在kubernetes1.17.2上结合ceph部署efk

时间：2020-03-18 20:28:36 阅读：84 评论：0 收藏：0 [点我收藏+]

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简绍

应用程序和系统日志可以帮助我们了解集群内部的运行情况，日志对于我们调试问题和监视集群情况也是非常有用的。而且大部分的应用都会有日志记录，对于传统的应用大部分都会写入到本地的日志文件之中。对于容器化应用程序来说则更简单，只需要将日志信息写入到 stdout 和 stderr 即可，容器默认情况下就会把这些日志输出到宿主机上的一个 JSON 文件之中，同样也可以通过 docker logs 或者 kubectl logs 来查看到对应的日志信息。

Kubernetes 中比较流行的日志收集解决方案是 Elasticsearch、Fluentd 和 Kibana（EFK）技术栈，也是官方现在比较推荐的一种方案。
Elasticsearch 是一个实时的、分布式的可扩展的搜索引擎，允许进行全文、结构化搜索，它通常用于索引和搜索大量日志数据，也可用于搜索许多不同类型的文档。Elasticsearch 通常与 Kibana 一起部署。

Kibana 是 Elasticsearch 的一个功能强大的数据可视化 Dashboard，Kibana 允许你通过 web 界面来浏览 Elasticsearch 日志数据。
Fluentd是一个流行的开源数据收集器，我们将在 Kubernetes 集群节点上安装 Fluentd，通过获取容器日志文件、过滤和转换日志数据，然后将数据传递到 Elasticsearch 集群，在该集群中对其进行索引和存储。

拓扑图

技术图片

ps: 因为我的物理机资源有限，并且还要在集群中部署myweb、prometheus、jenkins等，所以这里我只部署EFK，正常情况，这套方案也足够使用了。
配置启动一个可扩展的 Elasticsearch 集群，然后在 Kubernetes 集群中创建一个 Kibana 应用，最后通过 DaemonSet 来运行 Fluentd，以便它在每个 Kubernetes 工作节点上都可以运行一个 Pod。

检查集群状态

ceph集群

# ceph -s
  cluster:
    id:     ed4d59da-c861-4da0-bbe2-8dfdea5be796
    health: HEALTH_WARN
            application not enabled on 1 pool(s)
 
  services:
    mon: 3 daemons, quorum bs-k8s-harbor,bs-k8s-gitlab,bs-k8s-ceph
    mgr: bs-k8s-ceph(active), standbys: bs-k8s-harbor, bs-k8s-gitlab
    osd: 6 osds: 6 up, 6 in
 
  data:
    pools:   1 pools, 128 pgs
    objects: 92  objects, 285 MiB
    usage:   6.7 GiB used, 107 GiB / 114 GiB avail
    pgs:     128 active+clean

原因：这是因为未在池上启用应用程序。
解决：
# ceph osd lspools
6 webapp
# ceph osd pool application enable webapp rbd
enabled application 'rbd' on pool 'webapp'
# ceph -s
......
    health: HEALTH_OK

