From e5bc70d5fc98bc974fb729725831c60524fb327a Mon Sep 17 00:00:00 2001 From: lcjqyml <0818lcjth> Date: Tue, 13 Dec 2016 13:19:46 +0800 Subject: [PATCH] init blog --- bak/.2016-10-17-ELK.markdown_ | 236 -------------- bak/2016-05-05-Docker-K8S.markdown | 473 ----------------------------- 2 files changed, 709 deletions(-) delete mode 100644 bak/.2016-10-17-ELK.markdown_ delete mode 100644 bak/2016-05-05-Docker-K8S.markdown diff --git a/bak/.2016-10-17-ELK.markdown_ b/bak/.2016-10-17-ELK.markdown_ deleted file mode 100644 index d074e9855bd..00000000000 --- a/bak/.2016-10-17-ELK.markdown_ +++ /dev/null @@ -1,236 +0,0 @@ ---- -layout: post -title: "用ELK搭建监控系统" -subtitle: "" -date: 2016-10-17 -author: "mubanjiu" -catalog: true -tags: - - 技术 - - ELK - - devops ---- - -# 用ELK搭建监控系统 - -> ELK=ElasticSearch + Logstash + Kibana - -对于一个软件或互联网公司来说，对计算资源和应用进行监控和告警是非常基础的需求。对于大公司或成熟公司，一个高度定制化的监控系统应该已经存在了很长时间并且非常成熟了。而对于一个初创公司或小公司来说，如何利用现有开源工具快速搭建一套日志监控及分析平台是需要探索的事情。 - -### 监控系统的用户： -运维，开发，产品 - -### 监控系统应该可以解决如下的问题： -1. 监控server的各项基础指标，比如memory,cpu,load,network等 -2. 监控应用的状态。 -3. 搜集应用日志，并进行分析和统计。通过日志分析和统计可得到应用的访问统计，异常统计，业务统计。具有进行大规模日志数据的分析和处理能力。 -4. 可制定告警规则。各种监控数据进入系统后，可以根据条件触发告警，实时的将应用异常情况推送到运维、开发或业务人员的IM/SMS上。 -5. 可定制的看板。可以将各种实时统计或报表直观的显示出来。 - - -### 可选方案： -1. 日志宝，日志易，Logtail(阿里云) - -优势：使用简单 - -劣势：需上传日志到外部，不灵活，不易扩展，需付费 - -2. flume-ng + kafka + spark streaming + hbase(es/mysql) + zepplin/自研web展示 - -优势：灵活，易于扩展，数据分析和处理能力强 - -劣势：开发难度高，周期长，维护成本高 - -3. ELK - -优势：开源成熟解决方案，使用简单，扩展能力强 - -劣势：日志分析和处理依靠logstash完成，处理能力较低，无法适应复杂的日志分析场景 - - - -结论：初步选择ELK搭建起监控平台，其能够满足当前较为简单的监控和分析需求。未来如果不能适应，再考虑其他方案。 - - - -### 搭建ELK测试环境 - -整个测试环境基于docker来搭建 - -#### 部署elasticsearch - -`docker run -d -v /data/es:/usr/share/elasticsearch/data -p 9200:9200 -p 9300:9300 elasticsearch:5.0` - - - -#### 部署kibana - -经测试，kibana官方的5.0的image不能用，无法在es 5.0上创建index。所以选择在centos image上自己手动下载部署kibana 5.0 - -`docker run -ti -p 5601:5601 centos /bin/bash ` - -到官方站点下载rpm包并安装：https://www.elastic.co/downloads/kibana - - -#### 安装X-Pack插件 -其为es推出的包含安全，监控，告警等插件的包 - -Take the Elastic Stack to the next level with Shield (security), Marvel (monitoring), Watcher (alerting), Graph, and reporting. - -在elasticsearch es_home下执行：bin/elasticsearch-plugin install x-pack - -重启es - -在kibana kibana_home下执行：bin/kibana-plugin install x-pack - -重启kibana - -访问kibana，默认账号是elastic/changeme 然后新建一个logstash账号给logstash使用 - -#### 部署logstash - -logstash的配置文件位于/opt/logstash/logstash.conf - -准备一个access.log用于测试，将该文件也置于/opt/logstash/下 - -内容为： - - #character at the beginning of a line indicates a comment. Use - # comments to describe your configuration. - input { - file { - path => /opt/logstash/access.log - } - } - - # The filter part of this file is commented out to indicate that it is - # optional. - filter { - grok { - match => { "message" => "%{COMBINEDAPACHELOG}"} - } - } - - output { - elasticsearch { - hosts => [ "192.168.99.100:9200" ] - user => logstash - password => logstash - } - } - -hosts和user/password需设置为真实的值 - -`docker run -v /opt/logstash:/opt/logstash logstash -f /opt/logstash/logstash.conf ` - - -logstash起来后，可通过kibana页面直接查看access log的情况 - -![image](/img/elk/kibana-1.png) - -### 使用collectd+logstash+es+kibana+timelion来完成对系统的基础监控 - -对系统的基础状况进行监控：load,cpu,memory,network - -collectd 搜集数据通过UDP发送给logstash，logstash使用collectd codec进行解码，生成结构化数据后将数据发送给es；用户使用kibana的timelion插件来查询es中的数据，并进行图表展示。 - -在监控目标机器上安装collectd - -collectd 的配置文件片段： - - LoadPlugin network - - - - - - - -logstash的配置文件： - - #character at the beginning of a line indicates a comment. Use - # comments to describe your configuration. - input { - udp { - port => 25826 - buffer_size => 1452 - workers => 3 # Default is 2 - queue_size => 30000 # Default is 2000 - codec => collectd { } - type => "collectd" - } - } - - - output { - elasticsearch { - hosts => [ "192.168.99.100:9200" ] - user => logstash - password => logstash - } - } - -kibana timelion的查询： - -load: `.es(collectd_type:load,metric='avg:shortterm').label("1m"), .es(collectd_type:load,metric='avg:midterm').label("5m"), .es(collectd_type:load,metric='avg:longterm').label("15m").title('load')` - -cpu: `.es('collectd_type:cpu AND type_instance:user',metric='avg:value').derivative().label("user"),.es('collectd_type:cpu AND type_instance:idle',metric='avg:value').derivative().label("idle").hide(),.es('collectd_type:cpu AND type_instance:system',metric='avg:value').derivative().label("system"),.es('collectd_type:cpu AND type_instance:wait',metric='avg:value').derivative().label("wait").title("cpu")` - -network: `.es('collectd_type:if_packets AND plugin_instance:eth0',metric='avg:rx').derivative().label("rx"),.es('collectd_type:if_packets AND plugin_instance:eth0',metric='avg:tx').derivative().label("tx").title(network)` - -memory: `.es('collectd_type:memory AND type_instance:used',metric='avg:value').divide(1000000).label("used(MB)"),.es('collectd_type:memory AND type_instance:free',metric='avg:value').divide(1000000).label("free(MB)"),.es('collectd_type:memory AND type_instance:cached',metric='avg:value').divide(1000000).label("cached(MB)"),.es('collectd_type:memory AND type_instance:buffered',metric='avg:value').divide(1000000).label("buffered(MB)").title(memory)` - -![image](/img/elk/kibana-2.png) - - -### 使用logstash-logback-encoder+logstash+es+kibana 来完成对系统日志的汇总和查询 - -logstash-logback-encoder是一个应用内的logback appender，其将logback的日志（json格式）通过UDP/TCP发送给logstash，logstash将日志存储到es中，kibana 从es中查询日志数据 - -应用内添加如下依赖： - - - net.logstash.logback - logstash-logback-encoder - 4.5.1 - - -在logback.xml中添加将日志发送给logstash的appender - - - logstashServer - - 514 - {"appname":"myWebservice"} - - - - - -logstash的配置片段： - - #character at the beginning of a line indicates a comment. Use - # comments to describe your configuration. - input { - udp { - port => 25826 - buffer_size => 1452 - workers => 3 # Default is 2 - queue_size => 30000 # Default is 2000 - } - } - - - output { - elasticsearch { - hosts => [ "192.168.99.100:9200" ] - user => logstash - password => logstash - } - } - - -![image](/img/elk/kibana-3.png) - - -总结：ELK平台搭建和使用起来都十分简单，功能强大，能够满足对日志的汇总和简单分析的需求。在生产环境还需要进一步验证，比如对资源的占用情况，对现有应用是否有影响等。 