如何在Kubernetes上部署高可用和可扩展的Elasticsearch？
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。
- 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：
- 设置ES_JAVA_OPT环境变量。
- 设置CLUSTER_NAME环境变量。
- 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。
- 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://<External-Ip-Kibana-Service>/app/kibana#/home?_g=()
ElasticHQ Dasboard
http://<External-Ip-ES-Hq-Service>/#!/clusters/my-es
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。
本文转自DockOne-如何在Kubernetes上部署高可用和可扩展的Elasticsearch？
