mirror of https://github.com/k3s-io/k3s
307 lines
12 KiB
Markdown
307 lines
12 KiB
Markdown
<!-- BEGIN MUNGE: UNVERSIONED_WARNING -->
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<!-- BEGIN STRIP_FOR_RELEASE -->
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<img src="http://kubernetes.io/img/warning.png" alt="WARNING"
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width="25" height="25">
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<img src="http://kubernetes.io/img/warning.png" alt="WARNING"
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width="25" height="25">
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<img src="http://kubernetes.io/img/warning.png" alt="WARNING"
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width="25" height="25">
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<img src="http://kubernetes.io/img/warning.png" alt="WARNING"
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<img src="http://kubernetes.io/img/warning.png" alt="WARNING"
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width="25" height="25">
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<h2>PLEASE NOTE: This document applies to the HEAD of the source tree</h2>
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If you are using a released version of Kubernetes, you should
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refer to the docs that go with that version.
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<strong>
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The latest release of this document can be found
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[here](http://releases.k8s.io/release-1.1/examples/spark/README.md).
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Documentation for other releases can be found at
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[releases.k8s.io](http://releases.k8s.io).
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</strong>
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--
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<!-- END STRIP_FOR_RELEASE -->
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<!-- END MUNGE: UNVERSIONED_WARNING -->
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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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The Docker images are heavily based on https://github.com/mattf/docker-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](../../docs/user-guide/services.md) is the master service
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for a Spark cluster.
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Use the
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[`examples/spark/spark-master-controller.yaml`](spark-master-controller.yaml)
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file to create a
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[replication controller](../../docs/user-guide/replication-controller.md)
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running the Spark Master service.
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```console
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$ kubectl create -f examples/spark/spark-master-controller.yaml
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replicationcontrollers/spark-master-controller
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```
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Then, use the
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[`examples/spark/spark-master-service.yaml`](spark-master-service.yaml) file to
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create a logical service endpoint that Spark workers can use to access the
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Master pod.
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```console
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$ kubectl create -f examples/spark/spark-master-service.yaml
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services/spark-master
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```
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You can then create a service for the Spark Master WebUI:
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```console
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$ kubectl create -f examples/spark/spark-webui.yaml
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services/spark-webui
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```
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### Check to see if Master is running and accessible
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```console
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$ kubectl get pods
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NAME READY STATUS RESTARTS AGE
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spark-master-controller-5u0q5 1/1 Running 0 8m
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```
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Check logs to see the status of the master. (Use the pod retrieved from the previous output.)
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```sh
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$ kubectl logs spark-master-controller-5u0q5
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starting org.apache.spark.deploy.master.Master, logging to /opt/spark-1.5.1-bin-hadoop2.6/sbin/../logs/spark--org.apache.spark.deploy.master.Master-1-spark-master-controller-g0oao.out
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Spark Command: /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java -cp /opt/spark-1.5.1-bin-hadoop2.6/sbin/../conf/:/opt/spark-1.5.1-bin-hadoop2.6/lib/spark-assembly-1.5.1-hadoop2.6.0.jar:/opt/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar:/opt/spark-1.5.1-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar -Xms1g -Xmx1g org.apache.spark.deploy.master.Master --ip spark-master --port 7077 --webui-port 8080
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========================================
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15/10/27 21:25:05 INFO Master: Registered signal handlers for [TERM, HUP, INT]
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15/10/27 21:25:05 INFO SecurityManager: Changing view acls to: root
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15/10/27 21:25:05 INFO SecurityManager: Changing modify acls to: root
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15/10/27 21:25:05 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
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15/10/27 21:25:06 INFO Slf4jLogger: Slf4jLogger started
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15/10/27 21:25:06 INFO Remoting: Starting remoting
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15/10/27 21:25:06 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@spark-master:7077]
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15/10/27 21:25:06 INFO Utils: Successfully started service 'sparkMaster' on port 7077.
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15/10/27 21:25:07 INFO Master: Starting Spark master at spark://spark-master:7077
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15/10/27 21:25:07 INFO Master: Running Spark version 1.5.1
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15/10/27 21:25:07 INFO Utils: Successfully started service 'MasterUI' on port 8080.
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15/10/27 21:25:07 INFO MasterWebUI: Started MasterWebUI at http://spark-master:8080
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15/10/27 21:25:07 INFO Utils: Successfully started service on port 6066.
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15/10/27 21:25:07 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066
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15/10/27 21:25:07 INFO Master: I have been elected leader! New state: ALIVE
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```
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After you know the master is running, you can use the [cluster
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proxy](../../docs/user-guide/accessing-the-cluster.md#using-kubectl-proxy) to
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connect to the Spark WebUI:
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```console
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kubectl proxy --port=8001
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```
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At which point the UI will be available at
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[http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/](http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/).
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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.yaml`](spark-worker-controller.yaml) file to create a
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[replication controller](../../docs/user-guide/replication-controller.md) that manages the worker pods.
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```console
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$ kubectl create -f examples/spark/spark-worker-controller.yaml
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```
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### Check to see if the workers are running
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If you launched the Spark WebUI, your workers should just appear in the UI when
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they're ready. (It may take a little bit to pull the images and launch the
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pods.) You can also interrogate the status in the following way:
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```console
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$ kubectl get pods
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NAME READY STATUS RESTARTS AGE
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spark-master-controller-5u0q5 1/1 Running 0 25m
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spark-worker-controller-e8otp 1/1 Running 0 6m
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spark-worker-controller-fiivl 1/1 Running 0 6m
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spark-worker-controller-ytc7o 1/1 Running 0 6m
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$ kubectl logs spark-master-controller-5u0q5
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[...]
