EFK日志采集系统简介

EFK = ElasticSearch + Fluentd + Kibana

Elasticsearch 是一个分布式的搜索和分析引擎，可以用于全文检索、结构化检索和分析，并能将这三者结合起来。Elasticsearch 基于 Lucene 开发，现在是使用最广的开源搜索引擎之一。

Fluentd是一个优秀的log信息收集的开源免费软件

Kibana是一个开源的分析与可视化平台，可以用kibana搜索、查看存放在Elasticsearch索引里

部署Fluentd

DaemonSet fluentd-es 只会调度到设置了标签 beta.kubernetes.io/fluentd-ds-ready=true的 Node，需要在期望运行 fluentd 的 Node 上设置该标签

# kubectl get nodes
NAME           STATUS    ROLES     AGE       VERSION
172.31.22.16   Ready     <none>    31d       v1.8.3+f0efb3cb88375
172.31.22.3    Ready     <none>    31d       v1.8.3+f0efb3cb88375
172.31.22.6    Ready     <none>    31d       v1.8.3+f0efb3cb88375

# kubectl label nodes 172.31.22.16 172.31.22.3 172.31.22.6 beta.kubernetes.io/fluentd-ds-ready=true

创建fluentd-es configmap

# kubectl apply -f fluentd-es-configmap.yaml

fluentd-es-configmap.yaml文件如下

apiVersion: v1
kind: ConfigMap
metadata:
  name: fluentd-es-config-v0.1.4
  namespace: kube-system
  labels:
    addonmanager.kubernetes.io/mode: Reconcile
data:
  system.conf: |-
    <system>
      root_dir /tmp/fluentd-buffers/
    </system>

  containers.input.conf: |-
    # This configuration file for Fluentd / td-agent is used
    # to watch changes to Docker log files. The kubelet creates symlinks that
    # capture the pod name, namespace, container name & Docker container ID
    # to the docker logs for pods in the /var/log/containers directory on the host.
    # If running this fluentd configuration in a Docker container, the /var/log
    # directory should be mounted in the container.
    #
    # These logs are then submitted to Elasticsearch which assumes the
    # installation of the fluent-plugin-elasticsearch & the
    # fluent-plugin-kubernetes_metadata_filter plugins.
    # See https://github.com/uken/fluent-plugin-elasticsearch &
    # https://github.com/fabric8io/fluent-plugin-kubernetes_metadata_filter for
    # more information about the plugins.
    #
    # Example
    # =======
    # A line in the Docker log file might look like this JSON:
    #
    # {"log":"2014/09/25 21:15:03 Got request with path wombat\n",
    #  "stream":"stderr",
    #   "time":"2014-09-25T21:15:03.499185026Z"}
    #
    # The time_format specification below makes sure we properly
    # parse the time format produced by Docker. This will be
    # submitted to Elasticsearch and should appear like:
    # $ curl 'http://elasticsearch-logging:9200/_search?pretty'
    # ...
    # {
    #      "_index" : "logstash-2014.09.25",
    #      "_type" : "fluentd",
    #      "_id" : "VBrbor2QTuGpsQyTCdfzqA",
    #      "_score" : 1.0,
    #      "_source":{"log":"2014/09/25 22:45:50 Got request with path wombat\n",
    #                 "stream":"stderr","tag":"docker.container.all",
    #                 "@timestamp":"2014-09-25T22:45:50+00:00"}
    #    },
    # ...
    #
    # The Kubernetes fluentd plugin is used to write the Kubernetes metadata to the log
    # record & add labels to the log record if properly configured. This enables users
    # to filter & search logs on any metadata.
    # For example a Docker container's logs might be in the directory:
    #
    #  /data/docker/containers/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b
    #
    # and in the file:
    #
    #  997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b-json.log
    #
    # where 997599971ee6... is the Docker ID of the running container.
    # The Kubernetes kubelet makes a symbolic link to this file on the host machine
    # in the /var/log/containers directory which includes the pod name and the Kubernetes
    # container name:
    #
    #    synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log
    #    ->
    #    /data/docker/containers/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b/997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b-json.log
    #
    # The /var/log directory on the host is mapped to the /var/log directory in the container
    # running this instance of Fluentd and we end up collecting the file:
    #
    #   /var/log/containers/synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log
    #
    # This results in the tag:
    #
    #  var.log.containers.synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log
    #
    # The Kubernetes fluentd plugin is used to extract the namespace, pod name & container name
    # which are added to the log message as a kubernetes field object & the Docker container ID
    # is also added under the docker field object.
    # The final tag is:
    #
    #   kubernetes.var.log.containers.synthetic-logger-0.25lps-pod_default_synth-lgr-997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b.log
    #
    # And the final log record look like:
    #
    # {
    #   "log":"2014/09/25 21:15:03 Got request with path wombat\n",
    #   "stream":"stderr",
    #   "time":"2014-09-25T21:15:03.499185026Z",
    #   "kubernetes": {
    #     "namespace": "default",
    #     "pod_name": "synthetic-logger-0.25lps-pod",
    #     "container_name": "synth-lgr"
    #   },
    #   "docker": {
    #     "container_id": "997599971ee6366d4a5920d25b79286ad45ff37a74494f262e3bc98d909d0a7b"
    #   }
    # }
    #
    # This makes it easier for users to search for logs by pod name or by
    # the name of the Kubernetes container regardless of how many times the
    # Kubernetes pod has been restarted (resulting in a several Docker container IDs).