diff --git a/bak/2016-05-05-Docker-K8S.markdown b/bak/2016-05-05-Docker-K8S.markdown deleted file mode 100644 index f5cefd086be..00000000000 --- a/bak/2016-05-05-Docker-K8S.markdown +++ /dev/null @@ -1,473 +0,0 @@ ---- -layout: post -title: "CentOS7下Docker+k8s集群的安装过程" -subtitle: "" -date: 2016-05-05 -author: "mubanjiu" -catalog: true -tags: - - 技术 - - devops ---- - ->本docker集群采用docker-engine + flannel + k8s来搭建 ->将机器分为master和node两个角色，master上相对node多了registry，etcd，kube-apiserver,kube-scheduler,kube-controller-manager - ->master为：192.168.1.200 ->其他为node - -# 1.安装docker engine.(所有机器) -1.1 升级所有软件包及系统内核： `yum update` -1.2 `reboot` -1.3 安装docker-engine: `curl -fsSL https://get.docker.com/ | sh` -1.4 设为开机启动： `systemctl enable docker` - -# 2.修改registry(master) -docker官方的Docker Hub由于各种原因在国内基本无法访问，好在有daocloud.io 提供的国内镜像。 -在/etc/systemd/system/docker.service.d/下新建docker.conf文件，内容为： - - [Service] - ExecStart= - ExecStart=/usr/bin/docker daemon -H fd:// --graph="/data/docker-data" --storage-driver=overlay --registry-mirror=http://78967139.m.daocloud.io --insecure-registry=78967139.m.daocloud.io - -重启docker： -`systemctl daemon-reload` -`systemctl restart docker` - -# 3.安装私有仓库(master) -registry v2和v1之间有些区别，v2中如果registry配置了proxy，那这个registry就不能push image上去了。我们建私仓当然得push image上去了，所以私仓和proxy就得分开搭建。私仓用来管理我们自己的私有image，proxy用来加速Registry Hub。 -由于是搭建的内部集群，datacenter以外访问不了，故不考虑安全问题，将registry运行在container中： -私仓： -`docker run -d -p 80:5000 -v /data/docker-registry/private:/var/lib/registry --restart=always --name=registry_private registry:2` -Proxy: -`docker run -d -p 5000:5000 -v /data/docker-registry/proxy:/var/lib/registry -e REGISTRY_PROXY_REMOTEURL=http://78967139.m.daocloud.io --restart=always --name=registry_proxy registry:2` - - - -# 4.registry配置改成私有仓库(所有机器) -修改/etc/systemd/system/docker.service.d/下的docker.conf文件，将相应地址改成私有仓库的地址即可 - - [Service] - ExecStart= - ExecStart=/usr/bin/docker daemon -H fd:// --graph="/data/docker-data" --storage-driver=overlay --registry-mirror=http://192.168.1.200:5000 --insecure-registry=192.168.1.200 - -然后重启docker -`systemctl daemon-reload` -`systemctl restart docker` - -# 5.安装etcd(master) -etcd是给flannel和k8s用的配置管理中心，和zookeeper类似。本来想用docker跑etcd，但是由于flannel依赖etcd，而docker会依赖flannel，如果etcd再依赖docker，这样就变成了循环依赖，在启动的时候不知道哪个应该最先启动了。所以etcd还是只能直接跑在宿主机上。只部署了单台的etcd，考虑到HA，未来需扩展成为etcd集群。 -`yum install etcd` -`systemctl enable etcd` -修改etcd配置文件/etc/etcd/etcd.conf： -修改这项：ETCD_LISTEN_CLIENT_URLS=http://0.0.0.0:2379,http://0.0.0.0:4001 -默认是监听的localhost，这样会导致其他机器无法访问etcd -启动etcd： -`systemctl start etcd` -如果其他机器无法连接到etcd，可能是firewall的原因，根据情况配置或者直接关闭，我是直接关闭：`systemctl stop firewalld` - - - -# 6.安装flannel(所有机器) -docker下的网络解决访问主要有calico,flannel,weave,docker overlay.他们的使用和对比可以参考下面这个blog：http://chunqi.li/2015/11/15/Battlefield-Calico-Flannel-Weave-and-Docker-Overlay-Network/ -经过了一些粗浅的比较，在calico和flannel vxlan之间纠结了一下，最后我选择的是flannel vxlan. 选择主要考量是性能和易用性 -使用yum安装flannel: `yum install flannel` ，repository里的并不是最新版本的flannel，如果需要最新版本的可以自己下载安装. 仓库中的flannel的-etcd-prefix默认值是/atomic.io/network 而自己安装的是/coreos.com/network,在写配置到etcd时需注意。 - -flannel依赖etcd来获取配置和存储网络信息 -使用etcdctl将配置写入etcd: -`etcdctl set /atomic.io/network/config '{"Network": "10.0.0.0/16","SubnetLen": 24,"SubnetMin": "10.0.1.0","SubnetMax": "10.0.200.0","Backend": {"Type": "vxlan"}}'` - -修改flannel配置/etc/sysconfig/flanneld : -`FLANNEL_ETCD="http://192.168.1.200:2379"` -`FLANNEL_OPTIONS="-ip-masq=true"` - --ip-masq=true 这个参数的目的是让flannel进行ip伪装，而不让docker进行ip伪装。