Databricks Python Wheel Task: A Simple Example

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Databricks Python Wheel Task: A Simple Example

Hey guys! Ever wondered how to package your Python code into a neat little wheel and run it on Databricks? Well, you're in the right place! This article will walk you through creating and executing a Databricks Python Wheel task with a straightforward example. Buckle up, and let's dive in!

What is a Python Wheel?

Before we jump into Databricks, let's quickly understand what a Python wheel is. Simply put, a wheel is a packaged format for Python distributions. Think of it as a ZIP file but specifically designed for Python packages. Wheels are meant to be easily installed and distributed. They contain all the necessary code and metadata, making deployment a breeze. Using wheels ensures that your code runs consistently across different environments because all dependencies and configurations are packaged together.

Wheels are especially useful when you want to share your Python code with others or deploy it to a production environment. They eliminate the need for users to build the package from source, which can be time-consuming and error-prone. Instead, they can simply install the wheel file using pip, and everything is ready to go.

Why use wheels? They offer faster installation times, ensure consistency, and simplify the deployment process. If you're not already using wheels, now is the perfect time to start!

Why Use Python Wheels in Databricks?

So, why bother with Python wheels in Databricks? Databricks is a powerful platform for big data processing and analytics, and it supports Python seamlessly. However, managing dependencies and ensuring consistent execution across different Databricks clusters can be challenging. That's where Python wheels come to the rescue.

By packaging your Python code and its dependencies into a wheel, you can easily deploy it to Databricks and ensure that it runs exactly as expected. This is particularly useful when you have complex projects with multiple dependencies or when you need to share your code with other team members.

Benefits of using wheels in Databricks:

  • Dependency Management: Wheels encapsulate all the necessary dependencies, eliminating the risk of missing or conflicting packages.
  • Reproducibility: Wheels ensure that your code runs consistently across different Databricks clusters and environments.
  • Simplified Deployment: Deploying wheels to Databricks is straightforward and can be automated as part of your CI/CD pipeline.
  • Code Sharing: Wheels make it easy to share your code with other team members, as they can simply install the wheel file and start using your code.

Prerequisites

Before we get started, make sure you have the following prerequisites in place:

  • Databricks Account: You'll need a Databricks account with access to a Databricks workspace.

  • Databricks CLI: Install and configure the Databricks Command-Line Interface (CLI) on your local machine. You can find instructions on how to do this in the Databricks documentation.

  • Python: Make sure you have Python installed on your local machine. Python 3.6 or higher is recommended.

  • pip: Ensure that you have pip, the Python package installer, installed. It usually comes with Python by default.

  • setuptools and wheel: Install the setuptools and wheel packages. These are necessary for building Python wheels. You can install them using pip:

    pip install setuptools wheel

Step-by-Step Example: Creating and Running a Databricks Python Wheel Task

Let's walk through a step-by-step example of creating a Python wheel and running it as a Databricks task. We'll start with a simple Python script and package it into a wheel. Then, we'll upload the wheel to Databricks and create a job to execute it.

Step 1: Create a Simple Python Script

First, let's create a simple Python script that we want to run on Databricks. Create a new directory for your project and add a file named my_script.py with the following content:

# my_script.py

def hello_databricks(name):
    return f"Hello, {name}! Welcome to Databricks!"

if __name__ == "__main__":
    name = "World"
    message = hello_databricks(name)
    print(message)

This script defines a function hello_databricks that takes a name as input and returns a greeting message. When the script is executed, it calls the function with the name "World" and prints the resulting message.

