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apache / arrow / refs/heads/maint-0.11.x
commit0563245baa7b7c9fe59f414226c617059d1b5c70[log] [tgz]
authorKorn, Uwe <Uwe.Korn@blue-yonder.com>Fri Oct 19 10:00:15 2018 -0400
committerWes McKinney <wesm+git@apache.org>Fri Oct 19 10:42:30 2018 -0400
tree1678681a1736f765965800d8ae605a2abfcd7e76
parentb994b3f14ae328060b2e17b2474b1fe2f464b138 [diff]
ARROW-3565: [Python] Pin tensorflow to 1.11.0 in manylinux1 container

Verified that this does not increase the current pinned numpy version.

Author: Korn, Uwe <Uwe.Korn@blue-yonder.com>

Closes #2797 from xhochy/ARROW-3565 and squashes the following commits:

0307343a1 <Korn, Uwe> ARROW-3565:  Pin tensorflow to 1.11.0 in manylinux1 container
1 file changed
tree: 1678681a1736f765965800d8ae605a2abfcd7e76
  1. .github/
  2. c_glib/
  3. ci/
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  5. dev/
  6. format/
  7. go/
  8. integration/
  9. java/
  10. js/
  11. matlab/
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  15. rust/
  16. site/
  17. .clang-format
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  24. .readthedocs.yml
  25. .travis.yml
  26. appveyor.yml
  27. CHANGELOG.md
  28. docker-compose.yml
  29. header
  30. LICENSE.txt
  31. NOTICE.txt
  32. README.md
README.md

Apache Arrow

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 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

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!