Kubernetes 集群本身不提供日志收集的解决方案，一般来说有主要的3种方案来做日志收集：
1、在每个节点上运行一个 agent 来收集日志
由于这种 agent 必须在每个节点上运行，所以直接使用 DaemonSet 控制器运行该应用程序即可
这种方法也仅仅适用于收集输出到 stdout 和 stderr 的应用程序日志
简单来说，本方式就是在每个node上各运行一个日志代理容器，
对本节点/var/log和 /var/lib/docker/containers/两个目录下的日志进行采集
2、在每个 Pod 中包含一个 sidecar 容器来收集应用日志
在 sidecar 容器中运行日志采集代理程序会导致大量资源消耗，因为你有多少个要采集的 Pod，就需要运行多少个采集代理程序，另外还无法使用 kubectl logs 命令来访问这些日志
3、直接在应用程序中将日志信息推送到采集后端
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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
创建 Elasticsearch 集群
一般使用3个 Elasticsearch Pod 来避免高可用下多节点集群中出现的“脑裂”问题，并且使用StatefulSet控制器来创建Elasticsearch Pod
创建StatefulSet pod时，直接在其pvc模板中使用StorageClass自动生成pv和pvc，可以实现数据持久化，nfs-client-provisioner已经提前准备好了。
1、创建独立的命名空间
apiVersion: v1
kind: Namespace
metadata:
name: logging2、创建StorageClas，也可以使用已经存在的StorageClas
apiVersion: storage.k8s.io/v1
kind: StorageClass
metadata:
name: es-data-db
provisioner: fuseim.pri/ifs # 该值需要和 provisioner 配置的保持一致3、创建StatefulSet pod前需要先创建无头服务
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-node4、创建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服务器上会自动生成3个目录，用于这3个pod存储数据
$ 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/
检查es集群状态
$ kubectl port-forward es-cluster-0 9200:9200 --namespace=logging
在另外一个窗口执行
$ curl http://localhost:9200/_cluster/state?pretty
用deployment控制器创建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
访问kibana
http://192.168.1.243:32081
安装配置 Fluentd
1、通过 ConfigMap 对象来指定 Fluentd 配置文件
kind: ConfigMap
apiVersion: v1
metadata:
name: fluentd-config
namespace: logging
labels:
addonmanager.kubernetes.io/mode: Reconcile
data:
system.conf: |-
root_dir /tmp/fluentd-buffers/
containers.input.conf: |-
@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
@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
@id elasticsearch
@type elasticsearch
@log_level info
include_tag_key true
host elasticsearch
port 9200
logstash_format true
request_timeout 30s
@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
上面配置文件中我们配置了 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
2、使用DaemonSet创建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内的日志
还可以搜集docker服务和kubelet服务的日志
为了能够灵活控制哪些节点的日志可以被收集，所以我们这里还添加了一个 nodSelector 属性
nodeSelector:
beta.kubernetes.io/fluentd-ds-ready: "true"所以要给所有节点打标签：
$ kubectl get node
$ kubectl label nodes server243.example.com beta.kubernetes.io/fluentd-ds-ready=true
$ kubectl get nodes --show-labels
由于我们的集群使用的是 kubeadm 搭建的，默认情况下 master 节点有污点，所以要想也收集 master 节点的日志，则需要添加上容忍
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----第一步输入logstash-*，第二步选择@timestamp
4、创建测试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 页面，在上面的Discover页面搜索栏中输入kubernetes.pod_name:counter
