Young Kbt blog
Young Kbt
2023-06-23
目录

Kubernetes - ELK 日志管理

# ELK 组成

# Elasticsearch

ES 作为一个搜索型文档数据库，拥有优秀的搜索能力，以及提供了丰富的 REST API 让我们可以轻松的调用接口。

# Filebeat

Filebeat 是一款轻量的数据收集工具。

# Logstash

通过 Logstash 同样可以进行日志收集，但是若每一个节点都需要收集时，部署 Logstash 有点过重，因此这里主要用到 Logstash 的数据清洗能力，收集交给 Filebeat 去实现。

# Kibana

Kibana 是一款基于 ES 的可视化操作界面工具，利用 Kibana 可以实现非常方便的 ES 可视化操作。

# 流程图

k8s 内置 Docker，而 Docker 专门有个所有容器的统一日志收集目录 /var/log/containers，所以可以用 Filebeat 去这个目录收集容器的日志，收集后发给 Logstash（如果日志数据太大，可以先发给 Kafka 等中间件，然后 Logstash 去 Kafka 获取日志）。

Logstash 得到日志后，内部可以进行数据的清洗等操作，然后发给 ElasticSearch，接着 Kibana 通过 ElasticSearch 的 API 去 ElasticSearch 里检索日志，在页面展示给程序员、运维人员看。

Kibana 是一个 ElasticSearch 的可视化界面

如下图：

image-20230624151651646

# 集成 ELK

image-20230624151623028

# 部署 es 搜索服务

需要提前给 es 落盘节点打上标签

# 对应下面 es.yaml 的 127
kubectl label node <node name> es=data
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创建 es.yaml

--- 
apiVersion: v1 
kind: Service 
metadata: 
  name: elasticsearch-logging 
  namespace: kube-logging 
  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 
--- 
# RBAC authn and authz 
apiVersion: v1 
kind: ServiceAccount 
metadata: 
  name: elasticsearch-logging 
  namespace: kube-logging 
  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-logging 
  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-logging 
  apiGroup: "" 
roleRef: 
  kind: ClusterRole 
  name: elasticsearch-logging 
  apiGroup: "" 
--- 
# Elasticsearch deployment itself 
apiVersion: apps/v1 
kind: StatefulSet # 使用 statefulset 创建 Pod 
metadata: 
  name: elasticsearch-logging # pod 名称,使用 statefulSet 创建的 Pod 是有序号有顺序的 
  namespace: kube-logging  # 命名空间 
  labels: 
    k8s-app: elasticsearch-logging 
    kubernetes.io/cluster-service: "true" 
    addonmanager.kubernetes.io/mode: Reconcile 
    srv: srv-elasticsearch 
spec: 
  serviceName: elasticsearch-logging # 与 svc 相关联，这可以确保使用以下 DNS 地址访问 Statefulset 中的每个 pod (es-cluster-[0,1,2].elasticsearch.elk.svc.cluster.local)
  replicas: 1 # 副本数量,单节点 
  selector: 
    matchLabels: 
      k8s-app: elasticsearch-logging # 和 pod template 配置的 labels 相匹配 
  template:
    metadata: 
      labels: 
        k8s-app: elasticsearch-logging 
        kubernetes.io/cluster-service: "true" 
    spec: 
      serviceAccountName: elasticsearch-logging 
      containers: 
      - image: docker.io/library/elasticsearch:7.9.3 
        name: elasticsearch-logging 
        resources: 
          # need more cpu upon initialization, therefore burstable class 
          limits: 
            cpu: 1000m 
            memory: 2Gi 
          requests: 
            cpu: 100m 
            memory: 500Mi 
        ports: 
        - containerPort: 9200 
          name: db 
          protocol: TCP 
        - containerPort: 9300 
          name: transport 
          protocol: TCP 
        volumeMounts: 
        - name: elasticsearch-logging 
          mountPath: /usr/share/elasticsearch/data/  # 挂载点 
        env: 
        - name: "NAMESPACE" 
          valueFrom: 
            fieldRef: 
              fieldPath: metadata.namespace 
        - name: "discovery.type"  # 定义单节点类型 
          value: "single-node" 
        - name: ES_JAVA_OPTS # 设置 Java 的内存参数，可以适当进行加大调整 
          value: "-Xms512m -Xmx2g"  
      volumes: 
      - name: elasticsearch-logging 
        hostPath: 
          path: /data/es/ 
      nodeSelector: # 如果需要匹配落盘节点可以添加 nodeSelect 
        es: data 
      tolerations: 
      - effect: NoSchedule 
        operator: Exists 
      # 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: # 容器初始化前的操作 
      - name: elasticsearch-logging-init 
        image: alpine:3.6 
        command: ["/sbin/sysctl", "-w", "vm.max_map_count=262144"] # 添加 mmap 计数限制，太低可能造成内存不足的错误 
        securityContext:  # 仅应用到指定的容器上，并且不会影响 Volume 
          privileged: true # 运行特权容器 
      - name: increase-fd-ulimit 
        image: busybox 
        imagePullPolicy: IfNotPresent 
        command: ["sh", "-c", "ulimit -n 65536"] # 修改文件描述符最大数量 
        securityContext: 
          privileged: true 
      - name: elasticsearch-volume-init # es 数据落盘初始化，加上 777 权限 
        image: alpine:3.6 
        command: 
          - chmod 
          - -R 
          - "777" 
          - /usr/share/elasticsearch/data/ 
        volumeMounts: 
        - name: elasticsearch-logging 
          mountPath: /usr/share/elasticsearch/data/
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创建命名空间

