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Native Rust implementation of Apache Arrow and Parquet

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

This repo contains the following main components:

Crate Description Documentation
arrow Core functionality (memory layout, arrays, low level computations) (README)
parquet Support for Parquet columnar file format (README)
arrow-flight Support for Arrow-Flight IPC protocol (README)
object-store Support for object store interactions (aws, azure, gcp, local, in-memory) (README)

There are two related crates in a different repository

Crate Description Documentation
DataFusion In-memory query engine with SQL support (README)
Ballista Distributed query execution (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 joins and window functions.

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

Arrow Rust Community

The [email protected] 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 page. All major announcements and communications happen there.

The Rust Arrow community also uses the official ASF Slack 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 and feel free to ask for an invite via:

  1. the [email protected] mailing list
  2. the GitHub Discussions
  3. the Discord channel

The Rust implementation uses GitHub 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.

There is more information in the contributing guide.