经过一段时间的探索，我们先后完成了Kubernetes集群搭建，DNS、Dashboard、Heapster等插件安装，集群安全配置，搭建作为Persistent Volume的CephRBD，以及服务更新等探索和实现工作。现在Kubernetes集群层面的Logging需求逐渐浮上水面了。

随着一些小应用在我们的Kubernetes集群上的部署上线，集群的运行迈上了正轨。但问题随之而来，那就是如何查找和诊断集群自身的问题以及运行于Pod中应用的问题。日志，没错！我们也只能依赖Kubernetes组件以及Pod中应用输出的日志。不过目前我们仅能通过kubectl logs命令或Kubernetes Dashboard来查看Log。在没有cluster level logging的情况下，我们需要分别查看各个Pod的日志，操作繁琐，过程低效。我们迫切地需要为Kubernetes集群搭建一套集群级别的集中日志收集和分析设施。

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：

ElasticSearch stack实现的cluster level logging的一个优势在于其对Kubernetes集群中的Pod没有侵入性，Pod无需做任何配合性改动。同时EFK/ELK方案在业内也是相对成熟稳定的。

在本文中，我将为我们的Kubernetes 1.3.7集群安装ElasticSearch、Fluentd和Kibana。由于1.3.7版本略有些old，EFK能否在其上面run起来，我也是心中未知。能否像《生化危机：终章》那样有一个完美的结局，我们还需要一步一步“打怪升级”慢慢看。

一、Kubernetes 1.3.7集群的 “漏网之鱼”

Kubernetes 1.3.7集群是通过kube-up.sh搭建并初始化的。按照K8s官方文档有关elasticsearch logging的介绍，在kubernetes/cluster/ubuntu/config-default.sh中，我也发现了下面几个配置项：

// kubernetes/cluster/ubuntu/config-default.sh
# Optional: Enable node logging.
ENABLE_NODE_LOGGING=false
LOGGING_DESTINATION=${LOGGING_DESTINATION:-elasticsearch}

# Optional: When set to true, Elasticsearch and Kibana will be setup as part of the cluster bring up.
ENABLE_CLUSTER_LOGGING=false
ELASTICSEARCH_LOGGING_REPLICAS=${ELASTICSEARCH_LOGGING_REPLICAS:-1}

显然，当初如果搭建集群伊始时要是知道这些配置的意义，可能那个时候就会将elastic logging集成到集群中了。现在为时已晚，集群上已经跑了很多应用，无法重新通过kube-up.sh中断集群运行并安装elastic logging了。我只能手工进行安装了！

二、镜像准备

1.3.7源码中kubernetes/cluster/addons/fluentd-elasticsearch下的manifest已经比较old了，我们直接使用kubernetes最新源码中的manifest文件：

k8s.io/kubernetes/cluster/addons/fluentd-elasticsearch$ ls *.yaml
es-controller.yaml es-service.yaml fluentd-es-ds.yaml kibana-controller.yaml kibana-service.yaml

分析这些yaml，我们需要三个镜像：

 gcr.io/google_containers/fluentd-elasticsearch:1.22
 gcr.io/google_containers/elasticsearch:v2.4.1-1
 gcr.io/google_containers/kibana:v4.6.1-1

显然镜像都在墙外。由于生产环境下的Docker引擎并没有配置加速器代理，因此我们需要手工下载一下这三个镜像。我采用的方法是通过另外一台配置了加速器的机器上的Docker引擎将三个image下载，并重新打tag，上传到我在hub.docker.com上的账号下，以elasticsearch:v2.4.1-1为例：

# docker pull gcr.io/google_containers/elasticsearch:v2.4.1-1
# docker tag gcr.io/google_containers/elasticsearch:v2.4.1-1 bigwhite/elasticsearch:v2.4.1-1
# docker push bigwhite/elasticsearch:v2.4.1-1

下面是我们在后续安装过程中真正要使用到的镜像：

bigwhite/fluentd-elasticsearch:1.22
bigwhite/elasticsearch:v2.4.1-1
bigwhite/kibana:v4.6.1-1

三、启动fluentd

fluentd是以DaemonSet的形式跑在K8s集群上的，这样k8s可以保证每个k8s cluster node上都会启动一个fluentd(注意：将image改为上述镜像地址，如果你配置了加速器，那自然就不必了)。

