fc5c83b2ca
Contributed by Elek, Marton.
185 lines
5.7 KiB
Markdown
185 lines
5.7 KiB
Markdown
---
|
|
title: Spark in Kubernetes with OzoneFS
|
|
menu:
|
|
main:
|
|
parent: Recipes
|
|
---
|
|
<!---
|
|
Licensed to the Apache Software Foundation (ASF) under one or more
|
|
contributor license agreements. See the NOTICE file distributed with
|
|
this work for additional information regarding copyright ownership.
|
|
The ASF licenses this file to You under the Apache License, Version 2.0
|
|
(the "License"); you may not use this file except in compliance with
|
|
the License. You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
-->
|
|
|
|
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.OzoneFileSystem</value>
|
|
</property>
|
|
</configuration>
|
|
```
|
|
|
|
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 sh bucket 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=poc:yournamespace --namespace=yournamespace
|
|
```
|
|
## Execute the job
|
|
|
|
Execute the following spar-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 sh bucket path` 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
|
|
...
|
|
```
|