HDDS-1090. Add a recipe to the ozone docs about using Spark, OzoneFS, and K8s.
Contributed by Elek, Marton.
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---
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title: Spark in Kubernetes with OzoneFS
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menu:
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main:
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parent: Recipes
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---
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<!---
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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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Using Ozone from Apache Spark
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===
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This recipe shows how Ozone object store can be used from Spark using:
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- OzoneFS (Hadoop compatible file system)
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- Hadoop 2.7 (included in the Spark distribution)
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- Kubernetes Spark scheduler
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- Local spark client
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## Requirements
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Download latest Spark and Ozone distribution and extract them. This method is
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tested with the `spark-2.4.0-bin-hadoop2.7` distribution.
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You also need the following:
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* A container repository to push and pull the spark+ozone images. (In this recipe we will use the dockerhub)
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* A repo/name for the custom containers (in this recipe _myrepo/ozone-spark_)
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* A dedicated namespace in kubernetes (we use _yournamespace_ in this recipe)
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## Create the docker image for drivers
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### Create the base Spark driver/executor image
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First of all create a docker image with the Spark image creator.
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Execute the following from the Spark distribution
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```
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./bin/docker-image-tool.sh -r myrepo -t 2.4.0 build
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```
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_Note_: if you use Minikube add the `-m` flag to use the docker daemon of the Minikube image:
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```
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./bin/docker-image-tool.sh -m -r myrepo -t 2.4.0 build
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```
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`./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.
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### Customize the docker image
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Create a new directory for customizing the created docker image.
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Copy the `ozone-site.xml` from the cluster:
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```
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kubectl cp om-0:/opt/hadoop/etc/hadoop/ozone-site.xml .
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```
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And create a custom `core-site.xml`:
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```
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<configuration>
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<property>
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<name>fs.o3fs.impl</name>
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<value>org.apache.hadoop.fs.ozone.OzoneFileSystem</value>
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</property>
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</configuration>
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```
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Copy the `ozonefs.jar` file from an ozone distribution (__use the legacy version!__)
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```
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kubectl cp om-0:/opt/hadoop/share/ozone/lib/hadoop-ozone-filesystem-lib-legacy-0.4.0-SNAPSHOT.jar .
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```
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Create a new Dockerfile and build the image:
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```
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FROM myrepo/spark:2.4.0
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ADD core-site.xml /opt/hadoop/conf/core-site.xml
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ADD ozone-site.xml /opt/hadoop/conf/ozone-site.xml
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ENV HADOOP_CONF_DIR=/opt/hadoop/conf
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ENV SPARK_EXTRA_CLASSPATH=/opt/hadoop/conf
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ADD hadoop-ozone-filesystem-lib-legacy-0.4.0-SNAPSHOT.jar /opt/hadoop-ozone-filesystem-lib-legacy.jar
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```
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```
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docker build -t myrepo/spark-ozone
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```
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For remote kubernetes cluster you may need to push it:
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```
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docker push myrepo/spark-ozone
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```
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## Create a bucket and identify the ozonefs path
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Download any text file and put it to the `/tmp/alice.txt` first.
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```
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kubectl port-forward s3g-0 9878:9878
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aws s3api --endpoint http://localhost:9878 create-bucket --bucket=test
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aws s3api --endpoint http://localhost:9878 put-object --bucket test --key alice.txt --body /tmp/alice.txt
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kubectl exec -it scm-0 ozone sh bucket path test
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```
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The output of the last command is something like this:
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```
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Volume name for S3Bucket is : s3asdlkjqiskjdsks
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Ozone FileSystem Uri is : o3fs://test.s3asdlkjqiskjdsks
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```
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Write down the ozone filesystem uri as it should be used with the spark-submit command.
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## Create service account to use
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```
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kubectl create serviceaccount spark -n yournamespace
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kubectl create clusterrolebinding spark-role --clusterrole=edit --serviceaccount=poc:yournamespace --namespace=yournamespace
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```
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## Execute the job
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Execute the following spar-submit command, but change at least the following values:
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* the kubernetes master url (you can check your ~/.kube/config to find the actual value)
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* the kubernetes namespace (yournamespace in this example)
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* serviceAccountName (you can use the _spark_ value if you folllowed the previous steps)
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* container.image (in this example this is myrepo/spark-ozone. This is pushed to the registry in the previous steps)
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* location of the input file (o3fs://...), use the string which is identified earlier with the `ozone sh bucket path` command
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```
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bin/spark-submit \
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--master k8s://https://kubernetes:6443 \
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--deploy-mode cluster \
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--name spark-word-count \
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--class org.apache.spark.examples.JavaWordCount \
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--conf spark.executor.instances=1 \
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--conf spark.kubernetes.namespace=yournamespace \
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--conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
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--conf spark.kubernetes.container.image=myrepo/spark-ozone \
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--conf spark.kubernetes.container.image.pullPolicy=Always \
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--jars /opt/hadoop-ozone-filesystem-lib-legacy.jar \
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local:///opt/spark/examples/jars/spark-examples_2.11-2.4.0.jar \
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o3fs://bucket.volume/alice.txt
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```
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Check the available `spark-word-count-...` pods with `kubectl get pod`
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Check the output of the calculation with `kubectl logs spark-word-count-1549973913699-driver`
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You should see the output of the wordcount job. For example:
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```
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...
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name: 8
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William: 3
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this,': 1
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SOUP!': 1
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`Silence: 1
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`Mine: 1
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ordered.: 1
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considering: 3
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muttering: 3
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candle: 2
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...
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```
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