在Kubernetes上搭建EFK（Fluentd＋Elasticsearch＋Kibana）

1. 前言

kubernetes 默认会将容器的stdout和stderr录入node(minion)的/var/log/containers目录下,而kubernetes 组件的日志默认放置在/var/log目录下。

如果你是用kube-up启动的kubernetes集群，那么恭喜你,你可 以方便的启动k8s的日志功能。参考:http://kubernetes.io/docs/getting-started-guides/logging-elasticsearch/ 或者在k8s安装包的解压目录kubernetes/cluster/addons/fluentd-elasticsearch中找到安装文件。

如果你是通过命令或者脚本启动的k8s集群,那么也恭喜你,折腾等着你。

2. 实现需求

将各个pod中容器的stdout和stderr中的日志集中展示。

3. 部署结构

待传。

4. Elasticsearch & Kibana

这两个使用官网的镜像。Elasticsearch如果需要集群化,参考:

https://hub.docker.com/r/fabric8/elasticsearch/tags/

https://github.com/fabric8io/elasticsearch-cloud-kubernetes

由于本人的k8s环境并不通外网,需要先从官网下载镜像，再打上私有仓库的标签。

docker pull elasticsearch:2.3 docker pull kibana:4.5 docker tag elasticsearch:2.3 10.10.50.161:5000/elasticsearch:2.3 docker tag kibana:4.5 10.10.50.161:5000/kibana:4.5 docker push 10.10.50.161:5000/elasticsearch:2.3 docker push 10.10.50.161:5000/kibana:4.5

生成rc和svc:

kubectl create -f elasticsearch-kibana-svc.yaml kubectl create -f elasticsearch-kibana-rc.yaml

elasticsearch-kibana-rc.yaml:

apiVersion: v1 kind: ReplicationController metadata: name: elasticsearch-kibana namespace: kube-system labels: k8s-app: elasticsearch-kibana version: v1 kubernetes.io/cluster-service: "true" spec: replicas: 1 selector: k8s-app: elasticsearch-kibana version: v1 template: metadata: labels: k8s-app: elasticsearch-kibana version: v1 kubernetes.io/cluster-service: "true" spec: containers: - image: 10.10.50.161:5000/elasticsearch:2.3 name: elasticsearch resources: # keep request = limit to keep this container in guaranteed class limits: cpu: 100m requests: cpu: 100m ports: - containerPort: 9200 name: http protocol: TCP - containerPort: 9300 name: transport protocol: TCP volumeMounts: - name: es-persistent-storage mountPath: /usr/share/elasticsearch/data - image: 10.10.50.161:5000/kibana:4.5 name: kibana resources: # keep request = limit to keep this container in guaranteed class limits: cpu: 100m requests: cpu: 100m ports: - containerPort: 5601 name: ui protocol: TCP env: - name: ELASTICSEARCH_URL value: http://localhost:9200 volumes: - name: es-persistent-storage emptyDir: {}

elasticsearch-kibana-svc.yaml:

apiVersion: v1 kind: Service metadata: name: elasticsearch-kibana namespace: kube-system labels: k8s-app: elasticsearch-kibana kubernetes.io/cluster-service: "true" kubernetes.io/name: "elasticsearch-kibana" spec: type: NodePort ports: - name: elasticsearch-http port: 9200 protocol: TCP targetPort: http - name: elasticsearch-transport port: 9300 protocol: TCP targetPort: transport - name: kibana port: 5601 protocol: TCP targetPort: ui nodePort: 30016 selector: k8s-app: elasticsearch-kibana

注意:这里我把elasticsearch的日志存储放在了pod的empty volumns里了，真正的运行环境应该使用nodeSelector选定一台node，并把日志存储放在node里的/usr/share/elasticsearch/data。

5. Fluentd

重点就是fluentd,addons里的依赖kube.up里引用的make ca制作认证。而我的环境进行认证时，使用了前面一同事部署heapster+influxdb+grafana时用的单域名认证,之后再进行多域名认证时,搞得乱七八糟，索性决定绕过k8s内部环境。