# kubectl create -f fluentd-es-ds.yaml --record
daemonset "fluentd-es-v1.22" created

查看daemonset中的Pod的启动情况，我们发现：

kube-system fluentd-es-v1.22-as3s5 0/1 CrashLoopBackOff 2 43s 172.16.99.6 10.47.136.60
kube-system fluentd-es-v1.22-qz193 0/1 CrashLoopBackOff 2 43s 172.16.57.7 10.46.181.146

fluentd Pod启动失败，fluentd的日志可以通过/var/log/fluentd.log查看：

# tail -100f /var/log/fluentd.log

2017-03-02 02:27:01 +0000 [info]: reading config file path="/etc/td-agent/td-agent.conf"
2017-03-02 02:27:01 +0000 [info]: starting fluentd-0.12.31
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-mixin-config-placeholders' version '0.4.0'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-mixin-plaintextformatter' version '0.2.6'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-docker_metadata_filter' version '0.1.3'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-elasticsearch' version '1.5.0'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-kafka' version '0.4.1'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-kubernetes_metadata_filter' version '0.24.0'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-mongo' version '0.7.16'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-rewrite-tag-filter' version '1.5.5'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-s3' version '0.8.0'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-scribe' version '0.10.14'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-td' version '0.10.29'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-td-monitoring' version '0.2.2'
2017-03-02 02:27:01 +0000 [info]: gem 'fluent-plugin-webhdfs' version '0.4.2'
2017-03-02 02:27:01 +0000 [info]: gem 'fluentd' version '0.12.31'
2017-03-02 02:27:01 +0000 [info]: adding match pattern="fluent.**" type="null"
2017-03-02 02:27:01 +0000 [info]: adding filter pattern="kubernetes.**" type="kubernetes_metadata"
2017-03-02 02:27:02 +0000 [error]: config error file="/etc/td-agent/td-agent.conf" error="Invalid Kubernetes API v1 endpoint https://192.168.3.1:443/api: 401 Unauthorized"
2017-03-02 02:27:02 +0000 [info]: process finished code=256
2017-03-02 02:27:02 +0000 [warn]: process died within 1 second. exit.

从上述日志中的error来看：fluentd访问apiserver secure port(443)出错了：Unauthorized! 通过分析 cluster/addons/fluentd-elasticsearch/fluentd-es-image/build.sh和td-agent.conf，我们发现是fluentd image中的fluent-plugin-kubernetes_metadata_filter要去访问API Server以获取一些kubernetes的metadata信息。不过未做任何特殊配置的fluent-plugin-kubernetes_metadata_filter，我猜测它使用的是kubernetes为Pod传入的环境变量：KUBERNETES_SERVICE_HOST和KUBERNETES_SERVICE_PORT来得到API Server的访问信息的。但API Server在secure port上是开启了安全身份验证机制的，fluentd直接访问必然是失败的。

我们找到了fluent-plugin-kubernetes_metadata_filter项目在github.com上的主页，在这个页面上我们看到了fluent-plugin-kubernetes_metadata_filter支持的其他配置，包括：ca_file、client_cert、client_key等，显然这些字眼非常眼熟。我们需要修改一下fluentd image中td-agent.conf的配置，为fluent-plugin-kubernetes_metadata_filter增加一些配置项，比如：

// td-agent.conf
... ...
<filter kubernetes.**>
 type kubernetes_metadata
 ca_file /srv/kubernetes/ca.crt
 client_cert /srv/kubernetes/kubecfg.crt
 client_key /srv/kubernetes/kubecfg.key
</filter>
... ...

这里我不想重新制作image，那么怎么办呢？Kubernetes提供了ConfigMap这一强大的武器，我们可以将新版td-agent.conf制作成kubernetes的configmap资源，并挂载到fluentd pod的相应位置以替换image中默认的td-agent.conf。

需要注意两点：
* 在基于td-agent.conf创建configmap资源之前，需要将td-agent.conf中的注释行都删掉，否则生成的configmap的内容可能不正确；
* fluentd pod将创建在kube-system下，因此configmap资源也需要创建在kube-system namespace下面，否则kubectl create无法找到对应的configmap。

# kubectl create configmap td-agent-config --from-file=./td-agent.conf -n kube-system
configmap "td-agent-config" created

# kubectl get configmaps -n kube-system
NAME DATA AGE
td-agent-config 1 9s

# kubectl get configmaps td-agent-config -o yaml
apiVersion: v1
data:
 td-agent.conf: |
 <match fluent.**>
 type null
 </match>