    # Json Log Example:
    # {"log":"[info:2016-02-16T16:04:05.930-08:00] Some log text here\n","stream":"stdout","time":"2016-02-17T00:04:05.931087621Z"}
    # CRI Log Example:
    # 2016-02-17T00:04:05.931087621Z stdout F [info:2016-02-16T16:04:05.930-08:00] Some log text here
    <source>
      @id fluentd-containers.log
      @type tail
      path /var/log/containers/*.log
      pos_file /var/log/es-containers.log.pos
      time_format %Y-%m-%dT%H:%M:%S.%NZ
      tag raw.kubernetes.*
      read_from_head true
      <parse>
        @type multi_format
        <pattern>
          format json
          time_key time
          time_format %Y-%m-%dT%H:%M:%S.%NZ
        </pattern>
        <pattern>
          format /^(?<time>.+) (?<stream>stdout|stderr) [^ ]* (?<log>.*)$/
          time_format %Y-%m-%dT%H:%M:%S.%N%:z
        </pattern>
      </parse>
    </source>

    # Detect exceptions in the log output and forward them as one log entry.
    <match raw.kubernetes.**>
      @id raw.kubernetes
      @type detect_exceptions
      remove_tag_prefix raw
      message log
      stream stream
      multiline_flush_interval 5
      max_bytes 500000
      max_lines 1000
    </match>

  system.input.conf: |-
    # Example:
    # 2015-12-21 23:17:22,066 [salt.state       ][INFO    ] Completed state [net.ipv4.ip_forward] at time 23:17:22.066081
    <source>
      @id minion
      @type tail
      format /^(?<time>[^ ]* [^ ,]*)[^\[]*\[[^\]]*\]\[(?<severity>[^ \]]*) *\] (?<message>.*)$/
      time_format %Y-%m-%d %H:%M:%S
      path /var/log/salt/minion
      pos_file /var/log/salt.pos
      tag salt
    </source>

    # Example:
    # Dec 21 23:17:22 gke-foo-1-1-4b5cbd14-node-4eoj startupscript: Finished running startup script /var/run/google.startup.script
    <source>
      @id startupscript.log
      @type tail
      format syslog
      path /var/log/startupscript.log
      pos_file /var/log/es-startupscript.log.pos
      tag startupscript
    </source>

    # Examples:
    # time="2016-02-04T06:51:03.053580605Z" level=info msg="GET /containers/json"
    # time="2016-02-04T07:53:57.505612354Z" level=error msg="HTTP Error" err="No such image: -f" statusCode=404
    # TODO(random-liu): Remove this after cri container runtime rolls out.
    <source>
      @id docker.log
      @type tail
      format /^time="(?<time>[^)]*)" level=(?<severity>[^ ]*) msg="(?<message>[^"]*)"( err="(?<error>[^"]*)")?( statusCode=($<status_code>\d+))?/
      path /var/log/docker.log
      pos_file /var/log/es-docker.log.pos
      tag docker
    </source>

    # Example:
    # 2016/02/04 06:52:38 filePurge: successfully removed file /var/etcd/data/member/wal/00000000000006d0-00000000010a23d1.wal
    <source>
      @id etcd.log
      @type tail
      # Not parsing this, because it doesn't have anything particularly useful to
      # parse out of it (like severities).
      format none
      path /var/log/etcd.log
      pos_file /var/log/es-etcd.log.pos
      tag etcd
    </source>

    # Multi-line parsing is required for all the kube logs because very large log
    # statements, such as those that include entire object bodies, get split into
    # multiple lines by glog.

