k8s集群中的EFK日志搜集系統

Kubernetes 集群本身不提供日志收集的解決方案，一般來說有主要的3種方案來做日志收集：
1、在每個節點上運行一個 agent 來收集日志
由于這種 agent 必須在每個節點上運行，所以直接使用 DaemonSet 控制器運行該應用程序即可
這種方法也僅僅適用于收集輸出到 stdout 和 stderr 的應用程序日志
簡單來說，本方式就是在每個node上各運行一個日志代理容器，
對本節點/var/log和 /var/lib/docker/containers/兩個目錄下的日志進行采集
2、在每個 Pod 中包含一個 sidecar 容器來收集應用日志
在 sidecar 容器中運行日志采集代理程序會導致大量資源消耗，因為你有多少個要采集的 Pod，就需要運行多少個采集代理程序，另外還無法使用 kubectl logs 命令來訪問這些日志
3、直接在應用程序中將日志信息推送到采集后端

Kubernetes 中比較流行的日志收集解決方案是 Elasticsearch、Fluentd 和 Kibana（EFK）技術棧，也是官方現在比較推薦的一種方案
Elasticsearch 是一個實時的、分布式的可擴展的搜索引擎，允許進行全文、結構化搜索，它通常用于索引和搜索大量日志數據，也可用于搜索許多不同類型的文檔

創建 Elasticsearch 集群
一般使用3個 Elasticsearch Pod 來避免高可用下多節點集群中出現的“腦裂”問題，并且使用StatefulSet控制器來創建Elasticsearch Pod
創建StatefulSet pod時，直接在其pvc模板中使用StorageClass自動生成pv和pvc，可以實現數據持久化，nfs-client-provisioner已經提前準備好了。
1、創建獨立的命名空間

apiVersion: v1
kind: Namespace
metadata:
  name: logging

2、創建StorageClas，也可以使用已經存在的StorageClas

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: es-data-db
provisioner: fuseim.pri/ifs      # 該值需要和 provisioner 配置的保持一致

3、創建StatefulSet pod前需要先創建無頭服務

kind: Service
apiVersion: v1
metadata:
  name: elasticsearch
  namespace: logging
  labels:
    app: elasticsearch
spec:
  selector:
    app: elasticsearch
  clusterIP: None
  ports:
    - port: 9200
      name: rest
    - port: 9300
      name: inter-node

4、創建elasticsearch statefulset pod
$ docker pull docker.elastic.co/elasticsearch/elasticsearch-oss:6.4.3
$ docker pull busybox

apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: es-cluster
  namespace: logging
spec:
  serviceName: elasticsearch
  replicas: 3
  selector:
    matchLabels:
      app: elasticsearch
  template:
    metadata:
      labels:
        app: elasticsearch
    spec:
      containers:
      - name: elasticsearch
        image: docker.io/elasticsearch:latest
        resources:
            limits:
              cpu: 1000m
            requests:
              cpu: 100m
        ports:
        - containerPort: 9200
          name: rest
          protocol: TCP
        - containerPort: 9300
          name: inter-node
          protocol: TCP
        volumeMounts:
        - name: data
          mountPath: /usr/share/elasticsearch/data
        env:
          - name: cluster.name
            value: k8s-logs
          - name: node.name
            valueFrom:
              fieldRef:
                fieldPath: metadata.name
          - name: discovery.zen.ping.unicast.hosts
            value: "es-cluster-0.elasticsearch,es-cluster-1.elasticsearch,es-cluster-2.elasticsearch"
          - name: discovery.zen.minimum_master_nodes
            value: "2"
          - name: ES_JAVA_OPTS
            value: "-Xms512m -Xmx512m"
      initContainers:
      - name: fix-permissions
        image: busybox
        command: ["sh", "-c", "chown -R 1000:1000 /usr/share/elasticsearch/data"]
        securityContext:
          privileged: true
        volumeMounts:
        - name: data
          mountPath: /usr/share/elasticsearch/data
      - name: increase-vm-max-map
        image: busybox
        command: ["sysctl", "-w", "vm.max_map_count=262144"]
        securityContext:
          privileged: true
      - name: increase-fd-ulimit
        image: busybox
        command: ["sh", "-c", "ulimit -n 65536"]
        securityContext:
          privileged: true
  volumeClaimTemplates:
  - metadata:
      name: data
      labels:
        app: elasticsearch
    spec:
      accessModes: [ "ReadWriteOnce" ]
      storageClassName: es-data-db
      resources:
        requests:
          storage: 100Gi

$ kubectl get pod -n logging
NAME READY STATUS RESTARTS AGE
es-cluster-0 1/1 Running 0 42s
es-cluster-1 1/1 Running 0 10m
es-cluster-2 1/1 Running 0 9m49s
在nfs服務器上會自動生成3個目錄，用于這3個pod存儲數據
$ cd /data/k8s
$ ls
logging-data-es-cluster-0-pvc-98c87fc5-c581-11e9-964d-000c29d8512b/
logging-data-es-cluster-1-pvc-07872570-c590-11e9-964d-000c29d8512b/
logging-data-es-cluster-2-pvc-27e15977-c590-11e9-964d-000c29d8512b/
檢查es集群狀態
$ kubectl port-forward es-cluster-0 9200:9200 --namespace=logging
在另外一個窗口執行
$ curl http://localhost:9200/_cluster/state?pretty

