Flink operator实现自动扩缩容
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1.Autoscaler | Apache Flink Kubernetes Operator
2.Configuration | Apache Flink Kubernetes Operator
1.部署K8S集群
可参照我之前的文章k8s集群搭建
2.Helm安装Flink-Operator
helm repo add flink-operator-repo https://downloads.apache.org/flink/flink-kubernetes-operator-1.10.0/helm repo update--如果没有这个命名空间就创建
helm install flink-kubernetes-operator flink-operator-repo/flink-kubernetes-operator \
--namespace=flink-operator \
--create-namespace \
--set webhook.create=false \
--version 1.10.03.安装prometheus
operator通过监控prometheus实现自动扩缩容，过两天调整为helm
可以采用helm安装也可采用yaml，由于helm没安装成功我就采用yaml安装了# prometheus-basic.yaml
apiVersion: v1
kind: Namespace
metadata:name: monitoring
---
apiVersion: v1
kind: ConfigMap
metadata:name: prometheus-confignamespace: monitoring
data:prometheus.yml: |global:scrape_interval: 15sevaluation_interval: 15sscrape_configs:- job_name: 'flink'static_configs:- targets: ['flink-metrics.flink-apps.svc.cluster.local:9249']metrics_path: /metrics
---
apiVersion: apps/v1
kind: Deployment
metadata:name: prometheusnamespace: monitoring
spec:selector:matchLabels:app: prometheusreplicas: 1template:metadata:labels:app: prometheusspec:containers:- name: prometheusimage: prom/prometheus:v2.30.3args:- "--config.file=/etc/prometheus/prometheus.yml"- "--storage.tsdb.path=/prometheus"- "--web.enable-lifecycle"ports:- containerPort: 9090volumeMounts:- name: config-volumemountPath: /etc/prometheus/- name: storage-volumemountPath: /prometheusvolumes:- name: config-volumeconfigMap:name: prometheus-config- name: storage-volumeemptyDir: {}
---
apiVersion: v1
kind: Service
metadata:name: prometheusnamespace: monitoring
spec:type: NodePortports:- port: 9090targetPort: 9090nodePort: 30090selector:app: prometheus
4.制作镜像包
Dockerfile内容,flink-test-1.0-SNAPSHOT.jar为测试代码ARG FLINK_VERSION=1.18.1
FROM flink:${FLINK_VERSION}-scala_2.12
RUN mkdir -p /opt/flink/usrlib
COPY flink-test-1.0-SNAPSHOT.jar /opt/flink/usrlib/
COPY flink-metrics-prometheus-1.18.1.jar /opt/flink/lib/
COPY flink-statebackend-rocksdb-1.18.1.jar /opt/flink/lib/
COPY flink-connector-files-1.18.1.jar /opt/flink/lib/
WORKDIR /opt/flink# 1. 构建 Docker 镜像
# -t: 指定镜像名称和标签
# .: 使用当前目录的 Dockerfile
# --no-cache: 不使用缓存，从头构建
docker build -t zht-flink:1.18.1 . --no-cache# 2. 为本地镜像添加远程仓库标签
# 格式: registry地址/命名空间/镜像名:标签
docker tag zht-flink:1.18.1 registry.cn-hangzhou.aliyuncs.com/dinkyhub/zht-flink:1.18.1# 3. 推送镜像到阿里云镜像仓库
# 将标记的镜像推送到远程仓库
docker push registry.cn-hangzhou.aliyuncs.com/dinkyhub/zht-flink:1.18.1
5.创建命名空间和serviceaccount等
kubectl create namespace flink-appskubectl -n flink-apps create serviceaccount flink-serviceaccountkubectl -n flink-apps create clusterrolebinding flink-role-binding --clusterrole=edit --serviceaccount=flink-apps:flink-serviceaccountkubectl create secret docker-registry flink-apps-secret \
--docker-server=registry.cn-hangzhou.aliyuncs.com \
--docker-username=xx \
--docker-password=xxxx \
-n flink-appskubectl patch serviceaccount flink-serviceaccount -p '{"imagePullSecrets": [{"name": "flink-apps-secret"}]}' -n flink-apps6.任务和扩缩容配置
apiVersion: flink.apache.org/v1beta1
kind: FlinkDeployment
