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apache / incubator-livy / 3440857a1f844f607215475d353e749b62819cac
commit3440857a1f844f607215475d353e749b62819cac[log] [tgz]
authorAlex Bozarth <ajbozart@us.ibm.com>Wed Jan 17 16:27:04 2018 -0800
committerAlex Bozarth <ajbozart@us.ibm.com>Wed Jan 17 16:27:04 2018 -0800
tree088cabc24e4303afe9943629cb6ea96d604ad270
parent85e8fba3ce5d0e180655fd9ee239cb320d550780 [diff]
[LIVY-104][CORE] Update Livy build to Scala 2.11

[LIVY-104](https://issues.apache.org/jira/browse/LIVY-104)

Livy currently builds using Scala 2.10 which has been EOL for a couple years. This update switches the build to use 2.11 instead.
This does not drop Livy support for Scala 2.10, it only updates the version used to build Livy itself.
We are holding off updating to Scala 2.12 since Livy still supports Spark 1.6, which doesn't support 2.12 and such an update would break UTs for 1.6, any update to 2.12 will have to wait until Spark 1.6 support is dropped.

This update also addresses any new deprecation warning raised in the update as well as updates various ITs to pass.

This locally passes both UTs and ITs build withe all supported versions of Spark.

Author: Alex Bozarth <ajbozart@us.ibm.com>

Closes #72 from ajbozarth/scala211.
5 files changed
tree: 088cabc24e4303afe9943629cb6ea96d604ad270
  1. .github/
  2. api/
  3. assembly/
  4. bin/
  5. client-common/
  6. client-http/
  7. conf/
  8. core/
  9. coverage/
  10. dev/
  11. docs/
  12. examples/
  13. integration-test/
  14. python-api/
  15. repl/
  16. rsc/
  17. scala/
  18. scala-api/
  19. server/
  20. test-lib/
  21. .gitignore
  22. .rat-excludes
  23. .travis.yml
  24. checkstyle-suppressions.xml
  25. checkstyle.xml
  26. DISCLAIMER
  27. LICENSE
  28. NOTICE
  29. pom.xml
  30. README.md
  31. scalastyle.xml
README.md

Apache Livy

Build Status

Apache Livy is an open source REST interface for interacting with Apache Spark from anywhere. It supports executing snippets of code or programs in a Spark context that runs locally or in Apache Hadoop YARN.

  • Interactive Scala, Python and R shells
  • Batch submissions in Scala, Java, Python
  • Multiple users can share the same server (impersonation support)
  • Can be used for submitting jobs from anywhere with REST
  • Does not require any code change to your programs

Pull requests are welcomed! But before you begin, please check out the Contributing section on the Community page of our website.

Online Documentation

Guides and documentation on getting started using Livy, example code snippets, and Livy API documentation can be found at livy.incubator.apache.org.

Before Building Livy

To build Livy, you will need:

Debian/Ubuntu:

  • mvn (from maven package or maven3 tarball)
  • openjdk-7-jdk (or Oracle Java7 jdk)
  • Python 2.6+
  • R 3.x

Redhat/CentOS:

  • mvn (from maven package or maven3 tarball)
  • java-1.7.0-openjdk (or Oracle Java7 jdk)
  • Python 2.6+
  • R 3.x

MacOS:

  • Xcode command line tools
  • Oracle's JDK 1.7+
  • Maven (Homebrew)
  • Python 2.6+
  • R 3.x

Required python packages for building Livy:

  • cloudpickle
  • requests
  • requests-kerberos
  • flake8
  • flaky
  • pytest

To run Livy, you will also need a Spark installation. You can get Spark releases at https://spark.apache.org/downloads.html.

Livy requires at least Spark 1.6 and supports both Scala 2.10 and 2.11 builds of Spark, Livy will automatically pick repl dependencies through detecting the Scala version of Spark.

Livy also supports Spark 2.0+ for both interactive and batch submission, you could seamlessly switch to different versions of Spark through SPARK_HOME configuration, without needing to rebuild Livy.

Building Livy

Livy is built using Apache Maven. To check out and build Livy, run:

git clone https://github.com/apache/incubator-livy.git
cd livy
mvn package

By default Livy is built against Apache Spark 1.6.2, but the version of Spark used when running Livy does not need to match the version used to build Livy. Livy internally uses reflection to mitigate the gaps between different Spark versions, also Livy package itself does not contain a Spark distribution, so it will work with any supported version of Spark (Spark 1.6+) without needing to rebuild against specific version of Spark.