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Spark on K8s — Run a Spark job on Amazon EKS cluster

Anh Tu (James) Nguyen
7 min readSep 5, 2019

It took me 2 weeks to successfully submit a Spark job on Amazon EKS cluster, because lack of documentations, or most of them are about running on Kubernetes with kops or GKE, not Amazon EKS.
In this tutorial, I will show you how-to run a sample Spark job on EKS cluster.

What is EKS?

According to AWS official document:

Amazon Elastic Kubernetes Service (Amazon EKS) makes it easy to deploy, manage, and scale containerized applications using Kubernetes on AWS.

Amazon EKS runs the Kubernetes management infrastructure for you across multiple AWS availability zones to eliminate a single point of failure. Amazon EKS is certified Kubernetes conformant so you can use existing tooling and plugins from partners and the Kubernetes community. Applications running on any standard Kubernetes environment are fully compatible and can be easily migrated to Amazon EKS.

Amazon EKS is generally available for all AWS customers.

Why choosing EKS?

The purpose we choose EKS instead of kops (Kubernetes Operation) because:

  • We want to migrate our existing EMR clusters and all the Spark jobs to Kubernetes (K8s). Actually, we have several production K8s clusters provisioning by kops, but we want to explore AWS EKS to compare it with other approaches.
  • No control plane to manage.

Amazon EKS runs the Kubernetes management infrastructure (kube-apiserver, etcd, kube-scheduler, kube-proxy and coredns, etc.) across multiple AWS Availability Zones, automatically detects and replaces unhealthy control plane nodes, and provides on-demand upgrades and patching. You simply provision worker nodes and connect them to the provided Amazon EKS endpoint.

  • Easy to integrate with other AWS services (IAM, Load Balancer, EC2 Spot Instances and CloudWatch log, etc.).
  • Our company have 24/7 Premium Support from AWS 😛.

You can read AWS EKS features for more reference.

Requirements

Below is the general information of my EKS cluster (IP addresses are fake ones):

➜  kubectl cluster-info
Kubernetes master is running at https://4A5<i_am_tu>545E6.sk1.ap-southeast-1.eks.amazonaws.com
CoreDNS is running at https://4A5<i_am_tu>545E6.sk1.ap-southeast-1.eks.amazonaws.com/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy
➜ kubectl get nodes
NAME STATUS ROLES AGE VERSION
ip-10–0–0–3.ap-southeast-1.compute.internal Ready <none> 2d9h v1.12.7
ip-10–0–0–5.ap-southeast-1.compute.internal Ready <none> 2d9h v1.12.7
ip-10–0–0–7.ap-southeast-1.compute.internal Ready <none> 2d9h v1.12.7
➜  kubectl get all -n kube-system
NAME                                 READY   STATUS    RESTARTS   AGE
pod/aws-node-fb5kv                   1/1     Running   0          2d9h
pod/aws-node-ldkqc                   1/1     Running   0          2d9h
pod/aws-node-vx95t                   1/1     Running   0          2d9h
pod/coredns-78966b4675-c7bms         1/1     Running   0          2d9h
pod/coredns-78966b4675-wd6hx         1/1     Running   0          2d9h
pod/kube-proxy-48g97                 1/1     Running   0          2d9h
pod/kube-proxy-9tvfk                 1/1     Running   0          2d9h
pod/kube-proxy-zk5z6                 1/1     Running   0          2d9h
pod/tiller-deploy-56d7789fd4-nc7z7   1/1     Running   0          2d7hNAME                    TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)         AGE
service/kube-dns        ClusterIP   172.20.0.10     <none>        53/UDP,53/TCP   2d9h
service/tiller-deploy   ClusterIP   172.20.63.157   <none>        44134/TCP       2d9hNAME                        DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR   AGE
daemonset.apps/aws-node     3         3         3       3            3           <none>          2d9h
daemonset.apps/kube-proxy   3         3         3       3            3           <none>          2d9hNAME                            DESIRED   CURRENT   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/coredns         2         2         2            2           2d9h
deployment.apps/tiller-deploy   1         1         1            1           2d9hNAME                                       DESIRED   CURRENT   READY   AGE
replicaset.apps/coredns-78966b4675         2         2         2       2d9h
replicaset.apps/tiller-deploy-56d7789fd4   1         1         1       2d7h
replicaset.apps/tiller-deploy-6656d56444   0         0         0       2d9h

