Kubernetes中EFK怎么用-创新互联

这篇文章给大家分享的是有关Kubernetes中EFK怎么用的内容。小编觉得挺实用的，因此分享给大家做个参考，一起跟随小编过来看看吧。

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一：前言

1.在安装Kubernetes集群的时候我们有下载过压缩包https://dl.k8s.io/v1.8.5/kubernetes-client-linux-amd64.tar.gz
解压缩后 在目录cluster\addons 下有各插件的yaml文件，大部分情况仅需少量改动即可使用。

2.在搭建Kubernetes的集群过程中，涉及到很多镜像的下载，建议可以在阿里云购买一个香港所在地的ECS服务器，镜像下载完成后通过docker save -o 将镜像导出，在通过docker load 导入镜像或者上传镜像到个人镜像仓库。

3.Kubernetes从1.8版本开始，EFK的安装中，elasticsearch-logging采用StatefulSet类型，但存在bug,会导致elasticsearch-logging-0 POD 一直无法成功创建。 所以建议还是采用1.8之前的版本采用ReplicationController。

4.要成功安装EFK，一定要先安装kube-dns前面的文章已有介绍。

5.EFK安装过程中elasticsearch和kibana版本要兼容。这里采用的镜像如下：
gcr.io/google_containers/elasticsearch:v2.4.1-2

gcr.io/google_containers/fluentd-elasticsearch:1.22

gcr.io/google_containers/kibana:v4.6.1-1

二：yaml文件


efk-rbac.yaml

点击(此处)折叠或打开

1. apiVersion: v1

2. kind: ServiceAccount

3. metadata:

4.   name: efk

5.   namespace: kube-system

7. ---

9. kind: ClusterRoleBinding

10. apiVersion: rbac.authorization.k8s.io/v1beta1

11. metadata:

12.   name: efk

13. subjects:

14.   - kind: ServiceAccount

15.     name: efk

16.     namespace: kube-system

17. roleRef:

18.   kind: ClusterRole

19.   name: cluster-admin

20.   apiGroup: rbac.authorization.k8s.io

es-controller.yaml

点击(此处)折叠或打开

1. apiVersion: v1

2. kind: ReplicationController

3. metadata:

4.   name: elasticsearch-logging-v1

5.   namespace: kube-system

6.   labels:

7.     k8s-app: elasticsearch-logging

8.     version: v1

9.     kubernetes.io/cluster-service: "true"

10.     addonmanager.kubernetes.io/mode: Reconcile

11. spec:

12.   replicas: 2

13.   selector:

14.     k8s-app: elasticsearch-logging

15.     version: v1

16.   template:

17.     metadata:

18.       labels:

19.         k8s-app: elasticsearch-logging

20.         version: v1

21.         kubernetes.io/cluster-service: "true"

22.     spec:

23.       serviceAccountName: efk

24.       containers:

25.       - image: gcr.io/google_containers/elasticsearch:v2.4.1-2

26.         name: elasticsearch-logging

27.         resources:

28.           # need more cpu upon initialization, therefore burstable class

29.           limits:

30.             cpu: 1000m

31.           requests:

32.             cpu: 100m

33.         ports:

34.         - containerPort: 9200

35.           name: db

36.           protocol: TCP

37.         - containerPort: 9300

38.           name: transport

39.           protocol: TCP

40.         volumeMounts:

41.         - name: es-persistent-storage

42.           mountPath: /data

43.         env:

44.         - name: "NAMESPACE"

45.           valueFrom:

46.             fieldRef:

47.               fieldPath: metadata.namespace

48.       volumes:

49.       - name: es-persistent-storage

50.         emptyDir: {}

es-service.yaml

点击(此处)折叠或打开

1. apiVersion: v1

2. kind: Service

3. metadata:

4.   name: elasticsearch-logging

5.   namespace: kube-system

6.   labels:

7.     k8s-app: elasticsearch-logging

8.     kubernetes.io/cluster-service: "true"

9.     addonmanager.kubernetes.io/mode: Reconcile

10.     kubernetes.io/name: "Elasticsearch"

11. spec:

12.   ports:

13.   - port: 9200

14.     protocol: TCP

15.     targetPort: db

16.   selector:

17.     k8s-app: elasticsearch-logging

fluentd-es-ds.yaml

点击(此处)折叠或打开

1. apiVersion: extensions/v1beta1

2. kind: DaemonSet

3. metadata:

4.   name: fluentd-es-v1.22

5.   namespace: kube-system

6.   labels:

7.     k8s-app: fluentd-es

8.     kubernetes.io/cluster-service: "true"

9.     addonmanager.kubernetes.io/mode: Reconcile

10.     version: v1.22

11. spec:

12.   template:

13.     metadata:

14.       labels:

15.         k8s-app: fluentd-es

16.         kubernetes.io/cluster-service: "true"

17.         version: v1.22

18.       # This annotation ensures that fluentd does not get evicted if the node

19.       # supports critical pod annotation based priority scheme.

