Databricks Python SDK: Examples & Usage Guide

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Databricks Python SDK: Examples & Usage Guide

Hey guys! Ever felt like you're wrestling with the Databricks REST API, writing tons of boilerplate code just to automate simple tasks? Well, say hello to the Databricks Python SDK! This awesome tool simplifies interacting with Databricks, making your life as a data engineer or data scientist way easier. In this guide, we're diving deep into practical examples to get you up and running in no time. We'll cover everything from basic authentication to more advanced operations like managing clusters and jobs. So, buckle up and let's get started!

Setting Up Your Environment

Before we dive into the examples, let's make sure your environment is all set. First, you'll need to install the databricks-sdk package. Open your terminal and run:

pip install databricks-sdk

Once the installation is complete, you'll need to configure your credentials. The Databricks SDK supports various authentication methods, including personal access tokens, OAuth, and Azure Active Directory. For simplicity, let's use a personal access token. Here’s how you can set it up:

  1. Generate a Personal Access Token:

    • Go to your Databricks workspace.
    • Click on your username in the top right corner and select "User Settings."
    • Go to the "Access Tokens" tab.
    • Click "Generate New Token."
    • Enter a comment (e.g., "Databricks SDK"), set an expiration date, and click "Generate."
    • Important: Copy the token and store it securely. You won't be able to see it again.

  2. Configure the SDK:

    You can configure the SDK in a few ways:

    • Environment Variables: Set the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables.

      export DATABRICKS_HOST="your_databricks_workspace_url"
export DATABRICKS_TOKEN="your_personal_access_token"

    • Directly in Code: You can also pass the host and token directly in your Python script.

      from databricks.sdk import WorkspaceClient

w = WorkspaceClient(
    host  = "your_databricks_workspace_url",
    token = "your_personal_access_token"
)

Now that you have your environment set up, let’s dive into some practical examples.

Example 1: Listing Clusters

One of the most common tasks is listing available clusters. The Databricks Python SDK makes this super easy. Here’s how:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

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

In this example, we first create a WorkspaceClient instance. If you've set the environment variables, the SDK will automatically pick up your credentials. Then, we use the clusters.list() method to retrieve a list of all clusters in your workspace. Finally, we iterate through the list and print some basic information about each cluster. This is a fantastic way to quickly get an overview of your Databricks environment.

The WorkspaceClient is your entry point to interacting with the Databricks API. It handles authentication and provides access to various services, such as cluster management, job management, and more. The clusters.list() method returns an iterator, allowing you to efficiently process a large number of clusters without loading them all into memory at once. Understanding how to list clusters is crucial for monitoring your Databricks environment and ensuring that your resources are being used effectively. Also, it's a good starting point for more complex operations like starting, stopping, or resizing clusters. Remember to handle exceptions properly in your production code. For instance, you might want to catch ApiException to handle cases where the API returns an error.

Example 2: Creating a New Cluster

Creating a new cluster is another essential task. With the SDK, you can define your cluster configuration in Python and create a cluster with just a few lines of code. Here’s an example:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.compute import CreateCluster, ClusterState

w = WorkspaceClient()

cluster = w.clusters.create(CreateCluster(
    cluster_name = "my-new-cluster",
    spark_version = "13.3.x-scala2.12",
    node_type_id = "Standard_D3_v2",
    autoscale = {
        "min_workers": 1,
        "max_workers": 3
    }
)).result(600)

print(f"Cluster created with ID: {cluster.cluster_id}")

In this example, we use the clusters.create() method to create a new cluster. We pass a CreateCluster object that defines the configuration of the cluster, including the cluster name, Spark version, node type, and autoscaling settings. The result(600) call waits for the cluster to be in a running state with a timeout of 600 seconds. After the cluster is created, we print the cluster ID. Creating clusters programmatically allows you to automate your infrastructure provisioning and ensure consistency across your Databricks environments. This is incredibly useful when you need to spin up clusters for specific tasks or projects. For instance, you might want to create a cluster specifically for running a data pipeline or for interactive data exploration. The ability to define your cluster configuration in code also makes it easier to version control your infrastructure and track changes over time. Additionally, using autoscaling helps you optimize resource utilization and reduce costs by automatically adjusting the number of workers based on the workload.

