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如何在Kubernetes上部署高可用和可扩展的Elasticsearch?

简介: 先决条件 Elasticsearch的基本知识,其Node类型及角色 运行至少有3个节点的Kubernetes集群(至少4Cores 4GB) Kibana的相关知识 部署架构图 Elasticsearch Data Node的Pod被部署为具有Headless Service的StatefulSets,以提供稳定的网络ID。
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先决条件

  1. Elasticsearch的基本知识,其Node类型及角色
  2. 运行至少有3个节点的Kubernetes集群(至少4Cores 4GB)
  3. Kibana的相关知识

部署架构图

01.jpeg
  • Elasticsearch Data Node的Pod被部署为具有Headless Service的StatefulSets,以提供稳定的网络ID。
  • Elasticsearch Master Node的Pod被部署为具有Headless Service的副本集,这将有助于自动发现。
  • Elasticsearch Client Node的Pod部署为具有内部服务的副本集,允许访问R/W请求的Data Node。
  • Kibana和ElasticHQ Pod被部署为副本集,其服务可在Kubernetes集群外部访问,但仍在您的子网内部(除非另有要求,否则不公开)。
  • 为Client Node部署HPA(Horizonal Pod Auto-scaler)以在高负载下实现自动伸缩。

要记住的重要事项:
  1. 设置ES_JAVA_OPT环境变量。
  2. 设置CLUSTER_NAME环境变量。
  3. 为Master Node的部署设置NUMBER_OF_MASTERS环境变量(防止脑裂问题)。如果有3个Masters,我们必须设置为2。
  4. 在类似的pod中设置正确的Pod-AntiAffinity策略,以便在工作节点发生故障时确保HA。

让我们直接将这些服务部署到我们的GKE集群。

Master节点部署:


apiVersion: v1
kind: Namespace
metadata:
name: elasticsearch
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
name: es-master
namespace: elasticsearch
labels:
component: elasticsearch
role: master
spec:
replicas: 3
template:
metadata:
 labels:
 component: elasticsearch
 role: master
spec:
 affinity:
 podAntiAffinity:
 preferredDuringSchedulingIgnoredDuringExecution:
 - weight: 100
 podAffinityTerm:
 labelSelector:
 matchExpressions:
 - key: role
 operator: In
 values:
 - master
 topologyKey: kubernetes.io/hostname
 initContainers:
 - name: init-sysctl
 image: busybox:1.27.2
 command:
 - sysctl
 - -w
 - vm.max_map_count=262144
 securityContext:
 privileged: true
 containers:
 - name: es-master
 image: quay.io/pires/docker-elasticsearch-kubernetes:6.2.4
 env:
 - name: NAMESPACE
 valueFrom:
 fieldRef:
 fieldPath: metadata.namespace
 - name: NODE_NAME
 valueFrom:
 fieldRef:
 fieldPath: metadata.name
 - name: CLUSTER_NAME
 value: my-es
 - name: NUMBER_OF_MASTERS
 value: "2"
 - name: NODE_MASTER
 value: "true"
 - name: NODE_INGEST
 value: "false"
 - name: NODE_DATA
 value: "false"
 - name: HTTP_ENABLE
 value: "false"
 - name: ES_JAVA_OPTS
 value: -Xms256m -Xmx256m
 - name: PROCESSORS
 valueFrom:
 resourceFieldRef:
 resource: limits.cpu
 resources:
 limits:
 cpu: 2
 ports:
 - containerPort: 9300
 name: transport
 volumeMounts:
 - name: storage
 mountPath: /data
 volumes:
 - emptyDir:
 medium: ""
 name: "storage"
---
apiVersion: v1
kind: Service
metadata:
name: elasticsearch-discovery
namespace: elasticsearch
labels:
component: elasticsearch
role: master
spec:
selector:
component: elasticsearch
role: master
ports:
- name: transport
port: 9300
protocol: TCP
clusterIP: None

root$ kubectl apply -f es-master.yml
root$ kubectl -n elasticsearch get all
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
deploy/es-master 3 3 3 3 32s
NAME DESIRED CURRENT READY AGE
rs/es-master-594b58b86c 3 3 3 31s
NAME READY STATUS RESTARTS AGE
po/es-master-594b58b86c-9jkj2 1/1 Running 0 31s
po/es-master-594b58b86c-bj7g7 1/1 Running 0 31s
po/es-master-594b58b86c-lfpps 1/1 Running 0 31s
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
svc/elasticsearch-discovery ClusterIP None <none> 9300/TCP 31s

