This course consists of four lab assignments that span over different topics of the course:
- Lab 1: In the first lab assignment you will practice the basics of the data intensive programming by setting up HDFS and Hadoop MapReduce and implementing a simple application on them.
- Lab 2: The second lab assignment is based of the Spark and Spark SQL, and you will learn how to use them to process and analyze data.
- Lab 3: The concentration of this lab assignment is to process streaming data. You will work with Spark Streaming in this lab to process data on-line as it flows.
- Lab 4: In the last assignment you will work with GraphX to process graph-based data.
We have used the IPython/Jupyter notebooks for the lab assignments. Notebooks are documents that contain both the programming code (e.g., python or scala), as well as human-readable text elements (e.g., paragraph, figures, and links). Since we are using Scala programming language and Spark platform in our assignments, in addition to IPthon/Jupyter, we also need to install ISpark. Below, we first explain how to install IPython/Jupyter, and then present the ISpark installation steps. Note that these steps are given for a Linux operating system, so if you do not have Linux, you need to install it either on your machine or on a VirtualBox. You can download VirtualBox from its page. You can also find different ready to use Linux distribution images for VirtualBox here.
The steps to install IPython/Jupyter:
- Install
pip, the package management system used to install and manage software packages written in Python.
sudo apt-get install python-dev libncurses-dev python-pip
2. Use `pip` to install IPython. The following command downloads and installs IPython and its main optional dependencies for the notebook, qtconsole, tests, and other functionality.
```bash
sudo pip install ipython[all]==3.2.1
-
Download and install Java and Spark on your machine. The easiest way to install Spark is to download its "pre-built for CDH 4" from the here.
-
Set the environment variables.
export JAVA_HOME=<JAVA_PATH> export SPARK_HOME=<SPARK_PATH> export PYTHONPATH=$SPARK_HOME/python/lib/py4j-0.8.2.1-src.zip:$SPARK_HOME/python export PYSPARK_SUBMIT_ARGS="--master local[2] pyspark-shell"
5. Create an IPython profile to support Python programming language.
```bash
ipython profile create pyspark
- Edit the created profile by copying the following lines in
00-pyspark-setup.py. This file is at~/.ipython/profile_pyspark/startup/00-pyspark-setup.py.
import os import sys spark_home = os.environ.get('SPARK_HOME', None) sys.path.insert(0, os.path.join(spark_home, 'python')) sys.path.insert(0, os.path.join(spark_home, 'python/lib/py4j-0.8.2.1-src.zip')) execfile(os.path.join(spark_home, 'python/pyspark/shell.py'))
7. Run and test the IPython notebook by running the following command.
```bash
ipython notebook --profile=pyspark --debug
ISpark is an Apache Spark-shell backend for IPython.
- Install Maven first, if you do not have it.
sudo apt-get install maven
2. Download ISpark from [here](https://github.com/tribbloid/ISpark/archive/master.zip).
3. ISpark needs to be compiled and packaged into a jar by Maven before being submitted and deployed. Extract the ISpark file you downloaded and run the following command.
```bash
cd ISpark-master
./mvn-install.sh
- Create an ISpark profile.
ipython profile create spark
5. Edit the profile by copying the following lines in `ipython_config.py`, which is at `~/.ipython/profile_spark/ipython_config.py`. Below we are referring to `ispark-core-assembly-0.2.0-SNAPSHOT.jar`, which is built in step 3. It is located at `ISpark-master/core/target/scala-2.10`.
```python
import os
c = get_config()
spark_home = os.environ['SPARK_HOME']
master = 'local[2]'
c.KernelManager.kernel_cmd = [spark_home+"/bin/spark-submit",
"--master", master,
"--class", "org.tribbloid.ispark.Main",
"--executor-memory", "2G",
"<PATH ON YOUR MACIHE>/ispark-core-assembly-0.2.0-SNAPSHOT.jar",
"--profile", "{connection_file}",
"--parent"]
- Run and test the notebook.
ipython notebook --profile=spark --debug
## Things To Deliver
The assignments can be done individually or in group of two. For the labs 1 and 3, you should hand in the implemented codes in zip files, and for the other labs, you just need to deliver the completed IPython files.