kubernetes集群

# kubectl get pods --all-namespaces 
NAMESPACE     NAME                                         READY   STATUS    RESTARTS   AGE
kube-system   calico-kube-controllers-6cf5b744d7-rxt86     1/1     Running   0          47h
kube-system   calico-node-25dlc                            1/1     Running   2          2d4h
kube-system   calico-node-49q4n                            1/1     Running   2          2d4h
kube-system   calico-node-4gmcp                            1/1     Running   1          2d4h
kube-system   calico-node-gt4bt                            1/1     Running   1          2d4h
kube-system   calico-node-svcdj                            1/1     Running   1          2d4h
kube-system   calico-node-tkrqt                            1/1     Running   1          2d4h
kube-system   coredns-76b74f549-dkjxd                      1/1     Running   0          47h
kube-system   dashboard-metrics-scraper-64c8c7d847-dqbx2   1/1     Running   0          46h
kube-system   kubernetes-dashboard-85c79db674-bnvlk        1/1     Running   0          46h
kube-system   metrics-server-6694c7dd66-hsbzb              1/1     Running   0          47h
kube-system   traefik-ingress-controller-m8jf9             1/1     Running   0          47h
kube-system   traefik-ingress-controller-r7cgl             1/1     Running   0          47h
myweb         rbd-provisioner-9cf46c856-b9pm9              1/1     Running   1          7h2m
myweb         wordpress-6677ff7bd-sc45d                    1/1     Running   0          6h13m
myweb         wordpress-mysql-6d7bd496b4-62dps             1/1     Running   0          5h51m
# kubectl top nodes
NAME         CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%  
20.0.0.201   563m         14%    1321Mi          103%    
20.0.0.202   359m         19%    1288Mi          100%    
20.0.0.203   338m         18%    1272Mi          99%     
20.0.0.204   546m         14%    954Mi           13%     
20.0.0.205   516m         13%    539Mi           23%     
20.0.0.206   375m         9%     1123Mi          87%

创建namespace

这里我准备将所有efk放入assembly名称空间下。 assembly：组件

# vim namespace.yaml 

[root@bs-k8s-master01 efk]# pwd
/data/k8s/efk
[root@bs-k8s-master01 efk]# cat namespace.yaml 
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   namespace.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: Namespace
metadata:
  name: assembly

创建动态RBD StorageClass

创建assembly pool

bs-k8s-ceph
# ceph osd pool create assembly 128
pool 'assembly' created
# ceph auth get-or-create client.assembly mon 'allow r' osd 'allow class-read, allow rwx pool=assembly' -o ceph.client.assemply.keyring

创建Storageclass

bs-k8s-master01
# ceph auth get-key client.assembly | base64
QVFBWjIzRmVDa0RnSGhBQWQ0TXJWK2YxVThGTUkrMjlva1JZYlE9PQ==
# cat ceph-efk-secret.yaml 
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   ceph-jenkins-secret.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: Secret
metadata:
  name: ceph-admin-secret
  namespace: assembly 
data:
  key: QVFBaUptcGU0R3RDREJBQWhhM1E3NnowWG5YYUl1VVI2MmRQVFE9PQ==
type: kubernetes.io/rbd
---
apiVersion: v1
kind: Secret
metadata:
  name: ceph-assembly-secret
  namespace: assembly 
data:
  key: QVFBWjIzRmVDa0RnSGhBQWQ0TXJWK2YxVThGTUkrMjlva1JZYlE9PQ==
type: kubernetes.io/rbd
# kubectl apply -f ceph-efk-secret.yaml
secret/ceph-admin-secret created
secret/ceph-assembly-secret created
# cat ceph-efk-storageclass.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   ceph-jenkins-storageclass.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
  name: ceph-efk
  namespace: assembly
  annotations:
    storageclass.kubernetes.io/is-default-class: "false"
provisioner: ceph.com/rbd
reclaimPolicy: Retain
parameters:
  monitors: 20.0.0.205:6789,20.0.0.206:6789,20.0.0.207:6789
  adminId: admin
  adminSecretName: ceph-admin-secret
  adminSecretNamespace: assembly
  pool: assembly
  fsType: xfs
  userId: assembly
  userSecretName: ceph-assembly-secret
  imageFormat: "2"
  imageFeatures: "layering"
# kubectl apply -f ceph-efk-storageclass.yaml
storageclass.storage.k8s.io/ceph-efk created

