Import Python Functions Across Files In Databricks

by Jhon Lennon 51 views

Hey everyone! So, you're working on a Databricks project, and you've got this awesome Python code in one notebook, but you need to use it in another, right? Maybe you've written a bunch of helper functions, or perhaps you have a class that you want to reuse. Well, fear not, because importing functions from another Python file in Databricks is totally doable and, honestly, a lifesaver for keeping your code organized and efficient. We're going to dive deep into how you can make this happen smoothly, so stick around!

Why You'd Want to Import Functions in Databricks

First off, let's chat about why you'd even bother importing functions from another Python file in Databricks. Guys, it's all about efficiency and maintainability. Imagine you have a complex data cleaning process. You write all the cool functions for it in data_cleaning.py. Now, you have three different notebooks analyzing different aspects of that data. Instead of copy-pasting those cleaning functions into each notebook (which is a nightmare to update, by the way!), you can just import them. This means: write once, use everywhere. Plus, when you need to fix a bug or improve a function, you only have to change it in one place – the original file. How sweet is that? It keeps your projects tidy, reduces redundancy, and makes collaboration way easier. Think of it as building your own mini-library within your Databricks workspace. This is especially crucial when you're dealing with larger projects or when multiple people are working on the same codebase. Keeping your code modular not only prevents errors but also speeds up development. When you have well-defined modules, you can test them independently, ensuring each piece works perfectly before integrating it into the larger workflow. It's like building with LEGOs – each brick is solid, and you can assemble them in countless ways. So, if you're tired of repetitive code and want to level up your Databricks game, mastering this import technique is a must. It’s a foundational skill that will save you tons of headaches down the line and make your code more robust.

The Basics: Python's Import Mechanism

Before we jump into Databricks specifics, let's quickly recap how Python's import system generally works. Python looks for modules (which are just .py files) in a few places:

  1. The current directory: If the file you're trying to import is in the same directory as your script or notebook.
  2. Directories listed in the PYTHONPATH environment variable: This is a list of directories that Python searches.
  3. The installation-dependent default paths: These are standard locations where Python libraries are installed.

When you write import my_module or from my_module import my_function, Python goes on a treasure hunt to find my_module.py (or a package named my_module). The key here is that Python needs to be able to find the file you want to import. This is where Databricks adds its own flavor, but the core principle remains the same.

Understanding this foundational concept is super important because Databricks, while powerful, operates within a distributed environment. This means the way files are accessed and managed can differ from your local machine. So, when we talk about importing, we're really talking about making sure Python, running on the Databricks cluster, can locate and load your custom Python files. The sys.path is a list of directories where Python looks for modules. By default, it includes the directory of the script being run, standard library paths, and any paths added via the PYTHONPATH environment variable. In Databricks, we'll often manipulate this path to include our custom module locations. It’s like giving Python a map with specific points of interest where it can find the code you need. So, keep this in mind as we move forward – we’re essentially telling Python where to look for your reusable code snippets within the Databricks ecosystem.

Method 1: Using %run in Databricks Notebooks

This is probably the simplest and most common way to use code from one Databricks notebook in another. The %run magic command executes another notebook and makes all its defined functions, variables, and classes available in the current notebook's scope. Think of it as literally running the other notebook's code first.

How it works:

  1. Create your Python file (or notebook): Let's say you have a notebook named utils.py (or utils if it's a notebook) in the same directory or a subdirectory. Inside utils.py, define your functions:

    # utils.py
def greet(name):
    return f"Hello, {name}!"

def add(a, b):
    return a + b

  2. Use %run in your main notebook: In your other notebook (let's call it main_notebook.py), you'd run:

    %run ./utils

# Now you can use the functions
message = greet("Databricks User")
print(message)

result = add(5, 10)
print(f"The sum is: {result}")

Important Notes about %run:

  • Path matters: The path ./utils assumes utils.py is in the same directory as main_notebook.py. You can use relative paths (like ../shared_utils/common_functions) or absolute paths starting from the workspace root (like /Shared/utils).
  • Variables and Functions are shared: All top-level variables and function definitions from the run notebook become available. Be careful about variable name collisions!
  • Execution Order: The notebook specified in %run is executed first. If there are any errors in that notebook, they will halt the execution here.
  • Not true Python import: This isn't a standard Python import statement. It's a Databricks-specific magic command. This means if you try to use %run locally in your Python IDE, it won't work.

