How to : deploy a local Spark cluster (standalone) w/Docker (Linux)

Dockerized env : [JupyterLab server => Spark (master <-> 1 worker) ]

Deploying a local spark cluster (standalone) can be tricky
Most of online ressources focus on single driver installation w/ Spark in a custom env or using jupyter-docker-stacks
Here my notes to work with Spark locally, with a Jupyter Labs interface, with one Master and one Worker, using Docker Compose.
All the PySpark dependencies already configured in a container, access to your local files (in an existing directory)
You might also want to do it the easy way --not local though, using Databricks community (free)

1. Prerequisites

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• Install Docker Engine, either through Docker Desktop or directly Docker engine. Personally, using the latter.
• Make sure Docker Compose is installed or install it.
• Ressources :
  Medium article install and basic use of Docker . Docker official ressources should be enough though.
  Jupyter-Docker-Stacks. "A set of ready-to-run Docker images containing Jupyter applications".
  The source article I (very slightly) adapted the docker-compose file from.
  Install Docker engine (apt get), official ressource.
  Install Docker compose (apt get), official ressource.

2. How to

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After Docker Engine/compose installation, on linux, do not forget the post-installation steps

1. Git clone this repository or create a new one (name of your choice)
2. Open terminal, cd into custom directory, make sure docker-compose.yml file is present (copy it in if needed)
   

   spark-cluster/docker-compose.yml

   Lines 1 to 30 in 6e5cb82

   	version: '3'
   	services:
   	spark:
   	image: bitnami/spark:3.3.1
   	environment:
   	- SPARK_MODE=master
   	ports:
   	- '8080:8080'
   	- '7077:7077'
   	volumes:
   	- $PWD:/home/jovyan/work
   	spark-worker:
   	image: bitnami/spark:3.3.1
   	environment:
   	- SPARK_MODE=worker
   	- SPARK_MASTER_URL=spark://spark:7077
   	- SPARK_WORKER_MEMORY=4G
   	- SPARK_EXECUTOR_MEMORY=4G
   	- SPARK_WORKER_CORES=4
   	ports:
   	- '8081:8081'
   	volumes:
   	- $PWD:/home/jovyan/work
   	jupyter:
   	image: jupyter/pyspark-notebook:spark-3.3.1
   	ports:
   	- '8888:8888'
   	volumes:
   	- $PWD:/home/jovyan/work

Basically, the yml file tells Docker compose how to run the Spark Master, Worker, Jupyterlab. You will have access to your local disk/current working directory every time you run this command :

3. Run docker compose

cd my-directory
docker compose up
# or depending of your Docker Compose install:   
docker-compose up

Docker compose will automatically download the needed images (spark:3.3.1 for Master and Worker, pyspark-notebook for the JupyterLab interface) and run the whole thing. The next times, it will only run them.

3. Profit : JupyterLab interface, Spark cluster (standalone) mode

Access the different interfaces with :

Jupyter lab interface : http://localhost:8888
Spark Master : http://localhost:8080
Spark Worker : http://localhost:8081

You can use the demo notebook spark-cluster.ipynb for a ready-to-use PySpark notebook, or simply create a new one and run this as a SparkSession:

from pyspark.sql import SparkSession

# SparkSession
URL_SPARK = "spark://spark:7077"

spark = (
    SparkSession.builder
    .appName("spark-ml")
    .config("executor.memory", "4g")
    .master(URL_SPARK)
    .getOrCreate()
)

Bonus : Notebook, predict using spark.ml Pipeline()

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If you use spark-cluster.ipynb, a demo example shows how to build a spark.ml predict Pipeline() with a random forest regressor, on a well known dataset.