This is a project for processing large amounts of football data and computing statistics on teams and players on previous data as well as real-time data being streamed into the project.
Using Apache Hadoop HDFS to store raw data, Spark for processing data and YARN for orchestrating the spark jobs, RethinkDB for storing processed data, Kafka to handle streaming of new data from the scraper and Nextjs (create-t3-app) for the web app.
This project is composed of 2 pipelines, one for streaming data and another for batch processing.
- Run
bash start.sh(note that if you are on windows you can run it on a git bash terminal)
If running for the first time run bash start.sh download-data in order to download the datasets we're going to be using.
-
Run
docker compose upto start the whole project. Make sure you have enough space on disk in order for everything to work smoothly. Running this onwindowswill cost extra performance overhead (and noticeable too). -
You can check the UI at
http://localhost:3010. It probably will take some time depending on your resources in order for the initial processed data to be ready. You can check out the progress athttp://localhost:8088/cluster/apps. There should be 9 different jobs completed.
-
Run
docker compose run spark bashto launch a shell inside the container. -
Run
../bin/spark-submit mount/spark/main.pyand it will load all datasets file into spark. -
Run an sql query on it.
- Running
docker system prunehelps getting rid of all volumes, build cache, etc ... that take up space on your machine. BE CAUTIOUS! Running this command will remove all stopped containers (among other unused stuff). You can also try its sibling commands:docker buildx prune(for clearing unused build cache) anddocker volume prune(for clearing unused volumes).
