Databricks: Pass Parameters To Notebooks With Python

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Databricks: Pass Parameters to Notebooks with Python

Hey everyone! Ever needed to make your Databricks notebooks more dynamic? One common task is passing parameters into your notebooks, so you can reuse them with different configurations. This article will guide you through the process of passing parameters to a Databricks notebook using Python.

Why Pass Parameters to Databricks Notebooks?

Before we dive into the how-to, let's quickly cover the why. Passing parameters to Databricks notebooks offers several advantages:

  • Reusability: Instead of creating multiple notebooks for slightly different tasks, you can use a single notebook and pass in parameters to change its behavior.
  • Automation: When you automate your workflows with tools like Databricks Jobs, you can dynamically pass different parameters each time the job runs.
  • Flexibility: Parameters make your notebooks more adaptable to various scenarios without modifying the core logic.
  • Efficiency: By using parameterization, you reduce redundancy and streamline your data processing pipelines, leading to more efficient resource utilization and faster execution times.
  • Scalability: Parameterized notebooks are easier to scale since you can run the same notebook with different configurations in parallel, enabling you to process large datasets more effectively.
  • Maintainability: When your logic is centralized in a single notebook with parameters, it becomes easier to maintain and update. Changes only need to be made in one place, reducing the risk of errors and inconsistencies.

By mastering parameter passing, you'll significantly enhance your ability to create robust, scalable, and maintainable data solutions in Databricks.

Method 1: Using %run and dbutils.widgets

One of the most common methods to pass parameters is by using the %run magic command in conjunction with dbutils.widgets. Here’s how it works:

Step 1: Create the Target Notebook

First, let's create the notebook that will receive the parameters. This notebook will use dbutils.widgets to define and retrieve the parameters. This setup allows the notebook to dynamically adjust its behavior based on the input parameters, making it highly versatile.

# Target Notebook (receiving parameters)

# Create widgets
dbutils.widgets.text("input_param", "", "Input Parameter")

# Get parameter value
input_value = dbutils.widgets.get("input_param")

print(f"Received parameter: {input_value}")

In this snippet, dbutils.widgets.text creates a text input widget named input_param with an empty default value. The dbutils.widgets.get function retrieves the value of the input_param widget, which is then printed to the console. This is a fundamental step in setting up a parameterized notebook.

Step 2: Create the Calling Notebook

Next, create the notebook that will call the target notebook and pass the parameter using the %run command. This notebook will initiate the execution of the target notebook and supply the necessary parameters, making the entire process seamless and automated.

# Calling Notebook (passing parameters)

parameter_value = "Hello Databricks!"

%run ./TargetNotebook input_param=$parameter_value

Here, %run ./TargetNotebook executes the TargetNotebook. The input_param=$parameter_value part passes the value of parameter_value to the input_param widget in the target notebook. This mechanism allows you to dynamically control the behavior of the target notebook from the calling notebook.

Step 3: Execute the Calling Notebook

When you run the calling notebook, it executes the target notebook, and the target notebook prints the received parameter value.

Advantages:

  • Simple and straightforward.
  • Easy to understand and implement.

Disadvantages:

  • Limited to the %run command, which might not be suitable for complex scenarios.
  • Parameters are passed as strings, requiring type conversion in the target notebook if needed.

Method 2: Using dbutils.notebook.run

A more flexible approach is to use dbutils.notebook.run. This method allows you to pass parameters as a dictionary, giving you more control over the data types and complexity of the parameters.

Step 1: Create the Target Notebook

As before, create the target notebook that will receive the parameters. Use dbutils.widgets to define the widgets and retrieve their values. This setup ensures that the target notebook can dynamically adapt based on the input it receives.

# Target Notebook (receiving parameters)

# Create widgets
dbutils.widgets.text("input_param", "", "Input Parameter")
dbutils.widgets.number("number_param", "0", "Number Parameter")

# Get parameter values
input_value = dbutils.widgets.get("input_param")
number_value = dbutils.widgets.get("number_param")

print(f"Received string parameter: {input_value}")
print(f"Received number parameter: {number_value}")

In this example, we create two widgets: input_param as a text widget and number_param as a number widget. The values are retrieved using dbutils.widgets.get and printed. This demonstrates how to handle different data types in the target notebook.

Step 2: Create the Calling Notebook

Now, create the calling notebook that will use dbutils.notebook.run to execute the target notebook and pass the parameters as a dictionary. This approach provides greater flexibility and control over the parameters being passed.

# Calling Notebook (passing parameters)

params = {
    "input_param": "Hello Databricks from dbutils.notebook.run!",
    "number_param": "42"
}

result = dbutils.notebook.run("./TargetNotebook", 60, params)

print(f"Result from TargetNotebook: {result}")

Here, dbutils.notebook.run executes the TargetNotebook with a timeout of 60 seconds and passes the parameters in the params dictionary. The result of the target notebook (if it returns anything) is stored in the result variable and printed. This is a more robust and flexible way to pass parameters.