How it work

From Spark official guideline:

spark-submit can be directly used to submit a Spark application to a Kubernetes cluster. The submission mechanism works as follows:

- Spark creates a Spark driver running within a Kubernetes pod.

- The driver creates executors which are also running within Kubernetes pods and connects to them, and executes application code.

- When the application completes, the executor pods terminate and are cleaned up, but the driver pod persists logs and remains in “completed” state in the Kubernetes API until it’s eventually garbage collected or manually cleaned up.

Note that in the completed state, the driver pod does not use any computational or memory resources.

The driver and executor pod scheduling is handled by Kubernetes. It is possible to schedule the driver and executor pods on a subset of available nodes through a node selector using the configuration property for it. It will be possible to use more advanced scheduling hints like node/pod affinities in a future release.

spark-submit with cluster mode (Image from Spark document)

A sample spark-submit looks like this

➜ bin/spark-submit \
    --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
    --deploy-mode cluster \
    --name spark-pi \
    --class org.apache.spark.examples.SparkPi \
    --conf spark.executor.instances=5 \
    --conf spark.kubernetes.container.image=<spark-image> \
    local:///path/to/examples.jar

Create namespace, service account and role for Jump pod

The jump pod is a special pod helps us to deploy cluster or client mode Spark applications into EKS cluster.

  • Create a file named spark_role.yaml as following
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: spark-pi
  namespace: spark-pi
automountServiceAccountToken: true
---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: spark-pi-role
  namespace: spark-pi
rules:
- apiGroups: [""]
  resources: ["pods", "services", "configmaps"]
  verbs: ["get", "list", "watch", "create", "delete", "update", "patch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: spark-pi-role-binding
  namespace: spark-pi
subjects:
- kind: ServiceAccount
  name: spark-pi
  namespace: spark-pi
roleRef:
  kind: Role
  name: spark-pi-role
  apiGroup: rbac.authorization.k8s.io
  • Run kubectl to create namespace and service account
➜  kubectl create namespace spark-pi
namespace/spark-pi created
➜  kubectl apply -f ~/lab/k8s/spark_role.yml
serviceaccount/spark-pi created
role.rbac.authorization.k8s.io/spark-pi-role created
rolebinding.rbac.authorization.k8s.io/spark-pi-role-binding created
  • Verify whether new service account has permission to create/delete pods
➜  kubectl auth can-i create pod --as=system:serviceaccount:spark-pi:spark-pi -n spark-pi
yes

Building Docker image to run spark-submit

I wrote a simple script to download spark source code, run mvn compile, build and push Docker images.

I’m using docker:dind image to run and build Docker image inside a Docker container

➜  docker container run \
    --privileged -it \
    --name spark-build \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v ${PWD}:/tmp \
    -e USER=<your_docker_username> \
    -e PASSWORD=<your_docker_password> \
    -w /opt \
    docker:dind \
    sh /tmp/build.sh

You can use the Docker image I’ve built from above script with tag vitamingaugau/spark:spark-2.4.4.

Specially thanks to Andy Grove and his post for pointing out the issue with Kubernetes client. We need to patch the Kubernetes client version before building Spark from source. The fix should be come together with Spark 2.4.5, hopefully 🤞.

java.net.ProtocolException: Expected HTTP 101 response but was '403 Forbidden' 
    at okhttp3.internal.ws.RealWebSocket.checkResponse(RealWebSocket.java:216) 
    at okhttp3.internal.ws.RealWebSocket$2.onResponse(RealWebSocket.java:183) 
    at okhttp3.RealCall$AsyncCall.execute(RealCall.java:141) 
    at okhttp3.internal.NamedRunnable.run(NamedRunnable.java:32) 
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) 
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) 
    at java.lang.Thread.run(Thread.java:748)

It’s show time!

