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）
image.png 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