有趣的是,可以从任何主节点pod的日志来见证它们之间的master选举,然后何时添加新的data和client节点。
root$ kubectl -n elasticsearch logs -f po/es-master-594b58b86c-9jkj2 | grep ClusterApplierService
[2018-10-21T07:41:54,958][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-9jkj2] detected_master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300}, added {{es-master-594b58b86c-lfpps}{wZQmXr5fSfWisCpOHBhaMg}{50jGPeKLSpO9RU_HhnVJCA}{10.9.124.81}{10.9.124.81:9300},{es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [3]])

可以看出,名为es-master-594b58b86c-bj7g7的es-master pod被选为master节点,其他2个Pod被添加到这个集群。

名为elasticsearch-discovery的Headless Service默认设置为Docker镜像中的env变量,用于在节点之间进行发现。 当然这是可以被改写的。

同样,我们可以部署Data和Client节点。 配置如下:

Data节点部署:
apiVersion: v1
kind: Namespace
metadata:
name: elasticsearch
---
apiVersion: storage.k8s.io/v1beta1
kind: StorageClass
metadata:
name: fast
provisioner: kubernetes.io/gce-pd
parameters:
type: pd-ssd
fsType: xfs
allowVolumeExpansion: true
---
apiVersion: apps/v1beta1
kind: StatefulSet
metadata:
name: es-data
namespace: elasticsearch
labels:
component: elasticsearch
role: data
spec:
serviceName: elasticsearch-data
replicas: 3
template:
metadata:
 labels:
 component: elasticsearch
 role: data
spec:
 affinity:
 podAntiAffinity:
 preferredDuringSchedulingIgnoredDuringExecution:
 - weight: 100
 podAffinityTerm:
 labelSelector:
 matchExpressions:
 - key: role
 operator: In
 values:
 - data
 topologyKey: kubernetes.io/hostname
 initContainers:
 - name: init-sysctl
 image: busybox:1.27.2
 command:
 - sysctl
 - -w
 - vm.max_map_count=262144
 securityContext:
 privileged: true
 containers:
 - name: es-data
 image: quay.io/pires/docker-elasticsearch-kubernetes:6.2.4
 env:
 - name: NAMESPACE
 valueFrom:
 fieldRef:
 fieldPath: metadata.namespace
 - name: NODE_NAME
 valueFrom:
 fieldRef:
 fieldPath: metadata.name
 - name: CLUSTER_NAME
 value: my-es
 - name: NODE_MASTER
 value: "false"
 - name: NODE_INGEST
 value: "false"
 - name: HTTP_ENABLE
 value: "false"
 - name: ES_JAVA_OPTS
 value: -Xms256m -Xmx256m
 - name: PROCESSORS
 valueFrom:
 resourceFieldRef:
 resource: limits.cpu
 resources:
 limits:
 cpu: 2
 ports:
 - containerPort: 9300
 name: transport
 volumeMounts:
 - name: storage
 mountPath: /data
volumeClaimTemplates:
- metadata:
 name: storage
 annotations:
 volume.beta.kubernetes.io/storage-class: "fast"
spec:
 accessModes: [ "ReadWriteOnce" ]
 storageClassName: fast
 resources:
 requests:
 storage: 10Gi
---
apiVersion: v1
kind: Service
metadata:
name: elasticsearch-data
namespace: elasticsearch
labels:
component: elasticsearch
role: data
spec:
ports:
- port: 9300
name: transport
clusterIP: None
selector:
component: elasticsearch
role: data

Headless Service为Data节点提供稳定的网络ID,有助于它们之间的数据传输。

在将持久卷附加到pod之前格式化它是很重要的。 这可以通过在创建storage class时指定卷类型来完成。 我们还可以设置标志以允许动态扩展。 这里可以阅读更多内容。
...
parameters: 
type: pd-ssd 
fsType: xfs
allowVolumeExpansion: true
...

