KubeDirector - an easy way to run complex stateful applications in Kubernetes

Original author: Thomas Phelan
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Note trans. : The original article was written by representatives of BlueData, a company founded by people from VMware. She specializes in making more accessible (simpler, faster, cheaper) the deployment of solutions for Big Data-analytics and machine learning in various environments. This is also due to the recent initiative of the company called BlueK8s.in which authors want to compile a galaxy of open source tools “for deploying stateful applications and managing them in Kubernetes”. The article is devoted to the first of them - KubeDirector, which, according to the authors' idea, helps the enthusiast in the field of Big Data, who does not have special training in Kubernetes, to deploy applications like Spark, Cassandra or Hadoop in K8s. Brief instructions on how to do this are given in the article. However, keep in mind that the project has an early status of readiness - pre-alpha.



KubeDirector is an open source project created to simplify the launch of clusters from complex, scalable stateful applications in Kubernetes. KubeDirector is implemented using the Custom Resource Definition framework(CRD), uses the Kubernetes API native extension capabilities and relies on their philosophy. This approach provides transparent integration with the management of users and resources in Kubernetes, as well as with existing clients and utilities.

The recently announced KubeDirector project is part of a larger Open Source initiative for Kubernetes called BlueK8s. Now I am pleased to announce the availability of KubeDirector's early (pre-alpha) code . This post will show how it works.

KubeDirector offers the following features:

  • No need to modify the code to run in Kubernetes stateful-apps not from the category of cloud native. In other words, there is no need for decomposition of already existing applications to match the pattern of the microservice architecture.
  • Native support for storage-specific application configuration and state (state) .
  • An application-independent deployment pattern that minimizes the time it takes to launch new stateful applications in Kubernetes.

KubeDirector allows data scientists who are used to distributed data-intensive applications such as Hadoop, Spark, Cassandra, TensorFlow, Caffe2, etc., to run Kubernetes with a minimal learning curve and no need to write Go code. When these applications are monitored by KubeDirector, they are defined by simple metadata and the associated set of configurations. Application metadata is defined as a resource KubeDirectorApp.

To understand the components of KubeDirector, clone the repository on GitHub with a command like the following:

git clone http://<userid>@github.com/bluek8s/kubedirector.

The definition KubeDirectorAppfor the Spark 2.2.1 application is located in the file 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"
                    ],
…

An application cluster configuration is defined as a resource KubeDirectorCluster.

The definition KubeDirectorClusterfor the Spark 2.2.1 cluster example is available at 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 in Kubernetes with KubeDirector


Running Spark clusters in Kubernetes with KubeDirector is easy.

First, make sure that Kubernetes (version 1.9 or higher) is running - with the command 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"}

Deploy KubeDirector service and sample resource definitions KubeDirectorAppusing the following commands:

cd kubedirector
make deploy

As a result, it will launch under KubeDirector:

~> kubectl get pods
NAME                           READY     STATUS     RESTARTS     AGE
kubedirector-58cf59869-qd9hb   1/1       Running    0            1m

View the list of applications installed in KubeDirector by running kubectl get KubeDirectorApp:

~> kubectl get KubeDirectorApp
NAME           AGE
cassandra311   30m
spark211up     30m
spark221e2     30m

You can now start a Spark 2.2.1 cluster using the sample file for KubeDirectorClusterand the command kubectl create -f deploy/example_clusters/cr-cluster-spark211up.yaml. Check that it has 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

Spark also appeared in the list of running 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-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

If you access port 31533 in your browser, you can see Spark Master UI:



That's it! In the example above, in addition to the Spark cluster, we also deployed Jupyter Notebook .

To launch another application (for example, Cassandra) simply specify another file with KubeDirectorApp:

kubectl create -f deploy/example_clusters/cr-cluster-cassandra311.yaml

Verify that the Cassandra cluster has started:

~> 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

Now Kubernetes runs the Spark cluster (with Jupyter Notebook) and the Cassandra cluster. The list of services can be seen with the command kubectl get service:

~> 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


PS from translator


If you are interested in the KubeDirector project, you should also pay attention to its wiki . Unfortunately, it was not possible to find a public roadmap, but issues in GitHub shed light on the development of the project and the views of its main developers. In addition, for those interested in KubeDirector, the authors provide links to Slack-chat and Twitter .

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