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

fluentd-es-ds.yaml也要随之做一些改动，主要是增加两个mount: 一个是mount 上面的configmap td-agent-config，另外一个就是mount hostpath：/srv/kubernetes以获取到相关client端的数字证书：

 spec:
 containers:
 - name: fluentd-es
 image: bigwhite/fluentd-elasticsearch:1.22
 command:
 - '/bin/sh'
 - '-c'
 - '/usr/sbin/td-agent 2>&1 >> /var/log/fluentd.log'
 resources:
 limits:
 memory: 200Mi
 #requests:
 #cpu: 100m
 #memory: 200Mi
 volumeMounts:
 - name: varlog
 mountPath: /var/log
 - name: varlibdockercontainers
 mountPath: /var/lib/docker/containers
 readOnly: true
 - name: td-agent-config
 mountPath: /etc/td-agent
 - name: tls-files
 mountPath: /srv/kubernetes
 terminationGracePeriodSeconds: 30
 volumes:
 - name: varlog
 hostPath:
 path: /var/log
 - name: varlibdockercontainers
 hostPath:
 path: /var/lib/docker/containers
 - name: td-agent-config
 configMap:
 name: td-agent-config
 - name: tls-files
 hostPath:
 path: /srv/kubernetes

接下来，我们重新创建fluentd ds，步骤不赘述。这回我们的创建成功了：

kube-system fluentd-es-v1.22-adsrx 1/1 Running 0 1s 172.16.99.6 10.47.136.60
kube-system fluentd-es-v1.22-rpme3 1/1 Running 0 1s 172.16.57.7 10.46.181.146

但通过查看/var/log/fluentd.log，我们依然能看到“问题”：

2017-03-02 03:57:58 +0000 [warn]: temporarily failed to flush the buffer. next_retry=2017-03-02 03:57:59 +0000 error_class="Fluent::ElasticsearchOutput::ConnectionFailure" error="Can not reach Elasticsearch cluster ({:host=>\"elasticsearch-logging\", :port=>9200, :scheme=>\"http\"})!" plugin_id="object:3fd99fa857d8"
 2017-03-02 03:57:58 +0000 [warn]: suppressed same stacktrace
2017-03-02 03:58:00 +0000 [warn]: temporarily failed to flush the buffer. next_retry=2017-03-02 03:58:03 +0000 error_class="Fluent::ElasticsearchOutput::ConnectionFailure" error="Can not reach Elasticsearch cluster ({:host=>\"elasticsearch-logging\", :port=>9200, :scheme=>\"http\"})!" plugin_id="object:3fd99fa857d8"
2017-03-02 03:58:00 +0000 [info]: process finished code=9
2017-03-02 03:58:00 +0000 [error]: fluentd main process died unexpectedly. restarting.

由于ElasticSearch logging还未创建，这是连不上elasticsearch所致。

四、启动elasticsearch

启动elasticsearch：

# kubectl create -f es-controller.yaml
replicationcontroller "elasticsearch-logging-v1" created

# kubectl create -f es-service.yaml
service "elasticsearch-logging" created

get pods：

kube-system elasticsearch-logging-v1-3bzt6 1/1 Running 0 7s 172.16.57.8 10.46.181.146
kube-system elasticsearch-logging-v1-nvbe1 1/1 Running 0 7s 172.16.99.10 10.47.136.60

elastic search logging启动成功后，上述fluentd的fail日志就没有了！

不过elastic search真的运行ok了么？我们查看一下elasticsearch相关Pod日志：

# kubectl logs -f elasticsearch-logging-v1-3bzt6 -n kube-system
F0302 03:59:41.036697 8 elasticsearch_logging_discovery.go:60] kube-system namespace doesn't exist: the server has asked for the client to provide credentials (get namespaces kube-system)
goroutine 1 [running]:
k8s.io/kubernetes/vendor/github.com/golang/glog.stacks(0x19a8100, 0xc400000000, 0xc2, 0x186)
... ...
main.main()
 elasticsearch_logging_discovery.go:60 +0xb53