kubectl create ns kube-logging
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创建服务

kubectl create -f es.yaml
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查看 pod 启用情况

kubectl get pod -n kube-logging
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# 部署 logstash 数据清洗

创建 logstash.yaml 并部署服务

--- 
apiVersion: v1 
kind: Service 
metadata: 
  name: logstash 
  namespace: kube-logging 
spec: 
  ports: 
  - port: 5044 
    targetPort: beats 
  selector: 
    type: logstash 
  clusterIP: None 
--- 
apiVersion: apps/v1 
kind: Deployment 
metadata: 
  name: logstash 
  namespace: kube-logging 
spec: 
  selector: 
    matchLabels: 
      type: logstash 
  template: 
    metadata: 
      labels: 
        type: logstash 
        srv: srv-logstash 
    spec: 
      containers: 
      - image: docker.io/kubeimages/logstash:7.9.3 # 该镜像支持 arm64 和 amd64 两种架构 
        name: logstash 
        ports: 
        - containerPort: 5044 
          name: beats 
        command: 
        - logstash 
        - '-f' 
        - '/etc/logstash_c/logstash.conf' 
        env: 
        - name: "XPACK_MONITORING_ELASTICSEARCH_HOSTS" 
          value: "http://elasticsearch-logging:9200" 
        volumeMounts: 
        - name: config-volume 
          mountPath: /etc/logstash_c/ 
        - name: config-yml-volume 
          mountPath: /usr/share/logstash/config/ 
        - name: timezone 
          mountPath: /etc/localtime 
        resources: # logstash 一定要加上资源限制，避免对其他业务造成资源抢占影响 
          limits: 
            cpu: 1000m 
            memory: 2048Mi 
          requests: 
            cpu: 512m 
            memory: 512Mi 
      volumes: 
      - name: config-volume 
        configMap: 
          name: logstash-conf 
          items: 
          - key: logstash.conf 
            path: logstash.conf 
      - name: timezone 
        hostPath: 
          path: /etc/localtime 
      - name: config-yml-volume 
        configMap: 
          name: logstash-yml 
          items: 
          - key: logstash.yml 
            path: logstash.yml 
 
