对于企业来说，日志的重要性不言而喻，日志收集分析展示平台的选择，这里给出几点选择ELK的理由。ELK是一套非常成熟的系统，她本身的构架非常适合Kubernetes集群，这里官网当中也是选用的 Elasticsearch作为Sample的，GitHub上下载的kubernetes二进制包中本身就有这个.yaml文件，所以使用ELK作为收集日志的理由相当充分。
对于任何基础设施或后端服务系统，日志都是极其重要的。对于受Google内部容器管理系统Borg启发而催生出的Kubernetes项目来说，自然少不了对Logging的支持。在“ Logging Overview “中，官方概要介绍了Kubernetes上的几个层次的Logging方案，并给出Cluster-level logging的参考架构：
Kubernetes还给出了参考实现：
– Logging Backend： Elastic Search stack(包括： Kibana )
– Logging-agent： fluentd
01
介绍
1. Fluentd是一个开源收集事件和日志系统，用与各node节点日志数据的收集、处理等等。
2. ElasticSearch是一个开源的，基于Lucene的搜索服务器。它提供了一个分布式多用户能力的全文搜索引擎，基于RESTful web接口。
3. Kibana是一个开源的用于数据可视化的web UI工具，可使用它对日志进行高效的搜索、可视化、分析等各种操作。
02
流程
每个node节点上面的fluentd监控并收集该节点上面的系统日志，并将处理过后的日志信息发送给ElasticSearch，Elasticsearch汇总各个node节点的日志信息，最后结合Kibana 实现web UI界面的数据展示。
03
安装实现
1.确保K8S集群正常工作(当然这是必须的....)
2.fluentd.yaml文件编写，这里要实现每个节点都能有fluentd跑起来，只需要将kind设置为DaemonSet即可。
apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
name: fluentd-elasticsearch
namespace: kube-system
labels:
k8s-app: fluentd-logging
spec:
template:
metadata:
labels:
name: fluentd-elasticsearch
spec:
containers:
- name: fluentd-elasticsearch
image: gcr.io/google-containers/fluentd-elasticsearch:1.20
resources:
limits:
memory: 200Mi
requests:
cpu: 100m
memory: 200Mi
volumeMounts:
- name: varlog
mountPath: /var/log
- name: varlibdockercontainers
mountPath: /var/lib/docker/containers
readOnly: true
terminationGracePeriodSeconds: 30
volumes:
- name: varlog
hostPath:
path: /var/log
- name: varlibdockercontainers
hostPath:
path: /var/lib/docker/containers
3.elasticsearch-rc.yaml&elasticsearch-svc.yaml
apiVersion: v1
kind: ReplicationController
metadata:
name: elasticsearch-logging-v1
namespace: kube-system
labels:
k8s-app: elasticsearch-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
replicas: 2
selector:
k8s-app: elasticsearch-logging
version: v1
template:
metadata:
labels:
k8s-app: elasticsearch-logging
version: v1
kubernetes.io/cluster-service: "true"
spec:
containers:
- image: gcr.io/google-containers/elasticsearch:v2.4.1
name: elasticsearch-logging
resources:
# need more cpu upon initialization, therefore burstable class
limits:
cpu: 1000m
requests:
cpu: 100m
ports:
- containerPort: 9200
name: db
protocol: TCP
- containerPort: 9300
name: transport
protocol: TCP
volumeMounts:
- name: es-persistent-storage
mountPath: /data
volumes:
- name: es-persistent-storage
emptyDir: {}
apiVersion: v1
kind: Service
metadata:
name: elasticsearch-logging
namespace: kube-system
labels:
k8s-app: elasticsearch-logging
kubernetes.io/cluster-service: "true"
kubernetes.io/name: "Elasticsearch"
spec:
ports:
- port: 9200
protocol: TCP
targetPort: db
selector:
k8s-app: elasticsearch-logging
4.kibana-rc.yaml&kibana-svc.yaml
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
name: kibana-logging
namespace: kube-system
labels:
k8s-app: kibana-logging
kubernetes.io/cluster-service: "true"
spec:
replicas: 1
selector:
matchLabels:
k8s-app: kibana-logging
template:
metadata:
labels:
k8s-app: kibana-logging
spec:
containers:
- name: kibana-logging
image: gcr.io/google-containers/kibana:v4.6.1
resources:
# keep request = limit to keep this container in guaranteed class
limits:
cpu: 100m
requests:
cpu: 100m
env:
- name: "ELASTICSEARCH_URL"
value: "http://elasticsearch-logging:9200"
- name: "KIBANA_BASE_URL"
value: "/api/v1/proxy/namespaces/kube-system/services/kibana-logging"
ports:
- containerPort: 5601
name: ui
protocol: TCP
apiVersion: v1
kind: Service
metadata:
name: kibana-logging
namespace: kube-system
labels:
k8s-app: kibana-logging
kubernetes.io/cluster-service: "true"
kubernetes.io/name: "Kibana"
spec:
ports:
- port: 5601
protocol: TCP
targetPort: ui
selector:
k8s-app: kibana-logging
5.kubectl create -f ****** ，这里可自由发挥。
