Apache Arrow is a multi-language toolbox for accelerated data interchange and in-memory processing

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  1. cbc17a9 GH-35618: [C++][Doc] Improve doc for Datum (#35794) by Jin Shang · 5 hours ago main
  2. d9c2d59 GH-35828: [Go] Add `array.WithUnorderedMapKeys` option for `array.ApproxEqual` (#35823) by Alex Shcherbakov · 8 hours ago
  3. fee8bf9 GH-35757: [C++][Parquet] using page-encoding-stats to build encodings (#35758) by mwish · 9 hours ago
  4. 5c772af GH-35845: [CI][Python] Fix usage of assert_frame_equal in test_hdfs.py (#35842) by Joris Van den Bossche · 11 hours ago
  5. 95df6cc GH-35820: [C++][CI] EnsureAlignment.Buffer fails on test-build-vcpkg-win (#35834) by Weston Pace · 16 hours ago

Apache Arrow

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Powering In-Memory Analytics

Apache Arrow is a development platform for in-memory analytics. It contains a set of technologies that enable big data systems to process and move data fast.

Major components of the project include:

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.

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: