Databricks REST API With Python: Examples & Guide

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Databricks REST API with Python: Examples & Guide

Hey guys! Ever wanted to automate your Databricks workflows, manage clusters programmatically, or interact with Databricks resources using Python? You've come to the right place! This guide will walk you through the Databricks REST API, showing you how to use it with Python, complete with practical examples.

What is the Databricks REST API?

The Databricks REST API is a powerful interface that allows you to interact with your Databricks workspace programmatically. Think of it as a way to control Databricks using code. This is incredibly useful for automating tasks, integrating Databricks with other systems, and building custom tools.

Databricks REST API is built upon standard HTTP methods (GET, POST, PUT, DELETE) and returns responses in JSON format. This makes it easy to use with any programming language that can make HTTP requests, and Python is an excellent choice due to its simplicity and extensive libraries.

Why use the REST API?

  • Automation: Automate cluster management, job execution, and other repetitive tasks.
  • Integration: Integrate Databricks with your existing data pipelines and workflows.
  • Customization: Build custom tools and applications that leverage Databricks functionality.

Prerequisites

Before we dive into the code, make sure you have the following:

  • Databricks Workspace: You'll need access to a Databricks workspace.

  • Python: Ensure you have Python 3.6 or higher installed.

  • requests Library: Install the requests library for making HTTP requests. You can install it using pip:

    pip install requests

  • API Token: Generate an API token from your Databricks workspace. Go to User Settings -> Access Tokens and create a new token. Keep this token safe, as it's your key to accessing the API.

Obtaining a Databricks API Token

First, log into your Databricks workspace. Click on your username in the top-right corner and select "User Settings". Navigate to the "Access Tokens" tab and click "Generate New Token". Enter a comment (e.g., "API Access") and set an optional expiration date. Copy the generated token and store it securely. Important: This token is like a password, so treat it with utmost care. Do not share it or commit it to version control systems. If compromised, immediately revoke the token and generate a new one. The API token authenticates your requests to the Databricks REST API, proving that you have the necessary permissions to perform actions within your Databricks workspace. Without a valid token, your requests will be rejected. Managing your tokens effectively is crucial for maintaining the security and integrity of your Databricks environment. Regularly review your tokens and their permissions, and promptly revoke any tokens that are no longer needed or suspected of being compromised. Databricks also supports using Azure Active Directory (Azure AD) tokens for authentication, which can provide enhanced security and simplified management in Azure-based deployments. However, for simplicity and clarity, this guide focuses on using Databricks API tokens. Remember to always prioritize security best practices when working with API tokens and other sensitive credentials. By following these guidelines, you can ensure that your Databricks environment remains secure and protected from unauthorized access.

Setting Up Authentication

To authenticate with the Databricks REST API, you'll need to include your API token in the request headers. Here's how you can do it in Python:

import requests

databricks_host = "YOUR_DATABRICKS_WORKSPACE_URL"  # e.g., "https://dbc-xxxxxxxx.cloud.databricks.com"
databricks_token = "YOUR_API_TOKEN"

headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
}

Replace YOUR_DATABRICKS_WORKSPACE_URL with your Databricks workspace URL and YOUR_API_TOKEN with the API token you generated.

Remember to keep your token secure!

Constructing the Base URL

The base URL for your Databricks REST API requests is derived from your Databricks workspace URL. It typically follows this format: https://<databricks-instance>/api/2.0. Replace <databricks-instance> with your actual workspace URL. For instance, if your Databricks workspace URL is https://dbc-1234567890abcdef.cloud.databricks.com, then your base API URL would be https://dbc-1234567890abcdef.cloud.databricks.com/api/2.0. This base URL serves as the foundation for all your API calls. You'll append specific endpoints to this base URL to access different functionalities within the Databricks REST API. For example, to list all clusters, you would append /clusters/list to the base URL. Constructing the correct base URL is essential for ensuring that your API requests are routed to the appropriate Databricks instance. Double-check your workspace URL and ensure that you are using the correct version of the API (e.g., 2.0) to avoid errors. Using an incorrect base URL will result in your requests failing, preventing you from interacting with your Databricks workspace programmatically. Therefore, it is always a good practice to verify the base URL before making any API calls. Remember, the base URL is the starting point for all your interactions with the Databricks REST API, so getting it right is crucial for success. Once you have the correct base URL, you can start exploring the various endpoints and functionalities offered by the API.