这么做的原因是如果docker进行ip伪装，流量再从flannel出去，其他host上看到的source ip就是flannel的网关ip，而不是docker容器的ip。hadoop和hbase会依赖这个ip进行集群管理，而把flannel的网关当做cluster node将会出现问题。 -参见：https://luqmansahaf.github.io/2015/11/29/on-setting-hadoop-hbase-docker-networking/ - -关闭firewall： -`systemctl stop firewalld` -`systemctl disable firewalld` - -启动flanneld -`systemctl start flanneld` -添加flanneld为开机启动 -`systemctl enable flanneld` - -修改docker启动脚本 /etc/systemd/system/docker.service.d/docker.conf： - - [Service] - ExecStart= - ExecStart=/usr/bin/docker daemon -H fd:// $DOCKER_NETWORK_OPTIONS --graph="/data/docker-data" --storage-driver=overlay --registry-mirror=http://192.168.1.200:5000 --insecure-registry=192.168.1.200 - -重启docker： -`systemctl daemon-reload` -`systemctl restart docker` -通过以下命令检查docker0是否应用到了flanneld获取到的网络配置 -`ip add` -`docker0: flags=4163 mtu 1450 - inet 10.0.94.1 netmask 255.255.255.0 broadcast 0.0.0.0 - inet6 fe80::42:faff:fef6:fe2 prefixlen 64 scopeid 0x20 - ether 02:42:fa:f6:0f:e2 txqueuelen 0 (Ethernet) - RX packets 78103 bytes 150034637 (143.0 MiB) - RX errors 0 dropped 0 overruns 0 frame 0 - TX packets 151950 bytes 160736481 (153.2 MiB) - TX errors 0 dropped 0 overruns 0 carrier 0 collisions 0` - -上面的信息说明docker0已经应用了 10.0.94.0/24 这个子网 -也可以通过`systemctl status docker` 查看docker的启动参数中是否有--bip=**的参数来验证 - -至此所有机器上已经安装好flannel了，为了测试flannel，可以分别在两台host上启动一个centos container，查看各自的ip，以及测试否能够互相ping通。如果可以ping通，则代表flannel能够正常工作了。 - -# 7.安装kube-node(所有机器) -kube-node上的组件有kubelet和kube-proxy -下载kubenetes的最新版本：https://github.com/kubernetes/kubernetes/releases -解压后再 -`tar -xzf kubernetes/server/kubernetes-server-linux-amd64.tar.gz -tar -xzf kubernetes/server/kubernetes-salt.tar.gz` -将kubernets/server/bin下的 kubelet，kube-proxy,kubectl copy 到/usr/local/bin/下面. -将kubernetes/saltbase/salt/kubelet/kubelet.service copy 到/lib/systemd/system/下面. -然后在/etc/sysconfig/下创建kubelet文件，内容为： - - DAEMON_ARGS="--api-servers=http://192.168.1.200:8080 --config=/etc/kubernetes/manifests --log-dir=/data/kube-node/ --logtostderr=false --allow-privileged=true --register-node=true --cluster-dns=10.0.201.10 --cluster-domain=test.com --docker-root=/data/docker-data --pod-infra-container-image=woodbird/pause:2.0" - -kube-proxy的kube-proxy.service文件kubenetes并未提供，原因可能是kubenetes希望将kube-proxy运行在docker里面，通过kubelet来启动。而我希望和kubelet一致，都作为服务启动。 -在/lib/systemd/system/下创建kube-proxy.service文件，其内容为： - - [Unit] - Description=kube-proxy - After=kubelet - Requires=kubelet - - [Service] - ExecStart=/usr/local/bin/kube-proxy --master=http://192.168.1.200:8080 --log-dir=/data/kube-node/ - - [Install] - WantedBy=multi-user.target - -需要将参数里面的kube-master替换成实际的值 - -最后将服务设置为自动启动，并启动服务 -`systemctl daemon-reload` -`systemctl enable kubelet` -`systemctl enable kube-proxy` -`systemctl start kubelet` -`systemctl start kube-proxy` - -# 8.安装kube-master(master) -kube-master的组件有kube-apiserver,kube-scheduler,kube-controller-manager. 将他们以daemon pod的方式运行在docker里面。 -在/etc/kubernetes/manifests/下创建相关pod声明文件： -kube-apiserver.yaml: - - apiVersion: v1 - kind: Pod - metadata: - name: kube-apiserver - namespace: kube-system - spec: - containers: - - command: - - /bin/sh - - -c - - /hyperkube apiserver - --etcd-servers=http://192.168.1.200:4001 - --service-cluster-ip-range=10.0.201.0/24 - --insecure-bind-address=0.0.0.0 - --allow-privileged=true - image: woodbird/hyperkube:v1.2.3 - imagePullPolicy: IfNotPresent - livenessProbe: - httpGet: - host: 127.0.0.1 - path: /healthz - port: 8080 - scheme: HTTP - initialDelaySeconds: 15 - timeoutSeconds: 15 - name: kube-apiserver - ports: - - containerPort: 443 - hostPort: 443 - name: https - protocol: TCP - - containerPort: 8080 - hostPort: 8080 - name: local - protocol: TCP - resources: - requests: - cpu: 250m - volumeMounts: - - mountPath: /srv/kubernetes - name: srvkube - readOnly: true - - mountPath: /etc/ssl - name: etcssl - readOnly: true - - mountPath: /usr/share/ca-certificates - name: usrsharecacerts - readOnly: true - - mountPath: /srv/sshproxy - name: srvsshproxy - dnsPolicy: ClusterFirst - hostNetwork: true - restartPolicy: Always - terminationGracePeriodSeconds: 30 - volumes: - - hostPath: - path: /srv/kubernetes - name: srvkube - - hostPath: - path: /etc/ssl - name: etcssl - - hostPath: - path: /usr/share/ca-certificates - name: usrsharecacerts - - hostPath: - path: /srv/sshproxy - name: srvsshproxy - -kube-sheduler.yaml: - - apiVersion: v1 - kind: Pod - metadata: - name: kube-scheduler - namespace: kube-system - spec: - containers: - - command: - - /bin/sh - - -c - - /hyperkube scheduler - --master=192.168.1.200:8080 - --address=0.0.0.0 - image: woodbird/hyperkube:v1.2.3 - imagePullPolicy: IfNotPresent - livenessProbe: - httpGet: - host: 127.0.0.1 - path: /healthz - port: 10251 - scheme: HTTP - initialDelaySeconds: 15 - timeoutSeconds: 15 - name: kube-scheduler - resources: - requests: - cpu: 100m - dnsPolicy: ClusterFirst - hostNetwork: true - nodeName: kubernetes-master - restartPolicy: Always - terminationGracePeriodSeconds: 30 - -kube-controller-manager.yaml: - - apiVersion: v1 - kind: Pod - metadata: - name: kube-controller - namespace: kube-system - spec: - containers: - - command: - - /bin/sh - - -c - - /hyperkube controller-manager - --master=192.168.1.200:8080 - --cluster-name=fxsoft - --cluster-cidr=10.0.0.0/16 - --address=0.0.0.0 - image: woodbird/hyperkube:v1.2.3 - imagePullPolicy: IfNotPresent - livenessProbe: - httpGet: - host: 127.0.0.1 - path: /healthz - port: 10252 - scheme: HTTP - initialDelaySeconds: 15 - timeoutSeconds: 15 - name: kube-controller-manager - resources: - limits: - cpu: 200m - requests: - cpu: 200m - volumeMounts: - - mountPath: /srv/kubernetes - name: srvkube - readOnly: true - - mountPath: /etc/ssl - name: etcssl - readOnly: true - - mountPath: /usr/share/ca-certificates - name: usrsharecacerts - readOnly: true - dnsPolicy: ClusterFirst - hostNetwork: true - restartPolicy: Always - terminationGracePeriodSeconds: 30 - volumes: - - hostPath: - path: /srv/kubernetes - name: srvkube - - hostPath: - path: /etc/ssl - name: etcssl - - hostPath: - path: /usr/share/ca-certificates - name: usrsharecacerts - - -由于GFW，无法访问google的registry，我将google registry上的需要用到的image tag 并push到docker Hub 上了。 - -# 9.安装skydns(master) -官方的dns addon职责是对service进行dns解析，即将service名称解析成为service ip。 -而在我的使用过程中，我还希望这个dns能做到host的解析，即能够将机器名解析成ip，同时也能支持反向解析，即通过ip解析成机器名。