Kubernetes 设置

Getting Started

This Getting Started guide describes how to deploy a Session cluster on Kubernetes.

Introduction

This page describes deploying a standalone Flink cluster on top of Kubernetes, using Flink’s standalone deployment. We generally recommend new users to deploy Flink on Kubernetes using native Kubernetes deployments.

Preparation

This guide expects a Kubernetes environment to be present. You can ensure that your Kubernetes setup is working by running a command like kubectl get nodes, which lists all connected Kubelets.

If you want to run Kubernetes locally, we recommend using MiniKube.

Note: If using MiniKube please make sure to execute minikube ssh 'sudo ip link set docker0 promisc on' before deploying a Flink cluster. Otherwise Flink components are not able to reference themselves through a Kubernetes service.

Starting a Kubernetes Cluster (Session Mode)

A Flink Session cluster is executed as a long-running Kubernetes Deployment. You can run multiple Flink jobs on a Session cluster. Each job needs to be submitted to the cluster after the cluster has been deployed.

A Flink Session cluster deployment in Kubernetes has at least three components:

  • a Deployment which runs a JobManager
  • a Deployment for a pool of TaskManagers
  • a Service exposing the JobManager’s REST and UI ports

Using the file contents provided in the the common resource definitions, create the following files, and create the respective components with the kubectl command:

  1.     # Configuration and service definition
  2.     $ kubectl create -f flink-configuration-configmap.yaml
  3.     $ kubectl create -f jobmanager-service.yaml
  4.     # Create the deployments for the cluster
  5.     $ kubectl create -f jobmanager-session-deployment.yaml
  6.     $ kubectl create -f taskmanager-session-deployment.yaml

Next, we set up a port forward to access the Flink UI and submit jobs:

  1. Run kubectl port-forward ${flink-jobmanager-pod} 8081:8081 to forward your jobmanager’s web ui port to local 8081.
  2. Navigate to http://localhost:8081 in your browser.
  3. Moreover, you could use the following command below to submit jobs to the cluster:
  1. $ ./bin/flink run -m localhost:8081 
  2. $ ./examples/streaming/TopSpeedWindowing.jar

You can tear down the cluster using the following commands:

  1.     $ kubectl delete -f jobmanager-service.yaml
  2.     $ kubectl delete -f flink-configuration-configmap.yaml
  3.     $ kubectl delete -f taskmanager-session-deployment.yaml
  4.     $ kubectl delete -f jobmanager-session-deployment.yaml

Deployment Modes

Deploy Application Cluster

A Flink Application cluster is a dedicated cluster which runs a single application.

A basic Flink Application cluster deployment in Kubernetes has three components:

  • an Application which runs a JobManager
  • a Deployment for a pool of TaskManagers
  • a Service exposing the JobManager’s REST and UI ports

Check the Application cluster specific resource definitions and adjust them accordingly:

The args attribute in the jobmanager-job.yaml has to specify the main class of the user job. See also how to specify the JobManager arguments to understand how to pass other args to the Flink image in the jobmanager-job.yaml.

The job artifacts should be available from the job-artifacts-volume in the resource definition examples. The definition examples mount the volume as a local directory of the host assuming that you create the components in a minikube cluster. If you do not use a minikube cluster, you can use any other type of volume, available in your Kubernetes cluster, to supply the job artifacts. Alternatively, you can build a custom image which already contains the artifacts instead.

After creating the common cluster components, use the Application cluster specific resource definitions to launch the cluster with the kubectl command:

  1.     $ kubectl create -f jobmanager-job.yaml
  2.     $ kubectl create -f taskmanager-job-deployment.yaml

To terminate the single application cluster, these components can be deleted along with the common ones with the kubectl command:

  1.     $ kubectl delete -f taskmanager-job-deployment.yaml
  2.     $ kubectl delete -f jobmanager-job.yaml

Per-Job Cluster Mode

Flink on Standalone Kubernetes does not support the Per-Job Cluster Mode.

Session Mode

Deployment of a Session cluster is explained in the Getting Started guide at the top of this page.

Flink on Standalone Kubernetes Reference

Configuration

All configuration options are listed on the configuration page. Configuration options can be added to the flink-conf.yaml section of the flink-configuration-configmap.yaml config map.