--- 
apiVersion: v1 
kind: ConfigMap 
metadata: 
  name: logstash-conf 
  namespace: kube-logging 
  labels: 
    type: logstash 
data: 
  logstash.conf: |- 
    input {
      beats { 
        port => 5044 
      } 
    } 
    filter {
      # 处理 ingress 日志 
      if [kubernetes][container][name] == "nginx-ingress-controller" {
        json {
          source => "message" 
          target => "ingress_log" 
        }
        if [ingress_log][requesttime] { 
          mutate { 
            convert => ["[ingress_log][requesttime]", "float"] 
          }
        }
        if [ingress_log][upstremtime] { 
          mutate { 
            convert => ["[ingress_log][upstremtime]", "float"] 
          }
        } 
        if [ingress_log][status] { 
          mutate { 
            convert => ["[ingress_log][status]", "float"] 
          }
        }
        if  [ingress_log][httphost] and [ingress_log][uri] {
          mutate { 
            add_field => {"[ingress_log][entry]" => "%{[ingress_log][httphost]}%{[ingress_log][uri]}"} 
          } 
          mutate { 
            split => ["[ingress_log][entry]","/"] 
          } 
          if [ingress_log][entry][1] { 
            mutate { 
              add_field => {"[ingress_log][entrypoint]" => "%{[ingress_log][entry][0]}/%{[ingress_log][entry][1]}"} 
              remove_field => "[ingress_log][entry]" 
            }
          } else { 
            mutate { 
              add_field => {"[ingress_log][entrypoint]" => "%{[ingress_log][entry][0]}/"} 
              remove_field => "[ingress_log][entry]" 
            }
          }
        }
      }
      # 处理以srv进行开头的业务服务日志 
      if [kubernetes][container][name] =~ /^srv*/ { 
        json { 
          source => "message" 
          target => "tmp" 
        } 
        if [kubernetes][namespace] == "kube-logging" { 
          drop{} 
        } 
        if [tmp][level] { 
          mutate{ 
            add_field => {"[applog][level]" => "%{[tmp][level]}"} 
          } 
          if [applog][level] == "debug"{ 
            drop{} 
          } 
        } 
        if [tmp][msg] { 
          mutate { 
            add_field => {"[applog][msg]" => "%{[tmp][msg]}"} 
          } 
        } 
        if [tmp][func] { 
          mutate { 
            add_field => {"[applog][func]" => "%{[tmp][func]}"} 
          } 
        } 
        if [tmp][cost]{ 
          if "ms" in [tmp][cost] { 
            mutate { 
              split => ["[tmp][cost]","m"] 
              add_field => {"[applog][cost]" => "%{[tmp][cost][0]}"} 
              convert => ["[applog][cost]", "float"] 
            } 
          } else { 
            mutate { 
              add_field => {"[applog][cost]" => "%{[tmp][cost]}"} 
            }
          }
        }
        if [tmp][method] { 
          mutate { 
            add_field => {"[applog][method]" => "%{[tmp][method]}"} 
          }
        }
        if [tmp][request_url] { 
          mutate { 
            add_field => {"[applog][request_url]" => "%{[tmp][request_url]}"} 
          } 
        }
        if [tmp][meta._id] { 
          mutate { 
            add_field => {"[applog][traceId]" => "%{[tmp][meta._id]}"} 
          } 
        } 
        if [tmp][project] { 
          mutate { 
            add_field => {"[applog][project]" => "%{[tmp][project]}"} 
          }
        }
        if [tmp][time] { 
          mutate { 
            add_field => {"[applog][time]" => "%{[tmp][time]}"} 
          }
        }
        if [tmp][status] { 
          mutate { 
            add_field => {"[applog][status]" => "%{[tmp][status]}"} 
            convert => ["[applog][status]", "float"] 
          }
        }
      }
      mutate { 
        rename => ["kubernetes", "k8s"] 
        remove_field => "beat" 
        remove_field => "tmp" 
        remove_field => "[k8s][labels][app]" 
      }
    }
    output { 
      elasticsearch { 
        hosts => ["http://elasticsearch-logging:9200"] 
        codec => json 
        index => "logstash-%{+YYYY.MM.dd}" # 索引名称以 logstash+ 日志进行每日新建 
      } 
    } 
---
 
