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Spark example

Following this example, you will create a functional Apache Spark cluster using Kubernetes and Docker.

You will setup a Spark master service and a set of Spark workers using Spark's standalone mode.

For the impatient expert, jump straight to the tl;dr section.

Sources

Source is freely available at:

Step Zero: Prerequisites

This example assumes you have a Kubernetes cluster installed and running, and that you have installed the kubectl command line tool somewhere in your path. Please see the getting started for installation instructions for your platform.

Step One: Start your Master service

The Master service is the master (or head) service for a Spark cluster.

Use the examples/spark/spark-master.json file to create a pod running the Master service.

$ kubectl create -f examples/spark/spark-master.json

Then, use the examples/spark/spark-master-service.json file to create a logical service endpoint that Spark workers can use to access the Master pod.

$ kubectl create -f examples/spark/spark-master-service.json

Ensure that the Master service is running and functional.

Check to see if Master is running and accessible

$ kubectl get pods,services
POD                             IP                  CONTAINER(S)        IMAGE(S)             HOST                          LABELS                                STATUS
spark-master                    192.168.90.14       spark-master        mattf/spark-master   172.18.145.8/172.18.145.8     name=spark-master                     Running
NAME                LABELS                                    SELECTOR            IP                  PORT
kubernetes          component=apiserver,provider=kubernetes   <none>              10.254.0.2          443
spark-master        name=spark-master                         name=spark-master   10.254.125.166      7077

Connect to http://192.168.90.14:8080 to see the status of the master.

$ links -dump 192.168.90.14:8080
  [IMG] 1.2.1 Spark Master at spark://spark-master:7077

     * URL: spark://spark-master:7077
     * Workers: 0
     * Cores: 0 Total, 0 Used
     * Memory: 0.0 B Total, 0.0 B Used
     * Applications: 0 Running, 0 Completed
     * Drivers: 0 Running, 0 Completed
     * Status: ALIVE
...

(Pull requests welcome for an alternative that uses the service IP and port)

Step Two: 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.json file to create a replication controller that manages the worker pods.

$ kubectl create -f examples/spark/spark-worker-controller.json

Check to see if the workers are running

$ links -dump 192.168.90.14:8080
  [IMG] 1.2.1 Spark Master at spark://spark-master:7077

     * URL: spark://spark-master:7077
     * Workers: 3
     * Cores: 12 Total, 0 Used
     * Memory: 20.4 GB Total, 0.0 B Used
     * Applications: 0 Running, 0 Completed
     * Drivers: 0 Running, 0 Completed
     * Status: ALIVE

    Workers

Id                                        Address             State Cores Memory
                                                                    4 (0  6.8 GB
worker-20150318151745-192.168.75.14-46422 192.168.75.14:46422 ALIVE Used) (0.0 B
                                                                          Used)
                                                                    4 (0  6.8 GB
worker-20150318151746-192.168.35.17-53654 192.168.35.17:53654 ALIVE Used) (0.0 B
                                                                          Used)
                                                                    4 (0  6.8 GB
worker-20150318151746-192.168.90.17-50490 192.168.90.17:50490 ALIVE Used) (0.0 B
                                                                          Used)
...

(Pull requests welcome for an alternative that uses the service IP and port)

Step Three: Do something with the cluster

$ kubectl get pods,services
POD                             IP                  CONTAINER(S)        IMAGE(S)             HOST                          LABELS                                STATUS
spark-master                    192.168.90.14       spark-master        mattf/spark-master   172.18.145.8/172.18.145.8     name=spark-master                     Running
spark-worker-controller-51wgg   192.168.75.14       spark-worker        mattf/spark-worker   172.18.145.9/172.18.145.9     name=spark-worker,uses=spark-master   Running
spark-worker-controller-5v48c   192.168.90.17       spark-worker        mattf/spark-worker   172.18.145.8/172.18.145.8     name=spark-worker,uses=spark-master   Running
spark-worker-controller-ehq23   192.168.35.17       spark-worker        mattf/spark-worker   172.18.145.12/172.18.145.12   name=spark-worker,uses=spark-master   Running
NAME                LABELS                                    SELECTOR            IP                  PORT
kubernetes          component=apiserver,provider=kubernetes   <none>              10.254.0.2          443
spark-master        name=spark-master                         name=spark-master   10.254.125.166      7077

$ sudo docker run -it mattf/spark-base sh

sh-4.2# echo "10.254.125.166 spark-master" >> /etc/hosts

sh-4.2# export SPARK_LOCAL_HOSTNAME=$(hostname -i)

sh-4.2# MASTER=spark://spark-master:7077 pyspark
Python 2.7.5 (default, Jun 17 2014, 18:11:42)
[GCC 4.8.2 20140120 (Red Hat 4.8.2-16)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 1.2.1
      /_/

Using Python version 2.7.5 (default, Jun 17 2014 18:11:42)
SparkContext available as sc.
>>> import socket, resource
>>> sc.parallelize(range(1000)).map(lambda x: (socket.gethostname(), resource.getrlimit(resource.RLIMIT_NOFILE))).distinct().collect()
[('spark-worker-controller-ehq23', (1048576, 1048576)), ('spark-worker-controller-5v48c', (1048576, 1048576)), ('spark-worker-controller-51wgg', (1048576, 1048576))]

tl;dr

kubectl create -f spark-master.json

kubectl create -f spark-master-service.json

Make sure the Master Pod is running (use: kubectl get pods).

kubectl create -f spark-worker-controller.json

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