- About this document
- Proposing a change
- Getting the code
- Setting up an environment
- Running
dbt
in development - Testing
- Submitting a Pull Request
This document is a guide intended for folks interested in contributing to dbt-core
. 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-core
, 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.
If you're new to python development or contributing to open-source software, we encourage you to read this document from start to finish. If you get stuck, drop us a line in the #dbt-core-development
channel on slack.
If you have an issue or code change suggestion related to a specific database adapter; please refer to that supported databases seperate repo for those contributions.
Please note that all contributors to dbt-core
must sign the Contributor License Agreement to have their Pull Request merged into the dbt-core
codebase. If you are unable to sign the CLA, then the dbt-core
maintainers will unfortunately be unable to merge your Pull Request. You are, however, welcome to open issues and comment on existing ones.
dbt-core
is Apache 2.0-licensed open source software. dbt-core
is what it is today because community members like you have opened issues, provided feedback, and contributed to the knowledge loop for the entire communtiy. Whether you are a seasoned open source contributor or a first-time committer, we welcome and encourage you to contribute code, documentation, ideas, or problem statements to this project.
If you have an idea for a new feature or if you've discovered a bug in dbt-core
, the first step is to open an issue. Please check the list of open issues before creating a new one. If you find a relevant issue, please add a comment to the open issue instead of creating a new one. There are hundreds of open issues in this repository and it can be hard to know where to look for a relevant open issue. The dbt-core
maintainers are always happy to point contributors in the right direction, so please err on the side of documenting your idea in a new issue if you are unsure where a problem statement belongs.
Note: All community-contributed Pull Requests must be associated with an open issue. If you submit a Pull Request that does not pertain to an open issue, you will be asked to create an issue describing the problem before the Pull Request can be reviewed.
After you open an issue, a dbt-core
maintainer will follow up by commenting on your issue (usually within 1-3 days) to explore your idea further and advise on how to implement the suggested changes. In many cases, community members will chime in with their own thoughts on the problem statement. If you as the issue creator are interested in submitting a Pull Request to address the issue, you should indicate this in the body of the issue. The dbt-core
maintainers are always happy to help contributors with the implementation of fixes and features, so please also indicate if there's anything you're unsure about or could use guidance around in the issue.
If an issue is appropriately well scoped and describes a beneficial change to the dbt-core
codebase, then anyone may submit a Pull Request to implement the functionality described in the issue. See the sections below on how to do this.
The dbt-core
maintainers will add a good first issue
label if an issue is suitable for a first-time contributor. This label often means that the required code change is small, limited to one database adapter, or a net-new addition that does not impact existing functionality. You can see the list of currently open issues on the Contribute page.
Here's a good workflow:
- Comment on the open issue, expressing your interest in contributing the required code change
- Outline your planned implementation. If you want help getting started, ask!
- Follow the steps outlined below to develop locally. Once you have opened a PR, one of the
dbt-core
maintainers will work with you to review your code. - Add a test! Tests are crucial for both fixes and new features alike. We want to make sure that code works as intended, and that it avoids any bugs previously encountered. Currently, the best resource for understanding
dbt-core
's unit and integration tests is the tests themselves. One of the maintainers can help by pointing out relevant examples. - Check your formatting and linting with Flake8, Black, and the rest of the hooks we have in our pre-commit config.
In some cases, the right resolution to an open issue might be tangential to the dbt-core
codebase. The right path forward might be a documentation update or a change that can be made in user-space. In other cases, the issue might describe functionality that the dbt-core
maintainers are unwilling or unable to incorporate into the dbt-core
codebase. When it is determined that an open issue describes functionality that will not translate to a code change in the dbt-core
repository, the issue will be tagged with the wontfix
label (see below) and closed.
The dbt-core
maintainers use labels to categorize open issues. Most labels describe the domain in the dbt-core
codebase germane to the discussion.
tag | description |
---|---|
triage | This is a new issue which has not yet been reviewed by a dbt-core maintainer. This label is removed when a maintainer reviews and responds to the issue. |
bug | This issue represents a defect or regression in dbt-core |
enhancement | This issue represents net-new functionality in dbt-core |
good first issue | This issue does not require deep knowledge of the dbt-core codebase to implement. This issue is appropriate for a first-time contributor. |
help wanted / discussion | Conversation around this issue in ongoing, and there isn't yet a clear path forward. Input from community members is most welcome. |
duplicate | This issue is functionally identical to another open issue. The dbt-core maintainers will close this issue and encourage community members to focus conversation on the other one. |
snoozed | This issue describes a good idea, but one which will probably not be addressed in a six-month time horizon. The dbt-core maintainers will revist these issues periodically and re-prioritize them accordingly. |
stale | This is an old issue which has not recently been updated. Stale issues will periodically be closed by dbt-core maintainers, but they can be re-opened if the discussion is restarted. |
wontfix | This issue does not require a code change in the dbt-core repository, or the maintainers are unwilling/unable to merge a Pull Request which implements the behavior described in the issue. |
dbt-core
has three types of branches:
- Trunks are where active development of the next release takes place. There is one trunk named
main
at the time of writing this, and will be the default branch of the repository. - Release Branches track a specific, not yet complete release of
dbt-core
. Each minor version release has a corresponding release branch. For example, the0.11.x
series of releases has a branch called0.11.latest
. This allows us to release new patch versions under0.11
without necessarily needing to pull them into the latest version ofdbt-core
. - Feature Branches track individual features and fixes. On completion they should be merged into the trunk branch or a specific release branch.
