Retrieving Notebook Bindings Dynamically
Fetching Run Parameters
Learn More about Notebooks and Context Objects
Summary
- Overview: An introduction to Databricks widgets, their usage, and how to retrieve their values.
- Dynamic Bindings: Explanation of dynamically fetching notebook bindings and when this is appropriate.
- Run Parameters: How to get job parameters when a notebook is executed as a job.
Understanding Databricks Widgets
Databricks widgets are UI components that enable users to input parameters interactively when running notebooks. They are essential for parameterizing notebooks and making them more dynamic and reusable.
Widgets can be created using dbutils.widgets and their values can be retrieved using dbutils.widgets.getArguments. Here is an example of creating and retrieving a widget value:
dbutils.widgets.text("input", "default")
input_value = dbutils.widgets.get("input")
print(f"The value of the input widget is: {input_value}")Retrieving Notebook Bindings Dynamically
Using Job Parameters
When a notebook is run as part of a Databricks job, job parameters can be fetched as a dictionary using the dbutils package. This method is useful for dynamically retrieving parameters and making notebooks more adaptable to different environments and use cases.
run_parameters = dbutils.notebook.entry_point.getCurrentBindings()
print(run_parameters)For example, if the job parameters were {"foo": "bar"}, the result of the code above would be the dictionary {'foo': 'bar'}. Note that Databricks only allows job parameter mappings of str to str, so keys and values will always be strings.
If the notebook is run interactively (not as a job), the dictionary will be empty.
The getCurrentBindings() method can also retrieve any active widget values for the notebook when run interactively.
Practical Applications
When to Use Dynamic Bindings
Dynamic bindings are particularly useful in the following scenarios:
Scheduled Jobs: When notebooks are run as scheduled jobs, dynamically fetching parameters ensures that the notebook uses the correct configuration.
Environment Configuration: Adjusting configurations based on the environment (development, staging, production) can be streamlined by dynamically fetching parameters.
Reusable Notebooks: Creating reusable notebooks that adapt to different inputs without manual intervention enhances productivity and reduces errors.
References & Further Reading
Links
Databricks Widgets Documentation
Databricks Job Parameters
[Dynamic Run Parameters] (https://stackoverflow.com/questions/63018871/how-do-you-get-the-run-parameters-and-runid-within-databricks-notebook)