Databricks Python Wheel Task: A Practical Example

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Databricks Python Wheel Task: A Practical Example

Hey guys! Today, we're diving deep into a super practical example of using Databricks with Python wheels. If you've been scratching your head on how to package your Python code into a neat, reusable component and deploy it on Databricks, you're in the right place. We'll walk through the entire process, step by step, so you can get your hands dirty and see how it all works.

Why Use Python Wheels in Databricks?

First off, let's quickly chat about why Python wheels are awesome, especially in a Databricks environment. In essence, Python wheels are pre-built distribution packages that make deploying Python code incredibly smooth. Think of them as zip files containing all the necessary code and metadata, ready to be installed without needing to compile anything from source.

  • Reproducibility: Python wheels ensure that your code runs the same way every time, regardless of the environment. This is crucial for maintaining consistency across different Databricks clusters.
  • Dependency Management: Wheels allow you to bundle all your dependencies together, so you don't have to worry about missing packages or version conflicts. This is especially helpful when working with complex projects that rely on multiple libraries.
  • Speed and Efficiency: Because wheels are pre-built, they install much faster than source distributions. This can significantly reduce the time it takes to deploy your code to Databricks.
  • Organization: Using wheels forces you to structure your projects in a modular way, making them easier to maintain and test. This is especially beneficial when working on large projects with multiple developers.

Prerequisites

Before we jump into the example, let's make sure you have everything you need to follow along:

  • Databricks Account: You'll need access to a Databricks workspace. If you don't have one already, you can sign up for a free trial.
  • Databricks CLI: The Databricks Command-Line Interface (CLI) is essential for interacting with your Databricks workspace from your local machine. You can install it using pip: pip install databricks-cli
  • Python 3.6 or Higher: Make sure you have a compatible version of Python installed. You can check your Python version by running python --version in your terminal.
  • Basic Python Knowledge: A basic understanding of Python syntax and package management will be helpful.

Step-by-Step Example: Creating and Deploying a Python Wheel in Databricks

Alright, let's dive into the fun stuff! We'll create a simple Python module, package it into a wheel, and then deploy it to Databricks.

Step 1: Create a Python Module

First, let's create a simple Python module that we can package into a wheel. Create a new directory for your project, and inside that directory, create a file named my_module.py. Add the following code to my_module.py:

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

This simple module defines a function called greet that takes a name as input and returns a friendly greeting. This is the core logic that we want to package and deploy to Databricks.

Step 2: Create a setup.py File

Next, we need to create a setup.py file, which tells Python how to build and package our module. Create a file named setup.py in the same directory as my_module.py, and add the following code:

from setuptools import setup, find_packages

setup(
    name='my_module',
    version='0.1.0',
    packages=find_packages(),
    install_requires=[
        # Add any dependencies here, e.g., 'requests'
    ],
)
  • name: This is the name of your package. Choose a unique name that reflects the purpose of your module.
  • version: This is the version number of your package. Follow semantic versioning (e.g., 0.1.0, 1.0.0) to indicate the maturity of your code.
  • packages: This tells setuptools to automatically find all packages in your project. Make sure your project is structured in a way that setuptools can easily discover your packages.
  • install_requires: This lists any dependencies that your module requires. If your module depends on other packages, list them here so that they are automatically installed when your wheel is installed.

Step 3: Build the Wheel

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

python setup.py bdist_wheel

This command will create a dist directory in your project directory, and inside that directory, you'll find your wheel file (e.g., my_module-0.1.0-py3-none-any.whl). This wheel file is the package that we will deploy to Databricks.

Step 4: Install the Databricks CLI

If you haven't already, install the Databricks CLI using pip:

pip install databricks-cli

Step 5: Configure the Databricks CLI

Configure the Databricks CLI with your Databricks workspace URL and authentication token. You can obtain an authentication token from your Databricks workspace. Run the following command and follow the prompts:

databricks configure

The CLI will prompt you for your Databricks host (e.g., https://your-databricks-workspace.cloud.databricks.com) and token. Enter the required information to configure the CLI.

Step 6: Upload the Wheel to DBFS

Next, we need to upload the wheel file to Databricks File System (DBFS). DBFS is a distributed file system that is accessible from your Databricks notebooks and jobs. Use the Databricks CLI to upload the wheel file to DBFS:

databricks fs cp dist/my_module-0.1.0-py3-none-any.whl dbfs:/FileStore/jars/

This command copies the wheel file from your local machine to the /FileStore/jars/ directory in DBFS. You can choose a different directory if you prefer, but make sure you have the necessary permissions to write to that directory.