ceph rbd和kubernetes结合需要第三方插件
# cat external-storage-rbd-provisioner.yaml 
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   external-storage-rbd-provisioner.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: ServiceAccount
metadata:
  name: rbd-provisioner
  namespace: assembly
---
kind: ClusterRole
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: rbd-provisioner
rules:
  - apiGroups: [""]
    resources: ["persistentvolumes"]
    verbs: ["get", "list", "watch", "create", "delete"]
  - apiGroups: [""]
    resources: ["persistentvolumeclaims"]
    verbs: ["get", "list", "watch", "update"]
  - apiGroups: ["storage.k8s.io"]
    resources: ["storageclasses"]
    verbs: ["get", "list", "watch"]
  - apiGroups: [""]
    resources: ["events"]
    verbs: ["create", "update", "patch"]
  - apiGroups: [""]
    resources: ["endpoints"]
    verbs: ["get", "list", "watch", "create", "update", "patch"]
  - apiGroups: [""]
    resources: ["services"]
    resourceNames: ["kube-dns"]
    verbs: ["list", "get"]
---
kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: rbd-provisioner
subjects:
  - kind: ServiceAccount
    name: rbd-provisioner
    namespace: assembly
roleRef:
  kind: ClusterRole
  name: rbd-provisioner
  apiGroup: rbac.authorization.k8s.io

---
apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  name: rbd-provisioner
  namespace: assembly
rules:
- apiGroups: [""]
  resources: ["secrets"]
  verbs: ["get"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
  name: rbd-provisioner
  namespace: assembly
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: Role
  name: rbd-provisioner
subjects:
- kind: ServiceAccount
  name: rbd-provisioner
  namespace: assembly

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: rbd-provisioner
  namespace: assembly
spec:
  replicas: 1
  selector:
    matchLabels:
      app: rbd-provisioner
  strategy:
    type: Recreate
  template:
    metadata:
      labels:
        app: rbd-provisioner
    spec:
      containers:
      - name: rbd-provisioner
        image: "harbor.linux.com/rbd/rbd-provisioner:latest"
        imagePullPolicy: IfNotPresent
        env:
        - name: PROVISIONER_NAME
          value: ceph.com/rbd
      imagePullSecrets: 
        - name: k8s-harbor-login
      serviceAccount: rbd-provisioner
      nodeSelector:             ## 设置node筛选器，在特定label的节点上启动
        rbd: "true"
# kubectl apply -f external-storage-rbd-provisioner.yaml
serviceaccount/rbd-provisioner created
clusterrole.rbac.authorization.k8s.io/rbd-provisioner unchanged
clusterrolebinding.rbac.authorization.k8s.io/rbd-provisioner configured
role.rbac.authorization.k8s.io/rbd-provisioner created
rolebinding.rbac.authorization.k8s.io/rbd-provisioner created
deployment.apps/rbd-provisioner created
# kubectl get pods -n assembly
NAME                              READY   STATUS    RESTARTS   AGE
rbd-provisioner-9cf46c856-6qzll   1/1     Running   0          71s

创建Elasticsearch

创建elasticsearch-svc.yaml
定义了一个名为 elasticsearch 的 Service，指定标签app=elasticsearch，当我们将 Elasticsearch StatefulSet 与此服务关联时，服务将返回带有标签app=elasticsearch的 Elasticsearch Pods 的 DNS A 记录，然后设置clusterIP=None，将该服务设置成无头服务。最后，我们分别定义端口9200、9300，分别用于与 REST API 交互，以及用于节点间通信。
# cat elasticsearch-svc.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   elasticsearch-svc.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
kind: Service
apiVersion: v1
metadata:
  name: elasticsearch
  namespace: assembly
  labels:
    app: elasticsearch
spec:
  selector:
    app: elasticsearch
  clusterIP: None
  ports:
    - port: 9200
      name: rest
    - port: 9300
      name: inter-node
# kubectl apply -f elasticsearch-svc.yaml
service/elasticsearch created
已经为 Pod 设置了无头服务和一个稳定的域名.elasticsearch.assmbly.svc.cluster.local，接下来通过 StatefulSet 来创建具体的 Elasticsearch 的 Pod 应用.
Kubernetes StatefulSet 允许为 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。

