HighlyAvailable and Scalable Elasticsearch on Kubernetes

ElasticSearch Kubernetes · 發表 2018-10-31 14:41:34

摘要： Kubernetes上部署高可用和可擴充套件的Elasticsearch 原文在上一篇文章中，我們通過擴充套件MongoDB副本集來了解有StatefulSets。在這篇文章中，我們將與ES-HQ和Kibana一起使用HA Elasticsearch叢集（具有不同的Master...

Kubernetes上部署高可用和可擴充套件的Elasticsearch

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在上一篇文章中，我們通過擴充套件MongoDB副本集來了解有StatefulSets。在這篇文章中，我們將與ES-HQ和Kibana一起使用HA Elasticsearch叢集（具有不同的Master，Data和Client節點）。

先決條件

Elasticsearch的基本知識，其Node型別及角色
執行至少有3個節點的Kubernetes叢集（至少4Cores 4GB）
Kibana的相關知識

部署架構圖

Elasticsearch Data Node的Pod被部署為具有Headless Service的StatefulSets，以提供穩定的網路ID
Elasticsearch Master Node的Pod被部署為具有Headless Service的副本集，這將有助於自動發現
Elasticsearch Client Node的Pod部署為具有內部服務的副本集，允許訪問R/W請求的Data Node
Kibana和ElasticHQ Pod被部署為副本集，其服務可在Kubernetes叢集外部訪問，但仍在您的子網內部（除非另有要求，否則不公開）
為Client Node部署HPA（Horizonal Pod Auto-scaler）以在高負載下實現自動伸縮

要記住的重要事項：

設定ES_JAVA_OPT環境變數
設定CLUSTER_NAME環境變數
為Master Node的部署設定NUMBER_OF_MASTERS環境變數（防止腦裂問題）。如果有3個Masters，我們必須設定為2。
在類似的pod中設定正確的Pod-AntiAffinity策略，以便在工作節點發生故障時確保HA。

讓我們直接將這些服務部署到我們的GKE叢集。

Master節點部署

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
NAMEDESIREDCURRENTUP-TO-DATEAVAILABLEAGE
deploy/es-master333332s
NAMEDESIREDCURRENTREADYAGE
rs/es-master-594b58b86c33331s
NAMEREADYSTATUSRESTARTSAGE
po/es-master-594b58b86c-9jkj21/1Running031s
po/es-master-594b58b86c-bj7g71/1Running031s
po/es-master-594b58b86c-lfpps1/1Running031s
NAMETYPECLUSTER-IPEXTERNAL-IPPORT(S)AGE
svc/elasticsearch-discoveryClusterIPNone<none>9300/TCP31s

有趣的是，可以從任何主節點pod的日誌來見證它們之間的master選舉，然後何時新增新的data和client節點。

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，名為es-master-594b58b86c-bj7g7的es-master pod被選為master節點，其他2個pod被新增到這個叢集。

名為elasticsearch-discovery的Headless Service預設設定為docker映象中的env變數，用於在節點之間進行發現。當然這是可以被改寫的。

同樣，我們可以部署Data和Client節點。配置如下：

Data節點部署：

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為Data節點提供穩定的網路ID，有助於它們之間的資料傳輸。

在將持久卷附加到pod之前格式化它是很重要的。這可以通過在建立storage class時指定卷型別來完成。我們還可以設定標誌以允許動態擴充套件。這裡可以閱讀更多內容。

...
parameters:
type: pd-ssd
fsType: xfs
allowVolumeExpansion: true
...

Client節點部署

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

此處部署的服務是從Kubernetes叢集外部訪問ES群集，但仍在我們的子網內部。註釋掉 cloud.google.com/load-balancer-type：Internal 可確保這一點。

但是，如果我們的ES叢集中的應用程式部署在叢集中，則可以通過 http://elasticsearch.elasticsearch:9200 來訪問ElasticSearch服務。