Client节点部署:
apiVersion: v1
kind: Namespace
metadata:
name: elasticsearch
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
name: es-client
namespace: elasticsearch
labels:
component: elasticsearch
role: client
spec:
replicas: 2
template:
metadata:
 labels:
 component: elasticsearch
 role: client
spec:
 affinity:
 podAntiAffinity:
 preferredDuringSchedulingIgnoredDuringExecution:
 - weight: 100
 podAffinityTerm:
 labelSelector:
 matchExpressions:
 - key: role
 operator: In
 values:
 - client
 topologyKey: kubernetes.io/hostname
 initContainers:
 - name: init-sysctl
 image: busybox:1.27.2
 command:
 - sysctl
 - -w
 - vm.max_map_count=262144
 securityContext:
 privileged: true
 containers:
 - name: es-client
 image: quay.io/pires/docker-elasticsearch-kubernetes:6.2.4
 env:
 - name: NAMESPACE
 valueFrom:
 fieldRef:
 fieldPath: metadata.namespace
 - name: NODE_NAME
 valueFrom:
 fieldRef:
 fieldPath: metadata.name
 - name: CLUSTER_NAME
 value: my-es
 - name: NODE_MASTER
 value: "false"
 - name: NODE_DATA
 value: "false"
 - name: HTTP_ENABLE
 value: "true"
 - name: ES_JAVA_OPTS
 value: -Xms256m -Xmx256m
 - name: NETWORK_HOST
 value: _site_,_lo_
 - name: PROCESSORS
 valueFrom:
 resourceFieldRef:
 resource: limits.cpu
 resources:
 limits:
 cpu: 1
 ports:
 - containerPort: 9200
 name: http
 - containerPort: 9300
 name: transport
 volumeMounts:
 - name: storage
 mountPath: /data
 volumes:
 - emptyDir:
 medium: ""
 name: storage
---
apiVersion: v1
kind: Service
metadata:
name: elasticsearch
namespace: elasticsearch
annotations: 
cloud.google.com/load-balancer-type: Internal
labels:
component: elasticsearch
role: client
spec:
selector:
component: elasticsearch
role: client
ports:
- name: http
port: 9200
type: LoadBalancer

此处部署的服务是从Kubernetes集群外部访问ES群集,但仍在我们的子网内部。 注释掉cloud.google.com/load-balancer-type:Internal可确保这一点。

但是,如果我们的ES集群中的应用程序部署在集群中,则可以通过 http://elasticsearch.elasticsearch:9200 来访问ElasticSearch服务。

创建这两个deployments后,新创建的client和data节点将自动添加到集群中。(观察master pod的日志)
root$ kubectl apply -f es-data.yml
root$ kubectl -n elasticsearch get pods -l role=data
NAME READY STATUS RESTARTS AGE
es-data-0 1/1 Running 0 48s
es-data-1 1/1 Running 0 28s
--------------------------------------------------------------------
root$ kubectl apply -f es-client.yml 
root$ kubectl -n elasticsearch get pods -l role=client
NAME READY STATUS RESTARTS AGE
es-client-69b84b46d8-kr7j4 1/1 Running 0 47s
es-client-69b84b46d8-v5pj2 1/1 Running 0 47s
--------------------------------------------------------------------
root$ kubectl -n elasticsearch get all
NAME DESIRED CURRENT UP-TO-DATE AVAILABLE AGE
deploy/es-client 2 2 2 2 1m
deploy/es-master 3 3 3 3 9m
NAME DESIRED CURRENT READY AGE
rs/es-client-69b84b46d8 2 2 2 1m
rs/es-master-594b58b86c 3 3 3 9m
NAME DESIRED CURRENT AGE
statefulsets/es-data 2 2 3m
NAME READY STATUS RESTARTS AGE
po/es-client-69b84b46d8-kr7j4 1/1 Running 0 1m
po/es-client-69b84b46d8-v5pj2 1/1 Running 0 1m
po/es-data-0 1/1 Running 0 3m
po/es-data-1 1/1 Running 0 3m
po/es-master-594b58b86c-9jkj2 1/1 Running 0 9m
po/es-master-594b58b86c-bj7g7 1/1 Running 0 9m
po/es-master-594b58b86c-lfpps 1/1 Running 0 9m
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
svc/elasticsearch LoadBalancer 10.9.121.160 10.9.120.8 9200:32310/TCP 1m
svc/elasticsearch-data ClusterIP None <none> 9300/TCP 3m
svc/elasticsearch-discovery ClusterIP None <none> 9300/TCP 9m
--------------------------------------------------------------------

