# <img src="https://raw.githubusercontent.com/streamlet-dev/tributary/main/docs/img/icon.png" width="300">
Python Data Streams
[](https://github.com/streamlet-dev/tributary/actions?query=workflow%3A%22Build+Status%22)
[](https://codecov.io/gh/streamlet-dev/tributary)
[](https://pypi.python.org/pypi/tributary)
[](https://pypi.python.org/pypi/tributary)
[](https://mybinder.org/v2/gh/streamlet-dev/tributary/main?urlpath=lab)
Tributary is a library for constructing dataflow graphs in python. Unlike many other DAG libraries in python ([airflow](https://airflow.apache.org), [luigi](https://luigi.readthedocs.io/en/stable/), [prefect](https://docs.prefect.io), [dagster](https://docs.dagster.io), [dask](https://dask.org), [kedro](https://github.com/quantumblacklabs/kedro), etc), tributary is not designed with data/etl pipelines or scheduling in mind. Instead, tributary is more similar to libraries like [mdf](https://github.com/man-group/mdf), [loman](https://github.com/janushendersonassetallocation/loman), [pyungo](https://github.com/cedricleroy/pyungo), [streamz](https://streamz.readthedocs.io/en/latest/), or [pyfunctional](https://github.com/EntilZha/PyFunctional), in that it is designed to be used as the implementation for a data model. One such example is the [greeks](https://github.com/streamlet-dev/greeks) library, which leverages tributary to build data models for [options pricing](https://www.investopedia.com/articles/optioninvestor/07/options_beat_market.asp).
# Installation
Install with pip:
`pip install tributary`
or with conda:
`conda install -c conda-forge tributary`
or from source:
`python setup.py install`
Note: If installing from source or with pip, you'll also need [Graphviz itself](https://www.graphviz.org/download/) if you want to visualize the graph using the `.graphviz()` method.
# Stream Types
Tributary offers several kinds of streams:
## Streaming
These are synchronous, reactive data streams, built using asynchronous python generators. They are designed to mimic complex event processors in terms of event ordering.
## Functional
These are functional streams, built by currying python functions (callbacks).
## Lazy
These are lazily-evaluated python streams, where outputs are propogated only as inputs change. They are implemented as directed acyclic graphs.
# Examples
- [Streaming](docs/examples/streaming/streaming.md): In this example, we construct a variety of forward propogating reactive graphs.
- [Lazy](docs/examples/lazy/lazy.md): In this example, we construct a variety of lazily-evaluated directed acyclic computation graphs.
- [Automatic Differentiation](docs/examples/autodiff/autodiff.md): In this example, we use `tributary` to perform automatic differentiation on both lazy and streaming graphs.
# Graph Visualization
You can visualize the graph with Graphviz. All streaming and lazy nodes support a `graphviz` method.
Streaming and lazy nodes also support [ipydagred3](https://github.com/timkpaine/ipydagred3) for live update monitoring.
## Streaming
Here green indicates executing, yellow indicates stalled for backpressure, and red indicates that `StreamEnd` has been propogated (e.g. stream has ended).
## Lazy
Here green indicates executing, and red indicates that the node is dirty. Note the the determination if a node is dirty is also done lazily (we can check with `isDirty` whcih will update the node's graph state.
## Catalog
See the [CATALOG](CATALOG.md) for a full list of functions, transforms, sources, and sinks.
## Support / Contributors
Thanks to the following organizations for providing code or financial support.
<a href="https://nemoulous.com"><img src="https://raw.githubusercontent.com/streamlet-dev/tributary/main/docs/img/nem.png" width="50"></a>
<a href="https://nemoulous.com">Nemoulous</a>
## License
This software is licensed under the Apache 2.0 license. See the [LICENSE](LICENSE) file for details.
## Alternatives
Here is an incomplete list of libraries which implement similar/overlapping functionality
- [man-group/mdf](https://github.com/man-group/mdf)
- [cedricleroy/pyungo](https://github.com/cedricleroy/pyungo)
- [python-streamz/streamz](https://github.com/python-streamz/streamz)
- [EntilZha/pyfunctional](https://github.com/EntilZha/PyFunctional)
- [stitchfix/hamilton](https://github.com/stitchfix/hamilton)
Raw data
{
"_id": null,
"home_page": "https://github.com/streamlet-dev/tributary",
"name": "tributary",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.7",
"maintainer_email": "",
"keywords": "streaming lazy graph dag dataflow reactive",
"author": "Tim Paine",
"author_email": "t.paine154@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/fb/cf/cf7180aa27943a361783c8bde4eb888cb63b5ac9da626df33151a66fa9df/tributary-0.2.1.tar.gz",
"platform": null,