Example 3: Running a Job

Databricks Jobs are a core component for running production workloads. The SDK makes it straightforward to define and run jobs. Let's look at an example of submitting a Python task:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.jobs import PythonTask, RunNow

w = WorkspaceClient()

job = w.jobs.create(
    name = "my-python-job",
    tasks = [
        {
            "task_key": "my-python-task",
            "python_task": PythonTask(
                python_file = "dbfs:/FileStore/my_script.py"
            ),
            "new_cluster": {
                "spark_version": "13.3.x-scala2.12",
                "node_type_id": "Standard_D3_v2",
                "autoscale": {
                    "min_workers": 1,
                    "max_workers": 3
                }
            }
        }
    ]
)

run = w.jobs.run_now(RunNow(job_id = job.job_id))

print(f"Job run ID: {run.run_id}")

In this example, we define a job with a single Python task. The PythonTask specifies the path to the Python file in DBFS (dbfs:/FileStore/my_script.py). We also define a new cluster configuration for the job. Then, we use the jobs.run_now() method to start the job. This is a powerful way to automate your data processing pipelines and ensure that your jobs are running reliably. Automating job submissions allows you to schedule your workloads and monitor their progress. For example, you can set up a job to run daily to process new data or to train a machine learning model. The SDK provides fine-grained control over job configuration, allowing you to specify dependencies, retry policies, and other settings. You can also use the SDK to monitor the status of your jobs and retrieve logs. This makes it easier to troubleshoot issues and ensure that your jobs are running as expected. Furthermore, using the SDK to manage your Databricks Jobs integrates seamlessly with your existing Python workflows and tools, improving productivity and collaboration.

Example 4: Working with DBFS

Databricks File System (DBFS) is a distributed file system that allows you to store and access data. The SDK provides methods for interacting with DBFS, such as uploading and downloading files. Here’s an example of uploading a file to DBFS:

from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

with open("my_local_file.txt", "rb") as f:
    w.dbfs.upload("/FileStore/my_uploaded_file.txt", f, overwrite = True)

print("File uploaded to DBFS")

In this example, we open a local file in binary read mode ("rb") and use the dbfs.upload() method to upload it to DBFS. The overwrite = True argument ensures that the file is overwritten if it already exists in DBFS. Interacting with DBFS programmatically allows you to automate data transfer and storage tasks. This is particularly useful when you need to move data between your local environment and Databricks or when you need to process large datasets stored in DBFS. The SDK also provides methods for listing files in DBFS, creating directories, and deleting files. This gives you full control over your data in DBFS and makes it easier to manage your data assets. Overall, managing files in DBFS becomes very easy with the Databricks SDK in Python.

Example 5: Managing Secrets

Managing secrets securely is crucial for any data engineering project. Databricks provides a secrets management system that allows you to store sensitive information, such as database passwords and API keys, securely. The SDK provides methods for interacting with the secrets management system. Here’s an example of creating a secret scope and putting a secret:

from databricks.sdk import WorkspaceClient
from databricks.sdk.service.secrets import CreateScope, PutSecret

w = WorkspaceClient()

# Create a secret scope
try:
    w.secret_scopes.create(CreateScope(
        name = "my-secret-scope",
        initial_manage_principal = "users"
    ))
except Exception as e:
    print(f"Secret scope already exists or error occurred: {e}")

# Put a secret into the scope
w.secrets.put_secret(PutSecret(
    scope = "my-secret-scope",
    key = "my-secret-key",
    string_value = "my-secret-value"
))

print("Secret created")

In this example, we first create a secret scope using the secret_scopes.create() method. We set the initial_manage_principal to "users", which means that all users in the workspace have access to manage the scope. Then, we use the secrets.put_secret() method to put a secret into the scope. Storing secrets securely ensures that your sensitive information is protected and that your data engineering pipelines are not compromised. The Databricks secrets management system provides a centralized and secure way to manage your secrets. The SDK makes it easy to integrate secrets management into your Python workflows and automate the process of creating and managing secrets. Also, it's important to handle exceptions properly when working with secrets, as unauthorized access or incorrect configuration can lead to security vulnerabilities.

Conclusion

The Databricks Python SDK is a game-changer for automating and simplifying your interactions with Databricks. From managing clusters and jobs to working with DBFS and secrets, the SDK provides a comprehensive set of tools for building robust and scalable data engineering pipelines. By using the examples in this guide, you can quickly get up and running with the SDK and start automating your Databricks workflows. So, go ahead and give it a try – you'll be amazed at how much time and effort you can save! Happy coding, and may your data pipelines always run smoothly!