所以自己新增了这部分功能,image 是：woodbird/pod2dns:0.5. 这个image中包含一个shell脚本，脚本会从kubernetes apiserver中拿到所有pod的信息，然后写到skydns的etcd当中 -https://github.com/woodbird/pod2sky -skydns-rc.yaml: - - apiVersion: v1 - kind: ReplicationController - metadata: - name: kube-dns-v10 - namespace: kube-system - labels: - k8s-app: kube-dns - version: v10 - kubernetes.io/cluster-service: "true" - spec: - replicas: 1 - selector: - k8s-app: kube-dns - version: v10 - template: - metadata: - labels: - k8s-app: kube-dns - version: v10 - kubernetes.io/cluster-service: "true" - spec: - containers: - - name: etcd - image: woodbird/etcd:2.0.9 - resources: - ## keep request = limit to keep this container in guaranteed class - limits: - cpu: 100m - memory: 50Mi - requests: - cpu: 100m - memory: 50Mi - command: - - /usr/local/bin/etcd - - -data-dir - - /var/etcd/data - - -listen-client-urls - - http://127.0.0.1:2379,http://127.0.0.1:4001 - - -advertise-client-urls - - http://127.0.0.1:2379,http://127.0.0.1:4001 - - -initial-cluster-token - - skydns-etcd - volumeMounts: - - name: etcd-storage - mountPath: /var/etcd/data - - name: kube2sky - image: woodbird/kube2sky:1.12 - resources: - ## keep request = limit to keep this container in guaranteed class - limits: - cpu: 100m - memory: 50Mi - requests: - cpu: 100m - memory: 50Mi - command: - - /kube2sky - args: - - -domain=fxsoft.com - - -kube_master_url=http://192.168.1.200:8080 - - name: pod2sky - image: woodbird/pod2sky:latest - resources: - ## keep request = limit to keep this container in guaranteed class - limits: - cpu: 100m - memory: 50Mi - requests: - cpu: 100m - memory: 50Mi - command: - - /bin/bash - - -c - - /usr/bin/schedulePod2sky.sh - --cluster_domain=test.com - --kube_master_url=http://192.168.1.200:8080 - - name: skydns - image: woodbird/skydns:2015-10-13-8c72f8c - resources: - ## keep request = limit to keep this container in guaranteed class - limits: - cpu: 100m - memory: 50Mi - requests: - cpu: 100m - memory: 50Mi - command: - - /skydns - args: - - -machines=http://127.0.0.1:4001 - - -addr=0.0.0.0:53 - - -ns-rotate=false - - -domain=test.com - - -nameservers=192.168.1.1:53 - ports: - - containerPort: 53 - name: dns - protocol: UDP - - containerPort: 53 - name: dns-tcp - protocol: TCP - livenessProbe: - httpGet: - path: /healthz - port: 8080 - scheme: HTTP - initialDelaySeconds: 30 - timeoutSeconds: 5 - readinessProbe: - httpGet: - path: /healthz - port: 8080 - scheme: HTTP - initialDelaySeconds: 1 - timeoutSeconds: 5 - - name: healthz - image: woodbird/exechealthz:1.0 - resources: - ## keep request = limit to keep this container in guaranteed class - limits: - cpu: 10m - memory: 20Mi - requests: - cpu: 10m - memory: 20Mi - command: - - /exechealthz - args: - - -cmd=nslookup kubernetes.default.svc.fxsoft.com 127.0.0.1 >/dev/null - - -port=8080 - ports: - - containerPort: 8080 - protocol: TCP - volumes: - - name: etcd-storage - emptyDir: {} - dnsPolicy: Default ## Don't use cluster DNS. - -skydns-rc.yaml和skydns-svc.yaml在kubernetes/addons/dns/目录下有，拿来稍微修改下就能用了 -`kubectl create -f skydns-rc.yaml` -`kubectl create -f skydns-svc.yaml` - -至此整个集群就基本搭建完成了。 \ No newline at end of file