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Spark in Kubernetes with OzoneFS

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Recipes

Using Ozone from Apache Spark

This recipe shows how Ozone object store can be used from Spark using:

OzoneFS (Hadoop compatible file system)
Hadoop 2.7 (included in the Spark distribution)
Kubernetes Spark scheduler
Local spark client

Requirements

Download latest Spark and Ozone distribution and extract them. This method is tested with the spark-2.4.0-bin-hadoop2.7 distribution.

You also need the following:

A container repository to push and pull the spark+ozone images. (In this recipe we will use the dockerhub)
A repo/name for the custom containers (in this recipe myrepo/ozone-spark)
A dedicated namespace in kubernetes (we use yournamespace in this recipe)

Create the docker image for drivers

Create the base Spark driver/executor image

First of all create a docker image with the Spark image creator. Execute the following from the Spark distribution

./bin/docker-image-tool.sh -r myrepo -t 2.4.0 build

Note: if you use Minikube add the -m flag to use the docker daemon of the Minikube image:

./bin/docker-image-tool.sh -m -r myrepo -t 2.4.0 build

./bin/docker-image-tool.sh is an official Spark tool to create container images and this step will create multiple Spark container images with the name myrepo/spark. The first container will be used as a base container in the following steps.

Customize the docker image

Create a new directory for customizing the created docker image.

Copy the ozone-site.xml from the cluster:

kubectl cp om-0:/opt/hadoop/etc/hadoop/ozone-site.xml .

And create a custom core-site.xml:

<configuration>
    <property>
        <name>fs.o3fs.impl</name>
        <value>org.apache.hadoop.fs.ozone.BasicOzoneFileSystem</value>
    </property>
</configuration>

Note: You may also use org.apache.hadoop.fs.ozone.OzoneFileSystem without the Basic prefix. The Basic version doesn't support FS statistics and encryption zones but can work together with older hadoop versions.

Copy the ozonefs.jar file from an ozone distribution (use the legacy version!)

kubectl cp om-0:/opt/hadoop/share/ozone/lib/hadoop-ozone-filesystem-lib-legacy-0.4.0-SNAPSHOT.jar .

Create a new Dockerfile and build the image:

FROM myrepo/spark:2.4.0
ADD core-site.xml /opt/hadoop/conf/core-site.xml
ADD ozone-site.xml /opt/hadoop/conf/ozone-site.xml
ENV HADOOP_CONF_DIR=/opt/hadoop/conf
ENV SPARK_EXTRA_CLASSPATH=/opt/hadoop/conf
ADD hadoop-ozone-filesystem-lib-legacy-0.4.0-SNAPSHOT.jar /opt/hadoop-ozone-filesystem-lib-legacy.jar

docker build -t myrepo/spark-ozone

For remote kubernetes cluster you may need to push it:

docker push myrepo/spark-ozone

Create a bucket and identify the ozonefs path

Download any text file and put it to the /tmp/alice.txt first.

kubectl port-forward s3g-0 9878:9878
aws s3api --endpoint http://localhost:9878 create-bucket --bucket=test
aws s3api --endpoint http://localhost:9878 put-object --bucket test --key alice.txt --body /tmp/alice.txt
kubectl exec -it scm-0 ozone s3 path test

The output of the last command is something like this:

Volume name for S3Bucket is : s3asdlkjqiskjdsks
Ozone FileSystem Uri is : o3fs://test.s3asdlkjqiskjdsks

Write down the ozone filesystem uri as it should be used with the spark-submit command.

Create service account to use

kubectl create serviceaccount spark -n yournamespace
kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=yournamespace:spark --namespace=yournamespace

Execute the job

Execute the following spark-submit command, but change at least the following values:

the kubernetes master url (you can check your ~/.kube/config to find the actual value)
the kubernetes namespace (yournamespace in this example)
serviceAccountName (you can use the spark value if you folllowed the previous steps)
container.image (in this example this is myrepo/spark-ozone. This is pushed to the registry in the previous steps)
location of the input file (o3fs://...), use the string which is identified earlier with the ozone s3 path <bucketname> command

bin/spark-submit \
    --master k8s://https://kubernetes:6443 \
    --deploy-mode cluster \
    --name spark-word-count \
    --class org.apache.spark.examples.JavaWordCount \
    --conf spark.executor.instances=1 \
    --conf spark.kubernetes.namespace=yournamespace \
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
    --conf spark.kubernetes.container.image=myrepo/spark-ozone \
    --conf spark.kubernetes.container.image.pullPolicy=Always \
    --jars /opt/hadoop-ozone-filesystem-lib-legacy.jar \
    local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0.jar \
    o3fs://bucket.volume/alice.txt

Check the available spark-word-count-... pods with kubectl get pod

Check the output of the calculation with kubectl logs spark-word-count-1549973913699-driver

You should see the output of the wordcount job. For example:

...
name: 8
William: 3
this,': 1
SOUP!': 1
`Silence: 1
`Mine: 1
ordered.: 1
considering: 3
muttering: 3
candle: 2
...

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