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add tl;dr version of Spark README.md
mention the spark cluster is standalone add detailed master & worker instructions add method to get master status add links option for master status add links option for worker status add example use of cluster add source location
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Matthew Farrellee
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examples/spark/README.md
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examples/spark/README.md
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# Spark example
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Following this example, you will create a functional [Apache
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Spark](http://spark.apache.org/) cluster using Kubernetes and
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[Docker](http://docker.io).
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You will setup a Spark master service and a set of
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Spark workers using Spark's [standalone mode](http://spark.apache.org/docs/latest/spark-standalone.html).
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For the impatient expert, jump straight to the [tl;dr](#tldr)
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section.
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### Sources
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Source is freely available at:
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* Docker image - https://github.com/mattf/docker-spark
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* Docker Trusted Build - https://registry.hub.docker.com/search?q=mattf/spark
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## Step Zero: Prerequisites
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This example assumes you have a Kubernetes cluster installed and
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running, and that you have installed the ```kubectl``` command line
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tool somewhere in your path. Please see the [getting
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started](../../docs/getting-started-guides) for installation
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instructions for your platform.
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## Step One: Start your Master service
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The Master service is the master (or head) service for a Spark
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cluster.
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Use the `examples/spark/spark-master.json` file to create a pod running
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the Master service.
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```shell
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$ kubectl create -f examples/spark/spark-master.json
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```
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Then, use the `examples/spark/spark-master-service.json` file to
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create a logical service endpoint that Spark workers can use to access
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the Master pod.
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```shell
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$ kubectl create -f examples/spark/spark-master-service.json
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```
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Ensure that the Master service is running and functional.
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### Check to see if Master is running and accessible
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```shell
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$ kubectl get pods,services
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POD IP CONTAINER(S) IMAGE(S) HOST LABELS STATUS
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spark-master 192.168.90.14 spark-master mattf/spark-master 172.18.145.8/172.18.145.8 name=spark-master Running
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NAME LABELS SELECTOR IP PORT
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kubernetes component=apiserver,provider=kubernetes <none> 10.254.0.2 443
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kubernetes-ro component=apiserver,provider=kubernetes <none> 10.254.0.1 80
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spark-master name=spark-master name=spark-master 10.254.125.166 7077
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```
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Connect to http://192.168.90.14:8080 to see the status of the master.
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```shell
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$ links -dump 192.168.90.14:8080
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[IMG] 1.2.1 Spark Master at spark://spark-master:7077
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* URL: spark://spark-master:7077
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* Workers: 0
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* Cores: 0 Total, 0 Used
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* Memory: 0.0 B Total, 0.0 B Used
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* Applications: 0 Running, 0 Completed
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* Drivers: 0 Running, 0 Completed
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* Status: ALIVE
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...
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```
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(Pull requests welcome for an alternative that uses the service IP and
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port)
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## Step Two: Start your Spark workers
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The Spark workers do the heavy lifting in a Spark cluster. They
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provide execution resources and data cache capabilities for your
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program.
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The Spark workers need the Master service to be running.
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Use the `examples/spark/spark-worker-controller.json` file to create a
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ReplicationController that manages the worker pods.
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```shell
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$ kubectl create -f examples/spark/spark-worker-controller.json
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```
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### Check to see if the workers are running
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```shell
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$ links -dump 192.168.90.14:8080
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[IMG] 1.2.1 Spark Master at spark://spark-master:7077
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* URL: spark://spark-master:7077
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* Workers: 3
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* Cores: 12 Total, 0 Used
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* Memory: 20.4 GB Total, 0.0 B Used
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* Applications: 0 Running, 0 Completed
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* Drivers: 0 Running, 0 Completed
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* Status: ALIVE
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Workers
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Id Address State Cores Memory
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4 (0 6.8 GB
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worker-20150318151745-192.168.75.14-46422 192.168.75.14:46422 ALIVE Used) (0.0 B
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Used)
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4 (0 6.8 GB
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worker-20150318151746-192.168.35.17-53654 192.168.35.17:53654 ALIVE Used) (0.0 B
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Used)
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4 (0 6.8 GB
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worker-20150318151746-192.168.90.17-50490 192.168.90.17:50490 ALIVE Used) (0.0 B
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Used)
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...
