Spark is a framework for distributed computations. Natively it works in Python, Scala and Java, although it can be used limitedly in Perl using pipes.

Apart from embarrassingly parallel computations, Spark framework is suitable for in-memory and/or iterative computations, making it suitable even for machine learning and complex data processing. (The Spark framework shares some underlying implementation with Hadoop, but it is quite different – Hadoop framework does not offer in-memory computations and has only limited support for iterative computations.)

The Spark framework can run either locally using one thread, locally using multiple threads or in a distributed fashion.

All Python, Scala and Java bindings work well in UFAL Environment. The displayed examples here are in Python and Scala. We do not discuss the Java binding, because it has the same API as Spark (and if you are a Java fan or know Java substantially better than Spark, you will be able to use it by yourself).

• Spark Introduction
• Official Quick Start
• Official Spark Programming Guide
• Official Python API Reference/Scala API Reference

Latest supported version of Spark is available in /net/projects/spark. To use it, add

export PATH="/net/projects/spark/bin:/net/projects/spark/sge:$PATH"

to your .bashrc (or .profile and log in again; or to your favourite shell config file). If you want to use Scala and do not have sbt already installed (or you do not know what it is), add also

export PATH="/net/projects/spark/sbt/bin:$PATH"

• Starting Spark Cluster in UFAL Environment
• Using Python
• Using Scala

• Reading Input and Writing Output
• Using Perl via Pipes