Kubernetes日志采集-EFK

1. 日志采集方案

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因为方案一在业界使用更为广泛，并且官方也更为推荐，所以我们基于方案一来做k8s的日志采集。

2. 架构选型

ELK
Filebeat（收集）、Logstash（过滤）、Kafka（缓冲）、Elasticsearch（存储）、Kibana（展示）

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EFK

Fluentbit （收集）、Fluentd（过滤）、Elasticsearch（存储）、Kibana（展示）

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本文使用Fluentd--->Elasticsearch -->Kibana

3. 部署Elasticsearch 集群

使用3个 Elasticsearch Pod 来避免高可用下多节点集群中出现的“脑裂”问题

创建一个名为 logging 的 namespace

$ kubectl  create  namespace logging

创建建一个名为 elasticsearch 的无头服务

elasticsearch-svc.yaml

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

创建服务资源对象

$ kubectl create -f elasticsearch-svc.yaml
$ kubectl  get svc -n logging
NAME            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
elasticsearch   ClusterIP   None                    9200/TCP,9300/TCP   13d

创建ES持久化存储

创建StorageClass

创建elasticsearch-storageclass.yaml

apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: es-data-db
provisioner: fuseim.pri/ifs

创建服务资源对象

$ kubectl create -f elasticsearch-storageclass.yaml 
$ kubectl get storageclass
NAME                PROVISIONER      AGE
es-data-db          fuseim.pri/ifs   13d

使用StatefulSet 创建Es Pod

Kubernetes StatefulSet 允许我们为 Pod 分配一个稳定的标识和持久化存储，Elasticsearch 需要稳定的存储来保证 Pod 在重新调度或者重启后的数据依然不变，所以需要使用 StatefulSet 来管理 Pod。
elasticsearch-statefulset.yaml

apiVersion: apps/v1
kind: StatefulSet  #定义了名为 es-cluster 的 StatefulSet 对象
metadata:
  name: es-cluster
  namespace: logging
spec:
  serviceName: elasticsearch # 和前面创建的 Service 相关联，这可以确保使用以下 DNS 地址访问 StatefulSet 中的每一个 Pod：es-cluster-[0,1,2].elasticsearch.logging.svc.cluster.local，其中[0,1,2]对应于已分配的 Pod 序号。
  replicas: 3 #3个副本
  selector:
    matchLabels:
      app: elasticsearch #设置匹配标签为app=elasticsearch
  template:
    metadata:
      labels:
        app: elasticsearch
    spec: #定义Pod模板
      containers:
      - name: elasticsearch
        image: docker.elastic.co/elasticsearch/elasticsearch-oss:6.4.3
        resources:
            limits:
              cpu: 1000m
            requests:
              cpu: 100m
        ports:
        - containerPort: 9200
          name: rest
          protocol: TCP
        - containerPort: 9300
          name: inter-node
          protocol: TCP
        volumeMounts: #声明数据持久化目录
        - name: data
          mountPath: /usr/share/elasticsearch/data
        env: #定义变量
          - name: cluster.name #Elasticsearch 集群的名称
            value: k8s-logs
          - name: node.name 
            valueFrom:
              fieldRef:
                fieldPath: metadata.name
          - name: discovery.zen.ping.unicast.hosts #设置在 Elasticsearch 集群中节点相互连接的发现方法。
            value: "es-cluster-0.elasticsearch,es-cluster-1.elasticsearch,es-cluster-2.elasticsearch" # 在一个namespace，EsPodDNS 域简写
          - name: discovery.zen.minimum_master_nodes #我们将其设置为(N/2) + 1，N是我们的群集中符合主节点的节点的数量。我们有3个 Elasticsearch 节点，因此我们将此值设置为2（向下舍入到最接近的整数）
            value: "2"
          - name: ES_JAVA_OPTS #JVM限制
            value: "-Xms512m -Xmx512m"
      initContainers: #环境初始化容器
      - 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: #定义持久化模板
  - metadata:
      name: data
      labels:
        app: elasticsearch
    spec:
      accessModes: [ "ReadWriteOnce" ] #设置访问模式，这意味着它只能被 mount 到单个节点上进行读写
      storageClassName: es-data-db  #使用Storagerclass自动创建PV
      resources:
        requests:
          storage: 100Gi #容量

