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.
The major Hadoop-related tools are avaiable on all of our systems, though we don't have an actual Hadoop cluster. We believe you can get at least as good performance from running tools on the local system as on a small cluster, particularly if you use one of the large systems such as ilab1, ilab2 and ilab3.
Here are the tools discussed on this page.
(As of summer, 2021, we are dropping support for Zeppelin, though we can put it back if there's serious need for it.)
If you need additional tools, please contact email@example.com. We're willing to install more tools, but will want your help in verifying that they work.
Most data science within computer science is done in Python. We have Anaconda-based environments available on our Linux systems. They have the major packages used for data science already loaded. If you need additional packages, please contact firstname.lastname@example.org.
- Do not simply type "python." That will give you a copy of python2. Support for python 2 was dropped Jan, 2020. Also, our default python2 doesn't have the data science packages loaded. Despite the fact no one should be using Python 2, the Python project wants the command "python" to run Python 2. So you should always use "python3".
- Use one of the Anaconda-based environments. See Using Python on our systems.
- You can install packages yourself, using "pip install --user". This will put the additional packages in your home directory.
- If our environments don't have what you need, you can download a copy of Python and create your own environment. Since these environments are large, consider putting it in /common/users/NETID, where you have a larger quota than your home directory.
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.
- Jupyter is installed in all of our Anaconda-based Python 3 environments. See Using Python on our systems for how to activate an environment.
- Once you've activated one, you can type a command like jupyter lab --ip=`hostname` --browser="none" That will display a URL to which you can point your browser.
- If all you want to do is run jupyter, you don't actually have to activate the environment. You can run the copy of jupyter directly from it, e.g. for the python39 environment, /koko/system/anaconda/envs/python39/bin/jupyter lab --ip=`hostname` --browser="none".
- We have installed Jupyter kernels for use with Spark. See the next section. If you want to use Scala interactively, but aren't interested in Spark, you can still use Spark in Scala. You'll just ignore the Spark features.
- We haven't installed a kernel for Java. Java isn't as well suited for interactive use as Scala. We suggest that Java programmers spend a few minutes to learn enough about Scala to use it. The languages are very similar.
As of May 2022, our primary version of Python is 3.9, and Spark is 3.2.1, although we have older versions of Python. We recommend using the newest one that will work with your software. Note that Spark 2 works with Python 3.7 but not 3.8, while Spark 3 only works with 3.8 and later.
For more information on our copy of Jupyter, see Jupyter.
Spark is available from all of our systems. Spark will run standalone on the node where you run it. On systems such as ilab.cs.rutgers.edu there are enough cores to get reasonable parallelism.
- The usual commands, e.g. pyspark, spark-shell, sparkR, spark-submit, are avaiable on our systems.
- If you run Jupyter on one of our systems, you'll see that there are kernels for Spark in Python and Scala. These effectively run the pyspark or spark-shell commands, respectively, in a Jupyter session.
- Spark is configured to use 8gb of memory by default. This should be enough for all classwork. With the command-line pyspark and spark-shell, you can add "--driver-memory NNNg" to override this. With the Jupyter Scala core, you can set an environment variable SPARK_OPTS="--conf spark.driver.memory=NNNg" before starting the notebook.
- Spark 3 (which is our current version of Spark) is configured to use Java 11. If you just type "java" on one of our systems, you get Java 11. Spark 2 won't work with anything more recent than Java 8, so the various Spark 2-related commands set an explicit JAVA_HOME to point to Java 8.
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.
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:
- spark - a pyspark.sql.SparkSession (using Hive)
- sc - a SparkContext
- sql - a bound method SparkSession.sql for the session
- sqlContext - an SQLContext [for compatibility]
- sqlCtx - an old name for sqlContext
The spark-shell command-line program, and the Spark in Scala session in Jupyter, set up a Spark context in Scala, with the following variables:
- spark - a pyspark.sql.SparkSession (using Hive)
- sc - a SparkContext
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, since it's less well documented elsewhere. Here's the official documentation: Apache Torree Quick Start.
Graphics in Scala
We are currently unable to support graphics in Scala from Jupyter. We suggest that you prepare data in Scala, write it out, and then use Python to graph it. While there are graphics packages for Scala, they either haven't been updated for the current version of Scala or won't install. (If you find one that works, please tell us. We'd be happy to install it.)
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 versionto load libraries. This command searches the Maven collection of libraries. Just about any library you could want is in Maven. 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 3.2.1, 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
- In Jupyter, if you define a class and use it in the same paragraph it may give an undefined error. You may have to execute the definition before the use. You probably won't see this in spark-shell, because it executes every line as it is complete.
- The Jupyter Scala kernel doesn't prefine the SQL context. Try "val sqlContext = new org.apache.spark.sql.hive.HiveContext(sc)"
- If you try to create a Hive table, you may get a permissions error from Derby. If so, you'll have to do "System.setSecurityManager(null)" before creating the SQL context.
The Hadoop software is installed in standalone mode on all of our systems. The only real use would be for Map/Reduce jobs. Note that 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.