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apache / incubator-livy / 19004b51a14de68a34526bab22b1b64a2376f042
commit19004b51a14de68a34526bab22b1b64a2376f042[log] [tgz]
authorEric Perry <eric@ericjperry.com>Fri Oct 20 10:04:12 2017 +0800
committerjerryshao <sshao@hortonworks.com>Fri Oct 20 10:04:12 2017 +0800
tree45ebbfb4d3c3d14f103ecf404f25eee7d76100c5
parent8056e4682531a5a4b717a0b13d992a46637fd412 [diff]
[LIVY-409] Livy shell UX improvements

I'm not sure how large of an audience the livy-shell has, but I've come to use it quite a bit in my testing/use of Livy and added a few small improvements that have made it a bit easier to use for me (and may help others):

* Replaced the use of httplib with requests to fix dropped connection issues (and a few other robustness issues).
* Added some context to the REPL prompt, by including the session `kind` and ID.
* Ignore `SIGINT` signal so that users can type `CTRL-C` to cancel out a command.

I know the use of `requests` is pretty ubiquitous but I included `livy-shell-requirements.txt` just in case 1) someone doesn't have it installed or 2) more dependencies are added down the road.

Author: Eric Perry <eric@ericjperry.com>

Closes #55 from ericjperry/feature/livy-shell-improvements.
1 file changed
tree: 45ebbfb4d3c3d14f103ecf404f25eee7d76100c5
  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.