This document is a guide intended for folks interested in contributing to dbt-fabricsparknb. Below, we document the process by which members of the community should create issues and submit pull requests (PRs) in this repository. It is not intended as a guide for using dbt-fabricsparknb, and it assumes a certain level of familiarity with Python concepts such as virtualenvs, pip, Python modules, and so on. This guide assumes you are using macOS or Linux and are comfortable with the command line.
For those wishing to contribute we highly suggest reading the dbt-core's contribution guide if you haven't already. Almost all of the information there is applicable to contributing here, too!
You will need git in order to download and modify the dbt-fabricsparknb source code. You can find directions here on how to install git.
If you are not a member of the Microsoft GitHub organization, you can contribute to dbt-fabricsparknb by forking the dbt-fabricsparknb repository. For a detailed overview on forking, check out the GitHub docs on forking. In short, you will need to:
- fork the
dbt-fabricsparknbrepository - clone your fork locally
- check out a new branch for your proposed changes
- push changes to your fork
- open a pull request against
microsoft/dbt-fabricsparknbfrom your forked repository
If you are a member of the Microsoft GitHub organization, you will have push access to the dbt-fabricsparknb repo. Rather than forking dbt-fabricsparknb to make your changes, just clone the repository, check out a new branch, and push directly to that branch.
First make sure that you set up your virtualenv as described in Setting up an environment. Ensure you have the latest version of pip installed with pip install --upgrade pip. Next, install dbt-fabricsparknb latest dependencies:
pip install -e . -r dev-requirements.txtWhen dbt-fabricsparknb is installed this way, any changes you make to the dbt-fabricsparknb source code will be reflected immediately in your next dbt-fabricsparknb run.
To confirm you have correct version of dbt-core installed please run dbt --version and which dbt.
dbt-fabricsparknb uses test credentials specified in a test.env file in the root of the repository. This test.env file is git-ignored, but please be extra careful to never check in credentials or other sensitive information when developing. To create your test.env file, copy the provided example file, then supply your relevant credentials.
cp test.env.example test.env
$EDITOR test.env
There are a few methods for running tests locally.
tox takes care of managing Python virtualenvs and installing dependencies in order to run tests. You can also run tests in parallel, for example you can run unit tests for Python 3.8, Python 3.9, and flake8 checks in parallel with tox -p. Also, you can run unit tests for specific python versions with tox -e <env>. The configuration of these tests are located in tox.ini.
Finally, you can also run a specific test or group of tests using pytest directly. With a Python virtualenv active and dev dependencies installed you can do things like:
# run all functional tests
python -m pytest --profile az_cli tests/functional/
# run specific functional tests
python -m pytest --profile az_cli tests/functional/adapter/basic/*
# run all unit tests in a file
python -m pytest tests/unit/test_adapter.py
# run a specific unit test
python -m pytest test/unit/test_adapter.py::TestSparkAdapter::test_profile_with_databaseMany changes will require and update to the dbt-fabricspark docs here are some useful resources.
- Docs are here.
- The docs repo for making changes is located here.
- The changes made are likely to impact one or both of Fabric Spark Profile, or Saprk Configs.
- We ask every community member who makes a user-facing change to open an issue or PR regarding doc changes.
Changelogs are managed manually for now. As you raise a PR, provide the changes made in your commits.
Microsoft provides a CI environment to test changes to the dbt-fabricsparknb adapter, and periodic checks against the development version of dbt-core through Github Actions.
A dbt-fabricsparknb maintainer will review your PR. They may suggest code revision for style or clarity, or request that you add unit or functional test(s). These are good things! We believe that, with a little bit of help, anyone can contribute high-quality code.
Once all requests and answers have been answered the dbt-fabricsparknb maintainer can trigger CI testing.
Once all tests are passing and your PR has been approved, a dbt-fabricsparknb maintainer will merge your changes into the active development branch. And that's it! Happy developing 🎉