[2017-03-02 03:59:42,587][INFO ][node ] [elasticsearch-logging-v1-3bzt6] version[2.4.1], pid[16], build[c67dc32/2016-09-27T18:57:55Z]
[2017-03-02 03:59:42,588][INFO ][node ] [elasticsearch-logging-v1-3bzt6] initializing ...
[2017-03-02 03:59:44,396][INFO ][plugins ] [elasticsearch-logging-v1-3bzt6] modules [reindex, lang-expression, lang-groovy], plugins [], sites []
... ...
[2017-03-02 03:59:44,441][INFO ][env ] [elasticsearch-logging-v1-3bzt6] heap size [1007.3mb], compressed ordinary object pointers [true]
[2017-03-02 03:59:48,355][INFO ][node ] [elasticsearch-logging-v1-3bzt6] initialized
[2017-03-02 03:59:48,355][INFO ][node ] [elasticsearch-logging-v1-3bzt6] starting ...
[2017-03-02 03:59:48,507][INFO ][transport ] [elasticsearch-logging-v1-3bzt6] publish_address {172.16.57.8:9300}, bound_addresses {[::]:9300}
[2017-03-02 03:59:48,547][INFO ][discovery ] [elasticsearch-logging-v1-3bzt6] kubernetes-logging/7_f_M2TKRZWOw4NhBc4EqA
[2017-03-02 04:00:18,552][WARN ][discovery ] [elasticsearch-logging-v1-3bzt6] waited for 30s and no initial state was set by the discovery
[2017-03-02 04:00:18,562][INFO ][http ] [elasticsearch-logging-v1-3bzt6] publish_address {172.16.57.8:9200}, bound_addresses {[::]:9200}
[2017-03-02 04:00:18,562][INFO ][node ] [elasticsearch-logging-v1-3bzt6] started
[2017-03-02 04:01:15,754][WARN ][discovery.zen.ping.unicast] [elasticsearch-logging-v1-3bzt6] failed to send ping to [{#zen_unicast_1#}{127.0.0.1}{127.0.0.1:9300}]
SendRequestTransportException[[][127.0.0.1:9300][internal:discovery/zen/unicast]]; nested: NodeNotConnectedException[[][127.0.0.1:9300] Node not connected];
... ...
Caused by: NodeNotConnectedException[[][127.0.0.1:9300] Node not connected]
 at org.elasticsearch.transport.netty.NettyTransport.nodeChannel(NettyTransport.java:1141)
 at org.elasticsearch.transport.netty.NettyTransport.sendRequest(NettyTransport.java:830)
 at org.elasticsearch.transport.TransportService.sendRequest(TransportService.java:329)
 ... 12 more

总结了一下，日志中有两个错误：
- 无法访问到API Server，这个似乎和fluentd最初的问题一样；
- elasticsearch两个节点间互ping失败。

要想找到这两个问题的原因，还得回到源头，去分析elastic search image的组成。

通过cluster/addons/fluentd-elasticsearch/es-image/run.sh文件内容：

/elasticsearch_logging_discovery >> /elasticsearch/config/elasticsearch.yml

chown -R elasticsearch:elasticsearch /data

/bin/su -c /elasticsearch/bin/elasticsearch elasticsearch

我们了解到image中，其实包含了两个程序，一个为/elasticsearch_logging_discovery，该程序执行后生成一个配置文件： /elasticsearch/config/elasticsearch.yml。该配置文件后续被另外一个程序：/elasticsearch/bin/elasticsearch使用。

我们查看一下已经运行的docker中的elasticsearch.yml文件内容：

# docker exec 3cad31f6eb08 cat /elasticsearch/config/elasticsearch.yml
cluster.name: kubernetes-logging

node.name: ${NODE_NAME}
node.master: ${NODE_MASTER}
node.data: ${NODE_DATA}

transport.tcp.port: ${TRANSPORT_PORT}
http.port: ${HTTP_PORT}

path.data: /data

network.host: 0.0.0.0

discovery.zen.minimum_master_nodes: ${MINIMUM_MASTER_NODES}
discovery.zen.ping.multicast.enabled: false

这个结果中缺少了一项：

discovery.zen.ping.unicast.hosts: ["172.30.0.11", "172.30.192.15"]

这也是导致第二个问题的原因。综上，elasticsearch logging的错误其实都是由于/elasticsearch_logging_discovery无法访问API Server导致 /elasticsearch/config/elasticsearch.yml没有被正确生成造成的，我们就来解决这个问题。

我查看了一下/elasticsearch_logging_discovery的源码，elasticsearch_logging_discovery是一个典型通过client-go通过service account访问API Server的程序，很显然这就是我在《在Kubernetes Pod中使用Service Account访问API Server》一文中提到的那个问题：默认的service account不好用。

解决方法：在kube-system namespace下创建一个新的service account资源，并在es-controller.yaml中显式使用该新创建的service account。

创建一个新的serviceaccount在kube-system namespace下：

//serviceaccount.yaml
apiVersion: v1
kind: ServiceAccount
metadata:
 name: k8s-efk

# kubectl create -f serviceaccount.yaml -n kube-system
serviceaccount "k8s-efk" created

# kubectl get serviceaccount -n kube-system
NAME SECRETS AGE
default 1 139d
k8s-efk 1 17s

在es-controller.yaml中，使用service account “k8s-efk”：

//es-controller.yaml
... ...
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:
 serviceAccount: k8s-efk
 containers:
... ...