Step 7: Create a Databricks Notebook

Now, let's create a Databricks notebook to test our wheel. Open your Databricks workspace and create a new notebook. Choose Python as the language for the notebook.

Step 8: Install the Wheel in the Notebook

In the first cell of your notebook, install the wheel using the %pip magic command:

%pip install /dbfs/FileStore/jars/my_module-0.1.0-py3-none-any.whl

This command tells Databricks to install the wheel file from the specified location in DBFS. Make sure to replace /dbfs/FileStore/jars/my_module-0.1.0-py3-none-any.whl with the actual path to your wheel file in DBFS. After running this cell, Databricks will install the wheel and its dependencies.

Step 9: Use the Module in the Notebook

Now that the wheel is installed, we can use the module in our notebook. In a new cell, import the module and call the greet function:

import my_module

name = "Databricks User"
message = my_module.greet(name)
print(message)

This code imports the my_module module, calls the greet function with the name "Databricks User", and prints the returned message. When you run this cell, you should see the following output:

Hello, Databricks User! Welcome to Databricks!

Congratulations! You've successfully created a Python wheel, deployed it to Databricks, and used it in a notebook. This is a fundamental step in building reusable and scalable data solutions on Databricks.

Automating Wheel Deployment with Databricks Jobs

Okay, so you've seen how to deploy a wheel and use it interactively in a notebook. But what if you want to automate the process as part of a Databricks job? No sweat, let's walk through that too.

Step 1: Create a Python Script

First, let's create a Python script that we can use as the entry point for our Databricks job. Create a file named main.py in your project directory, and add the following code:

import my_module

def main():
    name = "Databricks Job"
    message = my_module.greet(name)
    print(message)

if __name__ == "__main__":
    main()

This script imports the my_module module, defines a main function that calls the greet function, and then calls the main function when the script is executed. This script will be the entry point for our Databricks job.

Step 2: Upload the Script to DBFS

Next, we need to upload the script file to DBFS. Use the Databricks CLI to upload the script file to DBFS:

databricks fs cp main.py dbfs:/FileStore/scripts/

This command copies the script file from your local machine to the /FileStore/scripts/ directory in DBFS. Again, you can choose a different directory if you prefer.

Step 3: Create a Databricks Job

Now, let's create a Databricks job to run our script. Open your Databricks workspace and navigate to the Jobs section. Click the "Create Job" button to create a new job.

Step 4: Configure the Job

Configure the job with the following settings:

  • Task Type: Python Script
  • Main Python File: /dbfs/FileStore/scripts/main.py (or the path to your script in DBFS)
  • Cluster: Choose an existing cluster or create a new one.
  • Libraries: Add the path to your wheel file in DBFS as a library. Click "Add Library", select "DBFS/File", and enter the path to your wheel file (e.g., /dbfs/FileStore/jars/my_module-0.1.0-py3-none-any.whl).

The libraries setting ensures that the wheel is installed on the cluster before the script is executed. This is crucial for ensuring that your script can access the module in the wheel.

Step 5: Run the Job

Once you've configured the job, click the "Run now" button to run the job. Databricks will start a cluster, install the wheel, and execute the script. You can monitor the progress of the job in the Jobs section of your Databricks workspace.

Step 6: Verify the Output

After the job has completed, you can view the output of the script in the job's logs. The logs should contain the following message:

Hello, Databricks Job! Welcome to Databricks!

If you see this message in the logs, congratulations! You've successfully automated the deployment and execution of your Python code in Databricks.

Best Practices and Tips

Before we wrap up, here are a few best practices and tips to keep in mind when working with Python wheels in Databricks:

  • Use Virtual Environments: Always use virtual environments to isolate your project's dependencies. This helps prevent conflicts and ensures that your code runs consistently across different environments.
  • Version Control: Use version control (e.g., Git) to track changes to your code and collaborate with others. This makes it easier to manage your codebase and revert to previous versions if necessary.
  • Testing: Write unit tests to ensure that your code is working correctly. This helps prevent bugs and makes it easier to maintain your code over time.
  • Documentation: Document your code clearly and concisely. This makes it easier for others to understand your code and contribute to your project.
  • Dependency Management: Use a dependency management tool (e.g., pipenv, poetry) to manage your project's dependencies. This makes it easier to install and update dependencies, and it helps ensure that your project is reproducible.

Conclusion

Alright, that was a whirlwind tour of using Python wheels in Databricks! We covered everything from creating a simple module to deploying it as a wheel and automating its execution as part of a Databricks job. By following these steps, you can streamline your development workflow, improve the reproducibility of your code, and build scalable data solutions on Databricks. So go forth, package your code into wheels, and unleash the power of Databricks!