Now we’re ready to submit example Spark application to our EKS cluster

  • Running jump pod from my Docker image
➜  kubectl run --generator=run-pod/v1 jump-pod --rm -i --tty --serviceaccount=spark-pi --namespace=spark-pi --image vitamingaugau/spark:spark-2.4.4 sh
  • Inside the pod shell, prepare some variables
sh-4.4# export SA=spark-pi
sh-4.4# export NAMESPACE=spark-pi
sh-4.4# export TOKEN=/var/run/secrets/kubernetes.io/serviceaccount/token
sh-4.4# export CACERT=/var/run/secrets/kubernetes.io/serviceaccount/ca.crt
  • Run spark-submit command inside jump pod:
sh-4.4# /opt/spark/bin/spark-submit \
    --master=k8s://https://4A5<i_am_tu>545E6.sk1.ap-southeast-1.eks.amazonaws.com:443 \
    --deploy-mode cluster \
    --name spark-pi \
    --class org.apache.spark.examples.SparkPi \
    --conf spark.kubernetes.driver.pod.name=spark-pi-driver  \
    --conf spark.kubernetes.container.image=vitamingaugau/spark:spark-2.4.4 \
    --conf spark.kubernetes.namespace=$NAMESPACE \
    --conf spark.kubernetes.authenticate.driver.serviceAccountName=$SA \
    --conf spark.kubernetes.authenticate.submission.caCertFile=$CACERT \
    --conf spark.kubernetes.authenticate.submission.oauthTokenFile=$TOKEN \
    --conf spark.executor.instances=2 \
    local:///opt/spark/examples/target/scala-2.11/jars/spark-examples_2.11-2.4.4.jar 20000

Noted: Remember to define K8s apiserver port (e.g: 443) when run spark-submit command.

