Code for blog at: https://www.startdataengineering.com/post/docker-for-de/

In order to run the code in this post you'll need to install the following:

1. git version >= 2.37.1
2. Docker version >= 20.10.17 and Docker compose v2 version >= v2.10.2.

Windows users: please setup WSL and a local Ubuntu Virtual machine following the instructions here. Install the above prerequisites on your ubuntu terminal; if you have trouble installing docker, follow the steps here (only Step 1 is necessary). Please install the make command with sudo apt install make -y (if its not already present).

All the commands shown below are to be run via the terminal (use the Ubuntu terminal for WSL users).

git clone https://github.com/josephmachado/docker_for_data_engineers.git
cd efficient_data_processing_spark
# start containers
docker compose up --build -d --scale spark-worker=2
docker ps # see list of running docker containers and their settings
# stop containers
docker compose down

Using the exec command, you can submit commands to be run in a specific container. For example, we can use the following to open a bash terminal in our spark-master container:

docker exec -ti spark-master bash
# You will be in the master container bash shell
exit # exit the container

Note that the -ti indicates that this will be run in an interactive mode. As shown below, we can run a command without interactive mode and get an output.

docker exec spark-master echo hello
# prints hello

Code for blog at: https://www.startdataengineering.com/post/test-pyspark/

In our upstream (postgres db), we can create fake data with the datagen.py script, as shown:

docker exec spark-master bash -c "python3 /opt/spark/work-dir/capstone/upstream_datagen/datagen.py"

docker exec spark-master spark-submit --master spark://spark-master:7077 --deploy-mode client /opt/spark/work-dir/etl/simple_etl.py

docker exec spark-master bash -c 'python3 -m pytest --log-cli-level info -p no:warnings -v /opt/spark/work-dir/etl/tests'