Build & Deploy Databricks Assets With Python Wheels

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Build & Deploy Databricks Assets with Python Wheels

Hey data enthusiasts! Ever found yourself wrestling with the complexities of managing and deploying Databricks assets? Yeah, we've all been there. Thankfully, the Databricks Asset Bundle (DAB) and Python wheels have emerged as game-changers, offering a streamlined, efficient, and reproducible approach. In this article, we'll dive deep into how you can leverage these powerful tools to package, deploy, and manage your Databricks resources with ease. We'll explore the benefits, walk you through the setup, and provide practical examples to get you up and running in no time. So, buckle up, grab your favorite coding beverage, and let's get started!

Understanding Databricks Asset Bundles (DAB) & Python Wheels

So, what exactly are Databricks Asset Bundles (DAB) and why should you care? Think of a DAB as a single source of truth for all your Databricks-related assets. It allows you to define and manage everything from notebooks and jobs to workflows and more, all within a declarative configuration file (typically a YAML file). This approach provides several key advantages. It enforces version control, making it easy to track changes, revert to previous states, and collaborate with your team. It also promotes reproducibility, ensuring that your assets are deployed consistently across different environments (development, staging, production). Databricks Asset Bundles make deployments, especially for complex projects, a walk in the park.

Now, let's talk about Python wheels. In a nutshell, a Python wheel is a pre-built package that contains all the necessary files to install a Python project. It's like a neatly packaged bundle that includes your code, dependencies, and metadata, making it super easy to distribute and install. When you combine DABs with Python wheels, you unlock a powerful synergy. You can package your Python code (e.g., utility functions, custom libraries) into a wheel and then include that wheel within your DAB. This allows you to bundle your code with all the dependencies it needs in one centralized way, which simplifies dependency management, ensures consistency, and reduces the chance of deployment errors. We will show you how to do this in the upcoming sections.

Using Databricks Asset Bundles and Python wheels is an outstanding method to streamline the deployment of your assets, providing consistency and version control. DABs offer a declarative way to define your Databricks resources, and Python wheels package Python code and dependencies for easy distribution. The integration of these tools simplifies dependency management and promotes reproducibility. So, instead of manually deploying resources and fighting with dependencies, DABs and wheels let you focus on what really matters: crafting high-quality data products.

The Benefits of Using DABs and Wheels

Simplified Dependency Management: Python wheels neatly package all dependencies. When used in a DAB, deployment becomes more predictable and less error-prone. This is a massive win, especially in complex projects where dependency conflicts can be a real headache.

Version Control and Reproducibility: DABs and version control tools work well together. Rollback features become easy, and deployment consistency across various environments is a guarantee. This ensures that every deployment is identical and replicable.

Streamlined Deployment Process: Using DABs to define the resources needed for Databricks along with Python wheels to bundle project-specific Python code makes the entire deployment process faster and more efficient.

Improved Collaboration: DABs act as a single source of truth for your Databricks setup, which makes team collaboration much more efficient. Team members are aware of all the resources deployed. This removes the need for ad-hoc deployment practices.

Setting Up Your Environment

Alright, let's get your environment ready for action! Before you can start using Databricks Asset Bundles and Python wheels, you'll need to install a few tools and set up your Databricks workspace. Don't worry, it's not as daunting as it sounds. We'll walk you through each step.

Installing the Databricks CLI

First things first, you'll need the Databricks CLI (Command Line Interface). This is your primary tool for interacting with the Databricks platform. You can install it using pip, Python's package installer. Just open your terminal and run the following command:

pip install databricks-cli

After the installation is complete, it's a good idea to verify that the CLI is installed correctly. Run databricks --version to check its version. You should see the CLI version printed in the terminal.

Configuring Authentication

Next, you'll need to configure the Databricks CLI with your Databricks workspace credentials. You can do this in a few ways, but the most common method is to use personal access tokens (PATs). To get started, go to your Databricks workspace and generate a PAT. Then, in your terminal, run:

databricks configure

The CLI will prompt you to enter your Databricks host (e.g., https://<your-workspace-url>.cloud.databricks.com) and your PAT. Once you've entered these details, the CLI will save the configuration, allowing you to authenticate to your Databricks workspace.