Accessing Flink in Kubernetes

You can then access the Flink UI and submit jobs via different ways:

  1.     $ ./bin/flink run -m localhost:8081 
  2.     $ ./examples/streaming/TopSpeedWindowing.jar
  • Create a NodePort service on the rest service of jobmanager:
    1. Run kubectl create -f jobmanager-rest-service.yaml to create the NodePort service on jobmanager. The example of jobmanager-rest-service.yaml can be found in appendix.
    2. Run kubectl get svc flink-jobmanager-rest to know the node-port of this service and navigate to http://<public-node-ip>:<node-port> in your browser.
    3. If you use minikube, you can get its public ip by running minikube ip.
    4. Similarly to the port-forward solution, you can also use the following command below to submit jobs to the cluster:
  1.     $ ./bin/flink run -m <public-node-ip>:<node-port> 
  2.     $ ./examples/streaming/TopSpeedWindowing.jar

Debugging and Log Access

Many common errors are easy to detect by checking Flink’s log files. If you have access to Flink’s web user interface, you can access the JobManager and TaskManager logs from there.

If there are problems starting Flink, you can also use Kubernetes utilities to access the logs. Use kubectl get pods to see all running pods. For the quickstart example from above, you should see three pods:

  1. $ kubectl get pods
  2. NAME                                 READY   STATUS             RESTARTS   AGE
  3. flink-jobmanager-589967dcfc-m49xv    1/1     Running            3          3m32s
  4. flink-taskmanager-64847444ff-7rdl4   1/1     Running            3          3m28s
  5. flink-taskmanager-64847444ff-nnd6m   1/1     Running            3          3m28s

You can now access the logs by running kubectl logs flink-jobmanager-589967dcfc-m49xv

High-Availability with Standalone Kubernetes

For high availability on Kubernetes, you can use the existing high availability services.

Session Mode and Application Mode clusters support using the Kubernetes high availability service. Users just need to add the following Flink config options to flink-configuration-configmap.yaml. All other yamls do not need to be updated.

Note The filesystem which corresponds to the scheme of your configured HA storage directory must be available to the runtime. Refer to custom Flink image and enable plugins for more information.

  1. apiVersion: v1
  2. kind: ConfigMap
  3. metadata:
  4.   name: flink-config
  5.   labels:
  6.     app: flink
  7. data:
  8.   flink-conf.yaml: |+
  9.   ...
  10.     kubernetes.cluster-id: <cluster-id>
  11.     high-availability: org.apache.flink.kubernetes.highavailability.KubernetesHaServicesFactory
  12.     high-availability.storageDir: hdfs:///flink/recovery
  13.     restart-strategy: fixed-delay
  14.     restart-strategy.fixed-delay.attempts: 10
  15.   ...

Enabling Queryable State

You can access the queryable state of TaskManager if you create a NodePort service for it:

  1. Run kubectl create -f taskmanager-query-state-service.yaml to create the NodePort service for the taskmanager pod. The example of taskmanager-query-state-service.yaml can be found in appendix.
  2. Run kubectl get svc flink-taskmanager-query-state to get the &lt;node-port&gt; of this service. Then you can create the QueryableStateClient(<public-node-ip>, <node-port> to submit the state queries.

