mirror of https://github.com/k3s-io/k3s
216 lines
8.5 KiB
Markdown
216 lines
8.5 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 (or head) service for a Spark
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cluster.
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Use the [`examples/spark/spark-master.json`](spark-master.json) file to create a [pod](../../docs/user-guide/pods.md) running
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the Master service.
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```sh
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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`](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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```sh
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$ kubectl create -f examples/spark/spark-master-service.json
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```
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### Check to see if Master is running and accessible
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```sh
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$ kubectl get pods
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NAME READY STATUS RESTARTS AGE
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[...]
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spark-master 1/1 Running 0 25s
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```
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Check logs to see the status of the master.
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```sh
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$ kubectl logs spark-master
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starting org.apache.spark.deploy.master.Master, logging to /opt/spark-1.4.0-bin-hadoop2.6/sbin/../logs/spark--org.apache.spark.deploy.master.Master-1-spark-master.out
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Spark Command: /usr/lib/jvm/java-7-openjdk-amd64/jre/bin/java -cp /opt/spark-1.4.0-bin-hadoop2.6/sbin/../conf/:/opt/spark-1.4.0-bin-hadoop2.6/lib/spark-assembly-1.4.0-hadoop2.6.0.jar:/opt/spark-1.4.0-bin-hadoop2.6/lib/datanucleus-api-jdo-3.2.6.jar:/opt/spark-1.4.0-bin-hadoop2.6/lib/datanucleus-rdbms-3.2.9.jar:/opt/spark-1.4.0-bin-hadoop2.6/lib/datanucleus-core-3.2.10.jar -Xms512m -Xmx512m -XX:MaxPermSize=128m org.apache.spark.deploy.master.Master --ip spark-master --port 7077 --webui-port 8080
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========================================
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15/06/26 14:01:49 INFO Master: Registered signal handlers for [TERM, HUP, INT]
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15/06/26 14:01:50 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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15/06/26 14:01:51 INFO SecurityManager: Changing view acls to: root
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15/06/26 14:01:51 INFO SecurityManager: Changing modify acls to: root
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15/06/26 14:01:51 INFO SecurityManager: SecurityManager: authentication disabled; ui acls disabled; users with view permissions: Set(root); users with modify permissions: Set(root)
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15/06/26 14:01:51 INFO Slf4jLogger: Slf4jLogger started
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15/06/26 14:01:51 INFO Remoting: Starting remoting
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15/06/26 14:01:52 INFO Remoting: Remoting started; listening on addresses :[akka.tcp://sparkMaster@spark-master:7077]
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15/06/26 14:01:52 INFO Utils: Successfully started service 'sparkMaster' on port 7077.
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15/06/26 14:01:52 INFO Utils: Successfully started service on port 6066.
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15/06/26 14:01:52 INFO StandaloneRestServer: Started REST server for submitting applications on port 6066
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15/06/26 14:01:52 INFO Master: Starting Spark master at spark://spark-master:7077
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15/06/26 14:01:52 INFO Master: Running Spark version 1.4.0
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15/06/26 14:01:52 INFO Utils: Successfully started service 'MasterUI' on port 8080.
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15/06/26 14:01:52 INFO MasterWebUI: Started MasterWebUI at http://10.244.2.34:8080
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15/06/26 14:01:53 INFO Master: I have been elected leader! New state: ALIVE
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```
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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`](spark-worker-controller.json) 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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```sh
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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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```sh
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$ kubectl get pods
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NAME READY STATUS RESTARTS AGE
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[...]
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spark-master 1/1 Running 0 14m
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spark-worker-controller-hifwi 1/1 Running 0 33s
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spark-worker-controller-u40r2 1/1 Running 0 33s
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spark-worker-controller-vpgyg 1/1 Running 0 33s
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$ kubectl logs spark-master
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[...]
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15/06/26 14:15:43 INFO Master: Registering worker 10.244.2.35:46199 with 1 cores, 2.6 GB RAM
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15/06/26 14:15:55 INFO Master: Registering worker 10.244.1.15:44839 with 1 cores, 2.6 GB RAM
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15/06/26 14:15:55 INFO Master: Registering worker 10.244.0.19:60970 with 1 cores, 2.6 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](http://spark.apache.org/docs/latest/cluster-overview.html).
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```shell
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$ kubectl create -f examples/spark/spark-driver.json
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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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```shell
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$ kubectl get pods
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NAME READY REASON RESTARTS AGE
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[...]
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spark-master 1/1 Running 0 14m
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spark-driver 1/1 Running 0 10m
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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
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```
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$ kubectl exec spark-driver -it bash
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root@spark-driver:/#
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root@spark-driver:/# 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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15/06/26 14:25:28 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
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Welcome to
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____ __
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/ __/__ ___ _____/ /__
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_\ \/ _ \/ _ `/ __/ '_/
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/__ / .__/\_,_/_/ /_/\_\ version 1.4.0
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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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>>> import socket
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>>> sc.parallelize(range(1000)).map(lambda x:socket.gethostname()).distinct().collect()
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['spark-worker-controller-u40r2', 'spark-worker-controller-hifwi', 'spark-worker-controller-vpgyg']
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```
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## Result
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You now have services, replication controllers, and pods for the Spark master , Spark driver and Spark workers.
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You can take this example to the next step and start using the Apache Spark cluster
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you just created, see [Spark documentation](https://spark.apache.org/documentation.html)
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for more information.
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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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```kubectl create -f spark-driver.json```
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<!-- BEGIN MUNGE: GENERATED_ANALYTICS -->
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[]()
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<!-- END MUNGE: GENERATED_ANALYTICS -->
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