Examples of Common API Calls

Let's explore some common use cases with code examples.

1. Listing Clusters

To list all clusters in your Databricks workspace, use the /clusters/list endpoint.

import requests
import json

databricks_host = "YOUR_DATABRICKS_WORKSPACE_URL"
databricks_token = "YOUR_API_TOKEN"

headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
}

url = f"{databricks_host}/api/2.0/clusters/list"

try:
    response = requests.get(url, headers=headers)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)

    clusters = response.json().get("clusters", [])

    if clusters:
        print("Clusters:")
        for cluster in clusters:
            print(f"  - Name: {cluster['cluster_name']}, ID: {cluster['cluster_id']}")
    else:
        print("No clusters found.")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")
    if response is not None:
        print(f"Response status code: {response.status_code}")
        print(f"Response text: {response.text}")

This code sends a GET request to the /clusters/list endpoint and prints the names and IDs of the clusters.

Error Handling: The try...except block handles potential errors during the API call, such as network issues or invalid credentials. Always include error handling in your code to gracefully manage unexpected situations.

2. Creating a New Cluster

To create a new cluster, use the /clusters/create endpoint. You'll need to provide a JSON payload with the cluster configuration.

import requests
import json

databricks_host = "YOUR_DATABRICKS_WORKSPACE_URL"
databricks_token = "YOUR_API_TOKEN"

headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
}

url = f"{databricks_host}/api/2.0/clusters/create"

cluster_config = {
    "cluster_name": "My New Cluster",
    "spark_version": "12.2.x-scala2.12",
    "node_type_id": "Standard_DS3_v2",
    "autoscale": {
        "min_workers": 1,
        "max_workers": 3
    }
}

try:
    response = requests.post(url, headers=headers, json=cluster_config)
    response.raise_for_status()

    cluster_id = response.json().get("cluster_id")
    print(f"Cluster created with ID: {cluster_id}")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")
    if response is not None:
        print(f"Response status code: {response.status_code}")
        print(f"Response text: {response.text}")

This code sends a POST request to the /clusters/create endpoint with a JSON payload defining the cluster configuration. Remember to adjust the cluster configuration to match your needs. You'll need to choose appropriate values for spark_version, node_type_id, and other parameters. Refer to the Databricks REST API documentation for a complete list of available configuration options. The autoscale settings in this example configure the cluster to automatically scale between 1 and 3 worker nodes based on workload. This helps optimize resource utilization and cost. After the cluster is created, the code extracts the cluster_id from the response and prints it to the console. This ID can be used to manage the cluster further, such as starting, stopping, or resizing it. Error handling is crucial when creating clusters, as various issues can arise, such as invalid configuration parameters, insufficient permissions, or resource limitations. The try...except block in the code handles these potential errors and prints informative messages to help diagnose the problem. Always thoroughly test your cluster creation code and monitor the cluster creation process in the Databricks UI to ensure that everything is working as expected. Creating clusters programmatically via the REST API can significantly streamline your Databricks workflows and enable you to automate the provisioning of compute resources on demand.

3. Starting an Existing Cluster

To start an existing cluster, use the /clusters/start endpoint. You'll need to provide the cluster ID in the request body.

import requests
import json

databricks_host = "YOUR_DATABRICKS_WORKSPACE_URL"
databricks_token = "YOUR_API_TOKEN"

headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
}

cluster_id = "YOUR_CLUSTER_ID"  # Replace with the actual cluster ID
url = f"{databricks_host}/api/2.0/clusters/start"

data = {
    "cluster_id": cluster_id
}

try:
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status()

    print(f"Cluster {cluster_id} is starting...")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")
    if response is not None:
        print(f"Response status code: {response.status_code}")
        print(f"Response text: {response.text}")

