Databricks REST API With Python: Examples & Guide

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Databricks REST API with Python: Examples & Guide

Hey guys! Want to dive into the Databricks REST API using Python? You've come to the right place! This guide will walk you through everything you need to know, complete with practical examples. Whether you're automating tasks, integrating with other systems, or just exploring the power of Databricks, understanding the REST API is super useful. Let's get started!

What is the Databricks REST API?

The Databricks REST API is an interface that allows you to interact with Databricks programmatically. Instead of clicking around in the Databricks UI, you can use code to manage clusters, run jobs, access data, and much more. Think of it as a way to control Databricks using code, which is perfect for automation and integration.

The REST API uses standard HTTP methods like GET, POST, PUT, and DELETE to perform operations. You send requests to specific endpoints, and the API responds with data, usually in JSON format. This makes it easy to work with in various programming languages, including Python.

Why Use the REST API?

  • Automation: Automate repetitive tasks like starting and stopping clusters, deploying code, and scheduling jobs.
  • Integration: Integrate Databricks with other systems, such as CI/CD pipelines, data monitoring tools, and custom applications.
  • Scalability: Programmatically manage Databricks resources to scale your data processing workflows.
  • Flexibility: Access Databricks functionality from any environment that can make HTTP requests.

Setting Up Your Environment

Before we start coding, let's set up our environment. You'll need a few things:

  1. Databricks Account: Obviously, you need a Databricks account. If you don't have one, you can sign up for a free trial.

  2. Databricks Personal Access Token (PAT): You'll use this token to authenticate your API requests. To create a PAT:

    • 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 description and set an expiration date (or choose "No Expiration," but be careful with that!).
    • Click "Generate."
    • Important: Copy the token and store it securely. You won't be able to see it again.

  3. Python: Make sure you have Python installed. Version 3.6 or later is recommended.

  4. requests Library: This library makes it easy to send HTTP requests in Python. Install it using pip:

    pip install requests

Authentication

Authentication is key when working with the Databricks REST API. You'll use the Personal Access Token (PAT) you created earlier. Here’s how to include it in your requests:

import requests

# Your Databricks workspace URL
workspace_url = "YOUR_DATABRICKS_WORKSPACE_URL"

# Your Personal Access Token
token = "YOUR_PERSONAL_ACCESS_TOKEN"

# Set up the headers for authentication
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

Replace YOUR_DATABRICKS_WORKSPACE_URL with your Databricks workspace URL (e.g., https://dbc-xxxxxxxx.cloud.databricks.com) and YOUR_PERSONAL_ACCESS_TOKEN with your actual token. Keep these values safe!

Example 1: Listing Clusters

Let's start with something simple: listing all the clusters in your Databricks workspace. Here’s the code:

import requests
import json

workspace_url = "YOUR_DATABRICKS_WORKSPACE_URL"
token = "YOUR_PERSONAL_ACCESS_TOKEN"
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

# API endpoint for listing clusters
endpoint = f"{workspace_url}/api/2.0/clusters/list"

# Make the GET request
response = requests.get(endpoint, headers=headers)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON response
    clusters = response.json()["clusters"]
    
    # Print the cluster names
    for cluster in clusters:
        print(f"Cluster Name: {cluster['cluster_name']}")
else:
    print(f"Error: {response.status_code} - {response.text}")

This code sends a GET request to the /api/2.0/clusters/list endpoint, which returns a JSON object containing a list of clusters. We then parse the JSON and print the name of each cluster. Make sure to replace the placeholder values with your actual workspace URL and token. If all goes well, you should see a list of your cluster names in the output.

Example 2: Creating a New Cluster

Now, let's create a new cluster using the API. This requires a POST request with a JSON payload containing the cluster configuration. Here’s an example:

import requests
import json

workspace_url = "YOUR_DATABRICKS_WORKSPACE_URL"
token = "YOUR_PERSONAL_ACCESS_TOKEN"
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

# API endpoint for creating a cluster
endpoint = f"{workspace_url}/api/2.0/clusters/create"

# Define the cluster configuration
cluster_config = {
    "cluster_name": "My Awesome Cluster",
    "spark_version": "12.2.x-scala2.12",
    "node_type_id": "Standard_DS3_v2",
    "autoscale": {
        "min_workers": 1,
        "max_workers": 3
    }
}

# Make the POST request
response = requests.post(endpoint, headers=headers, json=cluster_config)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON response
    cluster_info = response.json()
    print(f"Cluster created with ID: {cluster_info['cluster_id']}")
else:
    print(f"Error: {response.status_code} - {response.text}")

In this example, we're creating a cluster named "My Awesome Cluster" with a specific Spark version and node type. The autoscale settings allow the cluster to automatically adjust the number of workers based on the workload. Feel free to customize the cluster configuration to suit your needs. After running this code, you should see the ID of the newly created cluster.