Step 3: Execute the Calling Notebook

When you run the calling notebook, it executes the target notebook with the specified parameters, and the target notebook prints the received values. The calling notebook also prints any result returned by the target notebook.

Advantages:

  • More flexible, allowing parameters to be passed as a dictionary.
  • Supports passing parameters of different data types.
  • Allows you to specify a timeout for the target notebook execution.

Disadvantages:

  • Slightly more complex than using %run.
  • Requires understanding of dictionaries and how to pass them correctly.

Method 3: Using Databricks Jobs

Databricks Jobs provide a way to schedule and automate your notebooks. When you create a Databricks Job, you can specify parameters that will be passed to the notebook when it runs. This is particularly useful for automating data processing pipelines and running notebooks with different configurations on a schedule.

Step 1: Create the Target Notebook

As with the previous methods, start by creating the target notebook that will receive parameters. Use dbutils.widgets to define the widgets and retrieve their values. This ensures that the notebook can dynamically adjust its behavior based on the input it receives from the Databricks Job.

# Target Notebook (receiving parameters from Databricks Job)

# Create widgets
dbutils.widgets.text("job_param", "", "Job Parameter")

# Get parameter value
job_value = dbutils.widgets.get("job_param")

print(f"Received parameter from Databricks Job: {job_value}")

In this code, dbutils.widgets.text creates a text input widget named job_param. The dbutils.widgets.get function retrieves the value of this widget, which is then printed to the console. This setup is essential for receiving parameters from a Databricks Job.

Step 2: Create a Databricks Job

  1. Go to the Jobs section in your Databricks workspace.
  2. Click Create Job.
  3. Configure the job settings:
    • Task type: Select Notebook.
    • Notebook: Choose the target notebook you created.
    • Parameters: Add parameters under the Parameters section. For example, add job_param with a value like Hello from Databricks Job!.

Step 3: Run the Job

Run the job manually or schedule it to run automatically. When the job runs, it passes the specified parameters to the target notebook.

Advantages:

  • Ideal for scheduling and automating notebook execution.
  • Parameters can be easily configured through the Databricks UI.
  • Integrates seamlessly with other Databricks features.

Disadvantages:

  • Requires navigating the Databricks UI to set up the job.
  • Less immediate than running notebooks directly with %run or dbutils.notebook.run.

Method 4: Using Environment Variables

Another approach is to use environment variables. This method is useful when you need to pass configuration values that are managed outside of the Databricks environment, such as secrets or API keys. You can set environment variables in your Databricks cluster configuration and then access them within your notebooks.

Step 1: Set Environment Variables

  1. Go to your Databricks cluster configuration.

  2. Under Advanced Options, find the Spark Config section.

  3. Set environment variables using the following format:

    spark.driver.extraJavaOptions -Dmy_env_var=my_value
spark.executor.extraJavaOptions -Dmy_env_var=my_value

    Replace my_env_var with the name of your environment variable and my_value with its value. Make sure to set these options for both the driver and the executor.

Step 2: Access Environment Variables in the Notebook

In your Databricks notebook, you can access the environment variables using Python's os module. This allows you to retrieve the values of the environment variables and use them within your notebook.

import os

# Access environment variable
env_value = os.environ.get("my_env_var")

print(f"Environment variable value: {env_value}")

Advantages:

  • Useful for managing sensitive information and configuration values outside of the notebook code.
  • Provides a secure way to pass secrets and API keys to your notebooks.

Disadvantages:

  • Requires cluster configuration changes, which may require administrative privileges.
  • Environment variables are set at the cluster level, affecting all notebooks running on that cluster.
  • Can be less straightforward to manage compared to other methods.

Best Practices for Passing Parameters

  • Use descriptive parameter names: Choose names that clearly indicate the purpose of the parameter.
  • Provide default values: Set default values for parameters to make the notebook easier to use and less prone to errors.
  • Document your parameters: Add comments to explain what each parameter does and what values it expects.
  • Handle data types: Ensure that you handle data types correctly, especially when passing parameters as strings.
  • Validate inputs: Validate the input parameters to prevent errors and ensure that the notebook behaves as expected.
  • Secure sensitive information: Avoid hardcoding sensitive information in your notebooks. Use environment variables or Databricks secrets to manage sensitive values.
  • Use dbutils.widgets.remove to Clear Widgets: If you are rerunning the same notebook multiple times during development and using dbutils.widgets, it's good practice to clear existing widgets to avoid conflicts. You can do this with `dbutils.widgets.remove(