This code sends a POST request to the /clusters/start endpoint with the cluster_id in the JSON payload. Make sure to replace YOUR_CLUSTER_ID with the actual ID of the cluster you want to start. The Databricks REST API requires the cluster_id to identify the specific cluster to be acted upon. This ID is a unique identifier assigned to each cluster within your Databricks workspace. You can obtain the cluster_id from the Databricks UI or by using the /clusters/list API endpoint, as demonstrated in the previous example. The JSON payload in this case is simple, containing only the cluster_id. However, for other API endpoints, the payload may be more complex, requiring various parameters to be specified. The requests.post method sends the POST request to the /clusters/start endpoint with the specified headers and JSON payload. The response.raise_for_status() method checks if the request was successful. If the request was not successful (e.g., due to an error), it raises an HTTPError exception, which is caught by the except block. The try...except block provides robust error handling, ensuring that your code can gracefully handle potential issues, such as network connectivity problems or invalid cluster_id values. After the cluster starts successfully, the code prints a message indicating that the cluster is starting. Note that it may take a few minutes for the cluster to fully start up and become ready for use. You can monitor the cluster's status in the Databricks UI.

4. Running a Databricks Job

To run a Databricks job, you can use the /jobs/run-now endpoint. This requires a job ID and optional parameters.

import requests
import json

databricks_host = "YOUR_DATABRICKS_WORKSPACE_URL"
databricks_token = "YOUR_API_TOKEN"

headers = {
    "Authorization": f"Bearer {databricks_token}",
    "Content-Type": "application/json",
}

job_id = "YOUR_JOB_ID"  # Replace with your Job ID
url = f"{databricks_host}/api/2.1/jobs/run-now"

data = {
    "job_id": job_id
}

try:
    response = requests.post(url, headers=headers, json=data)
    response.raise_for_status()

    run_id = response.json().get("run_id")
    print(f"Job submitted. Run ID: {run_id}")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")
    if response is not None:
        print(f"Response status code: {response.status_code}")
        print(f"Response text: {response.text}")

Remember to replace YOUR_JOB_ID with the actual ID of the job you want to run. It's also important to note that the endpoint for running jobs is /api/2.1/jobs/run-now, not /api/2.0/jobs/run-now. This reflects a later version of the Databricks API that includes enhancements and new features for job management. The job_id is a unique identifier assigned to each Databricks job. You can find the job_id in the Databricks UI when viewing the job details. The JSON payload for the /jobs/run-now endpoint is relatively simple, containing only the job_id. However, you can also include additional parameters in the payload to customize the job run, such as specifying different notebook parameters or overriding the job's default settings. The response.json().get("run_id") line extracts the run_id from the API response. The run_id is a unique identifier assigned to each individual run of the job. You can use the run_id to monitor the progress of the job run and retrieve its results. The try...except block provides error handling, allowing your code to gracefully handle potential issues, such as an invalid job_id or network connectivity problems. Always include error handling when working with the Databricks REST API to ensure that your code is robust and reliable. Running jobs programmatically via the REST API is a powerful way to automate your Databricks workflows and integrate them with other systems. By using the /jobs/run-now endpoint, you can easily trigger job runs from your Python scripts and monitor their progress.

Best Practices

  • Secure your API Token: Never hardcode your API token directly into your scripts. Use environment variables or a secure configuration management system.
  • Handle Errors: Always include error handling to gracefully manage API errors and prevent your scripts from crashing.
  • Use response.raise_for_status(): This method raises an HTTPError for bad responses (4xx or 5xx status codes), making it easier to identify and handle errors.
  • Rate Limiting: Be mindful of Databricks API rate limits. Implement retry logic with exponential backoff to avoid being throttled.
  • Use Databricks SDK: Consider using the Databricks SDK for Python, which provides a higher-level abstraction over the REST API and simplifies many common tasks.

Conclusion

The Databricks REST API is a powerful tool for automating and integrating with your Databricks workspace. With Python and the requests library, you can easily manage clusters, run jobs, and perform other administrative tasks programmatically. By following the examples and best practices in this guide, you'll be well on your way to mastering the Databricks REST API.

Keep experimenting and building awesome things with Databricks!