that the SDK is installed and configured correctly, you can run a simple Python script:
```
from databricks.sdk import WorkspaceClient

try:
    w = WorkspaceClient()
    me = w.current_user.me()
    print(f"Successfully connected to Databricks workspace. Current user: {me.user_name}")
except Exception as e:
    print(f"Failed to connect to Databricks workspace: {e}")
```
If everything is set up correctly, this script should print the username of the current user in your Databricks workspace. If you encounter any errors, double-check your installation and authentication settings. With the SDK set up and verified, we can now move on to using the workspace client to perform various operations.

Understanding the Workspace Client

The workspace client in the Databricks Python SDK is your gateway to interacting with various Databricks services. It provides methods to manage clusters, jobs, notebooks, secrets, and much more. Think of it as your central control panel for automating and managing your Databricks environment. It's super important to understand how to use it effectively. Let's explore some of the key components and operations.

Initializing the Workspace Client:

To start using the workspace client, you first need to initialize it. This is typically done at the beginning of your script:
```
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()
```
This creates an instance of the WorkspaceClient class, which you can then use to call various methods.
Key Components and Services:

The workspace client provides access to a wide range of Databricks services. Here are some of the most commonly used ones:
- Clusters:
  
  You can use the workspace client to create, start, stop, and manage Databricks clusters. Clusters are the compute resources where your Spark jobs and notebooks run. Managing clusters effectively is crucial for optimizing costs and performance.
- Jobs:
  
  The workspace client allows you to define and manage Databricks jobs. Jobs are automated tasks that run on a schedule or are triggered by events. With jobs, you can automate your data pipelines and other recurring tasks.
- Notebooks:
  
  You can use the workspace client to import, export, and manage Databricks notebooks. Notebooks are interactive documents that contain code, visualizations, and narrative text. They're great for data exploration and collaboration.
- Secrets:
  
  The workspace client provides access to the Databricks Secret Manager, which allows you to securely store and manage sensitive information like passwords and API keys. Keeping your secrets safe is paramount for security.
- Repos:
  
  You can manage Databricks Repos, which allow you to integrate your Databricks workspace with Git repositories for version control and collaboration. Repos are essential for managing your code and collaborating with your team.

Common Operations:

Here are some common operations you can perform using the workspace client:

Listing Clusters:

clusters = w.clusters.list()
for cluster in clusters:
    print(f"Cluster Name: {cluster.cluster_name}, ID: {cluster.cluster_id}")

This code snippet retrieves a list of all clusters in your workspace and prints their names and IDs.

Creating a Job:

from databricks.sdk.service.jobs import NotebookTask, Task, JobSettings

job = w.jobs.create(
    JobSettings=
        name="my-first-sdk-job",
        tasks=[
            Task=
                description="My first task",
                task_key="task_1",
                notebook_task=NotebookTask(notebook_path="/Users/me@example.com/my-notebook"), # replace with actual notebook path
            ]
)
print(f"Job created with ID: {job.job_id}")

This code creates a new Databricks job that runs a specified notebook. Make sure to replace /Users/me@example.com/my-notebook with the actual path to your notebook.

Importing a Notebook:

with open("my_notebook.ipynb", "rb") as f:
    content = f.read()

w.workspace.import_notebook(
    path="/Users/me@example.com/imported_notebook.ipynb", # replace with desired path
    content=content,
    format="IPYNB",
    overwrite=True
)
print("Notebook imported successfully.")

This code imports a notebook from a local file into your Databricks workspace. Replace my_notebook.ipynb with the path to your notebook file and /Users/me@example.com/imported_notebook.ipynb with the desired path in your workspace.

Managing Clusters with the Workspace Client

Databricks clusters are the backbone of your data processing and analytics workloads. The workspace client provides powerful tools to manage these clusters programmatically. Efficient cluster management is essential for optimizing resource utilization and reducing costs. Let's explore how to create, start, stop, and configure clusters using the Databricks Python SDK.

Creating a Cluster:

Creating a cluster involves specifying the desired configuration, such as the Databricks runtime version, node type, number of workers, and more. Here's an example of how to create a cluster:
```
from databricks.sdk.service.compute import ClusterSpec, CreateCluster, NodeType, AutoScale

cluster = w.clusters.create(
    CreateCluster=
        cluster_name="my-sdk-cluster",
        spark_version="13.3.x-scala2.12",
        node_type_id="i3.xlarge",
        autoscale=AutoScale(min_workers=1, max_workers=3)
)

print(f"Cluster created with ID: {cluster.cluster_id}")
```
In this example, we're creating a cluster named my-sdk-cluster with Databricks runtime version 13.3.x-scala2.12, node type i3.xlarge, and autoscaling enabled with a minimum of 1 worker and a maximum of 3 workers. You can adjust these parameters to suit your specific needs. Make sure to choose a node type that is appropriate for your workload and budget.
Starting and Stopping a Cluster:

Once a cluster is created, you can start and stop it using the workspace client. Starting a cluster provisions the compute resources, while stopping a cluster releases them. Here's how you can start and stop a cluster:
```
cluster_id = "1234-567890-abcdefg1"  # Replace with your cluster ID

w.clusters.start(cluster_id)
print(f"Cluster {cluster_id} starting...")

w.clusters.stop(cluster_id)
print(f"Cluster {cluster_id} stopping...")
```
Replace `