多域名认证参考:

https://coreos.com/kubernetes/docs/latest/openssl.html

http://www.linuxidc.com/Linux/2014-10/108222.htm

http://apetec.com/support/GenerateSAN-CSR.htm

https://certificates.heanet.ie/node/17

这里使用fabric8的fabric8/fluentd-kubernetes镜像,但需要重新制作镜像。

fabric8/fluentd-kubernetes本身不依赖https和认证,但里面的fluentd插件fluent-plugin-kubernetes_metadata_filter依赖了https和认证,这也是比较蛋疼的事。

首先pull官网的镜像

docker pull fabric8/fluentd-kubernetes:v1.14 docker tag fabric8/fluentd-kubernetes:v1.14 10.10.50.161:5000/fabric8/fluentd-kubernetes:v1.14

制作镜像:

mkdir myfluent cd fluent-plugin touch Dockerfile touch start-fluentd

Dockerfile:

FROM 10.10.50.161:5000/fabric8/fluentd-kubernetes:v1.14 MAINTAINER miaobainian <miaobainian36@163.com> ADD start-fluentd /start-fluentd

start-fluentd:

#!/bin/sh  ELASTICSEARCH_HOST=${ELASTICSEARCH_HOST:-es-logging.default.svc} ELASTICSEARCH_PORT=${ELASTICSEARCH_PORT:-9200} ELASTICSEARCH_SCHEME=${ELASTICSEARCH_SCHEME:-http} FLUENTD_FLUSH_INTERVAL=${FLUENTD_FLUSH_INTERVAL:-10s} FLUENTD_FLUSH_THREADS=${FLUENTD_FLUSH_THREADS:-1} FLUENTD_RETRY_LIMIT=${FLUENTD_RETRY_LIMIT:-10} FLUENTD_DISABLE_RETRY_LIMIT=${FLUENTD_DISABLE_RETRY_LIMIT:-true} FLUENTD_RETRY_WAIT=${FLUENTD_RETRY_WAIT:-1s} FLUENTD_MAX_RETRY_WAIT=${FLUENTD_MAX_RETRY_WAIT:-60s} FLUENTD_BUFFER_CHUNK_LIMIT=${FLUENTD_BUFFER_CHUNK_LIMIT:-8m} FLUENTD_BUFFER_QUEUE_LIMIT=${FLUENTD_BUFFER_QUEUE_LIMIT:-8192} FLUENTD_BUFFER_TYPE=${FLUENTD_BUFFER_TYPE:-memory} FLUENTD_BUFFER_PATH=${FLUENTD_BUFFER_PATH:-/var/fluentd/buffer} FLUENTD_LOGSTASH_FORMAT=${FLUENTD_LOGSTASH_FORMAT:-true} KUBERNETES_PRESERVE_JSON_LOG=${KUBERNETES_PRESERVE_JSON_LOG:-true} mkdir /etc/fluent cat << EOF >> /etc/fluent/fluent.conf <source> type tail path /var/log/containers/*.log pos_file /var/log/es-containers.log.pos time_format %Y-%m-%dT%H:%M:%S.%N tag kubernetes.* format json read_from_head true keep_time_key true </source> <filter kubernetes.**> type kubernetes_metadata preserve_json_log ${KUBERNETES_PRESERVE_JSON_LOG} kubernetes_url ${KUBERNETES_URL} verify_ssl ${VERIFY_SSL} </filter> <match **> type elasticsearch$([ "${ELASTICSEARCH_DYNAMIC}" == "true" ] && echo _dynamic) log_level info include_tag_key true time_key time host ${ELASTICSEARCH_HOST} port ${ELASTICSEARCH_PORT} scheme ${ELASTICSEARCH_SCHEME} $([ -n "${ELASTICSEARCH_USER}" ] && echo user ${ELASTICSEARCH_USER}) $([ -n "${ELASTICSEARCH_PASSWORD}" ] && echo password ${ELASTICSEARCH_PASSWORD}) buffer_type ${FLUENTD_BUFFER_TYPE} $([ "${FLUENTD_BUFFER_TYPE}" == "file" ] && echo buffer_path ${FLUENTD_BUFFER_PATH}) buffer_chunk_limit ${FLUENTD_BUFFER_CHUNK_LIMIT} buffer_queue_limit ${FLUENTD_BUFFER_QUEUE_LIMIT} flush_interval ${FLUENTD_FLUSH_INTERVAL} retry_limit ${FLUENTD_RETRY_LIMIT} $([ "${FLUENTD_DISABLE_RETRY_LIMIT}" == "true" ] && echo disable_retry_limit) retry_wait ${FLUENTD_RETRY_WAIT} max_retry_wait ${FLUENTD_MAX_RETRY_WAIT} num_threads ${FLUENTD_FLUSH_THREADS} logstash_format ${FLUENTD_LOGSTASH_FORMAT} $([ -n "${FLUENTD_LOGSTASH_PREFIX}" ] && echo logstash_prefix ${FLUENTD_LOGSTASH_PREFIX}) reload_connections false EOF cat << 'EOF' >> /etc/fluent/fluent.conf </match> EOF exec je fluentd