metadata:name: flink-autoscaling-sum-jobnamespace: flink-apps
spec:image: registry.cn-hangzhou.aliyuncs.com/dinkyhub/zht-flink:1.18.1flinkVersion: v1_18mode: nativeflinkConfiguration:taskmanager.numberOfTaskSlots: "2"parallelism.default: "2"state.backend: rocksdbstate.checkpoints.dir: file:///flink-data/checkpointsstate.savepoints.dir: file:///flink-data/savepointsmetrics.reporters: prometheusmetrics.reporter.prometheus.factory.class: org.apache.flink.metrics.prometheus.PrometheusReporterFactorymetrics.reporter.prometheus.port: "9249"execution.checkpointing.interval: "10000"execution.checkpointing.mode: "EXACTLY_ONCE"execution.checkpointing.timeout: "600000"execution.checkpointing.min.pause: "10000"execution.checkpointing.max.concurrent.checkpoints: "1"metrics.task.records.out.enable: "true"# 设置指标收集间隔metrics.fetcher.update-interval: "1000"metrics.latency.interval: "1000"# 启用 IO 指标metrics.io.enable: "true" jobmanager.scheduler: "adaptive"# 自动扩缩容配置job.autoscaler.enabled: "true"job.autoscaler.metrics.window: "20s"job.autoscaler.target.utilization: "0.30"job.autoscaler.scale.up.threshold: "0.05"job.autoscaler.scale.down.threshold: "0.1"job.autoscaler.metrics.memory.average: "1.0"job.autoscaler.metrics.memory.window: "5s"job.autoscaler.stabilization.interval: "5s"job.autoscaler.cooldown.period: "5s"job.autoscaler.scale.up.max.factor: "1.5"job.autoscaler.scale.down.max.factor: "0.5" serviceAccount: flink-serviceaccountjobManager:resource:memory: "1024m"cpu: 1replicas: 1taskManager:resource:memory: "1024m"cpu: 1job:jarURI: local:///opt/flink/usrlib/flink-test-1.0-SNAPSHOT.jarentryClass: com.zht.sumJobargs: []parallelism: 1upgradeMode: statelesspodTemplate:spec:volumes:- name: checkpoint-datahostPath:path: /data/flink-checkpointstype: DirectoryOrCreatecontainers:- name: flink-main-containervolumeMounts:- name: checkpoint-datamountPath: /flink-datametadata:annotations:prometheus.io/scrape: "true"prometheus.io/port: "9249"---
apiVersion: batch/v1
kind: Job
metadata:name: init-checkpoint-dirnamespace: flink-apps
spec:template:spec:serviceAccountName: flink-serviceaccountcontainers:- name: init-dirimage: busyboxcommand: ["/bin/sh", "-c"]args:- |mkdir -p /data/flink-checkpoints/checkpointsmkdir -p /data/flink-checkpoints/savepointschmod -R 777 /data/flink-checkpointsvolumeMounts:- name: checkpoint-datamountPath: /data/flink-checkpointsresources:limits:cpu: "0.1"memory: "64Mi"requests:cpu: "0.1"memory: "64Mi"volumes:- name: checkpoint-datahostPath:path: /data/flink-checkpointstype: DirectoryOrCreaterestartPolicy: NeverbackoffLimit: 4---
apiVersion: v1
kind: Service
metadata:name: flink-jobmanager-uinamespace: flink-apps
spec:type: NodePortports:- name: webuiport: 8081targetPort: 8081nodePort: 30081selector:component: jobmanagerapp: flink-autoscaling-sum-job---
apiVersion: v1
kind: Service
metadata:name: flink-metricsnamespace: flink-apps
spec:type: NodePortports:- name: metricsport: 9249targetPort: 9249nodePort: 30249selector:component: taskmanagerapp: flink-autoscaling-sum-job
注意点：1.添加 flink-metrics-prometheus-1.18.1.jar 不然启动不了metrics
2.注意先排查metrics是否启用成功。curl http://localhost:9249/metrics查看是否有值
3.之后查看prometheus页面的target是否有flink metrics
4.yaml或者flink任务配置好启用监控的配置