20.       # Note that this does not guarantee admission on the nodes (#40573).

21.       annotations:

22.         scheduler.alpha.kubernetes.io/critical-pod: ''

23.     spec:

24.       serviceAccountName: efk

25.       containers:

26.       - name: fluentd-es

27.         image: gcr.io/google_containers/fluentd-elasticsearch:1.22

28.         command:

29.           - '/bin/sh'

30.           - '-c'

31.           - '/usr/sbin/td-agent 2>&1 >> /var/log/fluentd.log'

32.         resources:

33.           limits:

34.             memory: 200Mi

35.           requests:

36.             cpu: 100m

37.             memory: 200Mi

38.         volumeMounts:

39.         - name: varlog

40.           mountPath: /var/log

41.         - name: varlibdockercontainers

42.           mountPath: /var/lib/docker/containers

43.           readOnly: true

44.       nodeSelector:

45.         beta.kubernetes.io/fluentd-ds-ready: "true"

46.       tolerations:

47.       - key : "node.alpha.kubernetes.io/ismaster"

48.         effect: "NoSchedule"

49.       terminationGracePeriodSeconds: 30

50.       volumes:

51.       - name: varlog

52.         hostPath:

53.           path: /var/log

54.       - name: varlibdockercontainers

55.         hostPath:

56.           path: /var/lib/docker/containers

kibana-controller.yaml  此处需要特殊说明，绿色标识的部分KIBANA_BASE_URL 的value要设置为空，默认值会导致Kibana访问出现问题。

点击(此处)折叠或打开

1. apiVersion: extensions/v1beta1

2. kind: Deployment

3. metadata:

4.   name: kibana-logging

5.   namespace: kube-system

6.   labels:

7.     k8s-app: kibana-logging

8.     kubernetes.io/cluster-service: "true"

9.     addonmanager.kubernetes.io/mode: Reconcile

10. spec:

11.   replicas: 1

12.   selector:

13.     matchLabels:

14.       k8s-app: kibana-logging

15.   template:

16.     metadata:

17.       labels:

18.         k8s-app: kibana-logging

19.     spec:

20.       serviceAccountName: efk

21.       containers:

22.       - name: kibana-logging

23.         image: gcr.io/google_containers/kibana:v4.6.1-1

24.         resources:

25.           # keep request = limit to keep this container in guaranteed class

26.           limits:

27.             cpu: 100m

28.           requests:

29.             cpu: 100m

30.         env:

31.           - name: "ELASTICSEARCH_URL"

32.             value: "http://elasticsearch-logging:9200"

33.           - name: "KIBANA_BASE_URL"

34.             value: ""

35.         ports:

36.         - containerPort: 5601

37.           name: ui

38.           protocol: TCP

kibana-service.yaml

点击(此处)折叠或打开

1. apiVersion: v1

2. kind: Service

3. metadata:

4.   name: kibana-logging

5.   namespace: kube-system

6.   labels:

7.     k8s-app: kibana-logging

8.     kubernetes.io/cluster-service: "true"

9.     addonmanager.kubernetes.io/mode: Reconcile

10.     kubernetes.io/name: "Kibana"

11. spec:

12.   ports:

13.   - port: 5601

14.     protocol: TCP

15.     targetPort: ui

16.   selector:

17.     k8s-app: kibana-logging

三：启动与验证

1. 创建资源
kubectl create -f .


2.通过 kubectl logs -f  查看相关pod的日志，确认是否正常启动。 其中kibana-logging-* POD 启动需要一定的时间。


3.elasticsearch验证（可以通过kube proxy创建代理）
http://IP：PORT/_cat/nodes?v

点击(此处)折叠或打开

1. host      ip        heap.percent ram.percent load node.role master name

2. 10.1.88.4 10.1.88.4            9          87 0.45 d         m      elasticsearch-logging-v1-hnfv2

3. 10.1.67.4 10.1.67.4            6          91 0.03 d         *      elasticsearch-logging-v1-zmtdl

http://IP：PORT/_cat/indices?v

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1. health status index               pri rep docs.count docs.deleted store.size pri.store.size

2. green open   logstash-2018.04.07   5   1        515            0      1.1mb        584.4kb

3. green open .kibana               1   1          2            0     22.2kb          9.7kb

4. green open   logstash-2018.04.06   5   1      15364            0      7.3mb          3.6mb

4.kibana验证
http://IP：PORT/app/kibana#/discover?_g


四：备注
要成功搭建EFK，需要注意一下几点：
1.确保已经成功安装了kube-dns
2.当前版本elasticsearch-logging采用ReplicationController
3.elasticsearch和kibana的版本要兼容
4.KIBANA_BASE_URL value设置为“”

感谢各位的阅读！关于“Kubernetes中EFK怎么用”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！

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