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Contributing to dbt-redshift

  1. About this document
  2. Getting the code
  3. Running dbt-redshift in development
  4. Testing
  5. Updating Docs
  6. Submitting a Pull Request

About this document

This document is a guide intended for folks interested in contributing to dbt-redshift. 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-redshift, and it assumes a certain level of familiarity with Python concepts such as virtualenvs, pip, python modules, filesystems, 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, if you haven't already. Almost all of the information there is applicable to contributing here, too!

Signing the CLA

Please note that all contributors to dbt-redshift must sign the Contributor License Agreement to have their Pull Request merged into an dbt-redshift codebase. If you are unable to sign the CLA, then the dbt-redshift maintainers will unfortunately be unable to merge your Pull Request. You are, however, welcome to open issues and comment on existing ones.

Getting the code

You will need git in order to download and modify the dbt-redshift source code. You can find direction here on how to install git.

External contributors

If you are not a member of the dbt-labs GitHub organization, you can contribute to dbt-redshift by forking the dbt-redshift repository. For a detailed overview on forking, check out the GitHub docs on forking. In short, you will need to:

  1. fork the dbt-redshift repository
  2. clone your fork locally
  3. check out a new branch for your proposed changes
  4. push changes to your fork
  5. open a pull request against dbt-labs/dbt-redshift from your forked repository

dbt Labs contributors

If you are a member of the dbt Labs GitHub organization, you will have push access to the dbt-redshift repo. Rather than forking dbt-redshift to make your changes, just clone the repository, check out a new branch, and push directly to that branch.

Running dbt-redshift in development

Installation

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-redshift latest dependencies:

pip install -e . -r ./dev-requirements.txt

When dbt-redshift is installed this way, any changes you make to the dbt-redshift source code will be reflected immediately in your next dbt run command that uses dbt-redshift.

To confirm you have correct dbt-core and adapter versions installed please run dbt --version and which dbt to check the correct executable path you wish to use for dbt-core is in your current virtualenv.

Testing

Initial Setup

dbt-redshift contains unit and functional tests. Functional tests require testing against an actual Redshift warehouse. We have CI set up to test against a Redshift warehouse during PR checks.

In order to run functional tests locally, you will need a test.env file in the root of the repository that contains credentials for your Redshift warehouse.

Note: 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

Test commands

There are a few methods for running tests locally.

tox

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, Python 3.10, and flake8 checks in parallel with tox -p. Also, you can run unit tests for specific python versions with tox -e py38. The configuration of these tests are located in tox.ini.

pytest

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 specific redshift functional tests
python -m pytest tests/functional/adapter/concurrent_transactions
# run specific redshift functional tests in a file
python -m pytest tests/functional/adapter/test_basic.py
# run all unit tests in a file
python -m pytest tests/unit/test_redshift_adapter.py
# run a specific unit test
python -m pytest tests/unit/test_redshift_adapter.py::TestRedshiftAdapterConversions::test_convert_date_type

Updating Docs

Many changes will require an update to the dbt-redshift docs. If so, here are some useful resources to find where the current behavior is documented.

  • Docs are here.
  • The docs repo for making changes is located here.
  • The changes made are likely to impact one or both of Redshift Profile, or Redshift Configs.
  • We ask every community member who makes a user-facing change to open an issue or PR regarding doc changes.

Adding CHANGELOG Entry

We use changie to generate CHANGELOG entries. Note: Do not edit the CHANGELOG.md directly. Your modifications will be lost.

Follow the steps to install changie for your system.

Once changie is installed and your PR is created, simply run changie new and changie will walk you through the process of creating a changelog entry. Commit the file that's created and your changelog entry is complete!

You don't need to worry about which dbt-redshift version your change will go into. Just create the changelog entry with changie, and open your PR against the main branch. All merged changes will be included in the next minor version of dbt-redshift. The Core maintainers may choose to "backport" specific changes in order to patch older minor versions. In that case, a maintainer will take care of that backport after merging your PR, before releasing the new version of dbt-redshift.

Submitting a Pull Request

dbt Labs provides a CI environment to test changes to the dbt-redshift adapter and periodic checks against the development version of dbt-core through Github Actions.

A dbt-redshift 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 tests are passing and your PR has been approved, a dbt-redshift maintainer will merge your changes into the active development branch. And that's it! Happy developing 🎉