创建动态pv
# cat elasticsearch-pvc.yaml 
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-18
#FileName:                   elasticsearch-pvc.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: ceph-elasticsearch
  namespace: assembly
  labels:
    app: elasticsearch
spec:
  storageClassName: ceph-efk
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 10Gi
#kubectl apply -f ceph-efk-storageclass.yaml 
# cat elasticsearch-statefulset.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   elasticsearch-storageclass.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: es-cluster
  namespace: assembly
spec:
  serviceName: elasticsearch
  selector:
    matchLabels:
      app: elasticsearch
  template:
    metadata:
      labels:
        app: elasticsearch
    spec:
      imagePullSecrets: 
        - name: k8s-harbor-login
      containers:
      - name: elasticsearch
        image: harbor.linux.com/efk/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:
        - 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
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: ceph-elasticsearch 
      nodeSelector:             ## 设置node筛选器，在特定label的节点上启动
        elasticsearch: "true"  
节点打标签
# kubectl label nodes 20.0.0.204 elasticsearch=true
node/20.0.0.204 labeled
# kubectl apply -f elasticsearch-statefulset.yaml
# kubectl get pods -n assembly
NAME                              READY   STATUS    RESTARTS   AGE
es-cluster-0                      1/1     Running   0          2m15s
rbd-provisioner-9cf46c856-6qzll   1/1     Running   0          37m

Pods 部署完成后，我们可以通过请求一个 REST API 来检查 Elasticsearch 集群是否正常运行。使用下面的命令将本地端口9200转发到 Elasticsearch 节点（es-cluster-0）对应的端口
# kubectl port-forward es-cluster-0 9200:9200 --namespace=assembly
Forwarding from 127.0.0.1:9200 -> 9200
#  curl http://localhost:9200/_cluster/state?pretty
{
  "cluster_name" : "k8s-logs",
  "compressed_size_in_bytes" : 234,
  "cluster_uuid" : "PopKT5FLROqyBYlRvvr7kw",
  "version" : 2,
  "state_uuid" : "ubOKSevGRVe4iR5JXODjDA",
  "master_node" : "vub5ot69Thu8igd4qeiZBg",
  "blocks" : { },
  "nodes" : {
    "vub5ot69Thu8igd4qeiZBg" : {
      "name" : "es-cluster-0",
      "ephemeral_id" : "9JjNmdOyRomyYsHAO1IQ5Q",
      "transport_address" : "172.20.46.85:9300",
      "attributes" : { }
    }
  },

创建Kibana

Elasticsearch 集群启动成功了，接下来可以来部署 Kibana 服务，新建一个名为 kibana.yaml 的文件。

# cat kibana.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   kibana.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: Service
metadata:
  name: kibana
  namespace: assembly
  labels:
    app: kibana
spec:
  ports:
  - port: 5601
  type: NodePort
  selector:
    app: kibana

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: kibana
  namespace: assembly
  labels:
    app: kibana
spec:
  selector:
    matchLabels:
      app: kibana
  template:
    metadata:
      labels:
        app: kibana
    spec:
      imagePullSecrets: 
        - name: k8s-harbor-login
      containers:
      - name: kibana
        image: harbor.linux.com/efk/kibana-oss:6.4.3
        resources:
          limits:
            cpu: 1000m
          requests:
            cpu: 100m
        env:
          - name: ELASTICSEARCH_URL
            value: http://elasticsearch:9200
        ports:
        - containerPort: 5601
      nodeSelector:             ## 设置node筛选器，在特定label的节点上启动
        kibana: "true"  
节点打标签
# kubectl label nodes 20.0.0.204 kibana=true
node/20.0.0.204 labeled
# kubectl apply -f kibana.yaml
service/kibana created
deployment.apps/kibana created
# kubectl get pods -n assembly
NAME                              READY   STATUS    RESTARTS   AGE
es-cluster-0                      1/1     Running   0          8m4s
kibana-598987f498-k8ff9           1/1     Running   0          70s
rbd-provisioner-9cf46c856-6qzll   1/1     Running   0          43m
定义了两个资源对象，一个 Service 和 Deployment，为了测试方便，我们将 Service 设置为了 NodePort 类型，Kibana Pod 中配置都比较简单，唯一需要注意的是我们使用 ELASTICSEARCH_URL 这个环境变量来设置Elasticsearch 集群的端点和端口，直接使用 Kubernetes DNS 即可，此端点对应服务名称为 elasticsearch，由于是一个 headless service，所以该域将解析为 Elasticsearch Pod 的 IP 地址列表
# kubectl get svc --namespace=assembly
NAME            TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)             AGE
elasticsearch   ClusterIP   None            <none>        9200/TCP,9300/TCP   50m
kibana          NodePort    10.68.123.234   <none>        5601:22693/TCP      2m22s