建立這兩個deployments後，新建立的client和data節點將自動新增到叢集中。（觀察master pod的日誌）

root$ kubectl apply -f es-data.yml
root$ kubectl -n elasticsearch get pods -l role=data
NAMEREADYSTATUSRESTARTSAGE
es-data-01/1Running048s
es-data-11/1Running028s
--------------------------------------------------------------------
root$ kubectl apply -f es-client.yml 
root$ kubectl -n elasticsearch get pods -l role=client
NAMEREADYSTATUSRESTARTSAGE
es-client-69b84b46d8-kr7j41/1Running047s
es-client-69b84b46d8-v5pj21/1Running047s
--------------------------------------------------------------------
root$ kubectl -n elasticsearch get all
NAMEDESIREDCURRENTUP-TO-DATEAVAILABLEAGE
deploy/es-client22221m
deploy/es-master33339m
NAMEDESIREDCURRENTREADYAGE
rs/es-client-69b84b46d82221m
rs/es-master-594b58b86c3339m
NAMEDESIREDCURRENTAGE
statefulsets/es-data223m
NAMEREADYSTATUSRESTARTSAGE
po/es-client-69b84b46d8-kr7j41/1Running01m
po/es-client-69b84b46d8-v5pj21/1Running01m
po/es-data-01/1Running03m
po/es-data-11/1Running03m
po/es-master-594b58b86c-9jkj21/1Running09m
po/es-master-594b58b86c-bj7g71/1Running09m
po/es-master-594b58b86c-lfpps1/1Running09m
NAMETYPECLUSTER-IPEXTERNAL-IPPORT(S)AGE
svc/elasticsearchLoadBalancer10.9.121.160 10.9.120.89200:32310/TCP1m
svc/elasticsearch-dataClusterIPNone<none>9300/TCP3m
svc/elasticsearch-discoveryClusterIPNone<none>9300/TCP9m
--------------------------------------------------------------------
#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的日誌清楚地描述了每個節點何時新增到叢集。這在除錯問題時非常有用。

部署完所有元件後，我們應驗證以下內容：

在kubernetes叢集內部使用ubuntu容器進行Elasticsearch部署的驗證。

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
}

在kubernetes叢集外部使用GCP內部LoadBalancer IP(這裡是10.9.120.8)進行Elasticsearch部署的驗證。

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
}

ES-Pods的Anti-Affinity規則驗證。

root$ kubectl -n elasticsearch get pods -o wide 
NAMEREADYSTATUSRESTARTSAGEIPNODE
es-client-69b84b46d8-kr7j41/1Running010m10.8.14.52gke-cluster1-pool1-d2ef2b34-t6h9
es-client-69b84b46d8-v5pj21/1Running010m10.8.15.53gke-cluster1-pool1-42b4fbc4-cncn
es-data-01/1Running012m10.8.16.58gke-cluster1-pool1-4cfd808c-kpx1
es-data-11/1Running012m10.8.15.52gke-cluster1-pool1-42b4fbc4-cncn
es-master-594b58b86c-9jkj21/1Running018m10.8.15.51gke-cluster1-pool1-42b4fbc4-cncn
es-master-594b58b86c-bj7g71/1Running018m10.8.16.57gke-cluster1-pool1-4cfd808c-kpx1
es-master-594b58b86c-lfpps1/1Running018m10.8.14.51gke-cluster1-pool1-d2ef2b34-t6h9

請注意，同一節點上沒有2個類似的pod。這可以在節點發生故障時確保HA。

Scaling相關注意事項

我們可以根據CPU閾值為client節點部署autoscalers。 Client節點的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

每當autoscaler啟動時，我們都可以通過觀察任何master pod的日誌來觀察新增到叢集中的新client節點pod。

對於Data Node Pod，我們必須使用K8 Dashboard或GKE控制檯增加副本數量。新建立的data節點將自動新增到叢集中，並開始從其他節點複製資料。

Master Node Pod不需要自動擴充套件，因為它們只儲存叢集狀態資訊，但是如果要新增更多data節點，請確保叢集中沒有偶數個master節點，同時環境變數NUMBER_OF_MASTERS也需要相應調整。

部署Kibana和ES-HQ

Kibana是一個視覺化ES資料的簡單工具，ES-HQ有助於管理和監控Elasticsearch叢集。對於我們的Kibana和ES-HQ部署，我們記住以下事項：

我們提供ES-Cluster的名稱作為docker映象的環境變數
訪問Kibana/ES-HQ部署的服務僅在我們組織內部，即不建立公共IP。我們使用GCP內部負載均衡。

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

我們可以使用新建立的Internal LoadBalancers訪問這兩個服務。

root$ kubectl -n elasticsearch get svc -l role=kibana
NAMETYPECLUSTER-IPEXTERNAL-IPPORT(S)AGE
kibanaLoadBalancer10.9.121.24610.9.120.1080:31400/TCP1m
root$ kubectl -n elasticsearch get svc -l role=hq
NAMETYPECLUSTER-IPEXTERNAL-IPPORT(S)AGE
hqLoadBalancer10.9.121.15010.9.120.980:31499/TCP1m

Kibana Dashboard http://<External-Ip-Kibana-Service>/app/kibana#/home?_g=()

ElasticHQ Dasboard http://<External-Ip-ES-Hq-Service>/#!/clusters/my-es

ES是最廣泛使用的分散式搜尋和分析系統之一，當與Kubernetes結合使用時，將消除有關擴充套件和HA的關鍵問題。此外，使用Kubernetes部署新的ES群集需要時間。我希望這個部落格對你有用，我真的很期待改進的建議。隨意評論或聯絡 LinkedIn 。