README.md

Running fully isolated PySpark application in YARN

This example project shows how to submit a PySpark application with complex dependencies and code structure to a YARN cluster without manually installing stuff on the workers. This is very useful when you want to share a cluster across different teams but don't want to each team globally installing their own set of potentially conflicting dependencies. At the same time you don't want to restrict the teams to a single set of available packages.

The only requirement is that spark-submit is in your PATH and you already can call it to send regular Spark scripts to YARN in cluster deploy mode.

Overview

We'll use Miniconda and conda-pack to fully package an environment with dependencies in a build/environment.tar.gz file and send to YARN using the --archives option. This archive will be decompressed and symlinked to a relative ./environment folder.

The spark.yarn.appMasterEnv.PYSPARK_PYTHON Spark configuration param allow us to set the PYSPARK_PYTHON variable on the cluster driver/workers to instruct Spark to use our packaged ./environment/bin/python for Python stuff.

We'll use the same strategy to push custom Python modules. Everything we need is packaged to build/app.zip and made available in a relative ./app folder. A top-level run.py file is the entrypoint to the Spark application.

Tutorial

1. Install Miniconda if you need. Follow the instructions.

2. Create a new environment from the provided environment.yaml file

   $ cd conda_pyspark
   $ conda env create -n conda_pyspark -f environment.yml
   $ conda activate conda_pyspark
   

3. Run ./pack.sh to create a build/environment.tar.gz file. During day to day development, you only need to call this if you change your dependencies.

4. Run ./submit.sh to submit the application to the cluster.

   • This script packs the application files to the app.zip, so edit as needed.