Project Structure

Now, let's talk about the project structure. This is how you'll organize your code and configuration files. A typical project structure might look like this:

my-databricks-project/
├── databricks.yml # The DAB configuration file
├── notebooks/
│   └── my_notebook.ipynb # Your Databricks notebook
├── src/
│   ├── __init__.py
│   └── my_module.py # Your Python code
├── pyproject.toml # Dependencies and wheel configuration
└── .gitignore
  • databricks.yml: This is the heart of your DAB. It defines your Databricks resources, such as notebooks, jobs, and workflows.
  • notebooks/: This directory contains your Databricks notebooks.
  • src/: This directory contains your Python code. You can structure it as a Python package.
  • pyproject.toml: This file is used to manage your project's dependencies and configure the creation of Python wheels. Using this file and the build package is recommended.
  • .gitignore: This file specifies files and directories that should be ignored by Git.

This structure provides a clean, organized way to manage your Databricks project. This structure simplifies organizing your Databricks project. It keeps all of your configuration, code, and dependencies in one central location.

Creating a Databricks Asset Bundle (DAB)

Let's get down to the nitty-gritty and create your first Databricks Asset Bundle. This is where you'll define your Databricks resources and how they should be deployed. We'll start with the databricks.yml file, which is the configuration file for your DAB.

The databricks.yml File

Here's a basic example of a databricks.yml file. This file will deploy a notebook and a job.

name: my-databricks-project

resources:
  notebooks:
    my_notebook:
      path: ./notebooks/my_notebook.ipynb
      destination_path: /Users/${workspace.user.userName}/my_notebooks
  jobs:
    my_job:
      name: My Job
      tasks:
        - notebook_task:
            notebook_path: /Users/${workspace.user.userName}/my_notebooks/my_notebook
            source: WORKSPACE
      schedule:
        quartz_cron_expression: '0 0 * * * ?'
        timezone_id: UTC

Let's break down this file:

  • name: This specifies the name of your DAB.
  • resources: This section defines the Databricks resources you want to deploy.
  • notebooks: This section defines a notebook. The path specifies the location of your notebook file, and destination_path specifies where it should be deployed in your Databricks workspace.
  • jobs: This section defines a job. The name specifies the name of the job. The tasks section defines the tasks that the job will execute. The notebook_task specifies that the job should run a notebook. The schedule section specifies a schedule to run the job daily at midnight.

Defining Resources

The databricks.yml file allows you to define various resources. You can define and deploy Databricks notebooks, jobs, and other resources. To create resources, you use a YAML file which is the central place to define all of your resources in a DAB. This declarative approach means that all resources are well-defined and reproducible.

Working with Variables

Using variables in your databricks.yml files can enhance flexibility and reusability. Databricks provides several built-in variables like ${workspace.user.userName} to use in your configurations. Variables ensure that your DAB is adaptable to various environments.

Creating a Python Wheel

Now, let's get your Python code packaged into a wheel. This is a crucial step for bundling your custom code and dependencies with your DAB. We'll use pyproject.toml and build to create a wheel.

The pyproject.toml File

Here's an example of a pyproject.toml file. This file will contain the project dependencies.

[build-system]
requires = ["build", "setuptools", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "my_module"
version = "0.1.0"
authors = [
  {name = "Your Name", email = "your.email@example.com"}
]
description = "A sample Python module for Databricks"
readme = "README.md"
license = {text = "MIT"}
requires-python = ">=3.7"
classifiers = [
    "Programming Language :: Python :: 3",
    "License :: OSI Approved :: MIT License",
    "Operating System :: OS Independent",
]

[tool.setuptools.packages.find]
where = ["src"]

[project.urls]
"Homepage" = "https://example.com"
"Bug Tracker" = "https://example.com/issues"
  • [build-system]: This section specifies the build system and its dependencies.
  • [project]: This section contains metadata about your project, such as its name, version, authors, description, and license.
  • [tool.setuptools.packages.find]: This section tells setuptools where to find your Python packages.
  • [project.urls]: This section specifies URLs for your project.

Building the Wheel

Once you have your pyproject.toml file set up, you can build your wheel. Navigate to the root directory of your project in your terminal and run:

python -m build

This command will create a wheel file in the dist/ directory. You will have a .whl file in the dist directory. You will need to take the file and put it in your DAB.

