KubeDirector: The easy way to run complex stateful applications on Kubernetes
Author: Thomas Phelan (BlueData)
KubeDirector is an open source project designed to make it easy to run complex stateful scale-out application clusters on Kubernetes. KubeDirector is built using the custom resource definition (CRD) framework and leverages the native Kubernetes API extensions and design philosophy. This enables transparent integration with Kubernetes user/resource management as well as existing clients and tools.
We recently
KubeDirector provides the following capabilities: KubeDirector enables data scientists familiar with data-intensive distributed applications such as Hadoop, Spark, Cassandra, TensorFlow, Caffe2, etc. to run these applications on Kubernetes -- with a minimal learning curve and no need to write GO code. The applications controlled by KubeDirector are defined by some basic metadata and an associated package of configuration artifacts. The application metadata is referred to as a KubeDirectorApp resource. To understand the components of KubeDirector, clone the repository on
The KubeDirectorApp definition for the Spark 2.2.1 application is located
in the file The configuration of an application cluster is referred to as a KubeDirectorCluster resource. The
KubeDirectorCluster definition for a sample Spark 2.2.1 cluster is located in the file
With KubeDirector, it’s easy to run Spark clusters on Kubernetes. First, verify that Kubernetes (version 1.9 or later) is running, using the command Deploy the KubeDirector service and the example KubeDirectorApp resource definitions with the commands: These will start the KubeDirector pod: List the installed KubeDirector applications with Now you can launch a Spark 2.2.1 cluster using the example KubeDirectorCluster file and the
The running services now include the Spark services: Pointing the browser at port 31533 connects to the Spark Master UI: That’s all there is to it!
In fact, in the example above we also deployed a Jupyter notebook along with the Spark cluster. To start another application (e.g. Cassandra), just specify another KubeDirectorApp file: See the running Cassandra cluster: Now you have a Spark cluster (with a Jupyter notebook) and a Cassandra cluster running on Kubernetes.
Use KubeDirector is a fully open source, Apache v2 licensed, project – the first of multiple open source projects within a broader initiative we call BlueK8s.
The pre-alpha code for KubeDirector has just been released and we would love for you to join the growing community of developers, contributors, and adopters.
Follow
git clone http://<userid>@github.com/bluek8s/kubedirector.
kubedirector/deploy/example_catalog/cr-app-spark221e2.json.~> cat kubedirector/deploy/example_catalog/cr-app-spark221e2.json
{
"apiVersion": "kubedirector.bluedata.io/v1alpha1",
"kind": "KubeDirectorApp",
"metadata": {
"name" : "spark221e2"
},
"spec" : {
"systemctlMounts": true,
"config": {
"node_services": [
{
"service_ids": [
"ssh",
"spark",
"spark_master",
"spark_worker"
],
…
kubedirector/deploy/example_clusters/cr-cluster-spark221.e1.yaml.~> cat kubedirector/deploy/example_clusters/cr-cluster-spark221.e1.yaml
apiVersion: "kubedirector.bluedata.io/v1alpha1"
kind: "KubeDirectorCluster"
metadata:
name: "spark221e2"
spec:
app: spark221e2
roles:
- name: controller
replicas: 1
resources:
requests:
memory: "4Gi"
cpu: "2"
limits:
memory: "4Gi"
cpu: "2"
- name: worker
replicas: 2
resources:
requests:
memory: "4Gi"
cpu: "2"
limits:
memory: "4Gi"
cpu: "2"
- name: jupyter
…
Running Spark on Kubernetes with KubeDirector
kubectl version~> kubectl version
Client Version: version.Info{Major:"1", Minor:"11", GitVersion:"v1.11.3", GitCommit:"a4529464e4629c21224b3d52edfe0ea91b072862", GitTreeState:"clean", BuildDate:"2018-09-09T18:02:47Z", GoVersion:"go1.10.3", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"11", GitVersion:"v1.11.3", GitCommit:"a4529464e4629c21224b3d52edfe0ea91b072862", GitTreeState:"clean", BuildDate:"2018-09-09T17:53:03Z", GoVersion:"go1.10.3", Compiler:"gc", Platform:"linux/amd64"}
cd kubedirector
make deploy
~> kubectl get pods
NAME READY STATUS RESTARTS AGE
kubedirector-58cf59869-qd9hb 1/1 Running 0 1m
kubectl get KubeDirectorApp~> kubectl get KubeDirectorApp
NAME AGE
cassandra311 30m
spark211up 30m
spark221e2 30m
kubectl create -f deploy/example_clusters/cr-cluster-spark211up.yaml command.
