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apache / arrow / refs/tags/apache-arrow-0.3.0-rc1
tag46bdc64cfa88bcabbf2178ac2f245b3692582ec0
taggerJulien Le Dem <julien@apache.org>Tue May 02 11:14:56 2017 -0700
objectd12fba2d6125477bb569387926c719fc786e6ae6 
[maven-release-plugin] copy for tag apache-arrow-0.3.0-rc1
commitd12fba2d6125477bb569387926c719fc786e6ae6[log] [tgz]
authorJulien Le Dem <julien@apache.org>Tue May 02 11:14:46 2017 -0700
committerJulien Le Dem <julien@apache.org>Tue May 02 11:14:46 2017 -0700
treed2b4b29050e1889eedaa41fcf7ff8906625c2948
parente9f2aec6a77a910514de9d3fa8acbbf840cb6e4e [diff]
[maven-release-plugin] prepare release apache-arrow-0.3.0-rc1

Change-Id: Ia5b4efe1bcff3b090451521cfa8fc1efbe9b488a
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tree: d2b4b29050e1889eedaa41fcf7ff8906625c2948
  1. c_glib/
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  6. integration/
  7. java/
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  9. python/
  10. site/
  11. .gitignore
  12. .readthedocs.yml
  13. .travis.yml
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  15. header
  16. KEYS
  17. LICENSE.txt
  18. NOTICE.txt
  19. README.md
README.md

Apache Arrow

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:

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

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)

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:

How to Contribute

We prefer to receive contributions in the form of GitHub pull requests. Please send pull requests against the github.com/apache/arrow repository.

If you are looking for some ideas on what to contribute, check out the JIRA issues for the Apache Arrow project. Comment on the issue and/or contact dev@arrow.apache.org with your questions and ideas.

If you’d like to report a bug but don’t have time to fix it, you can still post it on JIRA, or email the mailing list dev@arrow.apache.org

To contribute a patch:

  1. Break your work into small, single-purpose patches if possible. It’s much harder to merge in a large change with a lot of disjoint features.
  2. Create a JIRA for your patch on the Arrow Project JIRA.
  3. Submit the patch as a GitHub pull request against the master branch. For a tutorial, see the GitHub guides on forking a repo and sending a pull request. Prefix your pull request name with the JIRA name (ex: https://github.com/apache/arrow/pull/240).
  4. Make sure that your code passes the unit tests. You can find instructions how to run the unit tests for each Arrow component in its respective README file.
  5. Add new unit tests for your code.

Thank you in advance for your contributions!