Check logs of es-master leader pod

root$ kubectl -n elasticsearch logs po/es-master-594b58b86c-bj7g7 | grep ClusterApplierService [2018-10-21T07:41:53,731][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] new_master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300}, added {{es-master-594b58b86c-lfpps}{wZQmXr5fSfWisCpOHBhaMg}{50jGPeKLSpO9RU_HhnVJCA}{10.9.124.81}{10.9.124.81:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [1] source [zen-disco-elected-as-master ([1] nodes joined)[{es-master-594b58b86c-lfpps}{wZQmXr5fSfWisCpOHBhaMg}{50jGPeKLSpO9RU_HhnVJCA}{10.9.124.81}{10.9.124.81:9300}]]]) [2018-10-21T07:41:55,162][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] added {{es-master-594b58b86c-9jkj2}{x9Prp1VbTq6_kALQVNwIWg}{7NHUSVpuS0mFDTXzAeKRcg}{10.9.125.81}{10.9.125.81:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [3] source [zen-disco-node-join[{es-master-594b58b86c-9jkj2}{x9Prp1VbTq6_kALQVNwIWg}{7NHUSVpuS0mFDTXzAeKRcg}{10.9.125.81}{10.9.125.81:9300}]]]) [2018-10-21T07:48:02,485][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] added {{es-data-0}{SAOhUiLiRkazskZ_TC6EBQ}{qirmfVJBTjSBQtHZnz-QZw}{10.9.126.88}{10.9.126.88:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [4] source [zen-disco-node-join[{es-data-0}{SAOhUiLiRkazskZ_TC6EBQ}{qirmfVJBTjSBQtHZnz-QZw}{10.9.126.88}{10.9.126.88:9300}]]]) [2018-10-21T07:48:21,984][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] added {{es-data-1}{fiv5Wh29TRWGPumm5ypJfA}{EXqKGSzIQquRyWRzxIOWhQ}{10.9.125.82}{10.9.125.82:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [5] source [zen-disco-node-join[{es-data-1}{fiv5Wh29TRWGPumm5ypJfA}{EXqKGSzIQquRyWRzxIOWhQ}{10.9.125.82}{10.9.125.82:9300}]]]) [2018-10-21T07:50:51,245][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] added {{es-client-69b84b46d8-v5pj2}{MMjA_tlTS7ux-UW44i0osg}{rOE4nB_jSmaIQVDZCjP8Rg}{10.9.125.83}{10.9.125.83:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [6] source [zen-disco-node-join[{es-client-69b84b46d8-v5pj2}{MMjA_tlTS7ux-UW44i0osg}{rOE4nB_jSmaIQVDZCjP8Rg}{10.9.125.83}{10.9.125.83:9300}]]]) [2018-10-21T07:50:58,964][INFO ][o.e.c.s.ClusterApplierService] [es-master-594b58b86c-bj7g7] added {{es-client-69b84b46d8-kr7j4}{gGC7F4diRWy2oM1TLTvNsg}{IgI6g3iZT5Sa0HsFVMpvvw}{10.9.124.82}{10.9.124.82:9300},}, reason: apply cluster state (from master [master {es-master-594b58b86c-bj7g7}{1aFT97hQQ7yiaBc2CYShBA}{Q3QzlaG3QGazOwtUl7N75Q}{10.9.126.87}{10.9.126.87:9300} committed version [7] source [zen-disco-node-join[{es-client-69b84b46d8-kr7j4}{gGC7F4diRWy2oM1TLTvNsg}{IgI6g3iZT5Sa0HsFVMpvvw}{10.9.124.82}{10.9.124.82:9300}]]])
leading master pod的日志清楚地描述了每个节点何时添加到集群。 这在调试问题时非常有用。

