Powering Columnar In-Memory Analytics
Arrow is a set of technologies that enable big-data systems to process and move data fast.
Initial implementations include:
What's in the Arrow libraries?
The reference Arrow implementations 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
- Low-overhead IO interfaces to files on disk, HDFS (C++ only)
- 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 (e.g. Python's pandas library)
Right now the primary audience for Apache Arrow are the developers of data systems; most people will use Apache Arrow indirectly through systems that use it for internal data handling and interoperating with other Arrow-enabled systems.
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: