https://github.com/sequenceiq/docker-spark
http://blog.sequenceiq.com/blog/2015/01/09/spark-1-2-0-docker/

Apache Spark on Docker

This repository contains a Docker file to build a Docker image with Apache Spark. This Docker image depends on our previous Hadoop Docker image, available at the SequenceIQ GitHub page. The base Hadoop Docker image is also available as an official Docker image.

Pull the image from Docker Repository

$sudo docker pull sequenceiq/spark:1.5.1

Building the image

$sudo docker build --rm -t sequenceiq/spark:1.5.1 .

Running the image

• if using boot2docker make sure your VM has more than 2GB memory
• in your /etc/hosts file add $(boot2docker ip) as host 'sandbox' to make it easier to access your sandbox UI
• open yarn UI ports when running container

$sudo docker run -it -p 8088:8088 -p 8042:8042 -h sandbox sequenceiq/spark:1.5.1 bash

or

$sudo docker run -it -h sandbox sequenceiq/spark:1.5.1 bash

$cd /usr/local/spark 

or

$sudo docker run -d -h sandbox sequenceiq/spark:1.5.1 -d

Versions

Hadoop 2.6.0 and Apache Spark v1.5.1 on Centos 

Testing

There are two deploy modes that can be used to launch Spark applications on YARN.

Disable logs

Just execute this command in the spark directory:

cp conf/log4j.properties.template conf/log4j.properties

Edit log4j.properties:

Replace at the first line:

log4j.rootCategory=INFO, console

by

log4j.rootCategory=WARN, console   or  log4j.rootCategory=ERROR, console

Save and restart your shell. It works for me for Spark 1.1.0 and Spark 1.5.1 on OS X.

YARN-client mode

In yarn-client mode, the driver runs in the client process, and the application master is only used for requesting resources from YARN.

# run the spark shell
spark-shell \
--master yarn-client \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 1

# execute the the following command which should return 1000
scala> sc.parallelize(1 to 1000).count()

YARN-cluster mode

In yarn-cluster mode, the Spark driver runs inside an application master process which is managed by YARN on the cluster, and the client can go away after initiating the application.

Estimating Pi (yarn-cluster mode):

# execute the the following command which should write the "Pi is roughly 3.1418" into the logs
# note you must specify --files argument in cluster mode to enable metrics
spark-submit \
--class org.apache.spark.examples.SparkPi \
--files $SPARK_HOME/conf/metrics.properties \
--master yarn-cluster \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 1 \
$SPARK_HOME/lib/spark-examples-1.5.1-hadoop2.6.0.jar

Estimating Pi (yarn-client mode):

# execute the the following command which should print the "Pi is roughly 3.1418" to the screen
spark-submit \
--class org.apache.spark.examples.SparkPi \
--master yarn-client \
--driver-memory 1g \
--executor-memory 1g \
--executor-cores 1 \
$SPARK_HOME/lib/spark-examples-1.5.1-hadoop2.6.0.jar