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PLEASE NOTE: This document applies to the HEAD of the source tree

If you are using a released version of Kubernetes, you should refer to the docs that go with that version. The latest release of this document can be found [here](http://releases.k8s.io/release-1.2/examples/spark/README.md). Documentation for other releases can be found at [releases.k8s.io](http://releases.k8s.io). -- # Spark example Following this example, you will create a functional [Apache Spark](http://spark.apache.org/) cluster using Kubernetes and [Docker](http://docker.io). You will setup a Spark master service and a set of Spark workers using Spark's [standalone mode](http://spark.apache.org/docs/latest/spark-standalone.html). For the impatient expert, jump straight to the [tl;dr](#tldr) section. ### Sources The Docker images are heavily based on https://github.com/mattf/docker-spark. And are curated in https://github.com/kubernetes/application-images/tree/master/spark ## Step Zero: Prerequisites This example assumes - You have a Kubernetes cluster installed and running. - That you have installed the ```kubectl``` command line tool somewhere in your path. - That a spark-master service which spins up will be automatically discoverable by your kube DNS impl, as 'spark-master' For details, you can look at the Dockerfiles in the Sources section. ## Step One: Create namespace ```sh $ kubectl create -f examples/spark/namespace-spark-cluster.yaml ``` Now list all namespaces: ```sh $ kubectl get namespaces NAME LABELS STATUS default Active spark-cluster name=spark-cluster Active ``` For kubectl client to work with namespace, we define one context and use it: ```sh $ kubectl config set-context spark --namespace=spark-cluster --cluster=${CLUSTER_NAME} --user=${USER_NAME} $ kubectl config use-context spark ``` You can view your cluster name and user name in kubernetes config at ~/.kube/config. ## Step Two: Start your Master service The Master [service](../../docs/user-guide/services.md) is the master service for a Spark cluster. Use the [`examples/spark/spark-master-controller.yaml`](spark-master-controller.yaml) file to create a [replication controller](../../docs/user-guide/replication-controller.md) running the Spark Master service. ```console $ kubectl create -f examples/spark/spark-master-controller.yaml replicationcontroller "spark-master-controller" created ``` Then, use the [`examples/spark/spark-master-service.yaml`](spark-master-service.yaml) file to create a logical service endpoint that Spark workers can use to access the Master pod. ```console $ kubectl create -f examples/spark/spark-master-service.yaml service "spark-master" created ``` You can then create a service for the Spark Master WebUI: ```console $ kubectl create -f examples/spark/spark-webui.yaml service "spark-webui" created ``` ### Check to see if Master is running and accessible ```console $ kubectl get pods NAME READY STATUS RESTARTS AGE spark-master-controller-5u0q5 1/1 Running 0 8m ``` Check logs to see the status of the master. (Use the pod retrieved from the previous output.) ```sh $ kubectl logs spark-master-controller-5u0q5 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 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 ======================================== 15/10/27 21:25:05 INFO Master: Registered signal handlers for [TERM, HUP, INT] 15/10/27 21:25:05 INFO SecurityManager: Changing view acls to: root 15/10/27 21:25:05 INFO SecurityManager: Changing modify acls to: root 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) 15/10/27 21:25:06 INFO Slf4jLogger: Slf4jLogger started 15/10/27 21:25:06 INFO Remoting: Starting remoting 15/10/27 21:25:06 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@spark-master:7077] 15/10/27 21:25:06 INFO Utils: Successfully started service 'sparkMaster' on port 7077. 15/10/27 21:25:07 INFO Master: Starting Spark master at spark://spark-master:7077 15/10/27 21:25:07 INFO Master: Running Spark version 1.5.1 15/10/27 21:25:07 INFO Utils: Successfully started service 'MasterUI' on port 8080. 15/10/27 21:25:07 INFO MasterWebUI: Started MasterWebUI at http://spark-master:8080 15/10/27 21:25:07 INFO Utils: Successfully started service on port 6066. 