Define sophisticated data pipelines and run them on different distributed systems (such as Argo Workflows).
- Define tasks and DAGs, and compose them together seamlessly.
- Create dynamic for loops and map-reduce operations.
- Run your DAGs locally or using a distributed workflow orchestrator (such as Argo Workflows).
- Take advantage of advanced runtime features (e.g. Retry strategies, Kubernetes scheduling directives, etc.)
- ... All with a simple Pythonic DSL that feels just like coding regular Python functions.
Other nice features of Dagger are: Zero dependencies, 100% test coverage, great documentation and plenty of examples to get you started.
Dagger is published to the Python Package Index (PyPI) under the name py-dagger. To install it, you can simply run:
pip install py-dagger
Check our Documentation Portal!
Dagger is built around 3 components:
- A set of core data structures that represent the intended behavior of a DAG.
- A domain-specific language (DSL) that uses metaprogramming to capture how a DAG should behave, and represents it using the core data structures.
- Multiple runtimes that inspect the core data structures to run the corresponding DAG, or prepare the DAG to run in a specific pipeline executor.
Do you have some feedback about the library? Have you implemented a Serializer or a Runtime that may be useful for the community? Do you think a tutorial or example could be improved?
Every contribution to Dagger is greatly appreciated.
Please read our Contribution Guidelines for more details.
We use Poetry to manage the dependencies of this library. In the codebase, you will find a Makefile with some useful commands to run and test your contributions. Namely:
make install- Install the project's dependenciesmake test- Run tests and report test coverage. It will fail if coverage is too low.make ci- Run all the quality checks we run for each commit/PR. This includes type hint checking, linting, formatting and documentation.make build- Build the project.make docker-build- Package the project in a Docker imagemake docs-build- Build the documentation portal.make docs-serve- Serve the documentation portal.make k3d-set-up- Create a k3d cluster and image registry for the project.make k3d-docker-push- Build and push the project's Docker image to the local k3d registry.make k3d-install-argo- Install Argo on k3d, for local testing of Argo Workflows.make k3d-tear-down- Destroy the k3d cluster and registry.