Integrating the Wheel into Your DAB

Now, let's combine your Python wheel with your Databricks Asset Bundle. This is where the magic happens, and your custom code becomes part of your Databricks deployment.

Including the Wheel in databricks.yml

You'll need to modify your databricks.yml file to include your Python wheel. Add a new section to your databricks.yml file, the artifacts section. This tells Databricks to upload the wheel file to a specific location in DBFS. This ensures that the wheel is available to the Databricks cluster when your notebooks or jobs run. Here's how to do it.

name: my-databricks-project

artifacts:
  - path: dist/*.whl
    name: my_module.whl
    destination_path: dbfs:/FileStore/wheels

resources:
  notebooks:
    my_notebook:
      path: ./notebooks/my_notebook.ipynb
      destination_path: /Users/${workspace.user.userName}/my_notebooks
  jobs:
    my_job:
      name: My Job
      tasks:
        - notebook_task:
            notebook_path: /Users/${workspace.user.userName}/my_notebooks/my_notebook
            source: WORKSPACE
      schedule:
        quartz_cron_expression: '0 0 * * * ?'
        timezone_id: UTC
  • artifacts: This section defines the artifacts you want to upload to DBFS. You will specify the file you want to upload with its local path, and the destination_path where you want the file to be uploaded.

Using the Wheel in Your Notebooks

In your Databricks notebooks, you can install the wheel using the %pip magic command. This ensures that the dependencies packaged within the wheel are available to your code. Make sure that you install the wheel before importing the modules in the wheel. Here is an example of what this looks like.

# Install the wheel from DBFS
%pip install /dbfs/FileStore/wheels/my_module.whl --force-reinstall

# Import your module
from my_module.my_module import my_function

# Use your function
result = my_function()
print(result)

This workflow ensures that your Python code and its dependencies are readily available within your Databricks notebooks and jobs.

Deploying Your DAB

Now that you've created your DAB and included your Python wheel, it's time to deploy your resources to Databricks. This is where the Databricks CLI comes into play.

Deploying with the Databricks CLI

To deploy your DAB, navigate to the root directory of your project in your terminal and run the following command:

databricks bundle deploy

The Databricks CLI will read your databricks.yml file, upload your resources (including your Python wheel) to Databricks, and deploy them. You'll see output in the terminal indicating the progress of the deployment. Once deployment is complete, your notebooks, jobs, and other resources will be available in your Databricks workspace.

Verifying the Deployment

After deployment, it's always a good idea to verify that everything has been deployed correctly. You can check your Databricks workspace to see if your notebooks and jobs are present. You can also run your jobs to ensure that your code is executing as expected. If you run into issues, carefully check the output logs and the configuration file for any errors.

Advanced Tips and Tricks

Let's level up your DAB game with some advanced tips and tricks. These techniques will help you manage your Databricks assets more efficiently and effectively.

Using Environments

One of the best practices is using environments. Create different environments (e.g., development, staging, production) in your databricks.yml file to match different Databricks workspaces. This allows you to deploy different versions of your resources to different environments. This is a crucial step for testing and managing different stages of your deployment pipeline.

environments:
  dev:
    databricks:
      host: <dev-host>
  prod:
    databricks:
      host: <prod-host>

You can then deploy to a specific environment using the --environment flag:

databricks bundle deploy --environment dev

Automating Deployment with CI/CD

To really supercharge your deployment process, integrate your DAB with a CI/CD (Continuous Integration/Continuous Deployment) pipeline. This will automate the process of building, testing, and deploying your resources. Tools like GitHub Actions, Azure DevOps, and GitLab CI can be used to trigger deployments automatically when changes are pushed to your repository.

Best Practices

  • Version Control: Always use version control (e.g., Git) for your code and your databricks.yml file.
  • Testing: Test your code and your deployments thoroughly.
  • Documentation: Document your DAB and your deployment process.
  • Security: Follow security best practices when deploying your resources.

Conclusion

And there you have it, folks! You've now got the knowledge and tools to harness the power of Databricks Asset Bundles and Python wheels. We've covered the what, why, and how, from setting up your environment to deploying your resources. By using DABs and wheels, you'll be able to streamline your Databricks deployments, enhance reproducibility, and boost collaboration within your team. Happy coding, and may your data pipelines always run smoothly!