Computer Science Data Science Facilities

This page describes primary software and systems for general use within the department. Individuals and research groups may, of course, have their own.

We currently have a 3-node Hadoop cluster. However the major tools are avaiable on all of our systems. The primary use of the cluster is for courses where you want to show students what a Hadoop cluster looks like. For serious work, you can get at least as good performance from running tools on the local system, particularly if you use one of the large systems such as ilab1, ilab2 and ilab3.

Here are the tools discussed on this page. Except for the last entry, these are all the versions outside the Hadoop cluster, i.e. those available on all of our systems.

If you need additional tools, please contact


Most data science within computer science is done in Python. We have Anaconda-based environments available on our Centos systems. They have the major packages used for data science already loaded. If you need additional packages, please contact


Jupyter is a "notebook." It's a web interface designed to make it easy to do quick analysis, primarily in python. (We have also installed a kernel for Scala.) We don't recommend it for large programs, but many people use it for data analysis.

Spark Outside Hadoop

Spark is available from all of our systems. Except within Hadoop, Spark will run standalone on the node where you run it. On systems such as there are enough cores to get reasonable parallelism.

If you want to write significant Spark programs, you'll probably be using the command line (and maybe an IDE). See Spark Programming for specifics in doing Spark programs here. The rest of this section describes use of Spark from Jupyter and the interactive commands.

NOTE: Spark is currently available in juypter when run from the base anaconda environment, the python36 enviornment, and the python37 environment. It's not in python35, because we're about to remove it, or in python2.

The pyspark command-line program, and the Spark in Python3 session in Jupyter, set up a Spark context for you in python3. The following variables are defined:

The spark-shell command-line program, and the Spark in Scala session in Jupyter, set up a Spark context, with the following variables:

Graphics is avaiable within Jupyter / ipython using matplotlib. E.g "%matplotlib inline".

Python support for Jupyter is well documented. See The Jupyter Notebook.

The rest of this section has information on the Scala kernel for Jupyter, and spark-shell. Here's the official documentation: Apache Torree Quick Start.

Graphics in Scala

Graphics is avaiable within Toree, however it's not builtin. There's no one dominant package. The following two can be loaded with one or two lines. Currently they probably only work in Jupyter, not spark-shell. This will be fixed when we get Spark 3.

Add classes to Scala

The Scala enviornment has access to a large set of Spark-related libraries, as well as other standard libraries such as Apache Commons. Try "ls /koko/system/spark/jars/" to see them all. If you need more, in Jupyter, you can use "%classpath" to load them. See the FAQ for more information. They tell use to use

%AddDeps group-id artifact-id version
to load libraries. This command searches the Maven collection of libraries. Here's a search tool: Maven search That search will display the group ID, artifact ID, and latest version. For generic libraries you probably should use the most recent. With Spark-related libraries you may want to use version 2.4.3, so it matches the version of Spark we have installed.

In Spark-shell, the --jars and --packages options perform the same function. For --packages, arguments look like groupid:artifactid:version

Oddities in Scala


Hadoop is installed in standalone mode on all of our systems. The only real use would be for Map/Reduce jobs. Note that outside the cluster, jobs will run in local mode. However on our larger systems, e.g. ilab1, ilab2 and ilab3, you can get a reasonable amount of parallelism if you adjust the number of tasks. (By default only 2 map tasks are run.)

See Map/Reduce on CS Systems for details.


We have a small Hadoop cluster, with 3 nodes. It has the typical tools: HDFS, YARN, Zookeeper, MapReduce, Hive, Hbase, Pig, Kafka. In addition, it has two web-based noteoobks: Jupyterhub and Zeppelin. It's intended primarily for coursework, so it has enough memory to survive reasonable size classes.

See Computer Science Hadoop Cluster for specifics.