apiVersion: v1 
kind: ConfigMap 
metadata: 
  name: logstash-yml 
  namespace: kube-logging 
  labels: 
    type: logstash 
data: 
  logstash.yml: |- 
    http.host: "0.0.0.0" 
    xpack.monitoring.elasticsearch.hosts: http://elasticsearch-logging:9200
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kubectl create -f logstash.yaml
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# 部署 filebeat 数据采集

创建 filebeat.yaml 并部署

--- 
apiVersion: v1 
kind: ConfigMap 
metadata: 
  name: filebeat-config 
  namespace: kube-logging 
  labels: 
    k8s-app: filebeat 
data: 
  filebeat.yml: |- 
    filebeat.inputs: 
    - type: container 
      enable: true
      paths: 
        - /var/log/containers/*.log # 这里是 filebeat 采集挂载到 pod 中的日志目录 
      processors: 
        - add_kubernetes_metadata: # 添加 k8s 的字段用于后续的数据清洗 
            host: ${NODE_NAME}
            matchers: 
            - logs_path: 
                logs_path: "/var/log/containers/" 
    # output.kafka:  # 如果日志量较大，es 中的日志有延迟，可以选择在 filebeat 和 logstash 中间加入 kafka 
    #  hosts: ["kafka-log-01:9092", "kafka-log-02:9092", "kafka-log-03:9092"] 
    # topic: 'topic-test-log' 
    #  version: 2.0.0 
    output.logstash: # 因为还需要部署 logstash 进行数据的清洗，因此 filebeat 是把数据推到 logstash 中 
       hosts: ["logstash:5044"] 
       enabled: true 
--- 
apiVersion: v1 
kind: ServiceAccount 
metadata: 
  name: filebeat 
  namespace: kube-logging 
  labels: 
    k8s-app: filebeat
--- 
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole 
metadata: 
  name: filebeat 
  labels: 
    k8s-app: filebeat 
rules: 
- apiGroups: [""] # "" indicates the core API group 
  resources: 
  - namespaces 
  - pods 
  verbs: ["get", "watch", "list"] 
--- 
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding 
metadata: 
  name: filebeat 
subjects: 
- kind: ServiceAccount 
  name: filebeat 
  namespace: kube-logging 
roleRef: 
  kind: ClusterRole 
  name: filebeat 
  apiGroup: rbac.authorization.k8s.io 
--- 
apiVersion: apps/v1 
kind: DaemonSet 
metadata: 
  name: filebeat 
  namespace: kube-logging 
  labels: 
    k8s-app: filebeat 
spec: 
  selector: 
    matchLabels: 
      k8s-app: filebeat 
  template: 
    metadata: 
      labels: 
        k8s-app: filebeat 
    spec: 
      serviceAccountName: filebeat 
      terminationGracePeriodSeconds: 30 
      containers: 
      - name: filebeat 
        image: docker.io/kubeimages/filebeat:7.9.3 # 该镜像支持 arm64 和 amd64 两种架构 
        args: [ 
          "-c", "/etc/filebeat.yml", 
          "-e","-httpprof","0.0.0.0:6060" 
        ] 
        #ports: 
        #  - containerPort: 6060 
        #    hostPort: 6068 
        env: 
        - name: NODE_NAME 
          valueFrom: 
            fieldRef: 
              fieldPath: spec.nodeName 
        - name: ELASTICSEARCH_HOST 
          value: elasticsearch-logging 
        - name: ELASTICSEARCH_PORT 
          value: "9200" 
        securityContext: 
          runAsUser: 0 
          # If using Red Hat OpenShift uncomment this: 
          #privileged: true 
        resources: 
          limits: 
            memory: 1000Mi 
            cpu: 1000m 
          requests: 
            memory: 100Mi 
            cpu: 100m 
        volumeMounts: 
        - name: config # 挂载的是 filebeat 的配置文件 
          mountPath: /etc/filebeat.yml 
          readOnly: true 
          subPath: filebeat.yml 
        - name: data # 持久化 filebeat 数据到宿主机上 
          mountPath: /usr/share/filebeat/data 
        - name: varlibdockercontainers # 这里主要是把宿主机上的源日志目录挂载到filebeat容器中，如果没有修改 docker 或者 containerd 的 runtime 进行了标准的日志落盘路径，可以把 mountPath 改为 /var/lib 
          mountPath: /var/lib
          readOnly: true 
        - name: varlog # 这里主要是把宿主机上 /var/log/pods和/var/log/containers 的软链接挂载到 filebeat 容器中 
          mountPath: /var/log/ 
          readOnly: true 
        - name: timezone 
          mountPath: /etc/localtime 
      volumes: 
      - name: config 
        configMap: 
          defaultMode: 0600 
          name: filebeat-config 
      - name: varlibdockercontainers 
        hostPath: # 如果没有修改docker或者containerd的runtime进行了标准的日志落盘路径，可以把path改为/var/lib 
          path: /var/lib
      - name: varlog 
        hostPath: 
          path: /var/log/ 
      # data folder stores a registry of read status for all files, so we don't send everything again on a Filebeat pod restart 
      - name: inputs 
        configMap: 
          defaultMode: 0600 
          name: filebeat-inputs 
      - name: data 
        hostPath: 
          path: /data/filebeat-data 
          type: DirectoryOrCreate 
      - name: timezone 
        hostPath: 
          path: /etc/localtime 
      tolerations: #加入容忍能够调度到每一个节点 
      - effect: NoExecute 
        key: dedicated 
        operator: Equal 
        value: gpu 
      - effect: NoSchedule 
        operator: Exists
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kubectl create -f filebeat.yaml
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# 部署 kibana 可视化界面

此处有配置 kibana 访问域名，如果没有域名则需要在本机配置 hosts

192.168.199.28 kibana.youngkbt.cn
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创建 kibana.yaml 并创建服务

---
apiVersion: v1
kind: ConfigMap
metadata:
  namespace: kube-logging
  name: kibana-config
  labels:
    k8s-app: kibana
data:
  kibana.yml: |-
    server.name: kibana
    server.host: "0"
    i18n.locale: zh-CN                      # 设置默认语言为中文
    elasticsearch:
      hosts: ${ELASTICSEARCH_HOSTS}         # es 集群连接地址，由于我这都都是 k8s 部署且在一个 ns 下，可以直接使用 service name 连接
--- 
apiVersion: v1 
kind: Service 
metadata: 
  name: kibana 
  namespace: kube-logging 
  labels: 
    k8s-app: kibana 
    kubernetes.io/cluster-service: "true" 
    addonmanager.kubernetes.io/mode: Reconcile 
    kubernetes.io/name: "Kibana" 
    srv: srv-kibana 
spec: 
  type: NodePort
  ports: 
  - port: 5601 
    protocol: TCP 
    targetPort: ui 
  selector: 
    k8s-app: kibana 
--- 
apiVersion: apps/v1 
kind: Deployment 
metadata: 
  name: kibana 
  namespace: kube-logging 
  labels: 
    k8s-app: kibana 
    kubernetes.io/cluster-service: "true" 
    addonmanager.kubernetes.io/mode: Reconcile 
    srv: srv-kibana 
spec: 
  replicas: 1 
  selector: 
    matchLabels: 
      k8s-app: kibana 
  template: 
    metadata: 
      labels: 
        k8s-app: kibana 
    spec: 
      containers: 
      - name: kibana 
        image: docker.io/kubeimages/kibana:7.9.3 # 该镜像支持 arm64 和 amd64 两种架构 
        resources: 
          # need more cpu upon initialization, therefore burstable class 
          limits: 
            cpu: 1000m 
          requests: 
            cpu: 100m 
        env: 
          - name: ELASTICSEARCH_HOSTS 
            value: http://elasticsearch-logging:9200 
        ports: 
        - containerPort: 5601 
          name: ui 
          protocol: TCP 
        volumeMounts:
        - name: config
          mountPath: /usr/share/kibana/config/kibana.yml
          readOnly: true
          subPath: kibana.yml
      volumes:
      - name: config
        configMap:
          name: kibana-config
--- 
apiVersion: networking.k8s.io/v1
kind: Ingress 
metadata: 
  name: kibana 
  namespace: kube-logging 
spec: 
  ingressClassName: nginx
  rules: 
  - host: kibana.youngkbt.cn
    http: 
      paths: 
      - path: / 
        pathType: Prefix
        backend: 
          service:
            name: kibana 
            port:
              number: 5601
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kubectl create -f kibana.yaml
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# Kibana 配置