15/10/27 21:25:07 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066 15/10/27 21:25:07 INFO Master: I have been elected leader! New state: ALIVE ``` After you know the master is running, you can use the [cluster proxy](../../docs/user-guide/accessing-the-cluster.md#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-webui/](http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/). ## 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/spark/spark-worker-controller.yaml`](spark-worker-controller.yaml) file to create a [replication controller](../../docs/user-guide/replication-controller.md) that manages the worker pods. ```console $ kubectl create -f examples/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 ``` Assuming you still have the `kubectl proxy` running from the previous section, you should now see the workers in the UI as well. *Note:* The UI will have links to worker Web UIs. The worker UI links do not work (the links will attempt to connect to cluster IPs, which Kubernetes won't proxy automatically). ## 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. ```console $ kubectl create -f examples/spark/zeppelin-controller.yaml replicationcontroller "zeppelin-controller" created ``` Zeppelin needs the Master service to be running. ### Check to see if Zeppelin is running ```console $ kubectl get pods -l component=zeppelin NAME READY STATUS RESTARTS AGE zeppelin-controller-ja09s 1/1 Running 0 53s ``` ## Step Five: Do something with the cluster Now you have two choices, depending on your predilections. You can do something graphical with the Spark cluster, or you can stay in the CLI. ### Do something fast with pyspark! Use the kubectl exec to connect to the Zeppelin driver and run a pipeline. ```console $ kubectl exec zeppelin-controller-ja09s -it pyspark Python 2.7.9 (default, Mar 1 2015, 12:57:24) [GCC 4.9.2] on linux2 Type "help", "copyright", "credits" or "license" for more information. Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /__ / .__/\_,_/_/ /_/\_\ version 1.5.1 /_/ Using Python version 2.7.9 (default, Mar 1 2015 12:57:24) SparkContext available as sc, HiveContext available as sqlContext. >>> sc.textFile("gs://dataflow-samples/shakespeare/*").map(lambda s: len(s.split())).sum() 939193 ``` Congratulations, you just counted all of the words in all of the plays of Shakespeare. ### Do something graphical and shiny! Take the Zeppelin pod from above and port-forward the WebUI port: ```console $ kubectl port-forward zeppelin-controller-ja09s 8080:8080 ``` This forwards `localhost` 8080 to container port 8080. You can then find Zeppelin at [https://localhost:8080/](https://localhost:8080/). Create a "New Notebook". In there, type: ``` %pyspark print sc.textFile("gs://dataflow-samples/shakespeare/*").map(lambda s: len(s.split())).sum() ``` ## Result You now have services and replication controllers for the Spark master, Spark workers and Spark driver. You can take this example to the next step and start using the Apache Spark cluster you just created, see [Spark documentation](https://spark.apache.org/documentation.html) for more information. ## tl;dr ```console kubectl create -f examples/spark ``` After it's setup: ```console kubectl get pods # Make sure everything is running kubectl proxy --port=8001 # Start an application proxy, if you want to see the Spark Master WebUI kubectl get pods -lcomponent=zeppelin # Get the driver pod to interact with. ``` At which point the Master UI will be available at [http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-webui/](http://localhost:8001/api/v1/proxy/namespaces/default/services/spark-webui/). You can either interact with the Spark cluster the traditional `spark-shell` / `spark-subsubmit` / `pyspark` commands by using `kubectl exec` against the `zeppelin-controller` pod, or if you want to interact with Zeppelin: ```console kubectl port-forward zeppelin-controller-abc123 8080:8080 & ``` Then visit [http://localhost:8080/](http://localhost:8080/). ## Known Issues With Spark * This provides a Spark configuration that is restricted to the cluster network, meaning the Spark master is only available as a cluster service. If you need to submit jobs using external client other than Zeppelin or `spark-submit` on the `zeppelin` pod, you will need to provide a way for your clients to get to the [`examples/spark/spark-master-service.yaml`](spark-master-service.yaml). See [Services](../../docs/user-guide/services.md) for more information. ## Known Issues With Zeppelin * The Zeppelin pod is large, so it may take a while to pull depending on your network. The size of the Zeppelin pod is something we're working on, see issue #17231. * Zeppelin may take some time (about a minute) on this pipeline the first time you run it. It seems to take considerable time to load. * On GKE, `kubectl port-forward` may not be stable over long periods of time. If you see Zeppelin go into `Disconnected` state (there will be a red dot on the top right as well), the `port-forward` probably failed and needs to be restarted. See #12179. [![Analytics](https://kubernetes-site.appspot.com/UA-36037335-10/GitHub/examples/spark/README.md?pixel)]()