Passing Parameters Between Notebooks that Belong to Different Databricks Workflows: A Comprehensive Guide

Are you tired of manually re-running notebooks in Databricks with different parameters every time you need to test or deploy a workflow? Do you wish there was a way to seamlessly pass parameters between notebooks that belong to different Databricks workflows? Well, you’re in luck because today we’re going to explore exactly that!

What’s the Problem?

In Databricks, notebooks are a great way to organize and execute code in a workflow. However, when it comes to passing parameters between notebooks that belong to different workflows, things can get a bit tricky. By default, Databricks doesn’t provide a straightforward way to share variables between notebooks, making it challenging to create modular and reusable code.

The Need for Parameter Passing

Imagine you’re working on a machine learning project, and you have a notebook for data preprocessing, another for model training, and a third for model deployment. Each notebook requires specific parameters to function correctly, such as the dataset path, model hyperparameters, or deployment environment. Without a way to pass these parameters between notebooks, you’d have to hardcode them or manually update them every time you need to run the workflow.

This approach can lead to:

• Inconsistent results due to manual errors
• Difficulty in maintaining and updating code
• Inefficient use of resources and time

The Solution: DBUtils and Widgets

Luckily, Databricks provides two powerful features that can help us overcome this challenge: DBUtils and widgets. By combining these features, we can create a robust and modular framework for passing parameters between notebooks that belong to different workflows.

DBUtils: A Utility Library for Databricks

DBUtils is a utility library in Databricks that provides a set of functions for working with Databricks notebooks, jobs, and workflows. One of the most useful functions in DBUtils is `dbutils.secrets.get`, which allows us to store and retrieve secrets and parameters securely.

Here’s an example of how to use `dbutils.secrets.get` to retrieve a secret value:

dbutils.secrets.get(scope="my-scope", key="my-secret")

Widgets: Interactive Parameters for Notebooks

Widgets are interactive parameters in Databricks notebooks that allow users to input values dynamically. We can create widgets using the `%widget` command, followed by the widget type and name. For example:

%widget text my_text_widget "Default text"

Widgets can be used to create input fields, dropdowns, and other interactive elements that can be used to capture user input.

Passing Parameters Between Notebooks

Now that we’ve introduced DBUtils and widgets, let’s see how we can use them to pass parameters between notebooks that belong to different workflows.

Step 1: Create a Workflow-Specific Scope

In Databricks, you can create a scope for each workflow to store and manage secrets and parameters. Let’s create a scope called `my-workflow-scope`:

dbutils.secrets.createScope(name="my-workflow-scope")

Step 2: Define and Store Parameters

In the source notebook, define the parameters you want to pass to the target notebook using `dbutils.secrets.put`:

dbutils.secrets.put(scope="my-workflow-scope", key="my-parameter", value="my-value")

Step 3: Create a Widget in the Target Notebook

In the target notebook, create a widget to capture the parameter value:

%widget text my_text_widget "Default text"

Step 4: Retrieve the Parameter Value

In the target notebook, use `dbutils.secrets.get` to retrieve the parameter value from the scope:

param_value = dbutils.secrets.get(scope="my-workflow-scope", key="my-parameter")

Step 5: Use the Parameter Value

Finally, use the retrieved parameter value in your target notebook:

print("The parameter value is:", param_value)

Example Scenario: Passing a Dataset Path

Let’s consider an example scenario where we want to pass a dataset path from a data preprocessing notebook to a model training notebook. Here’s how we can achieve this:

        dbutils.secrets.put(scope="my-workflow-scope", key="dataset-path", value="/path/to/dataset")
      

        %widget text dataset_path_widget "Default dataset path"
        dataset_path = dbutils.secrets.get(scope="my-workflow-scope", key="dataset-path")
        print("The dataset path is:", dataset_path)
      

In this example, we create a scope `my-workflow-scope` and store the dataset path as a secret in the data preprocessing notebook. In the model training notebook, we create a widget to capture the dataset path and retrieve the value from the scope using `dbutils.secrets.get`. Finally, we use the retrieved value to print the dataset path.

Best Practices and Considerations

When passing parameters between notebooks, keep the following best practices and considerations in mind:

1. Scope Management**: Create separate scopes for each workflow to avoid parameter conflicts and ensure secure storage.
2. Parameter Naming**: Use descriptive and unique names for your parameters to avoid confusion and ensure easy debugging.
3. Widget Configuration**: Configure widgets to match the expected input type and provide default values for easier testing.
4. Error Handling**: Implement robust error handling to handle cases where the parameter value is not available or invalid.
5. Security**: Use Databricks secrets and scope management to ensure secure storage and retrieval of sensitive parameters.

Conclusion

Passing parameters between notebooks that belong to different Databricks workflows is a crucial aspect of creating modular and reusable code. By leveraging DBUtils and widgets, you can create a robust framework for sharing parameters securely and efficiently. Remember to follow best practices and considerations to ensure a smooth and secure parameter passing experience.

Happy coding, and don’t forget to share your parameter-passing success stories with us!

Frequently Asked Question

Get ready to unlock the secrets of passing parameters between notebooks that belong to different Databricks workflows!