This method is fantastic for sharing code between notebooks within Databricks. It's super straightforward and requires minimal setup. You just need to ensure the notebook you're running is accessible from the notebook calling it. If your utils notebook is in a different folder, say /Shared/my_utils, you would use %run /Shared/my_utils. The power of %run lies in its simplicity for quick sharing of code assets. It effectively stitches together code from different notebooks into a single execution context. Just remember that any output or side effects from the %run notebook will also appear. So, if utils.py had a print statement, you'd see that output before the rest of your main_notebook.py code runs. It’s a seamless way to integrate reusable logic, but always be mindful of the execution flow and potential for naming conflicts if you're not careful with your function and variable names.

Method 2: Using Python Packages and sys.path

For more structured projects or when you want to use standard Python import statements, you can package your code and ensure Python can find it. This involves putting your Python code into a package and making sure that package is discoverable by your Databricks cluster.

Steps:

  1. Organize your code: Create a directory structure for your package. For example:

    my_project/
├── main_notebook.py
└── my_package/
    ├── __init__.py
    └── helpers.py
    • my_package/ is your package directory.
    • __init__.py makes Python treat the directory as a package (it can be empty).
    • helpers.py contains your functions:
      # my_package/helpers.py
def multiply(x, y):
    return x * y

def divide(x, y):
    if y == 0:
        raise ValueError("Cannot divide by zero!")
    return x / y

  2. Upload your package: You need to get this my_package directory onto your Databricks cluster. There are a few ways:

    • DBFS (Databricks File System): Upload the my_package folder to DBFS. For example, using the Databricks CLI or the UI, you might put it at /dbfs/my_code/my_package.
    • Workspace Files: If you're using Databricks Repos (connected to Git), you can place your package directory within your repo. This is generally the preferred method for version control and collaboration.

  3. Make the package discoverable: Python needs to know where to look for my_package. You can add the directory containing my_package to Python's search path (sys.path).

    • In a notebook:
      import sys
# If my_package is in /dbfs/my_code/
sys.path.insert(0, '/dbfs/my_code') 
# If using Databricks Repos and my_package is at the root of your repo
# sys.path.insert(0, '/Workspace/path/to/your/repo/root')

# Now you can import
from my_package import helpers

result = helpers.multiply(6, 7)
print(f"The product is: {result}")
    • Cluster Libraries: For a more permanent solution, you can configure your cluster to add paths to PYTHONPATH. Go to Cluster -> Edit -> Advanced Options -> Spark -> Environment Variables. Add an entry like PYTHONPATH=/dbfs/my_code:/another/path. Alternatively, you can package your code as a Python wheel (.whl) and install it as a cluster library.

Why this is powerful:

  • Standard Python: Uses the familiar import syntax.
  • Modularity: Encourages well-structured, reusable code.
  • Scalability: Works well for larger projects and complex dependencies.
  • Version Control: Especially when using Databricks Repos, your code is versioned.

This approach provides a much cleaner and more professional way to manage your Python codebase in Databricks. By treating your code as a package, you can leverage all the benefits of Python's import system. When using DBFS, remember that /dbfs/ is a mount point; the actual files reside in DBFS storage. So, paths like /dbfs/my_code/my_package mean Python will look inside the my_package folder located in the my_code directory within DBFS. For Databricks Repos, the path starts from /Workspace/. For instance, if your repo is cloned at Repos/my-git-repo, and my_package is inside it, the path might be /Workspace/Repos/your-email@example.com/my-git-repo/my_package. The key is that the sys.path.insert(0, ...) line adds the parent directory of your package (my_package) to the search path, allowing from my_package import ... to work. Installing your code as a wheel file is the most robust method, as it handles dependencies and makes your code available cluster-wide without manual path manipulation in each notebook. This is the gold standard for production environments.

Method 3: Using Databricks Repos and Relative Imports

Databricks Repos is a game-changer for managing code in Databricks, especially if you're familiar with Git. It allows you to clone Git repositories directly into your workspace. This makes collaboration, version control, and code organization much smoother.

How it streamlines imports:

  1. Clone your Git repo: Ensure your Python files and package structure (like the my_package example from Method 2) are in your Git repository. Then, clone the repo into Databricks Repos.

  2. Set up your workspace: Once cloned, your repo appears under the