[![PyPI Version][pypi-image]][pypi-url]
[![Python Version][versions-image]][versions-url]
[![Github Stars][stars-image]][stars-url]
[![codecov][codecov-image]][codecov-url]
[![Build Status][build-image]][build-url]
[![Documentation][doc-image]][doc-url]
[![License][license-image]][license-url]
[![Downloads][downloads-image]][downloads-url]
[![Downloads][downloads-month-image]][downloads-month-url]
[![Code style: black][codestyle-image]][codestyle-url]
![Beavers Logo][5]
# Beavers
[Documentation][6] / [Installation][7] / [Repository][1] / [PyPI][8]
[Beavers][1] is a python library for stream processing, optimized for analytics.
It is used at [Tradewell Technologies][2],
to calculate analytics and serve model predictions,
for both realtime and batch jobs.
## Key Features
- Works in **real time** (eg: reading from Kafka) and **replay mode** (eg: reading from Parquet files).
- Optimized for analytics, using micro-batches (instead of processing records one by one).
- Similar to [incremental][3], it updates nodes in a dag incrementally.
- Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:
- **Stream**: ephemeral micro-batches of events (cleared after every cycle).
- **State**: durable state derived from streams.
- Clear separation between the business logic and the IO.
So the same dag can be used in real time mode, replay mode or can be easily tested.
- Functional interface: no inheritance or decorator required.
- Support for complicated joins, not just "linear" data flow.
## Limitations
- No concurrency support.
To speed up calculation use libraries like pandas, pyarrow or polars.
- No async code.
To speed up IO use kafka driver native thread or parquet IO thread pool.
- No support for persistent state.
Instead of saving state, replay historic data from kafka to prime stateful nodes.
[1]: https://github.com/tradewelltech/beavers
[2]: https://www.tradewelltech.co/
[3]: https://github.com/janestreet/incremental
[4]: https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/
[5]: https://raw.githubusercontent.com/tradewelltech/beavers/master/docs/static/icons/beavers/logo.svg
[6]: https://beavers.readthedocs.io/en/latest/
[7]: https://beavers.readthedocs.io/en/latest/install/
[8]: https://pypi.org/project/beavers/
[pypi-image]: https://img.shields.io/pypi/v/beavers
[pypi-url]: https://pypi.org/project/beavers/
[build-image]: https://github.com/tradewelltech/beavers/actions/workflows/ci.yaml/badge.svg
[build-url]: https://github.com/tradewelltech/beavers/actions/workflows/ci.yaml
[stars-image]: https://img.shields.io/github/stars/tradewelltech/beavers
[stars-url]: https://github.com/tradewelltech/beavers
[versions-image]: https://img.shields.io/pypi/pyversions/beavers
[versions-url]: https://pypi.org/project/beavers/
[doc-image]: https://readthedocs.org/projects/beavers/badge/?version=latest
[doc-url]: https://beavers.readthedocs.io/en/latest/?badge=latest
[license-image]: http://img.shields.io/:license-Apache%202-blue.svg
[license-url]: https://github.com/tradewelltech/beavers/blob/main/LICENSE
[codecov-image]: https://codecov.io/gh/tradewelltech/beavers/branch/main/graph/badge.svg?token=GY6KL7NT1Q
[codecov-url]: https://codecov.io/gh/tradewelltech/beavers
[downloads-image]: https://pepy.tech/badge/beavers
[downloads-url]: https://static.pepy.tech/badge/beavers
[downloads-month-image]: https://pepy.tech/badge/beavers/month
[downloads-month-url]: https://static.pepy.tech/badge/beavers/month
[codestyle-image]: https://img.shields.io/badge/code%20style-black-000000.svg
[codestyle-url]: https://github.com/ambv/black
[snyk-image]: https://snyk.io/advisor/python/beavers/badge.svg
[snyk-url]: https://snyk.io/advisor/python/beavers
Raw data
{
"_id": null,
"home_page": "https://github.com/tradewelltech/beavers",
"name": "beavers",
"maintainer": "0x26res",
"docs_url": null,
"requires_python": ">=3.10,<3.13",
"maintainer_email": "0x26res@gmail.com",
"keywords": "apache-arrow,streaming,data",
"author": "Tradewell Tech",
"author_email": "engineering@tradewelltech.co",
"download_url": "https://files.pythonhosted.org/packages/e7/d0/787251c7fbc1e9ea3cb5918ae71fe51464d59d5ef68167fe553be306392f/beavers-0.5.0.tar.gz",
"platform": null,
"description": "\n[![PyPI Version][pypi-image]][pypi-url]\n[![Python Version][versions-image]][versions-url]\n[![Github Stars][stars-image]][stars-url]\n[![codecov][codecov-image]][codecov-url]\n[![Build Status][build-image]][build-url]\n[![Documentation][doc-image]][doc-url]\n[![License][license-image]][license-url]\n[![Downloads][downloads-image]][downloads-url]\n[![Downloads][downloads-month-image]][downloads-month-url]\n[![Code style: black][codestyle-image]][codestyle-url]\n\n![Beavers Logo][5]\n\n# Beavers\n\n[Documentation][6] / [Installation][7] / [Repository][1] / [PyPI][8]\n\n[Beavers][1] is a python library for stream processing, optimized for analytics. \n\nIt is used at [Tradewell Technologies][2], \nto calculate analytics and serve model predictions,\nfor both realtime and batch jobs.