Databricks API with Python: A Comprehensive Guide

Hey guys! Ever wondered how to programmatically interact with Databricks using Python? Well, you’re in the right place! This comprehensive guide will walk you through everything you need to know about using the Databricks API with Python. We’ll cover setting up your environment, authenticating, and performing common tasks like managing clusters, running jobs, and working with data. So, buckle up and let’s dive in!

Setting Up Your Environment

Before we get started, let’s make sure you have everything you need. First off, you’ll need Python installed. I recommend using Python 3.6 or higher. You can download it from the official Python website. Next, you’ll need to install the databricks-sdk package. This package provides a convenient way to interact with the Databricks API. You can install it using pip, the Python package installer. Just open your terminal or command prompt and run:

pip install databricks-sdk

Make sure your pip is up to date to avoid any installation issues. It’s always a good idea to upgrade pip before installing new packages. You can do this by running:

pip install --upgrade pip

After installing the databricks-sdk , you’ll also need to configure your Databricks authentication. There are several ways to authenticate with the Databricks API, including using a personal access token (PAT), OAuth, or service principal. For this guide, we’ll focus on using a personal access token, as it’s the simplest to set up. To create a personal access token, 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 your token a descriptive name and set an expiration date. Copy the token and store it in a safe place. You’ll need it later.

Now that you have your personal access token, you can configure the databricks-sdk to use it. There are several ways to do this, including setting environment variables, using a configuration file, or passing the token directly in your code. For simplicity, we’ll use environment variables. Set the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables to your Databricks workspace URL and your personal access token, respectively. For example:

export DATABRICKS_HOST="https://your-databricks-workspace.cloud.databricks.com"
export DATABRICKS_TOKEN="your-personal-access-token"

Replace https://your-databricks-workspace.cloud.databricks.com with your actual Databricks workspace URL and your-personal-access-token with your actual personal access token. Remember to keep your token secure and never share it with anyone.

Authenticating with the Databricks API

Alright, now that we’ve got our environment set up, let’s get to the fun part: authenticating with the Databricks API. With the databricks-sdk installed and your environment variables configured, authenticating is a breeze. Just import the DatabricksClient class from the databricks.sdk module and create an instance of it:

from databricks.sdk import DatabricksClient

db = DatabricksClient()

That’s it! The DatabricksClient automatically reads your environment variables and uses them to authenticate with the Databricks API. If you prefer to pass your credentials directly, you can do so like this:

db = DatabricksClient(host="https://your-databricks-workspace.cloud.databricks.com", token="your-personal-access-token")

However, using environment variables is generally recommended, as it keeps your credentials out of your code. Once you have an authenticated DatabricksClient instance, you can start using it to interact with the Databricks API. The DatabricksClient provides access to various services, such as clusters, jobs, notebooks, and more.

To verify that you’re successfully authenticated, you can try calling a simple API method, such as listing the available clusters. Here’s how:

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

This code retrieves a list of all clusters in your Databricks workspace and prints their names and IDs. If you see a list of clusters, congratulations! You’ve successfully authenticated with the Databricks API. If you encounter any errors, double-check your environment variables and make sure your personal access token is still valid.

Managing Clusters with the API

Now that we’re authenticated, let’s explore how to manage clusters using the Databricks API. Clusters are the heart of Databricks, and the API provides a rich set of methods for creating, starting, stopping, and deleting them. Let’s start by creating a new cluster. To create a cluster, you’ll need to specify various parameters, such as the cluster name, Spark version, node type, and number of workers. Here’s an example:

from databricks.sdk.service.compute import CreateCluster, ClusterSpec, NodeType, AutoScale

cluster_name = "my-new-cluster"

new_cluster = db.clusters.create(CreateCluster(
    cluster_name=cluster_name,
    spark_version="13.3.x-scala2.12",
    node_type_id="Standard_DS3_v2",
    autoscale=AutoScale(min_workers=1, max_workers=3)
))

cluster_id = new_cluster.cluster_id
print(f"Created cluster with ID: {cluster_id}")

This code creates a new cluster with the name “my-new-cluster”, using Spark version 13.3, the Standard_DS3_v2 node type, and autoscaling enabled with a minimum of 1 worker and a maximum of 3 workers. You can customize these parameters to suit your needs. For example, you can choose a different Spark version, node type, or number of workers. You can also enable other features, such as auto-termination and cluster tags. After creating a cluster, you can start it using the start method:

db.clusters.start(cluster_id)
print(f"Starting cluster with ID: {cluster_id}")