注意这里比官方的start-fluentd增加关键的两行:

kubernetes_url ${KUBERNETES_URL}
verify_ssl ${VERIFY_SSL}

目的是绕过https://clusterIp:443的验证。

docker build -t 10.10.50.161:5000/fabric8/fluentd-kubernetes:v1.15 . docker push 10.10.50.161:5000/fabric8/fluentd-kubernetes:v1.15

ok,fluentd的镜像定义好了之后，就可以生成pod了，可以使用static pod,但是推荐使用daemon sets

touch fluentd-daemon.yaml

fluentd-daemon.yaml :

apiVersion: extensions/v1beta1 kind: DaemonSet metadata: name: fluentd-elasticsearch namespace: kube-system labels: k8s-app: fluentd-logging spec: template: metadata: labels: k8s-app: fluentd-logging spec: containers: - name: fluentd-elasticsearch image: 10.10.50.161:5000/fabric8/fluentd-kubernetes:v1.16 resources: limits: cpu: 100m volumeMounts: - name: varlog mountPath: /var/log - name: varlibdockercontainers mountPath: /var/lib/docker/containers readOnly: true env: - name: KUBERNETES_URL value: "http://10.10.50.156:8080/api" - name: VERIFY_SSL value: "false" - name: ELASTICSEARCH_HOST value: elasticsearch-kibana - name: ELASTICSEARCH_PORT value: "9200" - name: FLUENTD_FLUSH_INTERVAL value: "300s" volumes: - name: varlog hostPath: path: /var/log - name: varlibdockercontainers hostPath: path: /var/lib/docker/containers

kubectl create -f fluentd-daemon.yaml

注意这里的环境变量:

KUBERNETES_URL使用的是k8s master node的master api。

VERIFY_SSL为false表示不验证ca。

ELASTICSEARCH_HOST是前面部署的elasticsearch-kibana服务名,依赖于dns(服务名即dns名),如果没有装dns，你也可以使用kubectl get svc --namespace=kube-system找到elasticsearch-kibana的集群ip,配置集群ip也可以。但集群ip是可变的,这个要注意。

ELASTICSEARCH_PORT是elasticsearch-kibana中elasticsearch的服务内部端口。
FLUENTD_FLUSH_INTERVAL用于标识收集时间间隔,设置为300s是因为第一次收集时，花费的时间较长，时间间隔不够会导致elasticsearch不停的重新连接。

ok,现在all is already。

在浏览器打开:

http://10.10.50.155:30016/

10.10.50.155是我的k8s集群中的一个node,30016是elasticsearch-kibana服务的node port。

进行kibana的界面。

默认进入Settings的indices界面。

将Index contains time-based events的打勾去掉。点击下面的create。

ok，喝杯coffee or tea,等待k8s的日志出现就可以了。

本文转移开源中国-在Kubernetes上搭建EFK（Fluentd＋Elasticsearch＋Kibana）