摘要:ES集羣是進行大數據存儲和分析,快速檢索的利器,本文簡述了ES的集羣架構,並提供了在Kubernetes中快速部署ES集羣的樣例;對ES集羣的監控運維工具進行了介紹,並提供了部分問題定位經驗,最後總結了經常使用ES集羣的API調用方法。
本文分享自華爲雲社區《Kubernetes中部署ES集羣及運維》,原文做者:minucas。node
ES集羣分爲單點模式和集羣模式,其中單點模式通常在生產環境不推薦使用,推薦使用集羣模式部署。其中集羣模式又分爲Master節點與Data節點由同一個節點承擔,以及Master節點與Data節點由不一樣節點承擔的部署模式。Master節點與Data節點分開的部署方式可靠性更強。下圖爲ES集羣的部署架構圖:
nginx
一、採用k8s statefulset部署,可快速的進行擴縮容es節點,本例子採用 3 Master Node + 12 Data Node 方式部署
二、經過k8s service配置了對應的域名和服務發現,確保集羣能自動聯通和監控git
kubectl -s http://ip:port create -f es-master.yaml kubectl -s http://ip:port create -f es-data.yaml kubectl -s http://ip:port create -f es-service.yaml
apiVersion: apps/v1 kind: StatefulSet metadata: labels: addonmanager.kubernetes.io/mode: Reconcile k8s-app: es kubernetes.io/cluster-service: "true" version: v6.2.5 name: es-master namespace: default spec: podManagementPolicy: OrderedReady replicas: 3 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: es version: v6.2.5 serviceName: es template: metadata: labels: k8s-app: camp-es kubernetes.io/cluster-service: "true" version: v6.2.5 spec: containers: - env: - name: NAMESPACE valueFrom: fieldRef: apiVersion: v1 fieldPath: metadata.namespace - name: ELASTICSEARCH_SERVICE_NAME value: es - name: NODE_MASTER value: "true" - name: NODE_DATA value: "false" - name: ES_HEAP_SIZE value: 4g - name: ES_JAVA_OPTS value: -Xmx4g -Xms4g - name: cluster.name value: es image: elasticsearch:v6.2.5 imagePullPolicy: Always name: es ports: - containerPort: 9200 hostPort: 9200 name: db protocol: TCP - containerPort: 9300 hostPort: 9300 name: transport protocol: TCP resources: limits: cpu: "6" memory: 12Gi requests: cpu: "4" memory: 8Gi securityContext: capabilities: add: - IPC_LOCK - SYS_RESOURCE volumeMounts: - mountPath: /data name: es - command: - /bin/elasticsearch_exporter - -es.uri=http://localhost:9200 - -es.all=true image: elasticsearch_exporter:1.0.2 imagePullPolicy: IfNotPresent livenessProbe: failureThreshold: 3 httpGet: path: /health port: 9108 scheme: HTTP initialDelaySeconds: 30 periodSeconds: 10 successThreshold: 1 timeoutSeconds: 10 name: es-exporter ports: - containerPort: 9108 hostPort: 9108 protocol: TCP readinessProbe: failureThreshold: 3 httpGet: path: /health port: 9108 scheme: HTTP initialDelaySeconds: 10 periodSeconds: 10 successThreshold: 1 timeoutSeconds: 10 resources: limits: cpu: 100m memory: 128Mi requests: cpu: 25m memory: 64Mi securityContext: capabilities: drop: - SETPCAP - MKNOD - AUDIT_WRITE - CHOWN - NET_RAW - DAC_OVERRIDE - FOWNER - FSETID - KILL - SETGID - SETUID - NET_BIND_SERVICE - SYS_CHROOT - SETFCAP readOnlyRootFilesystem: true dnsPolicy: ClusterFirst initContainers: - command: - /sbin/sysctl - -w - vm.max_map_count=262144 image: alpine:3.6 imagePullPolicy: IfNotPresent name: elasticsearch-logging-init resources: {} securityContext: privileged: true restartPolicy: Always schedulerName: default-scheduler securityContext: {} volumes: - hostPath: path: /Data/es type: DirectoryOrCreate name: es
apiVersion: apps/v1 kind: StatefulSet metadata: labels: addonmanager.kubernetes.io/mode: Reconcile k8s-app: es kubernetes.io/cluster-service: "true" version: v6.2.5 name: es-data namespace: default spec: podManagementPolicy: OrderedReady replicas: 12 revisionHistoryLimit: 10 selector: matchLabels: k8s-app: es version: v6.2.5 serviceName: es template: metadata: labels: k8s-app: es kubernetes.io/cluster-service: "true" version: v6.2.5 spec: containers: - env: - name: NAMESPACE valueFrom: fieldRef: apiVersion: v1 fieldPath: metadata.namespace - name: ELASTICSEARCH_SERVICE_NAME value: es - name: NODE_MASTER value: "false" - name: NODE_DATA value: "true" - name: ES_HEAP_SIZE value: 16g - name: ES_JAVA_OPTS value: -Xmx16g -Xms16g - name: cluster.name value: es image: elasticsearch:v6.2.5 