    # Example:
    # I0204 07:32:30.020537    3368 server.go:1048] POST /stats/container/: (13.972191ms) 200 [[Go-http-client/1.1] 10.244.1.3:40537]
    <source>
      @id kubelet.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/kubelet.log
      pos_file /var/log/es-kubelet.log.pos
      tag kubelet
    </source>

    # Example:
    # I1118 21:26:53.975789       6 proxier.go:1096] Port "nodePort for kube-system/default-http-backend:http" (:31429/tcp) was open before and is still needed
    <source>
      @id kube-proxy.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/kube-proxy.log
      pos_file /var/log/es-kube-proxy.log.pos
      tag kube-proxy
    </source>

    # Example:
    # I0204 07:00:19.604280       5 handlers.go:131] GET /api/v1/nodes: (1.624207ms) 200 [[kube-controller-manager/v1.1.3 (linux/amd64) kubernetes/6a81b50] 127.0.0.1:38266]
    <source>
      @id kube-apiserver.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/kube-apiserver.log
      pos_file /var/log/es-kube-apiserver.log.pos
      tag kube-apiserver
    </source>

    # Example:
    # I0204 06:55:31.872680       5 servicecontroller.go:277] LB already exists and doesn't need update for service kube-system/kube-ui
    <source>
      @id kube-controller-manager.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/kube-controller-manager.log
      pos_file /var/log/es-kube-controller-manager.log.pos
      tag kube-controller-manager
    </source>

    # Example:
    # W0204 06:49:18.239674       7 reflector.go:245] pkg/scheduler/factory/factory.go:193: watch of *api.Service ended with: 401: The event in requested index is outdated and cleared (the requested history has been cleared [2578313/2577886]) [2579312]
    <source>
      @id kube-scheduler.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/kube-scheduler.log
      pos_file /var/log/es-kube-scheduler.log.pos
      tag kube-scheduler
    </source>

    # Example:
    # I1104 10:36:20.242766       5 rescheduler.go:73] Running Rescheduler
    <source>
      @id rescheduler.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/rescheduler.log
      pos_file /var/log/es-rescheduler.log.pos
      tag rescheduler
    </source>

    # Example:
    # I0603 15:31:05.793605       6 cluster_manager.go:230] Reading config from path /etc/gce.conf
    <source>
      @id glbc.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/glbc.log
      pos_file /var/log/es-glbc.log.pos
      tag glbc
    </source>

    # Example:
    # I0603 15:31:05.793605       6 cluster_manager.go:230] Reading config from path /etc/gce.conf
    <source>
      @id cluster-autoscaler.log
      @type tail
      format multiline
      multiline_flush_interval 5s
      format_firstline /^\w\d{4}/
      format1 /^(?<severity>\w)(?<time>\d{4} [^\s]*)\s+(?<pid>\d+)\s+(?<source>[^ \]]+)\] (?<message>.*)/
      time_format %m%d %H:%M:%S.%N
      path /var/log/cluster-autoscaler.log
      pos_file /var/log/es-cluster-autoscaler.log.pos
      tag cluster-autoscaler
    </source>

    # Logs from systemd-journal for interesting services.
    # TODO(random-liu): Remove this after cri container runtime rolls out.
    <source>
      @id journald-docker
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "docker.service" }]
      <storage>
        @type local
        persistent true
      </storage>
      read_from_head true
      tag docker
    </source>

    <source>
      @id journald-container-runtime
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "{{ container_runtime }}.service" }]
      <storage>
        @type local
        persistent true
      </storage>
      read_from_head true
      tag container-runtime
    </source>

    <source>
      @id journald-kubelet
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "kubelet.service" }]
      <storage>
        @type local
        persistent true
      </storage>
      read_from_head true
      tag kubelet
    </source>

    <source>
      @id journald-node-problem-detector
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "node-problem-detector.service" }]
      <storage>
        @type local
        persistent true
      </storage>
      read_from_head true
      tag node-problem-detector
    </source>