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```
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(Pull requests welcome for an alternative that uses the service IP and
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port)
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## Step Three: Do something with the cluster
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```shell
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$ kubectl get pods,services
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POD IP CONTAINER(S) IMAGE(S) HOST LABELS STATUS
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spark-master 192.168.90.14 spark-master mattf/spark-master 172.18.145.8/172.18.145.8 name=spark-master Running
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spark-worker-controller-51wgg 192.168.75.14 spark-worker mattf/spark-worker 172.18.145.9/172.18.145.9 name=spark-worker,uses=spark-master Running
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spark-worker-controller-5v48c 192.168.90.17 spark-worker mattf/spark-worker 172.18.145.8/172.18.145.8 name=spark-worker,uses=spark-master Running
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spark-worker-controller-ehq23 192.168.35.17 spark-worker mattf/spark-worker 172.18.145.12/172.18.145.12 name=spark-worker,uses=spark-master Running
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NAME LABELS SELECTOR IP PORT
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kubernetes component=apiserver,provider=kubernetes <none> 10.254.0.2 443
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kubernetes-ro component=apiserver,provider=kubernetes <none> 10.254.0.1 80
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spark-master name=spark-master name=spark-master 10.254.125.166 7077
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$ sudo docker run -it mattf/spark-base sh
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sh-4.2# echo "10.254.125.166 spark-master" >> /etc/hosts
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sh-4.2# export SPARK_LOCAL_HOSTNAME=$(hostname -i)
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sh-4.2# MASTER=spark://spark-master:7077 pyspark
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Python 2.7.5 (default, Jun 17 2014, 18:11:42)
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[GCC 4.8.2 20140120 (Red Hat 4.8.2-16)] on linux2
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Type "help", "copyright", "credits" or "license" for more information.
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Welcome to
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____ __
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/ __/__ ___ _____/ /__
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_\ \/ _ \/ _ `/ __/ '_/
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/__ / .__/\_,_/_/ /_/\_\ version 1.2.1
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/_/
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Using Python version 2.7.5 (default, Jun 17 2014 18:11:42)
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SparkContext available as sc.
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>>> import socket, resource
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>>> sc.parallelize(range(1000)).map(lambda x: (socket.gethostname(), resource.getrlimit(resource.RLIMIT_NOFILE))).distinct().collect()
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[('spark-worker-controller-ehq23', (1048576, 1048576)), ('spark-worker-controller-5v48c', (1048576, 1048576)), ('spark-worker-controller-51wgg', (1048576, 1048576))]
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```
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## tl;dr
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```kubectl create -f spark-master.json```
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```kubectl create -f spark-master-service.json```
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Make sure the Master Pod is running (use: ```kubectl get pods```).
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```kubectl create -f spark-worker-controller.json```
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9
examples/spark/spark-master-service.json
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9
examples/spark/spark-master-service.json
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{
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"id": "spark-master",
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"kind": "Service",
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"apiVersion": "v1beta1",
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"port": 7077,
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"containerPort": 7077,
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"selector": { "name": "spark-master" },
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"labels": { "name": "spark-master" }
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}
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20
examples/spark/spark-master.json
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20
examples/spark/spark-master.json
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{
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"id": "spark-master",
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"kind": "Pod",
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"apiVersion": "v1beta1",
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"desiredState": {
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"manifest": {
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"version": "v1beta1",
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"id": "spark-master",
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"containers": [{
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"name": "spark-master",
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"image": "mattf/spark-master",
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"cpu": 100,
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"ports": [{ "containerPort": 7077 }]
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}]
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}
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},
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"labels": {
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"name": "spark-master"
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}
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}
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28
examples/spark/spark-worker-controller.json
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28
examples/spark/spark-worker-controller.json
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{
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"id": "spark-worker-controller",
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"kind": "ReplicationController",
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"apiVersion": "v1beta1",
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"desiredState": {
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"replicas": 3,
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"replicaSelector": {"name": "spark-worker"},
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"podTemplate": {
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"desiredState": {
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"manifest": {
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"version": "v1beta1",
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"id": "spark-worker-controller",
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"containers": [{
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"name": "spark-worker",
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"image": "mattf/spark-worker",
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"cpu": 100,
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"ports": [{"containerPort": 8888, "hostPort": 8888}]
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}]
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}
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},
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"labels": {
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"name": "spark-worker",
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"uses": "spark-master"
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}
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}
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},
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"labels": {"name": "spark-worker"}
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}
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