\n\n## Key Features\n\n- Works in **real time** (eg: reading from Kafka) and **replay mode** (eg: reading from Parquet files).\n- Optimized for analytics, using micro-batches (instead of processing records one by one).\n- Similar to [incremental][3], it updates nodes in a dag incrementally.\n- Taking inspiration from [kafka streams][4], there are two types of nodes in the dag:\n - **Stream**: ephemeral micro-batches of events (cleared after every cycle).\n - **State**: durable state derived from streams.\n- Clear separation between the business logic and the IO. \n So the same dag can be used in real time mode, replay mode or can be easily tested.\n- Functional interface: no inheritance or decorator required.\n- Support for complicated joins, not just \"linear\" data flow.\n\n## Limitations\n\n- No concurrency support. \n To speed up calculation use libraries like pandas, pyarrow or polars.\n- No async code.\n To speed up IO use kafka driver native thread or parquet IO thread pool.\n- No support for persistent state. \n Instead of saving state, replay historic data from kafka to prime stateful nodes. \n\n[1]: https://github.com/tradewelltech/beavers\n[2]: https://www.tradewelltech.co/\n[3]: https://github.com/janestreet/incremental\n[4]: https://www.confluent.io/blog/kafka-streams-tables-part-1-event-streaming/\n[5]: https://raw.githubusercontent.com/tradewelltech/beavers/master/docs/static/icons/beavers/logo.svg\n[6]: https://beavers.readthedocs.io/en/latest/\n[7]: https://beavers.readthedocs.io/en/latest/install/\n[8]: https://pypi.org/project/beavers/\n\n[pypi-image]: https://img.shields.io/pypi/v/beavers\n[pypi-url]: https://pypi.org/project/beavers/\n[build-image]: https://github.com/tradewelltech/beavers/actions/workflows/ci.yaml/badge.svg\n[build-url]: https://github.com/tradewelltech/beavers/actions/workflows/ci.yaml\n[stars-image]: https://img.shields.io/github/stars/tradewelltech/beavers\n[stars-url]: https://github.com/tradewelltech/beavers\n[versions-image]: https://img.shields.io/pypi/pyversions/beavers\n[versions-url]: https://pypi.org/project/beavers/\n[doc-image]: https://readthedocs.org/projects/beavers/badge/?version=latest\n[doc-url]: https://beavers.readthedocs.io/en/latest/?badge=latest\n[license-image]: http://img.shields.io/:license-Apache%202-blue.svg\n[license-url]: https://github.com/tradewelltech/beavers/blob/main/LICENSE\n[codecov-image]: https://codecov.io/gh/tradewelltech/beavers/branch/main/graph/badge.svg?token=GY6KL7NT1Q\n[codecov-url]: https://codecov.io/gh/tradewelltech/beavers\n[downloads-image]: https://pepy.tech/badge/beavers\n[downloads-url]: https://static.pepy.tech/badge/beavers\n[downloads-month-image]: https://pepy.tech/badge/beavers/month\n[downloads-month-url]: https://static.pepy.tech/badge/beavers/month\n[codestyle-image]: https://img.shields.io/badge/code%20style-black-000000.svg\n[codestyle-url]: https://github.com/ambv/black\n[snyk-image]: https://snyk.io/advisor/python/beavers/badge.svg\n[snyk-url]: https://snyk.io/advisor/python/beavers\n",
"bugtrack_url": null,
"license": "Apache-2.0",
"summary": "Python stream processing",
"version": "0.5.0",
"project_urls": {
"Documentation": "https://beavers.readthedocs.io/en/latest/",
"Homepage": "https://github.com/tradewelltech/beavers",
"Repository": "https://github.com/tradewelltech/beavers"
},
"split_keywords": [
"apache-arrow",
"streaming",
"data"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ba9ea968cd70e5ccad28cbcb1415d0f7f7d85517cdf1efc3b284acbd7aa72736",
"md5": "4e3688d4d56589fd7f7f9d4d830ac56c",
"sha256": "aac3a7a0bd6a3f7252ab48ebf0a2632a3d02dcc59952547208d47d4698860741"
},
"downloads": -1,
"filename": "beavers-0.5.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4e3688d4d56589fd7f7f9d4d830ac56c",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10,<3.13",
"size": 24719,
"upload_time": "2024-01-23T11:14:21",
"upload_time_iso_8601": "2024-01-23T11:14:21.925123Z",
"url": "https://files.pythonhosted.org/packages/ba/9e/a968cd70e5ccad28cbcb1415d0f7f7d85517cdf1efc3b284acbd7aa72736/beavers-0.5.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "e7d0787251c7fbc1e9ea3cb5918ae71fe51464d59d5ef68167fe553be306392f",
"md5": "e89c76f0dc403f29ee1916c8c6a9bb5f",
"sha256": "708da6fe6a0830b5505180fe2069211891ccbab55777b6a4bfd56b47081e7933"
},
"downloads": -1,
"filename": "beavers-0.5.0.tar.gz",
"has_sig": false,
"md5_digest": "e89c76f0dc403f29ee1916c8c6a9bb5f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10,<3.13",
"size": 22994,
"upload_time": "2024-01-23T11:14:23",
"upload_time_iso_8601": "2024-01-23T11:14:23.474881Z",
"url": "https://files.pythonhosted.org/packages/e7/d0/787251c7fbc1e9ea3cb5918ae71fe51464d59d5ef68167fe553be306392f/beavers-0.5.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-01-23 11:14:23",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tradewelltech",
"github_project": "beavers",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"tox": true,
"lcname": "beavers"
}
JSON