imagePullPolicy: Always name: es ports: - containerPort: 9200 hostPort: 9200 name: db protocol: TCP - containerPort: 9300 hostPort: 9300 name: transport protocol: TCP resources: limits: cpu: "8" memory: 32Gi requests: cpu: "7" memory: 30Gi securityContext: capabilities: add: - IPC_LOCK - SYS_RESOURCE volumeMounts: - mountPath: /data name: es - command: - /bin/elasticsearch_exporter - -es.uri=http://localhost:9200 - -es.all=true image: elasticsearch_exporter:1.0.2 imagePullPolicy: IfNotPresent livenessProbe: failureThreshold: 3 httpGet: path: /health port: 9108 scheme: HTTP initialDelaySeconds: 30 periodSeconds: 10 successThreshold: 1 timeoutSeconds: 10 name: es-exporter ports: - containerPort: 9108 hostPort: 9108 protocol: TCP readinessProbe: failureThreshold: 3 httpGet: path: /health port: 9108 scheme: HTTP initialDelaySeconds: 10 periodSeconds: 10 successThreshold: 1 timeoutSeconds: 10 resources: limits: cpu: 100m memory: 128Mi requests: cpu: 25m memory: 64Mi securityContext: capabilities: drop: - SETPCAP - MKNOD - AUDIT_WRITE - CHOWN - NET_RAW - DAC_OVERRIDE - FOWNER - FSETID - KILL - SETGID - SETUID - NET_BIND_SERVICE - SYS_CHROOT - SETFCAP readOnlyRootFilesystem: true dnsPolicy: ClusterFirst initContainers: - command: - /sbin/sysctl - -w - vm.max_map_count=262144 image: alpine:3.6 imagePullPolicy: IfNotPresent name: elasticsearch-logging-init resources: {} securityContext: privileged: true restartPolicy: Always schedulerName: default-scheduler securityContext: {} volumes: - hostPath: path: /Data/es type: DirectoryOrCreate name: es
apiVersion: v1 kind: Service metadata: labels: addonmanager.kubernetes.io/mode: Reconcile k8s-app: es kubernetes.io/cluster-service: "true" kubernetes.io/name: Elasticsearch name: es namespace: default spec: clusterIP: None ports: - name: es port: 9200 protocol: TCP targetPort: 9200 - name: exporter port: 9108 protocol: TCP targetPort: 9108 selector: k8s-app: es sessionAffinity: None type: ClusterIP
工欲善其事必先利其器,中間件的運維首先要有充分的監控手段,ES集羣的監控經常使用的三種監控手段:exporter、eshead、kopf,因爲ES集羣是採用k8s架構部署,不少特性都會結合k8s來開展github
經過k8s部署es-exporter將監控metrics導出,prometheus採集監控數據,grafana定製dashboard展現segmentfault
github地址:https://github.com/mobz/elast...
ES-head組件可經過谷歌瀏覽器應用商店搜索安裝,使用Chrome插件可查看ES集羣的狀況 api
github地址:https://github.com/lmenezes/c...
瀏覽器
資源配置:關注ES的CPU、Memory以及Heap Size,Xms Xmx的配置,建議如機器是8u32g內存的狀況下,堆內存和Xms Xmx配置爲50%,官網建議單個node的內存不要超過64Gsession
索引配置:因爲ES檢索經過索引來定位,檢索的時候ES會將相關的索引數據裝載到內存中加快檢索速度,所以合理的對索引進行設置對ES的性能影響很大,當前咱們經過按日期建立索引的方法(個別數據量小的可不分割索引)架構
CPU和Load比較高的節點重點關注,可能的緣由是shard分配不均勻,此時可手動講不均衡的shard relocate一下
app
shard配置最好是data node數量的整數倍,shard數量不是越多越好,應該按照索引的數據量合理進行分片,確保每一個shard不要超過單個data node分配的堆內存大小,好比數據量最大的index單日150G左右,分爲24個shard,計算下來單個shard大小大概6-7G左右
副本數建議爲1,副本數過大,容易致使數據的頻繁relocate,加大集羣負載
curl -X DELETE "10.64.xxx.xx:9200/szv-prod-ingress-nginx-2021.05.01"
索引名可以使用進行正則匹配進行批量刪除,如:-2021.05.*
在定位問題的時候發現節點數據shard已經移走可是節點負載一直下不去,登入節點使用top命令發現節點kubelet的cpu佔用很是高,重啓kubelet也無效,重啓節點後負載才獲得緩解
ES集羣的健康狀態分爲三種:Green、Yellow、Red。
可經過API查詢集羣的健康狀態及未分配的分片:
GET _cluster/health { "cluster_name": "camp-es", "status": "green", "timed_out": false, "number_of_nodes": 15, "number_of_data_nodes": 12, "active_primary_shards": 2176, "active_shards": 4347, "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 }
GET /_cat/pending_tasks
其中 priority 字段則表示該 task 的優先級
GET _cluster/allocation/explain
其中reason 字段表示哪一種緣由致使的分片未分配,detail 表示詳細未分配的緣由
GET /_cat/indices?v&health=red
curl -s http://ip:port/_cat/shards | grep UNASSIGNED
POST _cluster/reroute?pretty" -d '{ "commands" : [ { "allocate_stale_primary" : { "index" : "xxx", "shard" : 1, "node" : "12345...", "accept_data_loss": true } } ] }
其中node爲es集羣節點的id,能夠經過curl ‘ip:port/_node/process?pretty’ 進行查詢
PUT /szv_ingress_*/settings { "index": { "number_of_replicas": 1 } }