The Spark SBT template image serves as a base image to build your own Scala application to run on a Spark cluster. See big-data-europe/docker-spark README for a description how to setup a Spark cluster.
sbt console will create you a Spark Context for testing your code like the
spark-shell:
docker run -it --rm bde2020/spark-sbt-template sbt console
You can also use directly your Docker image and test your own code that way.
You can build and launch your Scala application on a Spark cluster by extending
this image with your sources. The template uses
sbt as build tool, so you should take the
build.sbt file located in this directory and the project directory that
includes the
sbt-assembly.
When the Docker image is built using this template, you should get a Docker image that includes a fat JAR containing your application and all its dependencies.
- Create a Dockerfile in the root folder of your project (which also contains
a
build.sbt) - Extend the Spark Scala template Docker image
- Configure the following environment variables (unless the default value satisfies):
SPARK_MASTER_NAME(default: spark-master)SPARK_MASTER_PORT(default: 7077)SPARK_APPLICATION_MAIN_CLASS(default: Application)SPARK_APPLICATION_ARGS(default: "")
- Build and run the image:
docker build --rm=true -t bde/spark-app .
docker run --name my-spark-app --link spark-master:spark-master -d bde/spark-app
The sources in the project folder will be automatically added to /usr/src/app
if you directly extend the Spark Scala template image. Otherwise you will have
to add and package the sources by yourself in your Dockerfile with the
commands:
COPY . /usr/src/app
RUN cd /usr/src/app && sbt clean assembly
If you overwrite the template's CMD in your Dockerfile, make sure to execute
the /template.sh script at the end.
FROM bde2020/spark-sbt-template:3.3.0-hadoop3.3
MAINTAINER Cecile Tonglet <[email protected]>
ENV SPARK_APPLICATION_MAIN_CLASS eu.bde.my.Application
ENV SPARK_APPLICATION_ARGS "foo bar baz"
TODO