374 lines
15 KiB
Markdown
374 lines
15 KiB
Markdown
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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 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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And are curated in https://github.com/kubernetes/application-images/tree/master/spark
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The Spark UI Proxy is taken from https://github.com/aseigneurin/spark-ui-proxy.
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The PySpark examples are taken from http://stackoverflow.com/questions/4114167/checking-if-a-number-is-a-prime-number-in-python/27946768#27946768
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## Step Zero: Prerequisites
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This example assumes
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- You have a Kubernetes cluster installed and running.
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- That you have installed the ```kubectl``` command line tool installed in your path and configured to talk to your Kubernetes cluster
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- That your Kubernetes cluster is running [kube-dns](https://github.com/kubernetes/dns) or an equivalent integration.
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Optionally, your Kubernetes cluster should be configured with a Loadbalancer integration (automatically configured via kube-up or GKE)
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## Step One: Create namespace
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```sh
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$ kubectl create -f examples/spark/namespace-spark-cluster.yaml
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```
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Now list all namespaces:
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```sh
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$ kubectl get namespaces
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NAME LABELS STATUS
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default <none> Active
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spark-cluster name=spark-cluster Active
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```
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To configure kubectl to work with our namespace, we will create a new context using our current context as a base:
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```sh
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$ CURRENT_CONTEXT=$(kubectl config view -o jsonpath='{.current-context}')
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$ USER_NAME=$(kubectl config view -o jsonpath='{.contexts[?(@.name == "'"${CURRENT_CONTEXT}"'")].context.user}')
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$ CLUSTER_NAME=$(kubectl config view -o jsonpath='{.contexts[?(@.name == "'"${CURRENT_CONTEXT}"'")].context.cluster}')
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$ kubectl config set-context spark --namespace=spark-cluster --cluster=${CLUSTER_NAME} --user=${USER_NAME}
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$ kubectl config use-context spark
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```
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## Step Two: 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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replicationcontroller "spark-master-controller" created
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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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service "spark-master" created
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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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Once the master is started, we'll want to check the Spark WebUI. In order to access the Spark WebUI, we will deploy a [specialized proxy](https://github.com/aseigneurin/spark-ui-proxy). This proxy is neccessary to access worker logs from the Spark UI.
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Deploy the proxy controller with [`examples/spark/spark-ui-proxy-controller.yaml`](spark-ui-proxy-controller.yaml):
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```console
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$ kubectl create -f examples/spark/spark-ui-proxy-controller.yaml
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replicationcontroller "spark-ui-proxy-controller" created
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```
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We'll also need a corresponding Loadbalanced service for our Spark Proxy [`examples/spark/spark-ui-proxy-service.yaml`](spark-ui-proxy-service.yaml):
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```console
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$ kubectl create -f examples/spark/spark-ui-proxy-service.yaml
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service "spark-ui-proxy" created
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```
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After creating the service, you should eventually get a loadbalanced endpoint:
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```console
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$ kubectl get svc spark-ui-proxy -o wide
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NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE SELECTOR
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spark-ui-proxy 10.0.51.107 aad59283284d611e6839606c214502b5-833417581.us-east-1.elb.amazonaws.com 80/TCP 9m component=spark-ui-proxy
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```
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The Spark UI in the above example output will be available at http://aad59283284d611e6839606c214502b5-833417581.us-east-1.elb.amazonaws.com
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If your Kubernetes cluster is not equipped with a Loadbalancer integration, you will need to use the [kubectl 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/spark-cluster/services/spark-master:8080/](http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-master:8080/).
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## Step Three: 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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replicationcontroller "spark-worker-controller" created
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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 Four: 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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Deploy Zeppelin:
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```console
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$ kubectl create -f examples/spark/zeppelin-controller.yaml
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replicationcontroller "zeppelin-controller" created
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```
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And the corresponding service:
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```console
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$ kubectl create -f examples/spark/zeppelin-service.yaml
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service "zeppelin" created
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```
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Zeppelin needs the spark-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 -l component=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 Five: 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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For both choices, we will be working with this Python snippet:
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```python
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from math import sqrt; from itertools import count, islice
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def isprime(n):
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return n > 1 and all(n%i for i in islice(count(2), int(sqrt(n)-1)))
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nums = sc.parallelize(xrange(10000000))
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print nums.filter(isprime).count()
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```
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### Do something fast with pyspark!
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Simply copy and paste the python snippet into pyspark from within the zeppelin pod:
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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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>>> from math import sqrt; from itertools import count, islice
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>>>
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>>> def isprime(n):
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... return n > 1 and all(n%i for i in islice(count(2), int(sqrt(n)-1)))
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...
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>>> nums = sc.parallelize(xrange(10000000))
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>>> print nums.filter(isprime).count()
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664579
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```
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Congratulations, you now know how many prime numbers there are within the first 10 million numbers!
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### Do something graphical and shiny!
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Creating the Zeppelin service should have yielded you a Loadbalancer endpoint:
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```console
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$ kubectl get svc zeppelin -o wide
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NAME CLUSTER-IP EXTERNAL-IP PORT(S) AGE SELECTOR
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zeppelin 10.0.154.1 a596f143884da11e6839506c114532b5-121893930.us-east-1.elb.amazonaws.com 80/TCP 3m component=zeppelin
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```
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If your Kubernetes cluster does not have a Loadbalancer integration, then we will have to use port forwarding.
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Take the Zeppelin pod from before 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 [http://localhost:8080/](http://localhost:8080/).
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Once you've loaded up the Zeppelin UI, create a "New Notebook". In there we will paste our python snippet, but we need to add a `%pyspark` hint for Zeppelin to understand it:
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```
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%pyspark
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from math import sqrt; from itertools import count, islice
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def isprime(n):
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return n > 1 and all(n%i for i in islice(count(2), int(sqrt(n)-1)))
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nums = sc.parallelize(xrange(10000000))
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print nums.filter(isprime).count()
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```
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After pasting in our code, press shift+enter or click the play icon to the right of our snippet. The Spark job will run and once again we'll have our result!
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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 get svc -o wide # Get the Loadbalancer endpoints for spark-ui-proxy and zeppelin
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```
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At which point the Master UI and Zeppelin will be available at the URLs under the `EXTERNAL-IP` field.
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You can also interact with the Spark cluster using 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.
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If your Kubernetes cluster does not have a Loadbalancer integration, use `kubectl proxy` and `kubectl port-forward` to access the Spark UI and Zeppelin.
|
||
|
|
|
||
|
|
For Spark UI:
|
||
|
|
|
||
|
|
```console
|
||
|
|
kubectl proxy --port=8001
|
||
|
|
```
|
||
|
|
|
||
|
|
Then visit [http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-ui-proxy/](http://localhost:8001/api/v1/proxy/namespaces/spark-cluster/services/spark-ui-proxy/).
|
||
|
|
|
||
|
|
For 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.
|
||
|
|
|
||
|
|
<!-- BEGIN MUNGE: GENERATED_ANALYTICS -->
|
||
|
|
[]()
|
||
|
|
<!-- END MUNGE: GENERATED_ANALYTICS -->
|