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Deploy the proxy controller with [`examples/staging/spark/spark-ui-proxy-controller.yaml`](spark-ui-proxy-controller.yaml):
```console
$ kubectl create -f examples/staging/spark/spark-ui-proxy-controller.yaml
replicationcontroller "spark-ui-proxy-controller" created
```
We'll also need a corresponding Loadbalanced service for our Spark Proxy [`examples/staging/spark/spark-ui-proxy-service.yaml`](spark-ui-proxy-service.yaml):
```console
$ kubectl create -f examples/staging/spark/spark-ui-proxy-service.yaml
service "spark-ui-proxy" created
```
After creating the service, you should eventually get a loadbalanced endpoint:
```console
$ kubectl get svc spark-ui-proxy -o wide
NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE SELECTOR
spark-ui-proxy 10.0.51.107 aad59283284d611e6839606c214502b5-833417581.us-east-1.elb.amazonaws.com 80/TCP 9m component=spark-ui-proxy
```
The Spark UI in the above example output will be available at http://aad59283284d611e6839606c214502b5-833417581.us-east-1.elb.amazonaws.com
If your Kubernetes cluster is not equipped with a Loadbalancer integration, you will need to use the [kubectl proxy](https://kubernetes.io/docs/tasks/access-application-cluster/access-cluster/#using-kubectl-proxy) to
connect to the Spark WebUI:
```console
kubectl proxy --port=8001
```
At which point the UI will be available at
[http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-master:8080/](http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-master:8080/).
## Step Three: Start your Spark workers
The Spark workers do the heavy lifting in a Spark cluster. They
provide execution resources and data cache capabilities for your
program.
The Spark workers need the Master service to be running.
Use the [`examples/staging/spark/spark-worker-controller.yaml`](spark-worker-controller.yaml) file to create a
[replication controller](https://kubernetes.io/docs/concepts/workloads/controllers/replicationcontroller/) that manages the worker pods.
```console
$ kubectl create -f examples/staging/spark/spark-worker-controller.yaml
replicationcontroller "spark-worker-controller" created
```
### Check to see if the workers are running
If you launched the Spark WebUI, your workers should just appear in the UI when
they're ready. (It may take a little bit to pull the images and launch the
pods.) You can also interrogate the status in the following way:
```console
$ kubectl get pods
NAME READY STATUS RESTARTS AGE
spark-master-controller-5u0q5 1/1 Running 0 25m
spark-worker-controller-e8otp 1/1 Running 0 6m
spark-worker-controller-fiivl 1/1 Running 0 6m
spark-worker-controller-ytc7o 1/1 Running 0 6m
$ kubectl logs spark-master-controller-5u0q5
[...]
15/10/26 18:20:14 INFO Master: Registering worker 10.244.1.13:53567 with 2 cores, 6.3 GB RAM
15/10/26 18:20:14 INFO Master: Registering worker 10.244.2.7:46195 with 2 cores, 6.3 GB RAM
15/10/26 18:20:14 INFO Master: Registering worker 10.244.3.8:39926 with 2 cores, 6.3 GB RAM
```
## Step Four: Start the Zeppelin UI to launch jobs on your Spark cluster
The Zeppelin UI pod can be used to launch jobs into the Spark cluster either via
a web notebook frontend or the traditional Spark command line. See
[Zeppelin](https://zeppelin.incubator.apache.org/) and
[Spark architecture](https://spark.apache.org/docs/latest/cluster-overview.html)
for more details.
Deploy Zeppelin:
```console
$ kubectl create -f examples/staging/spark/zeppelin-controller.yaml
replicationcontroller "zeppelin-controller" created
```
And the corresponding service:
```console
$ kubectl create -f examples/staging/spark/zeppelin-service.yaml
service "zeppelin" created
```
Zeppelin needs the spark-master service to be running.
### Check to see if Zeppelin is running
```console
$ kubectl get pods -l component=zeppelin