Bespin is a software library that contains reference implementations of "big data" algorithms in MapReduce and Spark. It provides sample code for many of the algorithms we'll be discussing in class and also provides starting points for the assignments. You'll want to familiarize yourself with the library.

Linux Student CS Environment

Software needed for the course can be found in the environment. We will ensure that everything works correctly in this environment.

TL;DR. Just set up your environment as follows (in bash; adapt accordingly for your shell of choice):

export PATH=/usr/lib/jvm/java-8-openjdk-amd64/jre/bin:/u3/cs451/packages/spark/bin:/u3/cs451/packages/hadoop/bin:/u3/cs451/packages/maven/bin:/u3/cs451/packages/scala/bin:$PATH
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/jre

You'll want to add the above lines to your shell config file (e.g., .bash_profile).

Gory Details. For the course we need Java, Scala, Hadoop, Spark, and Maven. Java is already available in the default user environment (but we need to point to the right version). The rest of the packages are installed in /u3/cs451/packages/. The directories scala, hadoop, spark, and maven are actually symlinks to specific versions. This is so that we can transparently change the links to point to different versions if necessary without affecting downstream users. Currently, the versions are:

Installing Software Locally

You may wish to install all necessary software packages locally on your own machine. We provide basic installation instructions here, but the course staff cannot provide technical support due to the size of the class and the idiosyncrasies of individual systems. We will be responsible for making sure everything works properly in the Linux Student CS Environment (above), but if you want to install everything on your own machine for convenience, you're on your own.

Both Hadoop and Spark work fine on Mac OS X and Linux, but may be difficult to get working on Windows. Note that to run Hadoop and Spark on your local machine comfortably, you'll need at least 4 GB memory and plenty of disk space (at least 10 GB).

You'll also need Java (JDK 1.8), Scala (use Scala 2.11.x), and Maven (any reasonably recent version).

The versions of the packages installed on are as follows:

Download the above packages, unpack the tarball, add their respective bin/ directories to your path (and your shell config), and you should be go to go.

Alternatively, you can also install the various packages using a package manager, e.g., apt-get, MacPorts, etc. However, make sure you get the right version.

Altiscale Cluster

Altiscale Logo

In addition to running "toy" Hadoop on a single machine (which obviously defeats the point of a distributed framework), we're going to be playing with a modest cluster thanks to the generous support of Altiscale, which is a "Hadoop-as-a-service" provider. You'll be getting an email directly from Altiscale with account information.

Follow the instructions from the email:

  1. Set up your web profile at Altiscale Portal.
  2. Follow these instructions to upload your ssh keys: Uploading and Managing Your Public Key
  3. Follow these instructions to ssh into the "workspace": Connecting to the Workbench Using SSH. The workspace is the node from which you submit MapReduce/Spark jobs; it's also where you'll check out code, inspect HDFS data, etc. In class I sometimes refer to this as the "submit node".
  4. Follow these instructions to access the cluster webapps: Accessing Web UIs Through a SOCKS Proxy. In particular, you'll need to access the Resource Manager webapp to examine the status of your running jobs at

The TL;DR version. Configure your ~/.ssh/config file as follows:

Host altiscale
Port 1763
IdentityFile ~/.ssh/id_rsa
Compression yes
ServerAliveInterval 15
DynamicForward localhost:1080
TCPKeepAlive yes
Protocol 2,1

And you should be able to ssh into the workspace:

ssh altiscale

That should do it!

Running Spark on Altiscale — the TL;DR version: Add the following lines to you ~/.bash_profile to point at the correct version of Spark:


For additional details, consult the Altiscale Spark documentation.