Example 3: Running a Job

Next up, let's run a Databricks job using the API. This involves creating a job, submitting a run, and monitoring its progress. Here’s how:

import requests
import json
import time

workspace_url = "YOUR_DATABRICKS_WORKSPACE_URL"
token = "YOUR_PERSONAL_ACCESS_TOKEN"
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

# API endpoint for running a job
endpoint = f"{workspace_url}/api/2.1/jobs/run-now"

# Define the job configuration
job_config = {
    "job_id": 123,  # Replace with your job ID
    "python_params": ["param1", "param2"]  # Optional parameters for the job
}

# Make the POST request
response = requests.post(endpoint, headers=headers, json=job_config)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON response
    run_info = response.json()
    run_id = run_info["run_id"]
    print(f"Job run submitted with run ID: {run_id}")
    
    # Monitor the job run
    while True:
        # API endpoint for getting run status
        status_endpoint = f"{workspace_url}/api/2.1/jobs/runs/get?run_id={run_id}"
        status_response = requests.get(status_endpoint, headers=headers)
        status_data = status_response.json()
        
        # Check if the job has completed
        state = status_data["state"]["life_cycle_state"]
        if state in ["TERMINATED", "SKIPPED", "INTERNAL_ERROR"]:
            result_state = status_data["state"].get("result_state", "UNKNOWN")
            print(f"Job run finished with state: {result_state}")
            break
        else:
            print(f"Job run is still running. Current state: {state}")
            time.sleep(30)  # Check every 30 seconds
else:
    print(f"Error: {response.status_code} - {response.text}")

This code submits a job run and then monitors its status until it completes. Remember to replace 123 with the actual ID of your job. You can also pass parameters to the job using the python_params field. The script checks the job's status every 30 seconds until it finishes, providing updates along the way.

Example 4: Accessing DBFS

The Databricks File System (DBFS) is a distributed file system mounted into your Databricks workspace. You can use the REST API to interact with DBFS, such as listing files, creating directories, and uploading data. Here’s an example of listing files in a DBFS directory:

import requests
import json

workspace_url = "YOUR_DATABRICKS_WORKSPACE_URL"
token = "YOUR_PERSONAL_ACCESS_TOKEN"
headers = {
    "Authorization": f"Bearer {token}",
    "Content-Type": "application/json"
}

# API endpoint for listing files in DBFS
endpoint = f"{workspace_url}/api/2.0/dbfs/list"

# Define the path to list
data = {
    "path": "/"
}

# Make the POST request
response = requests.post(endpoint, headers=headers, json=data)

# Check if the request was successful
if response.status_code == 200:
    # Parse the JSON response
    files = response.json()["files"]
    
    # Print the file names
    for file in files:
        print(f"File Name: {file['path']}")
else:
    print(f"Error: {response.status_code} - {response.text}")

This code lists the files in the root directory of DBFS. You can change the path parameter to list files in a different directory. DBFS is super handy for storing and managing data within Databricks.

Error Handling

When working with the REST API, it's crucial to handle errors gracefully. The API returns HTTP status codes to indicate the success or failure of a request. Here are some common status codes:

  • 200 OK: The request was successful.
  • 400 Bad Request: The request was malformed or invalid.
  • 401 Unauthorized: Authentication failed.
  • 403 Forbidden: You don't have permission to perform the action.
  • 404 Not Found: The requested resource was not found.
  • 500 Internal Server Error: An error occurred on the server.

Always check the response.status_code and response.text to understand what went wrong. Implement proper error handling in your code to catch exceptions and provide informative messages.

Best Practices

  • Secure Your Token: Never hardcode your Personal Access Token directly into your code. Use environment variables or a secure configuration file to store it.
  • Rate Limiting: Be mindful of rate limits. The Databricks REST API has limits on the number of requests you can make in a given time period. Implement retry logic with exponential backoff to handle rate limiting errors.
  • Use API Versions: Specify the API version in your endpoint URLs (e.g., /api/2.0/). This ensures that your code continues to work even if the API changes.
  • Log Requests: Log your API requests and responses for debugging and monitoring purposes.
  • Test Thoroughly: Test your code in a development environment before deploying it to production.

Conclusion

Alright, guys! You've now got a solid understanding of how to use the Databricks REST API with Python. You've learned how to authenticate, list clusters, create clusters, run jobs, and access DBFS. With these examples, you can start automating your Databricks workflows and integrating them with other systems. Happy coding, and have fun exploring the power of the Databricks REST API! Remember to keep your tokens safe and handle errors gracefully. You're well on your way to becoming a Databricks API master!