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In this article, you will learn how to publish Kubernetes cluster events data to Amazon Elastic Search using Fluentd logging agent. The data will then be viewed using Kibana, an open-source visualization tool for Elasticsearch. Amazon ES consists of integrated Kibana integration.
We will walk you through with the following process:
Kubernetes is an open source platform created by Google to manage containerized applications. it enables you to manage, scale and deploy your containerized apps in a clustered environment. We can orchestrate our containers across various hosts with Kubernetes, scale the containerized apps with all resources on the fly, and have a centralized container management environment.
We will start with creating Kubernetes cluster and I’ll demonstrate you step by step, on how to install and configure Kubernetes on CentOS 7.
2. Disable SELinux by executing below commands
3. Enable br_netfilter Kernel Module
4. Disable SWAP by running below commands.
5. Install the latest version of Docker CE. Install the package dependencies for docker-ce by running below commands.
6. Install Kubernetes
[kubernetes] name=Kubernetes baseurl=https://packages.cloud.google.com/yum/repos/kubernetes-el7-x86_64 enabled=1 gpgcheck=1 repo_gpgcheck=1 gpgkey=https://packages.cloud.google.com/yum/doc/yum-key.gpg https://packages.cloud.google.com/yum/doc/rpm-package-key.gpg EOF
After the installation is complete, restart all those servers. After restart start the services docker and kubelet
9. Adding Nodes to the clusterConnect to the node01 server and run the kubeadm join command
Connect to the node02 server and run the kubeadm join command
Wait for some time and Validate the ‘k8s-master’ master cluster server, check the nodes and pods using the following command.
Kubernetes cluster master initialization and configuration has been completed.
Elasticsearch is an open source search and analytics engine which is used for log analysis and real-time monitoring of applications. Amazon Elasticsearch Service (Amazon ES) is an AWS service that allows the deployment, operation, and scale of Elasticsearch in the AWS cloud. You can use Amazon ES to analyze email sending events from your Amazon SES
We will create an Amazon ES cluster and then Deploy Fluentd logging agent to Kubernetes cluster which will collect logs and send to Amazon ES cluster
This section shows how to use the Amazon ES console to create an Amazon ES cluster.
To create an Amazon ES cluster
Note: The cluster will take up to ten minutes to deploy. Take note of your Kibana URL once you click the elastic search domain created.
Fluentd is an open source data collector, which lets you unify the data collection and consumption for better use and understanding of data. In this case, we will deploy Fluentd logging on Kubernetes cluster, which will collect the log files and send to the Amazon Elastic Search.
We will create a ClusterRole which provides permissions on pods and namespace objects to make get, list and watch request to cluster.
First, we need to configure RBAC (role-based access control) permissions so that Fluentd can access the appropriate components.
apiVersion: v1 kind: ServiceAccount metadata: name: fluentd namespace: kube-system --- apiVersion: rbac.authorization.k8s.io/v1beta1 kind: ClusterRole metadata: name: fluentd namespace: kube-system rules: - apiGroups: - "" resources: - pods - namespaces verbs: - get - list - watch --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1beta1 metadata: name: fluentd roleRef: kind: ClusterRole name: fluentd apiGroup: rbac.authorization.k8s.io subjects: - kind: ServiceAccount name: fluentd namespace: kube-system
Create: $ kubectl create -f kubernetes/fluentd-rbac.yaml
Now, we can create the DaemonSet.
apiVersion: extensions/v1beta1 kind: DaemonSet metadata: name: fluentd namespace: kube-system labels: k8s-app: fluentd-logging version: v1 kubernetes.io/cluster-service: "true" spec: template: metadata: labels: k8s-app: fluentd-logging version: v1 kubernetes.io/cluster-service: "true" spec: serviceAccount: fluentd serviceAccountName: fluentd tolerations: - key: node-role.kubernetes.io/master effect: NoSchedule containers: - name: fluentd image: fluent/fluentd-kubernetes-daemonset:v1.3-debian-elasticsearch env: - name: FLUENT_ELASTICSEARCH_HOST value: "elasticsearch.logging" - name: FLUENT_ELASTICSEARCH_PORT value: "9200" - name: FLUENT_ELASTICSEARCH_SCHEME value: "http" - name: FLUENT_UID value: "0" resources: limits: memory: 200Mi requests: cpu: 100m memory: 200Mi volumeMounts: - name: varlog mountPath: /var/log - name: varlibdockercontainers mountPath: /var/lib/docker/containers readOnly: true terminationGracePeriodSeconds: 30 volumes: - name: varlog hostPath: path: /var/log - name: varlibdockercontainers hostPath: path: /var/lib/docker/containers
Make sure to define FLUENT_ELASTICSEARCH_HOST & FLUENT_ELASTICSEARCH_PORT according to your elastic search environment
$ kubectl create -f kubernetes/fluentd-daemonset.yaml
$ kubectl logs fluentd-lwbt6 -n kube-system | grep Connection
You should see that Fluentd connect to Elasticsearch within the logs:
7. And Apply Changes
That’s it! This is how you can visualize the Kubernetes Pod created in Kibana.
Monitoring by log analysis is a critical component of any application deployment. You can gather and consolidate logs across your cluster in Kubernetes to monitor the whole cluster from one single dashboard. In our example, we have seen fluentd act as a mediator between kubernetes cluster and Amazon ES. Fluentd combines log collection and aggregation and sends logs to Amazon ES for log analytics and data visualization with kibana.
The above example shows how to add AWS Elastic search logging and kibana monitoring to kubernetes cluster using fluentd.
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