重新创建elasticsearch logging service后，我们再来查看elasticsearch-logging pod的日志：

# kubectl logs -f elasticsearch-logging-v1-dklui -n kube-system
[2017-03-02 08:26:46,500][INFO ][node ] [elasticsearch-logging-v1-dklui] version[2.4.1], pid[14], build[c67dc32/2016-09-27T18:57:55Z]
[2017-03-02 08:26:46,504][INFO ][node ] [elasticsearch-logging-v1-dklui] initializing ...
[2017-03-02 08:26:47,984][INFO ][plugins ] [elasticsearch-logging-v1-dklui] modules [reindex, lang-expression, lang-groovy], plugins [], sites []
[2017-03-02 08:26:48,073][INFO ][env ] [elasticsearch-logging-v1-dklui] using [1] data paths, mounts [[/data (/dev/vda1)]], net usable_space [16.9gb], net total_space [39.2gb], spins? [possibly], types [ext4]
[2017-03-02 08:26:48,073][INFO ][env ] [elasticsearch-logging-v1-dklui] heap size [1007.3mb], compressed ordinary object pointers [true]
[2017-03-02 08:26:53,241][INFO ][node ] [elasticsearch-logging-v1-dklui] initialized
[2017-03-02 08:26:53,241][INFO ][node ] [elasticsearch-logging-v1-dklui] starting ...
[2017-03-02 08:26:53,593][INFO ][transport ] [elasticsearch-logging-v1-dklui] publish_address {172.16.57.8:9300}, bound_addresses {[::]:9300}
[2017-03-02 08:26:53,651][INFO ][discovery ] [elasticsearch-logging-v1-dklui] kubernetes-logging/Ky_OuYqMRkm_918aHRtuLg
[2017-03-02 08:26:56,736][INFO ][cluster.service ] [elasticsearch-logging-v1-dklui] new_master {elasticsearch-logging-v1-dklui}{Ky_OuYqMRkm_918aHRtuLg}{172.16.57.8}{172.16.57.8:9300}{master=true}, added {{elasticsearch-logging-v1-vjxm3}{cbzgrfZATyWkHfQYHZhs7Q}{172.16.99.10}{172.16.99.10:9300}{master=true},}, reason: zen-disco-join(elected_as_master, [1] joins received)
[2017-03-02 08:26:56,955][INFO ][http ] [elasticsearch-logging-v1-dklui] publish_address {172.16.57.8:9200}, bound_addresses {[::]:9200}
[2017-03-02 08:26:56,956][INFO ][node ] [elasticsearch-logging-v1-dklui] started
[2017-03-02 08:26:57,157][INFO ][gateway ] [elasticsearch-logging-v1-dklui] recovered [0] indices into cluster_state
[2017-03-02 08:27:05,378][INFO ][cluster.metadata ] [elasticsearch-logging-v1-dklui] [logstash-2017.03.02] creating index, cause [auto(bulk api)], templates [], shards [5]/[1], mappings []
[2017-03-02 08:27:06,360][INFO ][cluster.metadata ] [elasticsearch-logging-v1-dklui] [logstash-2017.03.01] creating index, cause [auto(bulk api)], templates [], shards [5]/[1], mappings []
[2017-03-02 08:27:07,163][INFO ][cluster.routing.allocation] [elasticsearch-logging-v1-dklui] Cluster health status changed from [RED] to [YELLOW] (reason: [shards started [[logstash-2017.03.01][3], [logstash-2017.03.01][3]] ...]).
[2017-03-02 08:27:07,354][INFO ][cluster.metadata ] [elasticsearch-logging-v1-dklui] [logstash-2017.03.02] create_mapping [fluentd]
[2017-03-02 08:27:07,988][INFO ][cluster.metadata ] [elasticsearch-logging-v1-dklui] [logstash-2017.03.01] create_mapping [fluentd]
[2017-03-02 08:27:09,578][INFO ][cluster.routing.allocation] [elasticsearch-logging-v1-dklui] Cluster health status changed from [YELLOW] to [GREEN] (reason: [shards started [[logstash-2017.03.02][4]] ...]).

elasticsearch logging启动运行ok！

五、启动kibana

有了elasticsearch logging的“前车之鉴”，这次我们也把上面新创建的serviceaccount：k8s-efk显式赋值给kibana-controller.yaml:

//kibana-controller.yaml
... ...
spec:
 serviceAccount: k8s-efk
 containers:
 - name: kibana-logging
 image: bigwhite/kibana:v4.6.1-1
 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
... ...