This code starts the cluster with the specified ID. Starting a cluster can take several minutes, as Databricks needs to provision the necessary resources. You can check the status of the cluster using the get method:

cluster = db.clusters.get(cluster_id)
print(f"Cluster state: {cluster.state}")

This code retrieves the current state of the cluster. The state can be one of the following: PENDING , RUNNING , RESTARTING , TERMINATING , TERMINATED , or ERROR . Once the cluster is in the RUNNING state, you can start using it to run jobs and notebooks. When you’re finished using a cluster, you can stop it using the stop method:

db.clusters.stop(cluster_id)
print(f"Stopping cluster with ID: {cluster_id}")

Stopping a cluster releases the resources associated with it, which can help you save money. You can also terminate a cluster using the delete method:

db.clusters.delete(cluster_id)
print(f"Terminating cluster with ID: {cluster_id}")

Terminating a cluster permanently deletes it and all its associated data. Be careful when terminating clusters, as this action cannot be undone.

Running Jobs with the API

Now, let’s talk about running jobs using the Databricks API. Jobs are a way to automate tasks in Databricks, such as running notebooks, Spark applications, or Python scripts. The API provides methods for creating, running, and managing jobs. To create a job, you’ll need to specify various parameters, such as the job name, cluster ID, and task to run. Here’s an example of how to run a Databricks Notebook Job:

from databricks.sdk.service.jobs import NotebookTask, CreateJob, Deployment,

job_name = "my-new-job"
notebook_path = "/Users/your-email@example.com/my-notebook"

new_job = db.jobs.create(CreateJob(
    name=job_name,
    tasks=[
        {
            "task_key": "my_notebook_task",
            "notebook_task": {
              "notebook_path": notebook_path
            },
            "deployment": Deployment(target="ephemeral")
        }
    ]
))

job_id = new_job.job_id
print(f"Created job with ID: {job_id}")

This code creates a new job with the name “my-new-job” that runs the notebook located at /Users/your-email@example.com/my-notebook . Replace /Users/your-email@example.com/my-notebook with the actual path to your notebook. You can also specify other task types, such as SparkJarTask , SparkPythonTask , and SparkSubmitTask . Once you’ve created a job, you can run it using the run_now method:

run = db.jobs.run_now(job_id=job_id)
run_id = run.run_id
print(f"Running job with ID: {job_id}, run ID: {run_id}")

This code runs the job with the specified ID and returns a run ID. You can use the run ID to track the progress of the job. You can get the status of a job run using the get_run method:

run = db.jobs.get_run(run_id)
print(f"Run state: {run.state}")

This code retrieves the current state of the job run. The state can be one of the following: PENDING , RUNNING , TERMINATING , TERMINATED , SKIPPED , or INTERNAL_ERROR . You can also get the job run’s result state using the state.result_state attribute. The result state can be one of the following: SUCCESS , FAILURE , TIMED_OUT , or CANCELED . To list all jobs, you can use the list method:

jobs = db.jobs.list()
for job in jobs:
    print(f"Job Name: {job.settings.name}, ID: {job.job_id}")

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

Working with Data

The Databricks API also allows you to interact with data stored in Databricks. You can use the API to list files, upload data, and download data. To list files in a directory, you can use the dbfs.list method:

files = db.dbfs.list("/FileStore/")
for file in files:
    print(f"File Name: {file.path}, Size: {file.file_size}")

This code lists all files in the /FileStore/ directory and prints their names and sizes. To upload a file to DBFS, you can use the dbfs.upload method:

with open("my-local-file.txt", "rb") as f:
    db.dbfs.upload("/FileStore/my-uploaded-file.txt", f)
print("File uploaded successfully")

This code uploads the local file my-local-file.txt to /FileStore/my-uploaded-file.txt in DBFS. To download a file from DBFS, you can use the dbfs.download method:

with open("my-downloaded-file.txt", "wb") as f:
    db.dbfs.download("/FileStore/my-uploaded-file.txt", f)
print("File downloaded successfully")

This code downloads the file /FileStore/my-uploaded-file.txt from DBFS to the local file my-downloaded-file.txt .

Conclusion

Alright, folks! That’s it for this comprehensive guide to using the Databricks API with Python. We’ve covered setting up your environment, authenticating, managing clusters, running jobs, and working with data. With this knowledge, you’re well-equipped to automate your Databricks workflows and build powerful data applications. Happy coding!