进入 Kibana 界面，打开菜单中的 Stack Management 可以看到采集到的日志。

避免日志越来越大，占用磁盘过多，进入 索引生命周期策略 界面点击 创建策略 按钮。

  • 设置策略名称为 logstash-history-ilm-policy
  • 关闭热阶段
  • 开启删除阶段，设置保留天数为 7 天

保存配置

为了方便在 discover 中查看日志，选择 索引模式 然后点击 创建索引模式 按钮。

  • 索引模式名称 里面配置 logstash-*

  • 点击下一步

  • 时间字段 选择 @timestamp

  • 点击 创建索引模式 按钮

由于部署的单节点，产生副本后索引状态会变成 yellow，打开 dev tools，取消所有索引的副本数

PUT _all/_settings
{
    "number_of_replicas": 0
}
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为了标准化日志中的 map 类型，以及解决链接索引生命周期策略，我们需要修改默认模板

PUT _template/logstash 
{ 
    "order": 1, 
    "index_patterns": [ 
      "logstash-*" 
    ], 
    "settings": { 
      "index": { 
      "lifecycle" : { 
          "name" : "logstash-history-ilm-policy" 
        }, 
        "number_of_shards": "2", 
        "refresh_interval": "5s", 
        "number_of_replicas" : "0" 
      } 
    }, 
    "mappings": { 
        "properties": { 
          "@timestamp": { 
            "type": "date" 
          }, 
          "applog": { 
            "dynamic": true, 
            "properties": { 
              "cost": { 
                "type": "float" 
              }, 
              "func": { 
                "type": "keyword" 
              }, 
              "method": { 
                "type": "keyword" 
              } 
            } 
          }, 
          "k8s": { 
            "dynamic": true, 
            "properties": { 
              "namespace": { 
                "type": "keyword" 
              }, 
              "container": { 
                "dynamic": true, 
                "properties": { 
                  "name": { 
                    "type": "keyword" 
                  } 
                } 
              }, 
              "labels": { 
                "dynamic": true, 
                "properties": { 
                  "srv": { 
                    "type": "keyword" 
                  } 
                } 
              } 
            } 
          }, 
          "geoip": { 
            "dynamic": true, 
            "properties": { 
              "ip": { 
                "type": "ip" 
              }, 
              "latitude": { 
                "type": "float" 
              }, 
              "location": { 
                "type": "geo_point" 
              }, 
              "longitude": { 
                "type": "float" 
              } 
            } 
          } 
      } 
    }, 
    "aliases": {} 
  }
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最后即可通过 discover 进行搜索了。

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更新时间: 2023/10/23, 10:58:52
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