Verify that the Spark cluster has been started:~> kubectl get pods
NAME READY STATUS RESTARTS AGE
kubedirector-58cf59869-djdwl 1/1 Running 0 19m
spark221e2-controller-zbg4d-0 1/1 Running 0 23m
spark221e2-jupyter-2km7q-0 1/1 Running 0 23m
spark221e2-worker-4gzbz-0 1/1 Running 0 23m
spark221e2-worker-4gzbz-1 1/1 Running 0 23m
~> kubectl get service
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kubedirector ClusterIP 10.98.234.194 <none> 60000/TCP 1d
kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 1d
svc-spark221e2-5tg48 ClusterIP None <none> 8888/TCP 21s
svc-spark221e2-controller-tq8d6-0 NodePort 10.104.181.123 <none> 22:30534/TCP,8080:31533/TCP,7077:32506/TCP,8081:32099/TCP 20s
svc-spark221e2-jupyter-6989v-0 NodePort 10.105.227.249 <none> 22:30632/TCP,8888:30355/TCP 20s
svc-spark221e2-worker-d9892-0 NodePort 10.107.131.165 <none> 22:30358/TCP,8081:32144/TCP 20s
svc-spark221e2-worker-d9892-1 NodePort 10.110.88.221 <none> 22:30294/TCP,8081:31436/TCP 20s
kubectl create -f deploy/example_clusters/cr-cluster-cassandra311.yaml
~> kubectl get pods
NAME READY STATUS RESTARTS AGE
cassandra311-seed-v24r6-0 1/1 Running 0 1m
cassandra311-seed-v24r6-1 1/1 Running 0 1m
cassandra311-worker-rqrhl-0 1/1 Running 0 1m
cassandra311-worker-rqrhl-1 1/1 Running 0 1m
kubedirector-58cf59869-djdwl 1/1 Running 0 1d
spark221e2-controller-tq8d6-0 1/1 Running 0 22m
spark221e2-jupyter-6989v-0 1/1 Running 0 22m
spark221e2-worker-d9892-0 1/1 Running 0 22m
spark221e2-worker-d9892-1 1/1 Running 0 22m
kubectl get service to see the set of services.~> kubectl get service
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kubedirector ClusterIP 10.98.234.194 <none> 60000/TCP 1d
kubernetes ClusterIP 10.96.0.1 <none> 443/TCP 1d
svc-cassandra311-seed-v24r6-0 NodePort 10.96.94.204 <none> 22:31131/TCP,9042:30739/TCP 3m
svc-cassandra311-seed-v24r6-1 NodePort 10.106.144.52 <none> 22:30373/TCP,9042:32662/TCP 3m
svc-cassandra311-vhh29 ClusterIP None <none> 8888/TCP 3m
svc-cassandra311-worker-rqrhl-0 NodePort 10.109.61.194 <none> 22:31832/TCP,9042:31962/TCP 3m
svc-cassandra311-worker-rqrhl-1 NodePort 10.97.147.131 <none> 22:31454/TCP,9042:31170/TCP 3m
svc-spark221e2-5tg48 ClusterIP None <none> 8888/TCP 24m
svc-spark221e2-controller-tq8d6-0 NodePort 10.104.181.123 <none> 22:30534/TCP,8080:31533/TCP,7077:32506/TCP,8081:32099/TCP 24m
svc-spark221e2-jupyter-6989v-0 NodePort 10.105.227.249 <none> 22:30632/TCP,8888:30355/TCP 24m
svc-spark221e2-worker-d9892-0 NodePort 10.107.131.165 <none> 22:30358/TCP,8081:32144/TCP 24m
svc-spark221e2-worker-d9892-1 NodePort 10.110.88.221 <none> 22:30294/TCP,8081:31436/TCP 24m
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