livy

Apache Livy

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-8-jdk (or Oracle JDK 8)
• Python 2.6+
• R 3.x

Redhat/CentOS:

• mvn (from maven package or maven3 tarball)
• java-1.8.0-openjdk (or Oracle JDK 8)
• Python 2.6+
• R 3.x

MacOS:

• Xcode command line tools
• Oracle's JDK 1.8
• 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 Spark 2.2 or 2.3. You can switch to a different version of Spark by setting the SPARK_HOME environment variable in the Livy server process, 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 2.2.0, but the version of Spark used when running Livy does not need to match the version used to build Livy. Livy internally handles the differences between different Spark versions.

The Livy package itself does not contain a Spark distribution. It will work with any supported version of Spark without needing to rebuild.