启动kibana，并观察pod日志：

# kubectl create -f kibana-controller.yaml
# kubectl create -f kibana-service.yaml
# kubectl logs -f kibana-logging-3604961973-jby53 -n kube-system
ELASTICSEARCH_URL=http://elasticsearch-logging:9200
server.basePath: /api/v1/proxy/namespaces/kube-system/services/kibana-logging
{"type":"log","@timestamp":"2017-03-02T08:30:15Z","tags":["info","optimize"],"pid":6,"message":"Optimizing and caching bundles for kibana and statusPage. This may take a few minutes"}

kibana缓存着实需要一段时间，请耐心等待！可能是几分钟。之后你将会看到如下日志：

# kubectl logs -f kibana-logging-3604961973-jby53 -n kube-system
ELASTICSEARCH_URL=http://elasticsearch-logging:9200
server.basePath: /api/v1/proxy/namespaces/kube-system/services/kibana-logging
{"type":"log","@timestamp":"2017-03-02T08:30:15Z","tags":["info","optimize"],"pid":6,"message":"Optimizing and caching bundles for kibana and statusPage. This may take a few minutes"}
{"type":"log","@timestamp":"2017-03-02T08:40:04Z","tags":["info","optimize"],"pid":6,"message":"Optimization of bundles for kibana and statusPage complete in 588.60 seconds"}
{"type":"log","@timestamp":"2017-03-02T08:40:04Z","tags":["status","plugin:kibana@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:05Z","tags":["status","plugin:elasticsearch@1.0.0","info"],"pid":6,"state":"yellow","message":"Status changed from uninitialized to yellow - Waiting for Elasticsearch","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:05Z","tags":["status","plugin:kbn_vislib_vis_types@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:05Z","tags":["status","plugin:markdown_vis@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:05Z","tags":["status","plugin:metric_vis@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:06Z","tags":["status","plugin:spyModes@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:06Z","tags":["status","plugin:statusPage@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:06Z","tags":["status","plugin:table_vis@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from uninitialized to green - Ready","prevState":"uninitialized","prevMsg":"uninitialized"}
{"type":"log","@timestamp":"2017-03-02T08:40:06Z","tags":["listening","info"],"pid":6,"message":"Server running at http://0.0.0.0:5601"}
{"type":"log","@timestamp":"2017-03-02T08:40:11Z","tags":["status","plugin:elasticsearch@1.0.0","info"],"pid":6,"state":"yellow","message":"Status changed from yellow to yellow - No existing Kibana index found","prevState":"yellow","prevMsg":"Waiting for Elasticsearch"}
{"type":"log","@timestamp":"2017-03-02T08:40:14Z","tags":["status","plugin:elasticsearch@1.0.0","info"],"pid":6,"state":"green","message":"Status changed from yellow to green - Kibana index ready","prevState":"yellow","prevMsg":"No existing Kibana index found"}

接下来，通过浏览器访问下面地址就可以访问kibana的web页面了，注意：Kinaba的web页面加载也需要一段时间。

https://{API Server external IP}:{API Server secure port}/api/v1/proxy/namespaces/kube-system/services/kibana-logging/app/kibana#/settings/indices/

下面是创建一个index（相当于mysql中的一个database）页面：

取消“Index contains time-based events”，然后点击“Create”即可创建一个Index。

点击页面上的”Setting” -> “Status”，可以查看当前elasticsearch logging的整体状态，如果一切ok，你将会看到下图这样的页面：

创建Index后，可以在Discover下看到ElasticSearch logging中汇聚的日志：

六、小结

以上就是在Kubernetes 1.3.7集群上安装Fluentd和ElasticSearch stack，实现kubernetes cluster level logging的过程。在使用kubeadm安装的Kubernetes 1.5.1环境下安装这些，则基本不会遇到上述这些问题。

另外ElasticSearch logging默认挂载的volume是emptyDir，实验用可以。但要部署在生产环境，必须换成Persistent Volume，比如：CephRBD。

本文转自掘金-使用 Fluentd 和 ElasticSearch Stack 实现 Kubernetes 的集群 Logging