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:
- The Arrow Columnar In-Memory 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 heterogenous environments
- 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
- C# .NET libraries
- Gandiva: an LLVM-based Arrow expression compiler, part of the C++ codebase
- Go libraries
- Java libraries
- Plasma Object Store: a shared-memory blob store, part of the C++ codebase
- Python libraries
- R libraries
- Ruby libraries
- Rust libraries
What's in the Arrow libraries?
The reference Arrow libraries contain a number of 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)
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.
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: