Databricks Python Wheel Task: A Practical Example

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Databricks Python Wheel Task: A Practical Example

Hey guys! Ever wondered how to package your Python code into a neat, reusable component for your Databricks workflows? Well, you're in the right place! Today, we're diving deep into creating and using Python Wheel tasks within Databricks. Think of it as making your code portable and super easy to deploy. We’ll walk through a practical example to get you up and running in no time. This approach not only streamlines your development process but also ensures consistency and reliability across different environments. So, buckle up and let’s get started!

Understanding Python Wheels

First things first, let's break down what Python Wheels actually are. A Python Wheel is essentially a zipped archive with a .whl extension, and it's the standard format for distributing Python packages. Unlike source distributions, Wheels are built and ready to install, meaning they don't require compilation during installation. This makes the installation process faster and more reliable, which is especially crucial in a cloud environment like Databricks. Wheels contain all the necessary files, including Python code, compiled extensions, and metadata, all packaged neatly for easy deployment. This packaging ensures that your dependencies are managed efficiently, and your code runs as expected, regardless of the underlying system. Using Wheels also promotes reproducibility, as you can be confident that the same version of your package will be used every time, minimizing the risk of unexpected errors due to version conflicts or missing dependencies.

Furthermore, Wheels are designed to be platform-independent, allowing you to create a single package that can be installed on various operating systems and architectures. This is achieved by including pre-compiled binaries for different platforms within the Wheel, ensuring that the correct version is used during installation. The metadata included in the Wheel also provides valuable information about the package, such as its dependencies, version number, and author, making it easier to manage and track your projects. By adopting Wheels, you're not only simplifying your deployment process but also adhering to best practices in Python packaging, which can significantly improve the maintainability and scalability of your applications. Think of it as preparing a ready-to-eat meal versus gathering all the ingredients and cooking from scratch every time – Wheels offer convenience and consistency, especially in complex and dynamic environments like Databricks.

Benefits of Using Wheels in Databricks

So, why should you care about using Python Wheels in Databricks? There are several compelling reasons:

  • Faster Installation: As mentioned earlier, Wheels are pre-built, so installation is much faster compared to source distributions.
  • Dependency Management: Wheels make it easier to manage dependencies, ensuring that your code has everything it needs to run correctly.
  • Reproducibility: Using Wheels ensures that the same version of your package is used every time, reducing the risk of errors.
  • Portability: Wheels are platform-independent, making it easy to deploy your code across different environments.
  • Simplified Deployment: Packaging your code into a Wheel simplifies the deployment process, making it easier to manage and maintain your Databricks workflows.

Creating a Python Wheel

Okay, let's get our hands dirty and create a Python Wheel. We'll start with a simple Python script and then package it into a Wheel.

Step 1: Create a Python Script

Let's create a simple Python script called my_module.py that contains a function to calculate the square of a number.

# my_module.py

def square(x):
    return x * x

if __name__ == "__main__":
    num = 5
    result = square(num)
    print(f"The square of {num} is {result}")

Step 2: Create a setup.py File

Next, we need to create a setup.py file, which is the heart of the packaging process. This file tells Python how to build and package your code.

# setup.py

from setuptools import setup, find_packages

setup(
    name='my_module',
    version='0.1.0',
    description='A simple module to calculate the square of a number',
    author='Your Name',
    author_email='your.email@example.com',
    packages=find_packages(),
    install_requires=[],
)
  • name: The name of your package.
  • version: The version number of your package.
  • description: A brief description of your package.
  • author: Your name.
  • author_email: Your email address.
  • packages: A list of packages to include in the Wheel (using find_packages() automatically finds all packages in your project).
  • install_requires: A list of dependencies that your package requires (leave it empty for now).

Step 3: Build the Wheel

Now that we have our setup.py file, we can build the Wheel. Open your terminal, navigate to the directory containing my_module.py and setup.py, and run the following command:

python setup.py bdist_wheel

This command will create a dist directory containing the Wheel file (my_module-0.1.0-py3-none-any.whl).

Using the Python Wheel in Databricks

Alright, we've got our Wheel! Now let's see how to use it in Databricks.

Step 1: Upload the Wheel to Databricks

First, you need to upload the Wheel file to Databricks. You can do this using the Databricks UI:

  1. Go to your Databricks workspace.
  2. Click on Compute in the sidebar.
  3. Select a cluster (or create a new one).
  4. Click on the Libraries tab.
  5. Click on Install New.
  6. Select Upload and choose your Wheel file.
  7. Click Install.

Step 2: Create a Databricks Notebook

Now, let's create a Databricks notebook to use our Python Wheel. Create a new notebook and attach it to the cluster where you installed the Wheel.

Step 3: Import and Use the Module

In your notebook, you can now import and use the module from your Wheel.

from my_module import square

num = 10
result = square(num)
print(f"The square of {num} is {result}")

Run the cell, and you should see the output:

The square of 10 is 100

Congratulations! You've successfully created and used a Python Wheel in Databricks.

Creating a Databricks Job with a Python Wheel Task

Now, let's take it a step further and create a Databricks Job that uses our Python Wheel. This is where the real power of packaging your code becomes evident.

Step 1: Configure a Databricks Job

  1. Go to your Databricks workspace.
  2. Click on Workflows in the sidebar.
  3. Click on Jobs.
  4. Click on Create Job.

Step 2: Define the Task

  1. Give your job a name.
  2. In the Tasks section, click Add task.
  3. Choose a name for your task (e.g., square_calculation).
  4. For Type, select Python Wheel.

Step 3: Configure the Python Wheel Task

  1. Package Name: Enter the name of your package (my_module).
  2. Entry Point: Enter the name of the function to execute (square). If you want to run the if __name__ == "__main__": block from my_module.py, you can create a separate function that calls the main block and specify that function as the entry point.
  3. Parameters: Specify any parameters that your function requires. Since our square function takes one argument, you can pass it as a JSON string. For example, if you modify your script to accept an argument, you can pass {"x": 5}.
  4. Python File: (This field is not used when you specify a Package Name and Entry Point. It's used for executing a standalone Python file.)
  5. Installed Libraries: Ensure your cluster has the my_module wheel installed. If not, add it to the cluster libraries as described earlier.

Step 4: Set Cluster Configuration

  1. Choose an existing cluster or create a new one.
  2. Make sure the cluster has the necessary libraries installed (including your Python Wheel).

Step 5: Run the Job

  1. Click Create to save the job.
  2. Click Run now to start the job.

Step 6: Monitor the Job

  1. Monitor the job run to ensure it completes successfully.
  2. Check the logs to see the output of your Python code.

Here's an example of how you might modify my_module.py and setup.py to work seamlessly with the Databricks job configuration.

Modified my_module.py:

# my_module.py
import json

def square(x):
    return x * x

def main(input_str):
    data = json.loads(input_str)
    num = data['x']
    result = square(num)
    print(f"The square of {num} is {result}")

if __name__ == "__main__":
    # This block is now separate from the entry point
    num = 5
    result = square(num)
    print(f"The square of {num} is {result}")

Modified setup.py:

The setup.py file remains mostly the same. Ensure find_packages() is correctly set up to include your module.

# setup.py

from setuptools import setup, find_packages

setup(
    name='my_module',
    version='0.1.0',
    description='A simple module to calculate the square of a number',
    author='Your Name',
    author_email='your.email@example.com',
    packages=find_packages(),
    install_requires=[],
    entry_points={
        'console_scripts': [
            'my_module_main = my_module:main',
        ],
    },
)

In this setup:

  • The main function takes a JSON string as input.
  • The setup.py file is updated to include an entry_points section. This allows you to specify which function should be called when the package is executed as a script.

In the Databricks job configuration:

  • Package Name: my_module
  • Entry Point: main
  • Parameters: {"x": 5} (or any other number you want to square)

This setup allows the Databricks job to correctly call the main function within your module, passing the parameters as a JSON string. The json.loads() function then parses this string and extracts the required input.

Best Practices and Considerations

Before we wrap up, here are some best practices and considerations for using Python Wheels in Databricks:

  • Version Control: Always use version control (like Git) to manage your code and track changes.
  • Testing: Thoroughly test your code before packaging it into a Wheel.
  • Dependencies: Carefully manage your dependencies and specify them in the install_requires section of your setup.py file.
  • Documentation: Document your code and provide clear instructions on how to use it.
  • Secrets Management: Avoid hardcoding sensitive information (like passwords or API keys) in your code. Use Databricks secrets to securely manage sensitive information.

Conclusion

And there you have it! You've learned how to create a Python Wheel, upload it to Databricks, and use it in both a notebook and a Databricks Job. By following these steps, you can streamline your development process, improve the reliability of your code, and make your Databricks workflows more efficient. So go ahead, package your code into Wheels and unleash the power of reusable components in Databricks!