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777d3a6ec0
* Pod -> ReplicationController, which also forced me to hack around hostname issue on the master. (Spark master sees the incoming slave request to spark-master and assumes it's not meant for it, since it's name is spark-master-controller-abcdef.) * Remove service env dependencies (depend on DNS instead). * JSON -> YAML. * Add GCS connector. * Make example do something actually useful: A familiar example to anyone at Google, implement wordcount of all of Shakespeare's works. * Fix a minor service connection issue in the gluster example.
262 lines
10 KiB
Markdown
262 lines
10 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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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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<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 1.0.x release of this document can be found
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[here](http://releases.k8s.io/release-1.0/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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Optionally, you can create a service for the Spark Master WebUI at this point as
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well. If you are running on a cloud provider that supports it, this will create
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an external load balancer and open a firewall to the Spark Master WebUI on the
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cluster. **Note:** With the existing configuration, there is **ABSOLUTELY NO**
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authentication on this WebUI. With slightly more work, it would be
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straightforward to put an `nginx` proxy in front to password protect it.
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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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If you created the Spark WebUI and waited sufficient time for the load balancer
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to be create, the `spark-webui` service should look something like this:
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```console
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$ kubectl describe services/spark-webui
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Name: spark-webui
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Namespace: default
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Labels: <none>
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Selector: component=spark-master
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Type: LoadBalancer
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IP: 10.0.152.249
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LoadBalancer Ingress: 104.197.147.190
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Port: <unnamed> 8080/TCP
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NodePort: <unnamed> 31141/TCP
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Endpoints: 10.244.1.12:8080
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Session Affinity: None
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Events: [...]
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```
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You should now be able to visit `http://104.197.147.190:8080` and see the Spark
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Master UI. *Note:* After workers connect, this UI has links to worker Web
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UIs. The worker UI links do not work (the links attempt to connect to cluster
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IPs).
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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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## Step Three: Start your Spark driver to launch jobs on your Spark cluster
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The Spark driver is used to launch jobs into Spark cluster. You can read more about it in
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[Spark architecture](https://spark.apache.org/docs/latest/cluster-overview.html).
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```console
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$ kubectl create -f examples/spark/spark-driver-controller.yaml
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replicationcontrollers/spark-driver-controller
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```
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The Spark driver needs the Master service to be running.
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### Check to see if the driver is running
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```console
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$ kubectl get pods -lcomponent=spark-driver
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NAME READY STATUS RESTARTS AGE
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spark-driver-controller-vwb9c 1/1 Running 0 1m
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```
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## Step Four: Do something with the cluster
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Use the kubectl exec to connect to Spark driver and run a pipeline.
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```console
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$ kubectl exec spark-driver-controller-vwb9c -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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## 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/spark-master-controller.yaml
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kubectl create -f examples/spark/spark-master-service.yaml
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kubectl create -f examples/spark/spark-webui.yaml
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kubectl create -f examples/spark/spark-worker-controller.yaml
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kubectl create -f examples/spark/spark-driver-controller.yaml
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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 get services spark-webui # Get the IP of the Spark WebUI
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kubectl get pods -lcomponent=spark-driver # Get the driver pod to interact with.
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```
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<!-- BEGIN MUNGE: GENERATED_ANALYTICS -->
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[]()
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<!-- END MUNGE: GENERATED_ANALYTICS -->
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