用deployment控制器創建kibana

apiVersion: v1
kind: Service
metadata:
  name: kibana
  namespace: logging
  labels:
    app: kibana
spec:
  ports:
  - port: 5601
  type: NodePort
  selector:
    app: kibana

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: kibana
  namespace: logging
  labels:
    app: kibana
spec:
  selector:
    matchLabels:
      app: kibana
  template:
    metadata:
      labels:
        app: kibana
    spec:
      containers:
      - name: kibana
        image: docker.elastic.co/kibana/kibana-oss:6.4.3
        resources:
          limits:
            cpu: 1000m
          requests:
            cpu: 100m
        env:
          - name: ELASTICSEARCH_URL
            value: http://elasticsearch:9200
        ports:
        - containerPort: 5601

$ kubectl get svc -n logging |grep kibana
kibana NodePort 10.111.239.0 <none> 5601:32081/TCP 114m
訪問kibana
http://192.168.1.243:32081

安裝配置 Fluentd
1、通過 ConfigMap 對象來指定 Fluentd 配置文件

kind: ConfigMap
apiVersion: v1
metadata:
  name: fluentd-config
  namespace: logging
  labels:
    addonmanager.kubernetes.io/mode: Reconcile
data:
  system.conf: |-
    <system>
      root_dir /tmp/fluentd-buffers/
    </system>
  containers.input.conf: |-
    <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
      localtime
      tag raw.kubernetes.*
      format json
      read_from_head true
    </source>
    <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: |-
    <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-kubelet
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "kubelet.service" }]
      <storage>
        @type local
        persistent true
      </storage>
      read_from_head true
      tag kubelet
    </source>
  forward.input.conf: |-
    <source>
      @type forward
    </source>
  output.conf: |-
    <filter kubernetes.**>
      @type kubernetes_metadata
    </filter>
    <match **>
      @id elasticsearch
      @type elasticsearch
      @log_level info
      include_tag_key true
      host elasticsearch
      port 9200
      logstash_format true
      request_timeout    30s
      <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>

上面配置文件中我們配置了 docker 容器日志目錄以及 docker、kubelet 應用的日志的收集，收集到數據經過處理后發送到 elasticsearch:9200 服務
2、使用DaemonSet創建fluentd pod
$ docker pull cnych/fluentd-elasticsearch:v2.0.4
$ docker info
Docker Root Dir: /var/lib/docker

apiVersion: v1
kind: ServiceAccount
metadata:
  name: fluentd-es
  namespace: logging
  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"
---
kind: ClusterRoleBinding
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
subjects:
- kind: ServiceAccount
  name: fluentd-es
  namespace: logging
  apiGroup: ""
roleRef:
  kind: ClusterRole
  name: fluentd-es
  apiGroup: ""
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-es
  namespace: logging
  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
      annotations:
        scheduler.alpha.kubernetes.io/critical-pod: ''
    spec:
      serviceAccountName: fluentd-es
      containers:
      - name: fluentd-es
        image: cnych/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: varlibdockercontainers
          mountPath: /var/lib/docker/containers
          readOnly: true
        - name: config-volume
          mountPath: /etc/fluent/config.d
      nodeSelector:
        beta.kubernetes.io/fluentd-ds-ready: "true"
      tolerations:
      - key: node-role.kubernetes.io/master
        operator: Exists
        effect: NoSchedule
      terminationGracePeriodSeconds: 30
      volumes:
      - name: varlog
        hostPath:
          path: /var/log
      - name: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers
      - name: config-volume
        configMap:
          name: fluentd-config

可以搜集/var/log和/var/log/containers和/var/lib/docker/containers內的日志
還可以搜集docker服務和kubelet服務的日志
為了能夠靈活控制哪些節點的日志可以被收集，所以我們這里還添加了一個 nodSelector 屬性

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

所以要給所有節點打標簽：
$ kubectl get node
$ kubectl label nodes server243.example.com beta.kubernetes.io/fluentd-ds-ready=true
$ kubectl get nodes --show-labels
由于我們的集群使用的是 kubeadm 搭建的，默認情況下 master 節點有污點，所以要想也收集 master 節點的日志，則需要添加上容忍

tolerations:
- key: node-role.kubernetes.io/master
  operator: Exists
  effect: NoSchedule

$ kubectl get pod -n logging
NAME READY STATUS RESTARTS AGE
es-cluster-0 1/1 Running 0 10h
es-cluster-1 1/1 Running 0 10h
es-cluster-2 1/1 Running 0 10h
fluentd-es-rf6p6 1/1 Running 0 9h
fluentd-es-s99r2 1/1 Running 0 9h
fluentd-es-snmtt 1/1 Running 0 9h
kibana-bd6f49775-qsxb2 1/1 Running 0 11h
3、在kibana上配置
http://192.168.1.243:32081
Create index pattern----第一步輸入logstash-*，第二步選擇@timestamp
4、創建測試pod，在kibana上查看日志

apiVersion: v1
kind: Pod
metadata:
  name: counter
spec:
  containers:
  - name: count
    image: busybox
    args: [/bin/sh, -c,
            'i=0; while true; do echo "$i: $(date)"; i=$((i+1)); sleep 1; done']

回到 Kibana Dashboard 頁面，在上面的Discover頁面搜索欄中輸入kubernetes.pod_name:counter