Apache Arrow

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Powering In-Memory Analytics

Apache Arrow is a universal columnar format and multi-language toolbox for fast
data interchange and in-memory analytics. It contains a set of technologies that
enable data systems to efficiently store, process, and move data.

Major components of the project include:

• The Arrow Columnar Format:

a standard and efficient in-memory representation of various datatypes, plain or nested

• The Arrow IPC Format:

an efficient serialization of the Arrow format and associated metadata,
for communication between processes and heterogeneous environments

• ADBC (Arrow Database Connectivity) ↗: Arrow-powered API,

drivers, and libraries for access to databases and query engines

• The Arrow Flight RPC protocol:

based on the Arrow IPC format, a building block for remote services exchanging
Arrow data with application-defined semantics (for example a storage server or a database)

• C++ libraries
• C bindings using GLib
• .NET libraries ↗
• Gandiva:

an LLVM-based Arrow expression compiler, part of the C++ codebase

• Go libraries ↗
• Java libraries ↗
• JavaScript libraries ↗
• Julia implementation ↗
• Python libraries
• R libraries
• Ruby libraries
• Rust libraries ↗
• Swift libraries ↗

The ↗ icon denotes that this component of the project is maintained in a separate
repository.

Arrow is an Apache Software Foundation project. Learn more at
arrow.apache.org.

What's in the Arrow libraries?

The reference Arrow libraries contain many distinct software components:

• Columnar vector and table-like containers (similar to data frames) supporting

flat or nested types

• Fast, language agnostic metadata messaging layer (using Google's FlatBuffers

library)

• Reference-counted off-heap buffer memory management, for zero-copy memory

sharing and handling memory-mapped files

• IO interfaces to local and remote filesystems
• Self-describing binary wire formats (streaming and batch/file-like) for

remote procedure calls (RPC) and interprocess communication (IPC)

• Integration tests for verifying binary compatibility between the

implementations (e.g. sending data from Java to C++)

• Conversions to and from other in-memory data structures
• Readers and writers for various widely-used file formats (such as Parquet, CSV)

Implementation status

The official Arrow libraries in this repository are in different stages of
implementing the Arrow format and related features. See our current
feature matrix
on git main.

How to Contribute

Please read our latest [project contribution guide][4].

If you are using AI coding tools, please review our
[AI-generated code guidance][7].

Getting involved

Even if you do not plan to contribute to Apache Arrow itself or Arrow
integrations in other projects, we'd be happy to have you involved:

• Join the mailing list: send an email to

[dev-subscribe@arrow.apache.org][1]. Share your ideas and use cases for the
project.

• Follow our activity on [GitHub issues][3]
• [Learn the format][2]
• Contribute code to one of the reference implementations

Continuous Integration Sponsors

We use [runs-on][5] for managing the project self-hosted runners.
We use [AWS][6] for some of the required infrastructure for the project.

[1]: mailto:dev-subscribe@arrow.apache.org
[2]: https://github.com/apache/arrow/tree/main/format
[3]: https://github.com/apache/arrow/issues
[4]: https://arrow.apache.org/docs/dev/developers/index.html
[5]: https://runs-on.com/
[6]: https://aws.amazon.com/
[7]: https://arrow.apache.org/docs/dev/developers/overview.html#ai-generated-code