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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[![snyk][snyk-image]][snyk-url]\n\u003ca href=\"https://trackgit.com\"\u003e\n\u003cimg src=\"https://us-central1-trackgit-analytics.cloudfunctions.net/token/ping/m7c1fpbnueo78pkcetcm\" alt=\"trackgit-views\" /\u003e\n\u003c/a\u003e\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## Talks\n\n- [Unified batch and stream processing in python | PyData Global 2023][9]\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[9]: https://www.youtube.com/watch?v=8pUwsGA8SQM\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]: 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