3 weeks ago
Looking for ways to convert a Databricks notebook to Python library. Some context :
Thanks.
3 weeks ago
The best way to share code from a Databricks notebook as a reusable module while hiding implementation details from users—without using wheels or granting direct notebook execution permissions—is to convert your notebook into a Python module, store it securely in the Databricks workspace, and import it using relative workspace paths. This method allows importing custom Python modules directly from workspace files, which users can use without seeing source code, as long as permissions are set appropriately.
Python source files (.py) can be uploaded and stored alongside notebooks in the Databricks workspace.
These modules can be organized in folders, and notebooks can import functions/classes using Python’s standard import syntax and relative paths (e.g., from workspace.module import func).
By assigning workspace permissions, users can only read and use the module interface; actual code can be hidden if the file/folder access is restricted.
If workspace permissions are appropriately managed, this can hide the code implementation while exposing APIs for user notebooks to call.
Python wheels are commonly used for sharing and deploying modules, but strict environment policies can limit their use.
Library uploads (including wheels) are not always feasible due to administrative restrictions.
Notebook-scoped libraries are also possible but may not meet your code privacy requirements—users might still see source.
Code obfuscation/minification: Not ideal for Python, as bytecode isn’t very secure and users might still find ways to read code if they have access.
Docker containers: You can deploy Spark code in containers, hiding source, but this requires cluster admin and more setup.
Unity Catalog or secret management: These help protect sensitive data, but cannot fully hide code logic.
For your needs, using Python modules stored in workspace files with carefully set permissions is the most suitable way to share code without exposing internals—provided your environment supports this feature. Wheels and other binary approaches remain best but may be restricted. UDFs and notebook-scoped libraries do not fully solve the visibility and Spark referencing problems.
3 weeks ago
A Databricks notebook can help automate much of the wheel (whl) packaging and installation process, but it cannot fully eliminate the requirement of building the wheel artifact itself. However, you can create a notebook (or workflow) that covers most steps, from package building to deploying and installing the wheel onto your workspace or cluster, thereby minimizing manual intervention.
Building the wheel (whl) file automatically: With tools like setuptools, poetry, or uv, a notebook can run shell commands (%sh) or Magic commands to build the wheel directly from code stored within the Databricks workspace or fetched from a repository.
Uploading and installing the wheel: The notebook can upload the generated .whl to DBFS or a workspace path, then install it using %pip install /dbfs/path/to/your_package.whl or a similar command.
Automated configuration: Any additional setup, such as installing dependencies from a requirements.txt, can also be scripted within the same notebook.
You must still follow the basic structure of Python packaging: having a setup.py (or equivalent) and metadata files is necessary since these are required by the Python ecosystem to build wheels.
The initial setup (creating setup.py, organizing code, and writing build commands) happens once. Afterward, updating the wheel and deploying new versions can be fully automated in a notebook workflow.
Place your source code and setup files (e.g., setup.py, pyproject.toml) in a workspace or accessible location.
Use a notebook cell to run the wheel build process:
%sh
python setup.py bdist_wheel
Use another cell to upload and install the newly built wheel:
%pip install /dbfs/path/to/dist/your_package.whl
Optionally, automate the copying/upload of the wheel with the Databricks CLI or REST API.
This approach largely replaces manual building and uploading with a repeatable, notebook-driven process, streamlining your team's workflow.
In summary, while a notebook can't avoid the need for wheel-building prerequisites (setup files, code structure), it can effectively automate package creation, configuration, and installation to the point where manual intervention is minimal and repeatable updates become much easier.
3 weeks ago
The best way to share code from a Databricks notebook as a reusable module while hiding implementation details from users—without using wheels or granting direct notebook execution permissions—is to convert your notebook into a Python module, store it securely in the Databricks workspace, and import it using relative workspace paths. This method allows importing custom Python modules directly from workspace files, which users can use without seeing source code, as long as permissions are set appropriately.
Python source files (.py) can be uploaded and stored alongside notebooks in the Databricks workspace.
These modules can be organized in folders, and notebooks can import functions/classes using Python’s standard import syntax and relative paths (e.g., from workspace.module import func).
By assigning workspace permissions, users can only read and use the module interface; actual code can be hidden if the file/folder access is restricted.
If workspace permissions are appropriately managed, this can hide the code implementation while exposing APIs for user notebooks to call.
Python wheels are commonly used for sharing and deploying modules, but strict environment policies can limit their use.
Library uploads (including wheels) are not always feasible due to administrative restrictions.
Notebook-scoped libraries are also possible but may not meet your code privacy requirements—users might still see source.
Code obfuscation/minification: Not ideal for Python, as bytecode isn’t very secure and users might still find ways to read code if they have access.
Docker containers: You can deploy Spark code in containers, hiding source, but this requires cluster admin and more setup.
Unity Catalog or secret management: These help protect sensitive data, but cannot fully hide code logic.
For your needs, using Python modules stored in workspace files with carefully set permissions is the most suitable way to share code without exposing internals—provided your environment supports this feature. Wheels and other binary approaches remain best but may be restricted. UDFs and notebook-scoped libraries do not fully solve the visibility and Spark referencing problems.
3 weeks ago
Thanks for the great information. Our team has decided to do this as a wheel. Can a notebook be created that pushes new versions of code w/o having to go thru the manual process of creating a whl and other configuration files? In other words, can I create a notebook that will setup/configure and install the wheel?
3 weeks ago
A Databricks notebook can help automate much of the wheel (whl) packaging and installation process, but it cannot fully eliminate the requirement of building the wheel artifact itself. However, you can create a notebook (or workflow) that covers most steps, from package building to deploying and installing the wheel onto your workspace or cluster, thereby minimizing manual intervention.
Building the wheel (whl) file automatically: With tools like setuptools, poetry, or uv, a notebook can run shell commands (%sh) or Magic commands to build the wheel directly from code stored within the Databricks workspace or fetched from a repository.
Uploading and installing the wheel: The notebook can upload the generated .whl to DBFS or a workspace path, then install it using %pip install /dbfs/path/to/your_package.whl or a similar command.
Automated configuration: Any additional setup, such as installing dependencies from a requirements.txt, can also be scripted within the same notebook.
You must still follow the basic structure of Python packaging: having a setup.py (or equivalent) and metadata files is necessary since these are required by the Python ecosystem to build wheels.
The initial setup (creating setup.py, organizing code, and writing build commands) happens once. Afterward, updating the wheel and deploying new versions can be fully automated in a notebook workflow.
Place your source code and setup files (e.g., setup.py, pyproject.toml) in a workspace or accessible location.
Use a notebook cell to run the wheel build process:
%sh
python setup.py bdist_wheel
Use another cell to upload and install the newly built wheel:
%pip install /dbfs/path/to/dist/your_package.whl
Optionally, automate the copying/upload of the wheel with the Databricks CLI or REST API.
This approach largely replaces manual building and uploading with a repeatable, notebook-driven process, streamlining your team's workflow.
In summary, while a notebook can't avoid the need for wheel-building prerequisites (setup files, code structure), it can effectively automate package creation, configuration, and installation to the point where manual intervention is minimal and repeatable updates become much easier.
Passionate about hosting events and connecting people? Help us grow a vibrant local community—sign up today to get started!
Sign Up Now