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LETSQL is a deferred compute system focused on smart composition of AI pipelines. Optimize performance with cross-engine caching and static planning. Easily go from research to production with portable UDFs.

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xorq (formerly LETSQL)

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Data processing library built on top of Ibis and DataFusion to write multi-engine data workflows.

Note

We are changing the name from LETSQL to xorq.

Caution

This library does not currently have a stable release. Both the API and implementation are subject to change, and future updates may not be backward compatible.

Getting Started

Installation

LETSQL is available as xorq on PyPI:

pip install xorq

Usage

import urllib.request

import xorq as xq

urllib.request.urlretrieve("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv", "iris.csv")

con = xq.connect()
iris_table = con.read_csv("iris.csv", table_name="iris")

res = (
    iris_table.filter([iris_table.sepal_length > 5])
    .group_by("species")
    .agg(iris_table.sepal_width.sum())
    .execute()
)

for more examples on how to use letsql, check the examples directory, note that in order to run some of the scripts in there, you need to install the library with examples extra:

pip install 'xorq[examples]'

Contributing

Contributions are welcome and highly appreciated. To get started, check out the contributing guidelines.

Support

If you have any issues with this repository, please don't hesitate to raise them. It is actively maintained, and we will do our best to help you.

Acknowledgements

This project heavily relies on Ibis and DataFusion.

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License

This repository is licensed under the Apache License

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LETSQL is a deferred compute system focused on smart composition of AI pipelines. Optimize performance with cross-engine caching and static planning. Easily go from research to production with portable UDFs.

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