alonsoir / spark-optimization-2

The official repository for the Rock the JVM Spark Optimization 2 course

The official repository for the Rock the JVM Spark Optimization 2 course

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This repository contains the code we wrote during Rock the JVM's Spark Optimization 2 course. Unless explicitly mentioned, the code in this repository is exactly what was caught on camera.

Install and setup

• install IntelliJ IDEA
• install Docker Desktop
• either clone the repo or download as zip
• open with IntelliJ as an SBT project

As you open the project, the IDE will take care to download and apply the appropriate library dependencies.

To set up the dockerized Spark cluster we will be using in the course, do the following:

• open a terminal and navigate to spark-cluster
• run build-images.sh (if you don't have a bash terminal, just open the file and run each line one by one)
• run docker-compose up

To interact with the Spark cluster, the folders data and apps inside the spark-cluster folder are mounted onto the Docker containers under /opt/spark-data and /opt/spark-apps respectively.

To run a Spark shell, first run docker-compose up inside the spark-cluster directory, then in another terminal, do

docker exec -it spark-cluster_spark-master_1 bash

and then

/spark/bin/spark-shell

How to use intermediate states of this repository

Start by cloning this repository and checkout the start tag:

git checkout start

How to run an intermediate state

The repository was built while recording the lectures. Prior to each lecture, I tagged each commit so you can easily go back to an earlier state of the repo!

The tags are as follows:

• start
• 1.1-scala-recap
• 1.2-spark-recap
• 2.1-job-anatomy
• 2.2-query-plans
• 2.3-query-plans-exercises
• 2.4-spark-ui-dags
• 2.5-api-differences
• 2.6-deploy-config
• 2.7-catalyst
• 2.8-tungsten
• 3.2-caching
• 3.3-checkpointing
• 4.1-repartition-coalesce
• 4.2-partitioning-problems
• 4.3-partitioners
• 5.1-data-skews
• 5.2-serialization-problems
• 5.3-serialization-problems-2
• 5.4-kryo

When you watch a lecture, you can git checkout the appropriate tag and the repo will go back to the exact code I had when I started the lecture.

For questions or suggestions

If you have changes to suggest to this repo, either

• submit a GitHub issue
• tell me in the course Q/A forum
• submit a pull request!