创建服务资源对象

$ kubectl create -f elasticsearch-statefulset.yaml
statefulset.apps/es-cluster created
$ kubectl get pods -n logging
NAME                      READY     STATUS    RESTARTS   AGE
es-cluster-0              1/1       Running   0          20h
es-cluster-1              1/1       Running   0          20h
es-cluster-2              1/1       Running   0          20h

验证ES服务是否正常
将本地端口9200转发到 Elasticsearch 节点（如es-cluster-0）对应的端口：

$ kubectl port-forward es-cluster-0 9200:9200 --namespace=logging
Forwarding from 127.0.0.1:9200 -> 9200
Forwarding from [::1]:9200 -> 9200

其他终端请求es

$ curl http://localhost:9200/_cluster/state?pretty

{
  "cluster_name" : "k8s-logs",
  "compressed_size_in_bytes" : 348,
  "cluster_uuid" : "QD06dK7CQgids-GQZooNVw",
  "version" : 3,
  "state_uuid" : "mjNIWXAzQVuxNNOQ7xR-qg",
  "master_node" : "IdM5B7cUQWqFgIHXBp0JDg",
  "blocks" : { },
  "nodes" : {
    "u7DoTpMmSCixOoictzHItA" : {
      "name" : "es-cluster-1",
      "ephemeral_id" : "ZlBflnXKRMC4RvEACHIVdg",
      "transport_address" : "10.244.4.191:9300",
      "attributes" : { }
    }
-------

4. 部署Kibana服务

kibana.yaml

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

使用 kubectl 工具创建：

$ kubectl create -f kibana.yaml
service/kibana created
deployment.apps/kibana created
$ kubectl get pods --namespace=logging
NAME                      READY   STATUS    RESTARTS   AGE
es-cluster-0              1/1     Running   1          13d
es-cluster-1              1/1     Running   1          13d
es-cluster-2              1/1     Running   1          13d
kibana-7fc9f8c964-nnzx4   1/1     Running   1          13d
$ kubectl get svc  --namespace=logging
NAME            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
elasticsearch   ClusterIP   None                    9200/TCP,9300/TCP   13d
kibana          NodePort    10.111.51.138           5601:31284/TCP      13d

在浏览器中打开http://<任意节点IP>:31284

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5. 部署Fluent

使用DasemonSet 控制器来部署 Fluentd 应用，确保在集群中的每个节点上始终运行一个 Fluentd 容器
新建fluentd-configmap.yaml

kind: ConfigMap
apiVersion: v1
metadata:
  name: fluentd-config
  namespace: logging
  labels:
    addonmanager.kubernetes.io/mode: Reconcile
data:
  system.conf: |-
    
      root_dir /tmp/fluentd-buffers/
    
  containers.input.conf: |-
    
      @id fluentd-containers.log #表示引用该日志源的唯一标识符，该标识可用于进一步过滤和路由结构化日志数据
      @type tail #Fluentd 内置的指令，tail表示 Fluentd 从上次读取的位置通过 tail 不断获取数据，另外一个是http表示通过一个 GET 请求来收集数据。
      path /var/log/containers/*.log #tail类型下的特定参数，告诉 Fluentd 采集/var/log/containers目录下的所有日志，这是 docker 在 Kubernetes 节点上用来存储运行容器 stdout 输出日志数据的目录。
      pos_file /var/log/es-containers.log.pos #检查点，如果 Fluentd 程序重新启动了，它将使用此文件中的位置来恢复日志数据收集。
      time_format %Y-%m-%dT%H:%M:%S.%NZ
      localtime
      tag raw.kubernetes.*  #用来将日志源与目标或者过滤器匹配的自定义字符串，Fluentd 匹配源/目标标签来路由日志数据
      format json
      read_from_head true
    
    
      @id raw.kubernetes
      @type detect_exceptions
      remove_tag_prefix raw
      message log
      stream stream
      multiline_flush_interval 5
      max_bytes 500000
      max_lines 1000
    
  system.input.conf: |-
    
      @id journald-docker
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "docker.service" }]
      
