Logging

logging solution in Kubernetes with ECK Operator

In this article, we’ll deploy ECK Operator using helm to the Kubernetes cluster and build a quick-ready solution for logging using Elasticsearch, Kibana, and Filebeat.

What is ECK?

Built on the Kubernetes Operator pattern, Elastic Cloud on Kubernetes (ECK) extends the basic Kubernetes orchestration capabilities to support the setup and management of Elasticsearch, Kibana, APM Server, Enterprise Search, Beats, Elastic Agent, and Elastic Maps Server on Kubernetes.

With Elastic Cloud on Kubernetes, we can streamline critical operations, such as:

Managing and monitoring multiple clusters
Scaling cluster capacity and storage
Performing safe configuration changes through rolling upgrades
Securing clusters with TLS certificates
Setting up hot-warm-cold architectures with availability zone awareness

Install ECK

In this case we use helmfile to manage the helm deployments: helmfile.yaml

repositories:
- name: elastic
  url: https://helm.elastic.co

releases:
  - name: eck-operator
    version: 2.2.0
    chart: elastic/eck-operator
    namespace: monitoring
    values:
    - ./eck-operator/values.yaml

2. But we can do that just with helm: Installation using helm

helm repo add elastic https://helm.elastic.co
helm install elastic-operator elastic/eck-operator -n monitoring --create-namespace

After that we can see that the ECK pod is running:

kubectl get po elastic-operator-0 -n monitoring

NAME                 READY   STATUS    RESTARTS   AGE
elastic-operator-0   1/1     Running   0          43s

The pod is up and running

Creating Elasticsearch, Kibana, and Filebeat resources

There are a lot of different applications in Elastic Stack, such as:

Elasticsearch
Kibana
Beats (Filebeat/Metricbeat)
APM Server
Elastic Maps
etc

In our case, we’ll use only the first three of them, because we just want to deploy a classical EFK stack.

Let’s deploy the following in the order:

Elasticsearch cluster: This cluster has 3 nodes, each node with 100Gi of persistent storage, and intercommunication with a self-signed TLS-certificate.

# This sample sets up an Elasticsearch cluster with 3 nodes.
apiVersion: elasticsearch.k8s.elastic.co/v1
kind: Elasticsearch
metadata:
  name: elasticsearch-logging
  namespace: monitoring
spec:
  version: 8.2.0
  nodeSets:
  - name: default
    config:
      # most Elasticsearch configuration parameters are possible to set, e.g: node.attr.attr_name: attr_value
      node.roles: ["master", "data", "ingest", "ml"]
      # this allows ES to run on nodes even if their vm.max_map_count has not been increased, at a performance cost
      node.store.allow_mmap: true
    podTemplate:
      metadata:
        labels:
          # additional labels for pods
          purpose: logging
      spec:
        containers:
        - name: elasticsearch
          # specify resource limits and requests
          resources:
            limits:
              memory: 4Gi
              cpu: 2
          env:
          - name: ES_JAVA_OPTS
            value: "-Xms2g -Xmx2g"
    count: 3
    # request 100Gi of persistent data storage for pods in this topology element
    volumeClaimTemplates:
    - metadata:
        name: elasticsearch-data # Do not change this name unless you set up a volume mount for the data path.
      spec:
        accessModes:
        - ReadWriteOnce
        resources:
          requests:
            storage: 100Gi
        storageClassName: gp2
  http:
    tls:
      selfSignedCertificate:
        # add a list of SANs into the self-signed HTTP certificate
        subjectAltNames:
        - dns: elasticsearch-logging-es-http.monitoring.svc.cluster.local
        - dns: elasticsearch-logging-es-http.monitoring.svc
        - dns: "*.monitoring.svc"
        - dns: "*.monitoring.svc.cluster.local"

2. The next one is Kibana: Very simple, just referencing Kibana object to Elasticsearch in a simple way.

apiVersion: kibana.k8s.elastic.co/v1
kind: Kibana
metadata:
  name: kibana-logging
  namespace: monitoring
spec:
  version: 8.2.2
  count: 1
  elasticsearchRef:
    name: elasticsearch-logging
    namespace: monitoring
  http:
    tls:
      selfSignedCertificate:
        disabled: true

3. The next one is Filebeat: This manifest contains DaemonSet used by Filebeat and some ServiceAccount stuff.

---
apiVersion: beat.k8s.elastic.co/v1beta1
kind: Beat
metadata:
  name: filebeat
  namespace: monitoring
spec:
  type: filebeat
  version: 8.2.0
  elasticsearchRef:
    name: elasticsearch-logging
  kibanaRef:
    name: kibana-logging
  config:
    filebeat:
      autodiscover:
        providers:
        - type: kubernetes
          node: ${NODE_NAME}
          hints:
            enabled: true
            default_config:
              type: container
              paths:
              - /var/log/containers/*${data.kubernetes.container.id}.log
    setup:
      dashboards:
        enabled: true
  daemonSet:
    podTemplate:
      spec:
        serviceAccountName: filebeat
        automountServiceAccountToken: true
        terminationGracePeriodSeconds: 30
        dnsPolicy: ClusterFirstWithHostNet
        hostNetwork: true # Allows to provide richer host metadata
        priorityClassName: system-node-critical
        containers:
        - name: filebeat
          resources:
            limits:
              memory: 1Gi
              cpu: 1
          securityContext:
            runAsUser: 0
          volumeMounts:
          - name: varlogcontainers
            mountPath: /var/log/containers
          - name: varlogpods
            mountPath: /var/log/pods
          - name: varlibdockercontainers
            mountPath: /var/lib/docker/containers
          env:
            - name: NODE_NAME
              valueFrom:
                fieldRef:
                  fieldPath: spec.nodeName
        volumes:
        - name: varlogcontainers
          hostPath:
            path: /var/log/containers
        - name: varlogpods
          hostPath:
            path: /var/log/pods
        - name: varlibdockercontainers
          hostPath:
            path: /var/lib/docker/containers
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: filebeat
rules:
- apiGroups: [""] # "" indicates the core API group
  resources:
  - namespaces
  - pods
  - nodes
  verbs:
  - get
  - watch
  - list
---
apiVersion: v1
kind: ServiceAccount
metadata:
  name: filebeat
  namespace: monitoring
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: filebeat
subjects:
- kind: ServiceAccount
  name: filebeat
  namespace: monitoring
roleRef:
  kind: ClusterRole
  name: filebeat
  apiGroup: rbac.authorization.k8s.io

Testing

First of all, let’s get Kibana’s password: This password will be used to log in to Kibana

kubectl get secret elasticsearch-logging-es-elastic-user -ojson | jq '.data.elastic' -r | base64 -d

pCda48Ec32qbmFP1wdasd

2. Running port-forward to Kibana service: Port 5601 is forwarded to localhost

kubectl port-forward svc/kibana-logging-kb-http 5601:5601 -n monitoring 

Forwarding from 127.0.0.1:5601 -> 5601
Forwarding from [::1]:5601 -> 5601

3. Let’s log in to Kibana with the user elastic and password that we got before (http://localhost:5601), go to Analytics — Discover section and check logs:

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