部署完所有组件后,我们应验证以下内容:

1、在kubernetes集群内部使用ubuntu容器进行Elasticsearch部署的验证。
root$ kubectl run my-shell --rm -i --tty --image ubuntu -- bash
root@my-shell-68974bb7f7-pj9x6:/# curl http://elasticsearch.elasticsearch:9200/_cluster/health?pretty
{
"cluster_name" : "my-es",
"status" : "green",
"timed_out" : false,
"number_of_nodes" : 7,
"number_of_data_nodes" : 2,
"active_primary_shards" : 0,
"active_shards" : 0,
"relocating_shards" : 0,
"initializing_shards" : 0,
"unassigned_shards" : 0,
"delayed_unassigned_shards" : 0,
"number_of_pending_tasks" : 0,
"number_of_in_flight_fetch" : 0,
"task_max_waiting_in_queue_millis" : 0,
"active_shards_percent_as_number" : 100.0
} 

2、在kubernetes集群外部使用GCP内部LoadBalancer IP(这里是10.9.120.8)进行Elasticsearch部署的验证。
root$ curl http://10.9.120.8:9200/_cluster/health?pretty
{
"cluster_name" : "my-es",
"status" : "green",
"timed_out" : false,
"number_of_nodes" : 7,
"number_of_data_nodes" : 2,
"active_primary_shards" : 0,
"active_shards" : 0,
"relocating_shards" : 0,
"initializing_shards" : 0,
"unassigned_shards" : 0,
"delayed_unassigned_shards" : 0,
"number_of_pending_tasks" : 0,
"number_of_in_flight_fetch" : 0,
"task_max_waiting_in_queue_millis" : 0,
"active_shards_percent_as_number" : 100.0
} 

3、ES-Pods的Anti-Affinity规则验证。
root$ kubectl -n elasticsearch get pods -o wide 
NAME READY STATUS RESTARTS AGE IP NODE
es-client-69b84b46d8-kr7j4 1/1 Running 0 10m 10.8.14.52 gke-cluster1-pool1-d2ef2b34-t6h9
es-client-69b84b46d8-v5pj2 1/1 Running 0 10m 10.8.15.53 gke-cluster1-pool1-42b4fbc4-cncn
es-data-0 1/1 Running 0 12m 10.8.16.58 gke-cluster1-pool1-4cfd808c-kpx1
es-data-1 1/1 Running 0 12m 10.8.15.52 gke-cluster1-pool1-42b4fbc4-cncn
es-master-594b58b86c-9jkj2 1/1 Running 0 18m 10.8.15.51 gke-cluster1-pool1-42b4fbc4-cncn
es-master-594b58b86c-bj7g7 1/1 Running 0 18m 10.8.16.57 gke-cluster1-pool1-4cfd808c-kpx1
es-master-594b58b86c-lfpps 1/1 Running 0 18m 10.8.14.51 gke-cluster1-pool1-d2ef2b34-t6h9

请注意,同一节点上没有2个类似的Pod。 这可以在节点发生故障时确保HA。

Scaling相关注意事项

我们可以根据CPU阈值为client节点部署autoscalers。 Client节点的HPA示例可能如下所示:
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
name: es-client
namespace: elasticsearch
spec:
maxReplicas: 5
minReplicas: 2
scaleTargetRef:
apiVersion: extensions/v1beta1
kind: Deployment
name: es-client
targetCPUUtilizationPercentage: 80