    <source>
      @id kernel
      @type systemd
      filters [{ "_TRANSPORT": "kernel" }]
      <storage>
        @type local
        persistent true
      </storage>
      <entry>
        fields_strip_underscores true
        fields_lowercase true
      </entry>
      read_from_head true
      tag kernel
    </source>

  forward.input.conf: |-
    # Takes the messages sent over TCP
    <source>
      @type forward
    </source>

  monitoring.conf: |-
    # Prometheus Exporter Plugin
    # input plugin that exports metrics
    <source>
      @type prometheus
    </source>

    <source>
      @type monitor_agent
    </source>

    # input plugin that collects metrics from MonitorAgent
    <source>
      @type prometheus_monitor
      <labels>
        host ${hostname}
      </labels>
    </source>

    # input plugin that collects metrics for output plugin
    <source>
      @type prometheus_output_monitor
      <labels>
        host ${hostname}
      </labels>
    </source>

    # input plugin that collects metrics for in_tail plugin
    <source>
      @type prometheus_tail_monitor
      <labels>
        host ${hostname}
      </labels>
    </source>

  output.conf: |-
    # Enriches records with Kubernetes metadata
    <filter kubernetes.**>
      @type kubernetes_metadata
    </filter>

    <match **>
      @id elasticsearch
      @type elasticsearch
      @log_level info
      include_tag_key true
      host elasticsearch-logging
      port 9200
      logstash_format true
      <buffer>
        @type file
        path /var/log/fluentd-buffers/kubernetes.system.buffer
        flush_mode interval
        retry_type exponential_backoff
        flush_thread_count 2
        flush_interval 5s
        retry_forever
        retry_max_interval 30
        chunk_limit_size 2M
        queue_limit_length 8
        overflow_action block
      </buffer>
    </match>

使用fluentd-es-ds.yaml 部署fluentd daemonset，部署在kube-system这个namespace下

# kubectl apply -f fluentd-es-ds.yaml

# kubectl get ds -n kube-system
NAME                DESIRED   CURRENT   READY     UP-TO-DATE   AVAILABLE   NODE SELECTOR                              AGE
fluentd-es-v2.0.4   3         3         3         3            3           beta.kubernetes.io/fluentd-ds-ready=true   11d

[root@vm172-31-22-16 EFK]# kubectl get pods -n kube-system -o wide
NAME                                   READY     STATUS    RESTARTS   AGE       IP           NODE
fluentd-es-v2.0.4-465rh                1/1       Running   3          11d       10.0.97.8    172.31.22.16
fluentd-es-v2.0.4-9qtvr                1/1       Running   3          11d       10.0.71.8    172.31.22.3
fluentd-es-v2.0.4-qm6bb                1/1       Running   4          11d       10.0.75.18   172.31.22.6

fluentd-es-ds.yaml文件如下：

apiVersion: v1
kind: ServiceAccount
metadata:
  name: fluentd-es
  namespace: kube-system
  labels:
    k8s-app: fluentd-es
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: fluentd-es
  labels:
    k8s-app: fluentd-es
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups:
  - ""
  resources:
  - "namespaces"
  - "pods"
  verbs:
  - "get"
  - "watch"
  - "list"
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: fluentd-es
  labels:
    k8s-app: fluentd-es
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
subjects:
- kind: ServiceAccount
  name: fluentd-es
  namespace: kube-system
  apiGroup: ""
roleRef:
  kind: ClusterRole
  name: fluentd-es
  apiGroup: ""
---
apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
  name: fluentd-es-v2.0.4
  namespace: kube-system
  labels:
    k8s-app: fluentd-es
    version: v2.0.4
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
spec:
  selector:
    matchLabels:
      k8s-app: fluentd-es
      version: v2.0.4
  template:
    metadata:
      labels:
        k8s-app: fluentd-es
        kubernetes.io/cluster-service: "true"
        version: v2.0.4
      # This annotation ensures that fluentd does not get evicted if the node
      # supports critical pod annotation based priority scheme.
      # Note that this does not guarantee admission on the nodes (#40573).
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ''
    spec:
      priorityClassName: system-node-critical
      serviceAccountName: fluentd-es
      containers:
      - name: fluentd-es
        image: hub.kce.ksyun.com/ksyun/fluentd-elasticsearch:v2.0.4
        env:
        - name: FLUENTD_ARGS
          value: --no-supervisor -q
        resources:
          limits:
            memory: 500Mi
          requests:
            cpu: 100m
            memory: 200Mi
        volumeMounts:
        - name: varlog
          mountPath: /var/log
        - name: datadockercontainers
          mountPath: /data/docker/containers
          readOnly: true
        - name: config-volume
          mountPath: /etc/fluent/config.d
      nodeSelector:
        beta.kubernetes.io/fluentd-ds-ready: "true"
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: datadockercontainers
        hostPath:
          path: /data/docker/containers
      - name: config-volume
        configMap:
          name: fluentd-es-config-v0.1.4

