Skip to content

Latest commit

 

History

History
60 lines (38 loc) · 2.9 KB

README.md

File metadata and controls

60 lines (38 loc) · 2.9 KB
Raw

Data Science Course at SDU

The course/repo is run/maintained by Jakob Hviid [email protected].

Setup

In the root directory run the following from an administrative terminal:

docker-compose up -d
addroute.cmd

also, add a file to the HDFS setup by attaching to the namenode and running:

apt update
apt install wget
wget -O alice.txt https://www.gutenberg.org/files/11/11-0.txt
hdfs dfs -put alice.txt /

A sample of how to connect to spark is provided in example.py, which currently reads the alice.txt file and makes a word count.

Important! Currently pyspark requires that it is run with python version 3.7.5 or lower so if you have python 3.8 installed it will not work. See this issue for more info

Running pyspark code inside a container

To run spark code inside a container, an example was created in the pysparkExampleImage folder. The image can be created and deployed using the run.cmd command (needs to be run from inside the folder itself). Change the python file as needed, and change the dockerfile to fit with your needs. For example, add python packages inside this file. The first time it runs, it will take several minutes to complete. Subsequent runs should be ready within a second or two.

Note, to make this work, the container is attached to the "hadoop" network that is created by the docker-compose file. Also, the docker-compose file has been changed since the initial setup, which means it will have to be updated if you are running your own version. The changed components are only related to the network section of the file (added name) and the docker-compose version (changed to 3.5).

Kafka

A Kafka cluster can be found in the kafkaExampleImages folder. Simply run a docker-compose up, and the cluster should be running. All machines interacting with the cluster should be connecting to the kafkaNetwork.

Two images are provided:

  • Producer
  • Consumer

The producer is already implemented as an, but the consumer should be implemented by the students. Both images are automatically built bu running the run.cmd commands (On linux/mac cat them, and run the commands yourself).

Docker compose cheat sheet

Take a look here. For more background on Docker, see their official docker 101 slides.

Credits

This repo consists of several components created by Data Science Europe, but has been restructured into a part of a course run at the University of Southern Denmark. The repositories are as follows:

To see more about how the images that are used in this course are constructed, please visit these repositories and explore the DockerFiles in the corresponding directories.