Apache Arrow is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. It also provides computational libraries and zero-copy streaming messaging and interprocess communication. Languages currently supported include C, C++, Java, JavaScript, Python, and Ruby.

Clone this repo:
  1. 322cd01 ARROW-10760: [Rust] [DataFusion] Fixed error in filter push down over joins by Jorge C. Leitao · 11 hours ago master
  2. 71e2cb2 ARROW-10656: [Rust] Use DataType comparison without values by Christoph Schulze · 12 hours ago
  3. 33a4290 ARROW-10684: [Rust] Inherit struct nulls in child null equality by Neville Dipale · 12 hours ago
  4. 981cab7 ARROW-10763: [Rust] Speed up take for primitive / boolean for non-null arrays by Heres, Daniel · 19 hours ago
  5. a2d1708 ARROW-4544: [Rust] JSON nested struct reader by Neville Dipale · 19 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 master.

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: