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.

Single-Node Hadoop: Linux Student CS Environment

A single-node Hadoop cluster (also called "local" mode) comes pre-configured 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

Note that we do not advise you to add the above lines to your shell config file (e.g., .bash_profile), but rather to set up your environment explicitly every time you log in. The reason for this is to reduce the possibility of conflicts when you start using the Datasci cluster (see below).

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:

Single-Node Hadoop: Personal Install

As an alternative of using the single-node Hadoop cluster on, 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), 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.

Distributed Hadoop Cluster: Datasci

In addition to running "toy" Hadoop on a single node (which obviously defeats the point of a distributed framework), we're going to using the school's modest Hadoop teaching cluster called Datasci.

Accounts are already set up for students enrolled in the course. You should be able to log into the cluster as follows:

ssh -D 1080

The -D option specifies dynamic port forwarding, which you'll need for accessing the Hadoop UIs through a SOCKS proxy. The simplest approach is via the Firefox browser: go to preferences and access "Network Proxy" settings: your settings should look something like this. You should then be able to access the Resource Manager (RM) webapp at It's important that you get the proxy working, because the RM webapp is the primary point of access for examining and debugging jobs on the cluster.

With Firefox, the proxy setup limits your ability to access other sites; turn off the proxy once you're done with the cluster. One helpful tip while working on assignments is to access the cluster webapp in Firefox, and use another browser for accessing other sites. There are many other equivalent ways to set up your proxy (different OSes, different browsers, etc.) as well as alternative workflows. Feel free to share tips, experiences, etc. on Piazza.