  • The command will keep watching state of driver pod, from Creating -> Running -> Succeeded -> Terminated. You can see sample output as below:
19/09/05 20:20:08 INFO LoggingPodStatusWatcherImpl: State changed, new state:
         pod name: spark-pi-driver
         namespace: spark-pi
         labels: spark-app-selector -> spark-1d3977f57e224a3e8bd8429f78b83076, spark-role -> driver
         pod uid: 8cd8e900-d01a-11e9-9747-028eb46babf0
         creation time: 2019-09-05T20:20:06Z
         service account name: spark-pi
         volumes: spark-local-dir-1, spark-conf-volume, spark-pi-token-qp4d4
         node name: ip-10-150-58-108.ap-southeast-1.compute.internal
         start time: 2019-09-05T20:20:06Z
         container images: vitamingaugau/spark:spark-2.4.4
         phase: Running
         status: [ContainerStatus(containerID=docker://c115b2b8a29a1d5293449761cf39cd8b030bdb8290ef247fc7d6bfff4654053c, image=vitamingaugau/spark:spark-2.4.4, imageID=docker-pullable://vitamingaugau/spark@sha256:3e26a8bae5e210f26be5c64d33b110653bb97630ad19be9e415d20576ad75e91, lastState=ContainerState(running=null, terminated=null, waiting=null, additionalProperties={}), name=spark-kubernetes-driver, ready=true, restartCount=0, state=ContainerState(running=ContainerStateRunning(startedAt=2019-09-05T20:20:07Z, additionalProperties={}), terminated=null, waiting=null, additionalProperties={}), additionalProperties={})]
19/09/05 20:21:26 INFO LoggingPodStatusWatcherImpl: State changed, new state:
         pod name: spark-pi-driver
         namespace: spark-pi
         labels: spark-app-selector -> spark-1d3977f57e224a3e8bd8429f78b83076, spark-role -> driver
         pod uid: 8cd8e900-d01a-11e9-9747-028eb46babf0
         creation time: 2019-09-05T20:20:06Z
         service account name: spark-pi
         volumes: spark-local-dir-1, spark-conf-volume, spark-pi-token-qp4d4
         node name: ip-10-150-58-108.ap-southeast-1.compute.internal
         start time: 2019-09-05T20:20:06Z
         container images: vitamingaugau/spark:spark-2.4.4
         phase: Succeeded
         status: [ContainerStatus(containerID=docker://c115b2b8a29a1d5293449761cf39cd8b030bdb8290ef247fc7d6bfff4654053c, image=vitamingaugau/spark:spark-2.4.4, imageID=docker-pullable://vitamingaugau/spark@sha256:3e26a8bae5e210f26be5c64d33b110653bb97630ad19be9e415d20576ad75e91, lastState=ContainerState(running=null, terminated=null, waiting=null, additionalProperties={}), name=spark-kubernetes-driver, ready=false, restartCount=0, state=ContainerState(running=null, terminated=ContainerStateTerminated(containerID=docker://c115b2b8a29a1d5293449761cf39cd8b030bdb8290ef247fc7d6bfff4654053c, exitCode=0, finishedAt=2019-09-05T20:21:25Z, message=null, reason=Completed, signal=null, startedAt=2019-09-05T20:20:07Z, additionalProperties={}), waiting=null, additionalProperties={}), additionalProperties={})]
19/09/05 20:21:26 INFO LoggingPodStatusWatcherImpl: Container final statuses:Container name: spark-kubernetes-driver
         Container image: vitamingaugau/spark:spark-2.4.4
         Container state: Terminated
         Exit code: 0
19/09/05 20:21:26 INFO Client: Application spark-pi finished.
19/09/05 20:21:26 INFO ShutdownHookManager: Shutdown hook called
19/09/05 20:21:26 INFO ShutdownHookManager: Deleting directory /tmp/spark-48ee836f-53af-497e-a9ba-472a8106e5d5
  • Open another terminal and using kubectl to check
➜  kubectl -n spark-pi get all
NAME                                READY   STATUS    RESTARTS   AGE
pod/jump-pod                        1/1     Running   0          70m
pod/spark-pi-1567714805266-exec-1   1/1     Running   0          2s
pod/spark-pi-1567714805266-exec-2   1/1     Running   0          2s
pod/spark-pi-driver                 1/1     Running   0          7sNAME                                        TYPE        CLUSTER-IP   EXTERNAL-IP   PORT(S)             AGE
service/spark-pi-1567714805266-driver-svc   ClusterIP   None         <none>        7078/TCP,7079/TCP   8s
  • Checking driver pod logs, you will see a SparkUI endpoint has been created, you can access the UI to check the status of Spark application
➜  kubectl -n spark-pi logs pod/spark-pi-driver..................
19/09/05 20:17:34 INFO Utils: Successfully started service 'SparkUI' on port 4040.
19/09/05 20:17:34 INFO SparkUI: Bound SparkUI to 0.0.0.0, and started at http://spark-pi-1567714650240-driver-svc.spark-pi.svc:4040
..................
➜  kubectl -n spark-pi port-forward pod/spark-pi-driver 4040:4040

Now you can access the Spark UI via http://127.0.0.1:4040

What next?!

Checkout my new tutorials for Spark on K8s series:

How-to Perform a Spark-Submit to Amazon EKS Cluster With IRSA

In previous article, I have introduced how we submit a Spark job to an EKS cluster. As long as we’re using other AWS…

medium.com

Spark on K8s — Send Spark job’s metrics to DataDog using Autodiscovery

tunguyen9889.medium.com

Feel free to let me know if you’re facing any issue when follow this tutorial.

James (Anh-Tú) Nguyễn on about.me

I am a Cloud Infrastructure Engineer in Singapore. Read my blog.

about.me

Kubernetes
Eks
Spark
Docker
AWS

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Anh Tu (James) Nguyen

Written by Anh Tu (James) Nguyen

67 Followers

A Fulltime SRE and 24/7 Oncall Dad

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