"description": "# <img src=\"https://raw.githubusercontent.com/streamlet-dev/tributary/main/docs/img/icon.png\" width=\"300\">\nPython Data Streams\n\n[](https://github.com/streamlet-dev/tributary/actions?query=workflow%3A%22Build+Status%22)\n[](https://codecov.io/gh/streamlet-dev/tributary)\n[](https://pypi.python.org/pypi/tributary)\n[](https://pypi.python.org/pypi/tributary)\n[](https://mybinder.org/v2/gh/streamlet-dev/tributary/main?urlpath=lab)\n\n\nTributary is a library for constructing dataflow graphs in python. Unlike many other DAG libraries in python ([airflow](https://airflow.apache.org), [luigi](https://luigi.readthedocs.io/en/stable/), [prefect](https://docs.prefect.io), [dagster](https://docs.dagster.io), [dask](https://dask.org), [kedro](https://github.com/quantumblacklabs/kedro), etc), tributary is not designed with data/etl pipelines or scheduling in mind. Instead, tributary is more similar to libraries like [mdf](https://github.com/man-group/mdf), [loman](https://github.com/janushendersonassetallocation/loman), [pyungo](https://github.com/cedricleroy/pyungo), [streamz](https://streamz.readthedocs.io/en/latest/), or [pyfunctional](https://github.com/EntilZha/PyFunctional), in that it is designed to be used as the implementation for a data model. One such example is the [greeks](https://github.com/streamlet-dev/greeks) library, which leverages tributary to build data models for [options pricing](https://www.investopedia.com/articles/optioninvestor/07/options_beat_market.asp). \n\n\n\n\n# Installation\nInstall with pip:\n\n`pip install tributary`\n\nor with conda:\n\n`conda install -c conda-forge tributary`\n\nor from source:\n\n`python setup.py install`\n\nNote: If installing from source or with pip, you'll also need [Graphviz itself](https://www.graphviz.org/download/) if you want to visualize the graph using the `.graphviz()` method.\n\n# Stream Types\nTributary offers several kinds of streams:\n\n## Streaming\nThese are synchronous, reactive data streams, built using asynchronous python generators. They are designed to mimic complex event processors in terms of event ordering.\n\n## Functional\nThese are functional streams, built by currying python functions (callbacks). \n\n## Lazy\nThese are lazily-evaluated python streams, where outputs are propogated only as inputs change. They are implemented as directed acyclic graphs.\n\n# Examples\n- [Streaming](docs/examples/streaming/streaming.md): In this example, we construct a variety of forward propogating reactive graphs.\n- [Lazy](docs/examples/lazy/lazy.md): In this example, we construct a variety of lazily-evaluated directed acyclic computation graphs. \n- [Automatic Differentiation](docs/examples/autodiff/autodiff.md): In this example, we use `tributary` to perform automatic differentiation on both lazy and streaming graphs.\n\n# Graph Visualization\nYou can visualize the graph with Graphviz. All streaming and lazy nodes support a `graphviz` method.\n\nStreaming and lazy nodes also support [ipydagred3](https://github.com/timkpaine/ipydagred3) for live update monitoring.\n\n## Streaming\n\n\nHere green indicates executing, yellow indicates stalled for backpressure, and red indicates that `StreamEnd` has been propogated (e.g. stream has ended).\n\n## Lazy\n\n\nHere green indicates executing, and red indicates that the node is dirty. Note the the determination if a node is dirty is also done lazily (we can check with `isDirty` whcih will update the node's graph state.\n\n## Catalog\nSee the [CATALOG](CATALOG.md) for a full list of functions, transforms, sources, and sinks.\n\n## Support / Contributors\nThanks to the following organizations for providing code or financial support.\n\n\n<a href=\"https://nemoulous.com\"><img src=\"https://raw.githubusercontent.com/streamlet-dev/tributary/main/docs/img/nem.png\" width=\"50\"></a>\n\n<a href=\"https://nemoulous.com\">Nemoulous</a>\n\n## License\nThis software is licensed under the Apache 2.0 license. See the [LICENSE](LICENSE) file for details.\n\n## Alternatives\nHere is an incomplete list of libraries which implement similar/overlapping functionality\n\n- [man-group/mdf](https://github.com/man-group/mdf)\n- [cedricleroy/pyungo](https://github.com/cedricleroy/pyungo)\n- [python-streamz/streamz](https://github.com/python-streamz/streamz)\n- [EntilZha/pyfunctional](https://github.com/EntilZha/PyFunctional)\n- [stitchfix/hamilton](https://github.com/stitchfix/hamilton)\n\n",
"bugtrack_url": null,
"license": "Apache 2.0",
"summary": "Streaming reactive and dataflow graphs in Python",
"version": "0.2.1",
"project_urls": {
"Homepage": "https://github.com/streamlet-dev/tributary"
},
"split_keywords": [
"streaming",
"lazy",
"graph",
"dag",
"dataflow",
"reactive"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "0656d66d6705d1fb42af81ca3abdd6e102e16f0ec7360691139345774e0dea19",
"md5": "9ee6c1fed31dd3affd62566b95c90548",
"sha256": "206f033d090649dba375340c394a0f8874a4f59bdda123a216797ca866abf17c"
},
"downloads": -1,
"filename": "tributary-0.2.1-py2.py3-none-any.whl",
"has_sig": false,
"md5_digest": "9ee6c1fed31dd3affd62566b95c90548",
"packagetype": "bdist_wheel",
"python_version": "py2.py3",
"requires_python": ">=3.7",
"size": 113399,
"upload_time": "2023-05-10T10:37:59",
"upload_time_iso_8601": "2023-05-10T10:37:59.023349Z",
"url": "https://files.pythonhosted.org/packages/06/56/d66d6705d1fb42af81ca3abdd6e102e16f0ec7360691139345774e0dea19/tributary-0.2.1-py2.py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "fbcfcf7180aa27943a361783c8bde4eb888cb63b5ac9da626df33151a66fa9df",
"md5": "867c53dfd794976456d8071735cf3e77",
"sha256": "7f99e8257e989fabf5bb60b06dea5dd10d13c14b90de93490855d5b68d6c6fe6"
},
"downloads": -1,
"filename": "tributary-0.2.1.tar.gz",
"has_sig": false,
"md5_digest": "867c53dfd794976456d8071735cf3e77",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.7",
"size": 81658,
"upload_time": "2023-05-10T10:38:01",
"upload_time_iso_8601": "2023-05-10T10:38:01.779955Z",
"url": "https://files.pythonhosted.org/packages/fb/cf/cf7180aa27943a361783c8bde4eb888cb63b5ac9da626df33151a66fa9df/tributary-0.2.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-05-10 10:38:01",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "streamlet-dev",
"github_project": "tributary",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "tributary"
}
JSON