Step 2: Create a setup.py File

Next, we need to create a setup.py file in the same directory as my_script.py. This file contains the metadata about our package and tells Python how to build and install it. Here's an example setup.py file:

# setup.py

from setuptools import setup, find_packages

setup(
    name='my_databricks_wheel',
    version='0.1.0',
    packages=find_packages(),
    install_requires=[
        # List any dependencies here, e.g., 'requests>=2.20.0',
    ],
    entry_points={
        'console_scripts': [
            'my_script = my_script:main',
        ],
    },
)

Explanation of the setup.py file:

  • name: The name of your package.
  • version: The version number of your package.
  • packages: A list of packages to include in the distribution. find_packages() automatically finds all packages in your project.
  • install_requires: A list of dependencies that your package requires. You can list any external libraries that your code depends on here. For this example, we don't have any dependencies, so the list is empty.
  • entry_points: This section defines any command-line scripts that should be created when the package is installed. In this case, we're creating a script named my_script that executes the main function in the my_script.py file.

Step 3: Build the Python Wheel

Now that we have our Python script and setup.py file, we can build the Python wheel. Open a terminal in the project directory and run the following command:

python setup.py bdist_wheel

This command will build a wheel file in the dist directory. The wheel file will have a name like my_databricks_wheel-0.1.0-py3-none-any.whl.

Step 4: Install the Databricks CLI

If you haven't already, install the Databricks CLI. You can install it using pip:

pip install databricks-cli

Once installed, configure the Databricks CLI with your Databricks workspace URL and authentication token. You can do this by running the following command and following the prompts:

databricks configure

Step 5: Upload the Wheel to Databricks

Next, we need to upload the wheel file to Databricks. We can do this using the Databricks CLI. First, create a directory in the Databricks File System (DBFS) to store the wheel file:

databricks fs mkdirs dbfs:/FileStore/wheels

Then, upload the wheel file to DBFS:

databricks fs cp dist/my_databricks_wheel-0.1.0-py3-none-any.whl dbfs:/FileStore/wheels/

Step 6: Create a Databricks Job

Now that we have uploaded the wheel file to Databricks, we can create a Databricks job to execute it. You can create a job using the Databricks UI or the Databricks CLI. Here's how to create a job using the Databricks CLI:

First, create a JSON file named job.json with the following content:

{
    "name": "My Databricks Wheel Job",
    "tasks": [
        {
            "task_key": "run_wheel",
            "description": "Run the Python wheel",
            "python_wheel_task": {
                "package_name": "my_databricks_wheel",
                "entry_point": "hello_databricks",
                "parameters": ["Databricks"]
            },
            "libraries": [
                {
                    "whl": "dbfs:/FileStore/wheels/my_databricks_wheel-0.1.0-py3-none-any.whl"
                }
            ]
        }
    ],
    "format": "MULTI_TASK"
}

Explanation of the job.json file:

  • name: The name of the job.
  • tasks: A list of tasks to execute as part of the job. In this case, we have a single task named run_wheel.
  • task_key: A unique key for the task.
  • description: A description of the task.
  • python_wheel_task: Configuration for the Python wheel task.
    • package_name: The name of the Python package to execute.
    • entry_point: The entry point to execute in the package. This should be the name of a function in your Python code.
    • parameters: A list of parameters to pass to the entry point function.
  • libraries: A list of libraries to include in the job. In this case, we're including the wheel file that we uploaded to DBFS.

Now, create the job using the Databricks CLI:

databricks jobs create --json-file job.json

This command will create a new Databricks job and print the job ID. Make note of the job ID, as you'll need it to run the job.

Step 7: Run the Databricks Job

Finally, we can run the Databricks job using the Databricks CLI:

databricks jobs run-now --job-id <job_id>

Replace <job_id> with the actual job ID that you obtained in the previous step.

This command will start a new run of the Databricks job. You can view the job's output in the Databricks UI or using the Databricks CLI:

databricks jobs get-run --run-id <run_id>

Replace <run_id> with the actual run ID of the job. The output will show the message printed by our Python script: "Hello, Databricks! Welcome to Databricks!"

Conclusion

And there you have it! You've successfully created a Python wheel, uploaded it to Databricks, and executed it as a Databricks job. This is a simple example, but it demonstrates the basic steps involved in creating and running Python wheel tasks on Databricks. Now you can start packaging your own Python code into wheels and deploying it to Databricks with ease!

Remember to explore the Databricks documentation for more advanced options and configurations. Happy coding, guys!