部署ElasticSearch服务

考虑到日志存储对于磁盘空间的要求比较大，这里我们建议采用金山云云硬盘作为ES的存储

首先创建2个PV（注意：将yaml文件中的volumeId、name替换成自己的）

# kubectl apply -f disk-pv-1.yaml
# kubectl apply -f disk-pv-2.yaml

disk-pv-1.yaml文件如下

apiVersion: v1
kind: PersistentVolume
metadata:
  name: "844f520e-82fd-49c2-83e8-XXXXXXXX"
  namespace: kube-system
spec:
  capacity:
    storage: 20Gi
  storageClassName: disk
  accessModes:
    - ReadWriteOnce
  flexVolume:
    driver: "ksc/ebs"
    fsType: "ext4"
    options:
      volumeId: "844f520e-82fd-49c2-83e8-XXXXXXXX"

disk-pv-2.yaml文件如下：

apiVersion: v1
kind: PersistentVolume
metadata:
  name: "b3de4d13-1c3a-4c88-9b64-XXXXXXXX"
  namespace: kube-system
spec:
  capacity:
    storage: 20Gi
  storageClassName: disk
  accessModes:
    - ReadWriteOnce
  flexVolume:
    driver: "ksc/ebs"
    fsType: "ext4"
    options:
      volumeId: "b3de4d13-1c3a-4c88-9b64-XXXXXXXX"

使用 es-statefulset.yaml、es-service.yaml部署ES服务

备注：目前金山云售卖云主机中，通用性N1、通用型N2、IO优化型I2和IO优化型I3云主机支持挂载云硬盘（详见云硬盘使用限制）。如您选择云硬盘作为存储卷，建议在创建有状态服务时，将pod调度到以上机型，否则可能存在云硬盘无法挂载的情况出现。具体调度方式参考Assigning Pods to Nodes

# kubectl apply -f es-statefulset.yaml
# kubectl apply -f es-service.yaml

es-statefulset.yaml文件如下

# RBAC authn and authz
apiVersion: v1
kind: ServiceAccount
metadata:
  name: elasticsearch-logging
  namespace: kube-system
  labels:
    k8s-app: elasticsearch-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: elasticsearch-logging
  labels:
    k8s-app: elasticsearch-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
rules:
- apiGroups:
  - ""
  resources:
  - "services"
  - "namespaces"
  - "endpoints"
  verbs:
  - "get"
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  namespace: kube-system
  name: elasticsearch-logging
  labels:
    k8s-app: elasticsearch-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
subjects:
- kind: ServiceAccount
  name: elasticsearch-logging
  namespace: kube-system
  apiGroup: ""
roleRef:
  kind: ClusterRole
  name: elasticsearch-logging
  apiGroup: ""
---
# Elasticsearch deployment itself
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
  name: elasticsearch-logging
  namespace: kube-system
  labels:
    k8s-app: elasticsearch-logging
    version: v5.6.4
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
spec:
  serviceName: elasticsearch-logging
  replicas: 2
  selector:
    matchLabels:
      k8s-app: elasticsearch-logging
      version: v5.6.4
  template:
    metadata:
      labels:
        k8s-app: elasticsearch-logging
        version: v5.6.4
        kubernetes.io/cluster-service: "true"
    spec:
      serviceAccountName: elasticsearch-logging
      containers:
      - image: hub.kce.ksyun.com/ksyun/elasticsearch:v5.6.4
        name: elasticsearch-logging
        resources:
          # need more cpu upon initialization, therefore burstable class
          limits:
            cpu: 1000m
          requests:
            cpu: 100m
        ports:
        - containerPort: 9200
          name: db
          protocol: TCP
        - containerPort: 9300
          name: transport
          protocol: TCP
        volumeMounts:
        - name: elasticsearch-logging
          mountPath: /data
        env:
        - name: "NAMESPACE"
          valueFrom:
            fieldRef:
              fieldPath: metadata.namespace
        - name: "ES_JAVA_OPTS"
          value: "-XX:-AssumeMP -Xms2g -Xmx2g"
      # Elasticsearch requires vm.max_map_count to be at least 262144.
      # If your OS already sets up this number to a higher value, feel free
      # to remove this init container.
      initContainers:
      - image: alpine:3.6
        command: ["/sbin/sysctl", "-w", "vm.max_map_count=262144"]
        name: elasticsearch-logging-init
        securityContext:
          privileged: true
  volumeClaimTemplates:
  - metadata:
      name: elasticsearch-logging
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: "disk"
      resources:
        requests:
          storage: 20Gi

