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
docker pull gschmutz/spark:1.3.1
Building the image
docker build --rm -t gschmutz/spark:1.3.1 .
Running the image
docker run -i -t -h sandbox gschmutz/spark:1.3.1 bash
docker run -d -h sandbox gschmutz/spark:1.3.1 -d
Hadoop 2.6.0 and Apache Spark v1.3.1
There are two deploy modes that can be used to launch Spark applications on YARN.
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()
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 spark-submit --class org.apache.spark.examples.SparkPi --master yarn-cluster --driver-memory 1g --executor-memory 1g --executor-cores 1 $SPARK_HOME/lib/spark-examples-1.3.0-hadoop2.4.0.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-1.3.0-hadoop2.4.0.jar