Native Rust implementation of Apache Arrow and Apache Parquet

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

This repo contains the following main components:

The current development version the API documentation in this repo can be found here.

Release Versioning and Schedule

arrow and parquet crates

The Arrow Rust project releases approximately monthly and follows Semantic Versioning.

Due to available maintainer and testing bandwidth, arrow crates (arrow, arrow-flight, etc.) are released on the same schedule with the same versions as the parquet and parquet-derive crates.

This crate releases every month. We release new major versions (with potentially breaking API changes) at most once a quarter, and release incremental minor versions in the intervening months. See this ticket for more details.

To keep our maintenance burden down, we do regularly scheduled releases (major and minor) from the master branch. How we handle PRs with breaking API changes is described in the contributing guide.

Planned Release Schedule

object_store crate

The object_store crate is released independently of the arrow and parquet crates and follows Semantic Versioning. We aim to release new versions approximately every 2 months.

Related Projects

There are several related crates in different repositories

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

For example, you can write SQL queries or a DataFrame (using the datafusion crate) to read 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 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 on 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 dev@arrow.apache.org 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.

lib.rs:

Arrow ordering kernels

Sort RecordBatch

#
let a: ArrayRef = Arc::new(Int32Array::from(vec![1, 2, 3, 4]));
let b: ArrayRef = Arc::new(StringArray::from(vec!["b", "a", "e", "d"]));
let batch = RecordBatch::try_from_iter(vec![("a", a), ("b", b)]).unwrap();

// Sort by column 1
let indices = sort_to_indices(batch.column(1), None, None).unwrap();

// Apply indices to batch columns
let columns = batch.columns().iter().map(|c| take(&*c, &indices, None).unwrap()).collect();
let sorted = RecordBatch::try_new(batch.schema(), columns).unwrap();

let col1 = sorted.column(0).as_primitive::<Int32Type>();
assert_eq!(col1.values(), &[2, 1, 4, 3]);