# arrow-rs

**Repository Path**: mirrors_Azure/arrow-rs

## Basic Information

- **Project Name**: arrow-rs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-06
- **Last Updated**: 2026-05-23

## Categories & Tags

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**Tags**: None

## README

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# Native Rust implementation of Apache Arrow and Parquet

[![Coverage Status](https://codecov.io/gh/apache/arrow-rs/rust/branch/master/graph/badge.svg)](https://codecov.io/gh/apache/arrow-rs?branch=master)

Welcome to the implementation of Arrow, the popular in-memory columnar format, in [Rust][rust].

This repo contains the following main components:

| Crate        | Description                                                        | Documentation              |
| ------------ | ------------------------------------------------------------------ | -------------------------- |
| arrow        | Core functionality (memory layout, arrays, low level computations) | [(README)][arrow-readme]   |
| parquet      | Support for Parquet columnar file format                           | [(README)][parquet-readme] |
| arrow-flight | Support for Arrow-Flight IPC protocol                              | [(README)][flight-readme]  |

There are two related crates in a different repository

| Crate      | Description                             | Documentation                 |
| ---------- | --------------------------------------- | ----------------------------- |
| DataFusion | In-memory query engine with SQL support | [(README)][datafusion-readme] |
| Ballista   | Distributed query execution             | [(README)][ballista-readme]   |

Collectively, these crates support a vast array of functionality for analytic computations in Rust.

For example, you can write an SQL query or a `DataFrame` (using the `datafusion` crate), run it against a parquet file (using the `parquet` crate), evaluate it in-memory using Arrow's columnar format (using the `arrow` crate), and send to another process (using the `arrow-flight` crate).

Generally speaking, the `arrow` crate offers functionality for using Arrow arrays, and `datafusion` offers most operations typically found in SQL, including `join`s and window functions.

You can find more details about each crate in their respective READMEs.

## Arrow Rust Community

The `dev@arrow.apache.org` mailing list serves as the core communication channel for the Arrow community. Instructions for signing up and links to the archives can be found at the [Arrow Community](https://arrow.apache.org/community/) page. All major announcements and communications happen there.

The Rust Arrow community also uses the official [ASF Slack](https://s.apache.org/slack-invite) for informal discussions and coordination. This is
a great place to meet other contributors and get guidance on where to contribute. Join us in the `#arrow-rust` channel.

Unlike other parts of the Arrow ecosystem, the Rust implementation uses [GitHub issues][issues] as the system of record for new features
and bug fixes and this plays a critical role in the release process.

For design discussions we generally collaborate on Google documents and file a GitHub issue linking to the document.

[rust]: https://www.rust-lang.org/
[arrow-readme]: arrow/README.md
[parquet-readme]: parquet/README.md
[flight-readme]: arrow-flight/README.md
[datafusion-readme]: https://github.com/apache/arrow-datafusion/blob/master/README.md
[ballista-readme]: https://github.com/apache/arrow-datafusion/blob/master/ballista/README.md
[issues]: https://github.com/apache/arrow-rs/issues