代理kibana

这里我让kibana走traefik代理

# cat kibana-ingreeroute.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   kibana-ingreeroute.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: traefik.containo.us/v1alpha1
kind: IngressRoute
metadata:
  name: kibana
  namespace: assembly
spec:
  entryPoints:
    - web
  routes:
  - match: Host(`kibana.linux.com`)
    kind: Rule
    services:
    - name: kibana
      port: 5601
# kubectl apply -f kibana-ingreeroute.yaml
ingressroute.traefik.containo.us/kibana created

技术图片

traefik代理成功，本地主机hosts解析

技术图片

web访问成功！

创建Fluentd

Fluentd是一个高效的日志聚合器，是用 Ruby 编写的，并且可以很好地扩展。对于大部分企业来说，Fluentd 足够高效并且消耗的资源相对较少，另外一个工具Fluent-bit更轻量级，占用资源更少，但是插件相对 Fluentd 来说不够丰富，所以整体来说，Fluentd 更加成熟，使用更加广泛，所以这里使用 Fluentd 来作为日志收集工具。

工作原理

Fluentd 通过一组给定的数据源抓取日志数据，处理->转换成结构化的数据格式将它们转发给其他服务，比如 Elasticsearch、对象存储等等。Fluentd 支持超过300个日志存储和分析服务，所以在这方面是非常灵活的。主要运行步骤如下：

? 首先 Fluentd 从多个日志源获取数据

? 结构化并且标记这些数据

? 然后根据匹配的标签将数据发送到多个目标服务去

Fluentd拓扑图

技术图片

配置

通过一个配置文件来告诉 Fluentd 如何采集、处理数据的

日志源配置

比如这里为了收集 Kubernetes 节点上的所有容器日志，就需要做如下的日志源配置：

<source>
@id fluentd-containers.log
@type tail
path /var/log/containers/*.log
pos_file /var/log/fluentd-containers.log.pos
time_format %Y-%m-%dT%H:%M:%S.%NZ
tag raw.kubernetes.*
format json
read_from_head true
</source>

上面配置部分参数说明如下：

id：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：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：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：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。
tag：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。

路由配置

上面是日志源的配置，接下来看看如何将日志数据发送到 Elasticsearch：

<match **>
@id elasticsearch
@type elasticsearch
@log_level info
include_tag_key true
type_name fluentd
host "#{ENV['OUTPUT_HOST']}"
port "#{ENV['OUTPUT_PORT']}"
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 "#{ENV['OUTPUT_BUFFER_CHUNK_LIMIT']}"
queue_limit_length "#{ENV['OUTPUT_BUFFER_QUEUE_LIMIT']}"
overflow_action block
</buffer>

match：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：目标的一个唯一标识符。
type：支持的输出插件标识符，我们这里要输出到 Elasticsearch，所以配置成 elasticsearch，这是 Fluentd 的一个内置插件。
log_level：指定要捕获的日志级别，我们这里配置成info，表示任何该级别或者该级别以上（INFO、WARNING、ERROR）的日志都将被路由到 Elsasticsearch。
host/port：定义 Elasticsearch 的地址，也可以配置认证信息，我们的 Elasticsearch 不需要认证，所以这里直接指定 host 和 port 即可。
logstash_format：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。
Buffer： 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

要收集 Kubernetes 集群的日志，直接用 DasemonSet 控制器来部署 Fluentd 应用，这样，它就可以从 Kubernetes 节点上采集日志，确保在集群中的每个节点上始终运行一个 Fluentd 容器。

