Databricks API: Python Examples For Easy Automation

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Databricks API: Python Examples for Easy Automation

Let's dive into using the Databricks API with Python! If you're looking to automate tasks, manage your Databricks workspace programmatically, or integrate it with other systems, you've come to the right place. This article will walk you through practical examples, providing a solid foundation for leveraging the Databricks API using Python.

Getting Started with Databricks API and Python

To kick things off, you'll need a few things in place. First, make sure you have Python installed. I recommend using Python 3.6 or higher. You’ll also need the requests library, which makes sending HTTP requests super easy. You can install it using pip:

pip install requests

Next, you'll need your Databricks personal access token. To generate this, go to your Databricks workspace, click on your username in the top right corner, and select "User Settings." Then, go to the "Access Tokens" tab and click "Generate New Token." Give it a descriptive name and set an expiration date. Keep this token safe – it's like a password!

Finally, you'll need your Databricks workspace URL. This is the URL you use to access your Databricks workspace (e.g., https://your-databricks-instance.cloud.databricks.com).

Now that you have the essentials, let's look at some code examples.

Authentication

First, let's set up the authentication. You'll use your personal access token to authenticate your requests. Here’s how you can do it:

import requests

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

In this snippet, we import the requests library and define two variables: DATABRICKS_HOST for your workspace URL and DATABRICKS_TOKEN for your personal access token. We then create a headers dictionary that includes the authorization token. The authorization token is passed using the Bearer scheme. The content type is set to application/json because we'll be sending and receiving JSON data.

Example 1: Listing Clusters

One of the most common tasks is listing the available clusters in your Databricks workspace. Here’s how you can do it using the API:

import requests
import json

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

endpoint = f"{DATABRICKS_HOST}/api/2.0/clusters/list"

try:
    response = requests.get(endpoint, headers=headers)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    clusters = response.json().get("clusters", [])

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

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

In this example, we define the API endpoint for listing clusters. We then use the requests.get method to send a GET request to the endpoint, passing the headers for authentication. The response.json() method parses the JSON response, and we extract the list of clusters. Finally, we iterate through the clusters and print their IDs and names. Error handling is included to catch any request-related exceptions.

Example 2: Creating a New Cluster

Creating a new cluster is another common task. Here’s how you can do it:

import requests
import json

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

endpoint = 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(endpoint, headers=headers, json=cluster_config)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    cluster_id = response.json().get("cluster_id")

    if cluster_id:
        print(f"Cluster created with ID: {cluster_id}")
    else:
        print("Cluster creation failed.")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

Here, we define the API endpoint for creating clusters and a cluster_config dictionary that specifies the configuration for the new cluster, including the cluster name, Spark version, node type, and autoscaling settings. We then use the requests.post method to send a POST request to the endpoint, passing the headers and the cluster configuration as JSON data. The response.json() method parses the JSON response, and we extract the cluster ID. Error handling is included to catch any request-related exceptions.

Example 3: Starting a Cluster

Once you've created a cluster, you might want to start it. Here’s how:

import requests
import json

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

cluster_id = "1234-567890-abcdefg1"  # Replace with your cluster ID
endpoint = f"{DATABRICKS_HOST}/api/2.0/clusters/start"

data = {
    "cluster_id": cluster_id
}

try:
    response = requests.post(endpoint, headers=headers, json=data)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)

    if response.status_code == 200:
        print(f"Cluster {cluster_id} is starting...")
    else:
        print(f"Failed to start cluster {cluster_id}.")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

In this example, we define the API endpoint for starting a cluster and create a data dictionary that specifies the cluster ID. We then use the requests.post method to send a POST request to the endpoint, passing the headers and the data as JSON. The response.status_code attribute is used to check the status code of the response, and a message is printed to indicate whether the cluster started successfully. Error handling is included to catch any request-related exceptions.

Example 4: Running a Job

Running jobs is a crucial part of Databricks automation. Here’s a simple example:

import requests
import json

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

endpoint = f"{DATABRICKS_HOST}/api/2.1/jobs/run-now"

job_config = {
    "job_id": 123,  # Replace with your job ID
}

try:
    response = requests.post(endpoint, headers=headers, json=job_config)
    response.raise_for_status()  # Raise HTTPError for bad responses (4xx or 5xx)
    run_id = response.json().get("run_id")

    if run_id:
        print(f"Job run started with run ID: {run_id}")
    else:
        print("Job run failed to start.")

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

In this snippet, we define the API endpoint for running a job and create a job_config dictionary that specifies the job ID. We then use the requests.post method to send a POST request to the endpoint, passing the headers and the job configuration as JSON data. The response.json() method parses the JSON response, and we extract the run ID. Error handling is included to catch any request-related exceptions.

Example 5: Getting Job Details

To monitor your jobs, you'll often need to fetch details about specific job runs:

import requests
import json

DATABRICKS_HOST = "https://your-databricks-instance.cloud.databricks.com" # Replace with your Databricks workspace URL
DATABRICKS_TOKEN = "dapi********************************" # Replace with your personal access token

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

run_id = 456  # Replace with your run ID
endpoint = f"{DATABRICKS_HOST}/api/2.1/jobs/runs/get?run_id={run_id}"

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

    print(json.dumps(run_details, indent=4))

except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

Here, we define the API endpoint for getting job details and specify the run ID. We then use the requests.get method to send a GET request to the endpoint, passing the headers. The response.json() method parses the JSON response, and we print the run details in a nicely formatted JSON structure using json.dumps with an indent of 4.

Best Practices and Tips

When working with the Databricks API, keep these tips in mind:

  • Error Handling: Always include proper error handling. The Databricks API can return various error codes, so make sure you handle them gracefully.
  • Rate Limiting: Be aware of rate limits. The Databricks API has rate limits to prevent abuse. If you exceed the limits, you may receive error responses. Implement retry logic with exponential backoff to handle rate limiting.
  • Security: Keep your personal access tokens secure. Do not hardcode them in your scripts. Use environment variables or a secure configuration management system to store your tokens.
  • API Versions: Be mindful of the API versions. The Databricks API is versioned, and the endpoints may change between versions. Make sure you're using the correct API version for your needs.
  • Documentation: Refer to the official Databricks API documentation. The documentation provides detailed information about the available endpoints, request parameters, and response formats.

Conclusion

And that's a wrap, guys! Using the Databricks API with Python can greatly enhance your ability to automate and manage your Databricks workspace. By understanding the basics of authentication and exploring examples like listing clusters, creating clusters, starting clusters, running jobs, and getting job details, you can unlock powerful automation capabilities. Always remember to follow best practices for error handling, security, and rate limiting to ensure a smooth and efficient experience. Now go forth and automate! Happy coding! Good luck! You got this!