python-dlt 0.2.0a22

DLT is an open-source python-native scalable data loading framework that does not require any devops efforts to run.

data load tool (dlt)

data load tool (dlt) is a simple, open source Python library that makes data loading easy

• Automatically turn the JSON returned by any API into a live dataset stored wherever you want it
• pip install python-dlt and then include import dlt to use it in your Python loading script
• The dlt library is licensed under the Apache License 2.0, so you can use it for free forever

Read more about it on the dlt Docs

semantic versioning

python-dlt will follow the semantic versioning with MAJOR.MINOR.PATCH pattern. Currently we do pre-release versioning with major version being 0.

• minor version change means breaking changes
• patch version change means new features that should be backward compatible
• any suffix change ie. a10 -> a11 is a patch

development

python-dlt uses poetry to manage, build and version the package. It also uses make to automate tasks. To start

make install-poetry  # will install poetry, to be run outside virtualenv

then

make dev  # will install all deps including dev

Executing poetry shell and working in it is very convenient at this moment.

python version

Use python 3.8 for development which is the lowest supported version for python-dlt. You'll need distutils and venv:

sudo apt-get install python3.8
sudo apt-get install python3.8-distutils
sudo apt install python3.8-venv

You may also use pyenv as poetry suggests.

bumping version

Please use poetry version prerelease to bump patch and then make build-library to apply changes. The source of the version is pyproject.toml and we use poetry to manage it.

testing and linting

python-dlt uses mypy and flake8 with several plugins for linting. We do not reorder imports or reformat code.

pytest is used as test harness. make test-common will run tests of common components and does not require any external resources.

testing destinations

To test destinations use make test. You will need following external resources

1. BigQuery project
2. Redshift cluster
3. Postgres instance. You can find a docker compose for postgres instance here

See tests/.example.env for the expected environment variables. Then create tests/.env from it. You configure the tests as you would configure the dlt pipeline. We'll provide you with access to the resources above if you wish to test locally.

publishing

1. Make sure that you are on devel branch and you have the newest code that passed all tests on CI.
2. Verify the current version with poetry version
3. You'll need pypi access token and use poetry config pypi-token.pypi your-api-token then

make publish-library

4. Make a release on github, use version and git tag as release name

contributing

To contribute via pull request:

1. Create an issue with your idea for a feature etc.
2. Write your code and tests
3. Lint your code with make lint. Test the common modules with make test-common
4. If you work on a destination code then contact us to get access to test destinations
5. Create a pull request