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15/10/26 18:20:14 INFO Master: Registering worker 10.244.1.13:53567 with 2 cores, 6.3 GB RAM
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15/10/26 18:20:14 INFO Master: Registering worker 10.244.2.7:46195 with 2 cores, 6.3 GB RAM
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15/10/26 18:20:14 INFO Master: Registering worker 10.244.3.8:39926 with 2 cores, 6.3 GB RAM
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```
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Assuming you still have the `kubectl proxy` running from the previous section,
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you should now see the workers in the UI as well. *Note:* The UI will have links
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to worker Web UIs. The worker UI links do not work (the links will attempt to
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connect to cluster IPs, which Kubernetes won't proxy automatically).
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## Step Three: Start the Zeppelin UI to launch jobs on your Spark cluster
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The Zeppelin UI pod can be used to launch jobs into the Spark cluster either via
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a web notebook frontend or the traditional Spark command line. See
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[Zeppelin](https://zeppelin.incubator.apache.org/) and
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[Spark architecture](https://spark.apache.org/docs/latest/cluster-overview.html)
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for more details.
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```console
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$ kubectl create -f examples/spark/zeppelin-controller.yaml
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replicationcontrollers/zeppelin-controller
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```
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Zeppelin needs the Master service to be running.
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### Check to see if Zeppelin is running
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```console
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$ kubectl get pods -lcomponent=zeppelin
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NAME READY STATUS RESTARTS AGE
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zeppelin-controller-ja09s 1/1 Running 0 53s
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```
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## Step Four: Do something with the cluster
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Now you have two choices, depending on your predilections. You can do something
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graphical with the Spark cluster, or you can stay in the CLI.
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### Do something fast with pyspark!
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Use the kubectl exec to connect to the Zeppelin driver and run a pipeline.
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```console
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$ kubectl exec zeppelin-controller-ja09s -it pyspark
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Python 2.7.9 (default, Mar 1 2015, 12:57:24)
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[GCC 4.9.2] 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 1.5.1
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/_/
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Using Python version 2.7.9 (default, Mar 1 2015 12:57:24)
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SparkContext available as sc, HiveContext available as sqlContext.
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>>> sc.textFile("gs://dataflow-samples/shakespeare/*").map(lambda s: len(s.split())).sum()
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939193
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```
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Congratulations, you just counted all of the words in all of the plays of
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Shakespeare.
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### Do something graphical and shiny!
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Take the Zeppelin pod from above and port-forward the WebUI port:
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```console
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$ kubectl port-forward zeppelin-controller-ja09s 8080:8080
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```
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This forwards `localhost` 8080 to container port 8080. You can then find
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Zeppelin at (https://localhost:8080/)[https://localhost:8080/].
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Create a "New Notebook". In there, type:
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```
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%pyspark
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print sc.textFile("gs://dataflow-samples/shakespeare/*").map(lambda s: len(s.split())).sum()
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```
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## Result
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You now have services and replication controllers for the Spark master, Spark
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workers and Spark driver. You can take this example to the next step and start
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using the Apache Spark cluster you just created, see
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[Spark documentation](https://spark.apache.org/documentation.html) for more
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information.
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## tl;dr
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```console
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kubectl create -f examples/spark
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```
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After it's setup:
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```console
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kubectl get pods # Make sure everything is running
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kubectl proxy --port=8001 # Start an application proxy, if you want to see the Spark Master WebUI
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kubectl get pods -lcomponent=zeppelin # Get the driver pod to interact with.
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```
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At which point the Master UI will be available at
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[http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/](http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/).
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You can either interact with the Spark cluster the traditional `spark-shell` /
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`spark-subsubmit` / `pyspark` commands by using `kubectl exec` against the
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`zeppelin-controller` pod, or if you want to interact with Zeppelin:
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```console
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kubectl port-forward zeppelin-controller-abc123 8080:8080 &
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```
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Then visit [http://localhost:8080/](http://localhost:8080/).
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## Known Issues With Spark
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* This provides a Spark configuration that is restricted to the cluster network,
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meaning the Spark master is only available as a cluster service. If you need
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to submit jobs using external client other than Zeppelin or `spark-submit` on
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the `zeppelin` pod, you will need to provide a way for your clients to get to
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the
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[`examples/spark/spark-master-service.yaml`](spark-master-service.yaml). See
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[Services](../../docs/user-guide/services.md) for more information.
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## Known Issues With Zeppelin
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* The Zeppelin pod is large, so it may take a while to pull depending on your
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network. The size of the Zeppelin pod is something we're working on, see issue #17231.
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* Zeppelin may take some time (about a minute) on this pipeline the first time
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you run it. It seems to take considerable time to load.
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* On GKE, `kubectl port-forward` may not be stable over long periods of time. If
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you see Zeppelin go into `Disconnected` state (there will be a red dot on the
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top right as well), the `port-forward` probably failed and needs to be
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restarted. See #12179.
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<!-- BEGIN MUNGE: GENERATED_ANALYTICS -->
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[![Analytics](https://kubernetes-site.appspot.com/UA-36037335-10/GitHub/examples/spark/README.md?pixel)]()
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