You will need git
in order to download and modify the dbt-core
source code. On macOS, the best way to download git is to just install Xcode.
If you are not a member of the dbt-labs
GitHub organization, you can contribute to dbt-core
by forking the dbt-core
repository. For a detailed overview on forking, check out the GitHub docs on forking. In short, you will need to:
- fork the
dbt-core
repository - clone your fork locally
- check out a new branch for your proposed changes
- push changes to your fork
- open a pull request against
dbt-labs/dbt
from your forked repository
If you are a member of the dbt-labs
GitHub organization, you will have push access to the dbt-core
repo. Rather than forking dbt-core
to make your changes, just clone the repository, check out a new branch, and push directly to that branch.
There are some tools that will be helpful to you in developing locally. While this is the list relevant for dbt-core
development, many of these tools are used commonly across open-source python projects.
A short list of tools used in dbt-core
testing that will be helpful to your understanding:
tox
to manage virtualenvs across python versions. We currently target the latest patch releases for Python 3.7, Python 3.8, and Python 3.9pytest
to discover/run testsmake
- but don't worry too much, nobody really understands how make works and our Makefile is super simpleflake8
for code lintingblack
for code formattingmypy
for static type checking- Github Actions
A deep understanding of these tools in not required to effectively contribute to dbt-core
, but we recommend checking out the attached documentation if you're interested in learning more about them.
We strongly recommend using virtual environments when developing code in dbt-core
. We recommend creating this virtualenv
in the root of the dbt-core
repository. To create a new virtualenv, run:
python3 -m venv env
source env/bin/activate
This will create and activate a new Python virtual environment.
Docker and docker-compose are both used in testing. Specific instructions for you OS can be found here.
For testing, and later in the examples in this document, you may want to have psql
available so you can poke around in the database and see what happened. We recommend that you use homebrew for that on macOS, and your package manager on Linux. You can install any version of the postgres client that you'd like. On macOS, with homebrew setup, you can run:
brew install postgresql
First make sure that you set up your virtualenv
as described in Setting up an environment. Also ensure you have the latest version of pip installed with pip install --upgrade pip
. Next, install dbt-core
(and its dependencies) with:
make dev
# or
pip install -r dev-requirements.txt -r editable-requirements.txt
When dbt-core
is installed this way, any changes you make to the dbt-core
source code will be reflected immediately in your next dbt-core
run.
With your virtualenv activated, the dbt-core
script should point back to the source code you've cloned on your machine. You can verify this by running which dbt
. This command should show you a path to an executable in your virtualenv.
Configure your profile as necessary to connect to your target databases. It may be a good idea to add a new profile pointing to a local postgres instance, or a specific test sandbox within your data warehouse if appropriate.
Getting the dbt-core
integration tests set up in your local environment will be very helpful as you start to make changes to your local version of dbt-core
. The section that follows outlines some helpful tips for setting up the test environment.
Although dbt-core
works with a number of different databases, you won't need to supply credentials for every one of these databases in your test environment. Instead you can test all dbt-core code changes with Python and Postgres.
We recommend starting with dbt-core
's Postgres tests. These tests cover most of the functionality in dbt-core
, are the fastest to run, and are the easiest to set up. To run the Postgres integration tests, you'll have to do one extra step of setting up the test database:
make setup-db
or, alternatively:
docker-compose up -d database
PGHOST=localhost PGUSER=root PGPASSWORD=password PGDATABASE=postgres bash test/setup_db.sh
There are a few methods for running tests locally.
There are multiple targets in the Makefile to run common test suites and code checks, most notably:
# Runs unit tests with py38 and code checks in parallel.
make test
# Runs postgres integration tests with py38 in "fail fast" mode.
make integration
These make targets assume you have a local install of a recent version of
tox
for unit/integration testing and pre-commit for code quality checks, unless you use choose a Docker container to run tests. Runmake help
for more info.
Check out the other targets in the Makefile to see other commonly used test suites.
pre-commit
takes care of running all code-checks for formatting and linting. Run make dev
to install pre-commit
in your local environment. Once this is done you can use any of the linter-based make targets as well as a git pre-commit hook that will ensure proper formatting and linting.
tox
takes care of managing virtualenvs and install dependencies in order to run tests. You can also run tests in parallel, for example, you can run unit tests for Python 3.7, Python 3.8, and Python 3.9 checks in parallel with tox -p
. Also, you can run unit tests for specific python versions with tox -e py37
. The configuration for these tests in located in tox.ini
.
Finally, you can also run a specific test or group of tests using pytest
directly. With a virtualenv
active and dev dependencies installed you can do things like:
# run specific postgres integration tests
python -m pytest -m profile_postgres test/integration/001_simple_copy_test
# run all unit tests in a file
python -m pytest test/unit/test_graph.py
# run a specific unit test
python -m pytest test/unit/test_graph.py::GraphTest::test__dependency_list
Here is a list of useful command-line options for
pytest
to use while developing.
We use changie to generate CHANGELOG
entries. 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!
dbt Labs provides a CI environment to test changes to specific adapters, and periodic maintenance checks of dbt-core
through Github Actions. For example, if you submit a pull request to the dbt-redshift
repo, GitHub will trigger automated code checks and tests against Redshift.
A dbt-core
maintainer will review your PR. They may suggest code revision for style or clarity, or request that you add unit or integration test(s). These are good things! We believe that, with a little bit of help, anyone can contribute high-quality code.
- First time contributors should note code checks + unit tests require a maintainer to approve.
Once all tests are passing and your PR has been approved, a dbt-core
maintainer will merge your changes into the active development branch. And that's it! Happy developing 🎉