每当autoscaler启动时,我们都可以通过观察任何master pod的日志来观察添加到集群中的新client节点Pod。

对于Data Node Pod,我们必须使用K8 Dashboard或GKE控制台增加副本数量。 新创建的data节点将自动添加到集群中,并开始从其他节点复制数据。

Master Node Pod不需要自动扩展,因为它们只存储集群状态信息,但是如果要添加更多data节点,请确保集群中没有偶数个master节点,同时环境变量NUMBER_OF_MASTERS也需要相应调整。

部署Kibana和ES-HQ

Kibana是一个可视化ES数据的简单工具,ES-HQ有助于管理和监控Elasticsearch集群。 对于我们的Kibana和ES-HQ部署,我们记住以下事项:
  • 我们提供ES-Cluster的名称作为Docker镜像的环境变量
  • 访问Kibana/ES-HQ部署的服务仅在我们组织内部,即不创建公共IP。 我们使用GCP内部负载均衡。

Kibana部署:
apiVersion: v1
kind: Namespace
metadata:
name: elasticsearch
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
name: es-kibana
namespace: elasticsearch
labels:
component: elasticsearch
role: kibana
spec:
replicas: 1
template:
metadata:
 labels:
 component: elasticsearch
 role: kibana
spec:
 containers:
 - name: es-kibana
 image: docker.elastic.co/kibana/kibana-oss:6.2.2
 env:
 - name: CLUSTER_NAME
 value: my-es
 - name: ELASTICSEARCH_URL
 value: http://elasticsearch:9200
 resources:
 limits:
 cpu: 0.5
 ports:
 - containerPort: 5601
 name: http
---
apiVersion: v1
kind: Service
metadata:
name: kibana
annotations:
cloud.google.com/load-balancer-type: "Internal"
namespace: elasticsearch
labels:
component: elasticsearch
role: kibana
spec:
selector:
component: elasticsearch
role: kibana
ports:
- name: http
port: 80
targetPort: 5601
protocol: TCP
type: LoadBalancer

ES-HQ部署:
apiVersion: v1
kind: Namespace
metadata:
name: elasticsearch
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
name: es-hq
namespace: elasticsearch
labels:
component: elasticsearch
role: hq
spec:
replicas: 1
template:
metadata:
 labels:
 component: elasticsearch
 role: hq
spec:
 containers:
 - name: es-hq
 image: elastichq/elasticsearch-hq:release-v3.4.0
 env:
 - name: HQ_DEFAULT_URL
 value: http://elasticsearch:9200
 resources:
 limits:
 cpu: 0.5
 ports:
 - containerPort: 5000
 name: http
---
apiVersion: v1
kind: Service
metadata:
name: hq
annotations:
cloud.google.com/load-balancer-type: "Internal"
namespace: elasticsearch
labels:
component: elasticsearch
role: hq
spec:
selector:
component: elasticsearch
role: hq
ports:
- name: http
port: 80
targetPort: 5000
protocol: TCP
type: LoadBalancer

我们可以使用新创建的Internal LoadBalancers访问这两个服务。
root$ kubectl -n elasticsearch get svc -l role=kibana
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
kibana LoadBalancer 10.9.121.246 10.9.120.10 80:31400/TCP 1m
root$ kubectl -n elasticsearch get svc -l role=hq
NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE
hq LoadBalancer 10.9.121.150 10.9.120.9 80:31499/TCP 1m

Kibana Dashboard http://&lt;External-Ip-Kibana-Service>/app/kibana#/home?_g=()
02.png
ElasticHQ Dasboard http://&lt;External-Ip-ES-Hq-Service>/#!/clusters/my-es
03.png
ES是最广泛使用的分布式搜索和分析系统之一,当与Kubernetes结合使用时,将消除有关扩展和HA的关键问题。 此外,使用Kubernetes部署新的ES群集需要时间。

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