junity 0.2.3

A JupyterLab extension for Unity Catalog

junity

A JupyterLab extension for Unity Catalog

Requirements

• JupyterLab >= 4.0.0

Install

To install the extension, execute:

pip install junity

Uninstall

To remove the extension, execute:

pip uninstall junity

Configuration

The default unityCatalogHostUrl is set to http://localhost:8080/api/2.1/unity-catalog. the default token is set to "not-used".

To adjust the unityCatalogHostUrl or token, manually follow these steps:

1. Open JupyterLab.
2. Navigate to the settings menu.
3. Find the Junity setting.
4. Update the URL to your desired endpoint.
5. Update the token if needed

You can also set the UC_HOST_URL and UC_TOKEN environment variables to configure the extension.

If you can't see your catalogs being loaded in the Unity Catalog sidebar, make sure that the URL is correct and that the Unity Catalog server is running. If this is the case, then check Known Issues section below or check for errors in jupyter using a webinspector

Known Issues

This is more of a general problem with browsers trying to reach localhost services:

• If both Jupyter Labs and Unity Catalog are running on localhost, you may run into CORS issues. To resolve this, you can add CORS settings to the Unity Catalog server or set up a reverse proxy (For instance Nginx, or Apache). Checkout the docker example to see how to set up a reverse proxy with Nginx.

Docker Example

In the docker folder, you can find an example of how to run JupyterLab and Unity Catalog in Docker containers. To run the example, execute:

docker compose up --build -d

This will start JupyterLab on http://localhost:8888 and Unity Catalog on http://localhost:8080/api/2.1/unity-catalog. You can access the example notebook by opening this URL in your browser: http://localhost:8888/lab?token=junity/tree/example.ipynb

Contributing

Development install

Note: You will need NodeJS to build the extension package.

The jlpm command is JupyterLab's pinned version of yarn that is installed with JupyterLab. You may use yarn or npm in lieu of jlpm below.

# Clone the repo to your local environment
# Change directory to the junity directory
# Install package in development mode
pip install -e "."
# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite
# Rebuild extension Typescript source after making changes
jlpm build

You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.

# Watch the source directory in one terminal, automatically rebuilding when needed
jlpm watch
# Run JupyterLab in another terminal
jupyter lab

With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).

By default, the jlpm build command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

jupyter lab build --minimize=False

Development uninstall

pip uninstall junity

In development mode, you will also need to remove the symlink created by jupyter labextension develop command. To find its location, you can run jupyter labextension list to figure out where the labextensions folder is located. Then you can remove the symlink named junity within that folder.

Testing the extension

Frontend tests

This extension is using Jest for JavaScript code testing.

To execute them, execute:

jlpm
jlpm test

Integration tests

This extension uses Playwright for the integration tests (aka user level tests). More precisely, the JupyterLab helper Galata is used to handle testing the extension in JupyterLab.

More information are provided within the ui-tests README.

Packaging the extension

See RELEASE

Remarks

Also checkout my other library dunky for a matching jupyter kernel