首先，通过 ConfigMap 对象来指定 Fluentd 配置文件，新建 fluentd-configmap.yaml 文件。

# cat fluentd-configmap.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   fluentd-configmap.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
kind: ConfigMap
apiVersion: v1
metadata:
  name: fluentd-config
  namespace: assembly
  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>
    # 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: |-
    # Logs from systemd-journal for interesting services.
    <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: |-
    # Takes the messages sent over TCP
    <source>
      @type forward
    </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
      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>
# kubectl apply -f fluentd-configmap.yaml
configmap/fluentd-config created

上面配置文件中配置了 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。

然后新建一个 fluentd-daemonset.yaml 的文件

# cat fluentd-daemonset.yaml
##########################################################################
#Author:                     zisefeizhu
#QQ:                         2********0
#Date:                       2020-03-13
#FileName:                   fluentd-daemonset.yaml
#URL:                        https://www.cnblogs.com/zisefeizhu/
#Description:                The test script
#Copyright (C):              2020 All rights reserved
###########################################################################
apiVersion: v1
kind: ServiceAccount
metadata:
  name: fluentd-es
  namespace: assembly
  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: assembly
  apiGroup: ""
roleRef:
  kind: ClusterRole
  name: fluentd-es
  apiGroup: ""
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd-es
  namespace: assembly
  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:
      serviceAccountName: fluentd-es
      imagePullSecrets: 
        - name: k8s-harbor-login
      containers:
      - name: fluentd-es
        image: harbor.linux.com/efk/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: /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: varlibdockercontainers
        hostPath:
          path: /var/lib/docker/containers
      - name: config-volume
        configMap:
          name: fluentd-config
      nodeSelector:             ## 设置node筛选器，在特定label的节点上启动
        fluentd: "true" 
节点打标签
# kubectl apply -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 label nodes 20.0.0.204 fluentd=true
node/20.0.0.204 labeled
# kubectl label nodes 20.0.0.205 fluentd=true
node/20.0.0.205 labeled
# kubectl label nodes 20.0.0.206 fluentd=true
node/20.0.0.206 labeled
# kubectl get pods -n assembly -o wide
NAME                              READY   STATUS    RESTARTS   AGE     IP              NODE         NOMINATED NODE   READINESS GATES
es-cluster-0                      1/1     Running   0          30m     172.20.46.85    20.0.0.204   <none>           <none>
fluentd-es-5fgt7                  1/1     Running   0          5m36s   172.20.46.87    20.0.0.204   <none>           <none>
fluentd-es-l22nj                  1/1     Running   0          5m22s   172.20.145.9    20.0.0.205   <none>           <none>
fluentd-es-pnqk8                  1/1     Running   0          5m18s   172.20.208.29   20.0.0.206   <none>           <none>
kibana-598987f498-k8ff9           1/1     Running   0          23m     172.20.46.86    20.0.0.204   <none>           <none>
rbd-provisioner-9cf46c856-6qzll   1/1     Running   0          65m     172.20.46.84    20.0.0.204   <none>           <none>

技术图片

前面 Fluentd 配置文件中我们采集的日志使用的是 logstash 格式，这里只需要在文本框中输入logstash-*即可匹配到 Elasticsearch pod中的所有日志数据，然后点击下一步，进入以下页面：

技术图片

在该页面中配置使用哪个字段按时间过滤日志数据，在下拉列表中，选择@timestamp字段，然后点击Create index pattern，创建完成后，点击左侧导航菜单中的Discover，然后就可以看到一些直方图和最近采集到的日志数据了
技术图片

技术图片

至此完成了efk的部署

启动池

# ceph osd pool application enable assembly rbd
enabled application 'rbd' on pool 'assembly'

在kubernetes1.17.2上结合ceph部署efk

标签：scheduler text 解决方案 dashboard _for chown tmp 文档 with

原文地址：https://www.cnblogs.com/zisefeizhu/p/12519658.html

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