KubeDirector - an easy way to run complex stateful applications in Kubernetes
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 the authors want to assemble 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 (CRD) framework, uses the native extension capabilities of the Kubernetes API and is based 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 the early (pre-alpha) KubeDirector code. This post will show how it works.
KubeDirector offers the following features:
KubeDirector allows data scientists who are accustomed 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 an associated set of configurations. Application metadata is defined as a
To understand the components of KubeDirector, clone the repository on GitHub with a command like the following:
The
An application cluster configuration is defined as a
The
Starting Spark clusters in Kubernetes with KubeDirector is easy.
First, make sure that Kubernetes (version 1.9 or higher) is
Deploy the KubeDirector service and sample
As a result, it will launch under KubeDirector:
View the list of installed applications in KubeDirector by running
Now you can start a Spark 2.2.1 cluster using the sample file for
Spark also appeared in the list of running services:
If you access port 31533 in your browser, you can see the Spark Master UI:
That's all! 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
Verify that the Cassandra cluster has started:
Now Kubernetes has a Spark cluster (with Jupyter Notebook) and a Cassandra cluster running. The list of services can be seen with the command
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 .
Read also in our blog:
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 (CRD) framework, uses the native extension capabilities of the Kubernetes API and is based 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 the early (pre-alpha) KubeDirector code. This post will show how it works.
KubeDirector offers the following features:
- No need to modify the code to run non-cloud native stateful applications in Kubernetes. In other words, there is no need for decomposition of already existing applications to match the pattern of the microservice architecture.
- Native support for storing application-specific configuration and 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 accustomed 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 an associated set of configurations. Application metadata is defined as a
KubeDirectorApp resource.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
KubeDirectorApp definition for the Spark 2.2.1 application is located in the 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
KubeDirectorCluster resource.The
KubeDirectorCluster definition for 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
Starting Spark clusters in Kubernetes with KubeDirector is easy.
First, make sure that Kubernetes (version 1.9 or higher) is
kubectl version - using the kubectl version command: ~> 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 the KubeDirector service and sample
KubeDirectorApp resource KubeDirectorApp using 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 installed applications in KubeDirector by running
kubectl get KubeDirectorApp : ~> kubectl get KubeDirectorApp NAME AGE cassandra311 30m spark211up 30m spark221e2 30m Now you can start a Spark 2.2.1 cluster using the sample file for
KubeDirectorCluster and 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 the Spark Master UI:
That's all! 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 has a Spark cluster (with Jupyter Notebook) and a Cassandra cluster running. 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 .
Read also in our blog:
- “ Operators for Kubernetes: how to run stateful applications ”;
- “ Rook -“ self-service ”data store for Kubernetes ”;
- “ Kubernetes tips & tricks: speeding up the bootstrap of large databases ”;
- " Useful utilities when working with Kubernetes ";
- " Useful commands and tips when working with Kubernetes through the kubectl console utility ";
- “ We are getting acquainted with the alpha version of the snapshot volumes in Kubernetes ”;
- “ Introducing loghouse - an open source system for working with logs in Kubernetes .”
Source: https://habr.com/ru/post/428451/
All Articles