        @type local
        persistent true
      
      read_from_head true
      tag docker
    
    
      @id journald-kubelet
      @type systemd
      filters [{ "_SYSTEMD_UNIT": "kubelet.service" }]
      
        @type local
        persistent true
      
      read_from_head true
      tag kubelet
    
  forward.input.conf: |-
    
      @type forward
    
  output.conf: |-
    
      @type kubernetes_metadata
    
     #标识一个目标标签，后面是一个匹配日志源的正则表达式，我们这里想要捕获所有的日志并将它们发送给 Elasticsearch，所以需要配置成**。
      @id elasticsearch
      @type elasticsearch #输出到 Elasticsearch
      @log_level info #要捕获的日志级别 INFO级别及以上
      include_tag_key true
      host elasticsearch
      port 9200
      logstash_format true #Elasticsearch 服务对日志数据构建反向索引进行搜索，将 logstash_format 设置为true，Fluentd 将会以 logstash 格式来转发结构化的日志数据。
      request_timeout    30s
       # Fluentd 允许在目标不可用时进行缓存，比如，如果网络出现故障或者 Elasticsearch 不可用的时候。缓冲区配置也有助于降低磁盘的 IO。
        @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

配置 docker 容器日志目录以及 docker、kubelet 应用的日志的收集，收集到数据经过处理后发送到 elasticsearch:9200 服务

新建一个 fluentd-daemonset.yaml

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:  #节点选择
        beta.kubernetes.io/fluentd-ds-ready: "true" #节点需有这个标签才会部署收集
      tolerations: #添加容忍
      - 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

为节点添加标签

$ kubectl    label nodes  k8s-node01   beta.kubernetes.io/fluentd-ds-ready=true
$ kubectl get nodes --show-labels
NAME         STATUS   ROLES    AGE   VERSION   LABELS
k8s-node01   Ready       18d   v1.15.3   beta.kubernetes.io/arch=amd64,beta.kubernetes.io/fluentd-ds-ready=true,beta.kubernetes.io/os=linux,kubernetes.io/arch=amd64,kubernetes.io/hostname=k8s-node01,kubernetes.io/os=linux
kubesphere   Ready    master   20d   v1.15.3   beta.kubernetes.io/arch=amd64,beta.kubernetes.io/fluentd-ds-ready=true,beta.kubernetes.io/os=linux,kubernetes.io/arch=amd64,kubernetes.io/hostname=kubesphere,kubernetes.io/os=linux,node-role.kubernetes.io/master=

部署资源对象

$ kubectl create -f fluentd-configmap.yaml
configmap "fluentd-config" created
$ kubectl create -f fluentd-daemonset.yaml
serviceaccount "fluentd-es" created
clusterrole.rbac.authorization.k8s.io "fluentd-es" created
clusterrolebinding.rbac.authorization.k8s.io "fluentd-es" created
daemonset.apps "fluentd-es" created
$ kubectl get pods -n logging
NAME                      READY   STATUS    RESTARTS   AGE
es-cluster-0              1/1     Running   1          18d
es-cluster-1              1/1     Running   1          18d
es-cluster-2              1/1     Running   1          18d
fluentd-es-442jk          1/1     Running   0          4d22h
fluentd-es-knp2k          1/1     Running   0          4d22h
kibana-7fc9f8c964-nnzx4   1/1     Running   1          18d
$ kubectl  get svc -n logging
NAME            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
elasticsearch   ClusterIP   None                    9200/TCP,9300/TCP   18d
kibana          NodePort    10.111.51.138           5601:31284/TCP      18d

访问Kibana页面添加索引

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参考：
https://www.qikqiak.com/k8s-book/docs/62.%E6%90%AD%E5%BB%BA%20EFK%20%E6%97%A5%E5%BF%97%E7%B3%BB%E7%BB%9F.html

Kubernetes日志采集-EFK

1. 日志采集方案

2. 架构选型

3. 部署Elasticsearch 集群

创建一个名为 logging 的 namespace

创建建一个名为 elasticsearch 的无头服务

创建ES持久化存储

使用StatefulSet 创建Es Pod

4. 部署Kibana服务

5. 部署Fluent

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