es-service.yaml文件如下

apiVersion: v1
kind: Service
metadata:
  name: elasticsearch-logging
  namespace: kube-system
  labels:
    k8s-app: elasticsearch-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    kubernetes.io/name: "Elasticsearch"
spec:
  ports:
  - port: 9200
    protocol: TCP
    targetPort: db
  selector:
    k8s-app: elasticsearch-logging

通过ES服务的clusterIP和port，检查ES服务是否正常

# kubectl get svc --all-namespaces -o wide  
NAMESPACE     NAME                    TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)          AGE       SELECTOR
kube-system   elasticsearch-logging   ClusterIP   10.254.183.42    <none>        9200/TCP         11d       k8s-app=elasticsearch-logging

# curl 10.254.183.42:9200
{
  "name" : "elasticsearch-logging-1",
  "cluster_name" : "kubernetes-logging",
  "cluster_uuid" : "h3h9IPGZRsKZMaZLgOAvRw",
  "version" : {
    "number" : "5.6.4",
    "build_hash" : "8bbedf5",
    "build_date" : "2017-10-31T18:55:38.105Z",
    "build_snapshot" : false,
    "lucene_version" : "6.6.1"
  },
  "tagline" : "You Know, for Search"
}

部署Kibana

使用kibana-deployment.yaml和kibana-service.yaml部署Kibana服务

# kubectl apply -f kibana-deployment.yaml
# kubectl apply -f kibana-service.yaml

kibana-deployment.yaml文件如下

apiVersion: apps/v1beta1
kind: Deployment
metadata:
  name: kibana-logging
  namespace: kube-system
  labels:
    k8s-app: kibana-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
spec:
  replicas: 1
  selector:
    matchLabels:
      k8s-app: kibana-logging
  template:
    metadata:
      labels:
        k8s-app: kibana-logging
    spec:
      containers:
      - name: kibana-logging
        image: docker.elastic.co/kibana/kibana:5.6.4
        resources:
          # need more cpu upon initialization, therefore burstable class
          limits:
            cpu: 1000m
          requests:
            cpu: 100m
        env:
          - name: ELASTICSEARCH_URL
            value: http://elasticsearch-logging:9200
          #- name: SERVER_BASEPATH
          #  value: /api/v1/namespaces/kube-system/services/kibana-logging/proxy
          - name: XPACK_MONITORING_ENABLED
            value: "false"
          - name: XPACK_SECURITY_ENABLED
            value: "false"
        ports:
        - containerPort: 5601
          name: ui
          protocol: TCP

kibana-service.yaml文件如下

apiVersion: v1
kind: Service
metadata:
  name: kibana-logging
  namespace: kube-system
  labels:
    k8s-app: kibana-logging
    kubernetes.io/cluster-service: "true"
    addonmanager.kubernetes.io/mode: Reconcile
    kubernetes.io/name: "Kibana"
spec:
  ports:
  - port: 5601
    protocol: TCP
    targetPort: ui
  selector:
    k8s-app: kibana-logging
  type: NodePort

访问Kibana

kibana 第一次启动时会用较长时间(10-20分钟)来优化和 Cache 状态页面。kibana启动完成后，可以通过节点的Public IP+NodePort来访问kibana

# kubectl get svc --all-namespaces -o wide
kube-system   kibana-logging          NodePort    10.254.97.159    <none>        5601:31981/TCP   11d       k8s-app=kibana-logging

参数	类型	描述
InstanceId	String	节点id
InstanceName	String	实例名称
EpcInstancePara	EpcInstancePara数据类型	epc配置信息

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