Appendix

Common cluster resource definitions

flink-configuration-configmap.yaml

  1. apiVersion: v1
  2. kind: ConfigMap
  3. metadata:
  4.   name: flink-config
  5.   labels:
  6.     app: flink
  7. data:
  8.   flink-conf.yaml: |+
  9.     jobmanager.rpc.address: flink-jobmanager
  10.     taskmanager.numberOfTaskSlots: 2
  11.     blob.server.port: 6124
  12.     jobmanager.rpc.port: 6123
  13.     taskmanager.rpc.port: 6122
  14.     queryable-state.proxy.ports: 6125
  15.     jobmanager.memory.process.size: 1600m
  16.     taskmanager.memory.process.size: 1728m
  17.     parallelism.default: 2
  18.   log4j-console.properties: |+
  19.     # This affects logging for both user code and Flink
  20.     rootLogger.level = INFO
  21.     rootLogger.appenderRef.console.ref = ConsoleAppender
  22.     rootLogger.appenderRef.rolling.ref = RollingFileAppender
  24.     # Uncomment this if you want to _only_ change Flink's logging
  25.     #logger.flink.name = org.apache.flink
  26.     #logger.flink.level = INFO
  28.     # The following lines keep the log level of common libraries/connectors on
  29.     # log level INFO. The root logger does not override this. You have to manually
  30.     # change the log levels here.
  31.     logger.akka.name = akka
  32.     logger.akka.level = INFO
  33.     logger.kafka.name= org.apache.kafka
  34.     logger.kafka.level = INFO
  35.     logger.hadoop.name = org.apache.hadoop
  36.     logger.hadoop.level = INFO
  37.     logger.zookeeper.name = org.apache.zookeeper
  38.     logger.zookeeper.level = INFO
  40.     # Log all infos to the console
  41.     appender.console.name = ConsoleAppender
  42.     appender.console.type = CONSOLE
  43.     appender.console.layout.type = PatternLayout
  44.     appender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
  46.     # Log all infos in the given rolling file
  47.     appender.rolling.name = RollingFileAppender
  48.     appender.rolling.type = RollingFile
  49.     appender.rolling.append = false
  50.     appender.rolling.fileName = ${sys:log.file}
  51.     appender.rolling.filePattern = ${sys:log.file}.%i
  52.     appender.rolling.layout.type = PatternLayout
  53.     appender.rolling.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
  54.     appender.rolling.policies.type = Policies
  55.     appender.rolling.policies.size.type = SizeBasedTriggeringPolicy
  56.     appender.rolling.policies.size.size=100MB
  57.     appender.rolling.strategy.type = DefaultRolloverStrategy
  58.     appender.rolling.strategy.max = 10
  60.     # Suppress the irrelevant (wrong) warnings from the Netty channel handler
  61.     logger.netty.name = org.apache.flink.shaded.akka.org.jboss.netty.channel.DefaultChannelPipeline
  62.     logger.netty.level = OFF

jobmanager-service.yaml

  1. apiVersion: v1
  2. kind: Service
  3. metadata:
  4.   name: flink-jobmanager
  5. spec:
  6.   type: ClusterIP
  7.   ports:
  8.   - name: rpc
  9.     port: 6123
  10.   - name: blob-server
  11.     port: 6124
  12.   - name: webui
  13.     port: 8081
  14.   selector:
  15.     app: flink
  16.     component: jobmanager

jobmanager-rest-service.yaml. Optional service, that exposes the jobmanager rest port as public Kubernetes node’s port.

  1. apiVersion: v1
  2. kind: Service
  3. metadata:
  4.   name: flink-jobmanager-rest
  5. spec:
  6.   type: NodePort
  7.   ports:
  8.   - name: rest
  9.     port: 8081
  10.     targetPort: 8081
  11.     nodePort: 30081
  12.   selector:
  13.     app: flink
  14.     component: jobmanager

taskmanager-query-state-service.yaml. Optional service, that exposes the TaskManager port to access the queryable state as a public Kubernetes node’s port.

  1. apiVersion: v1
  2. kind: Service
  3. metadata:
  4.   name: flink-taskmanager-query-state
  5. spec:
  6.   type: NodePort
  7.   ports:
  8.   - name: query-state
  9.     port: 6125
  10.     targetPort: 6125
  11.     nodePort: 30025
  12.   selector:
  13.     app: flink
  14.     component: taskmanager

Session cluster resource definitions

jobmanager-session-deployment.yaml

  1. apiVersion: apps/v1
  2. kind: Deployment
  3. metadata:
  4.   name: flink-jobmanager
  5. spec:
  6.   replicas: 1
  7.   selector:
  8.     matchLabels:
  9.       app: flink
  10.       component: jobmanager
  11.   template:
  12.     metadata:
  13.       labels:
  14.         app: flink
  15.         component: jobmanager
  16.     spec:
  17.       containers:
  18.       - name: jobmanager
  19.         image: flink:1.12.0-scala_2.11
  20.         args: ["jobmanager"]
  21.         ports:
  22.         - containerPort: 6123
  23.           name: rpc
  24.         - containerPort: 6124
  25.           name: blob-server
  26.         - containerPort: 8081
  27.           name: webui
  28.         livenessProbe:
  29.           tcpSocket:
  30.             port: 6123
  31.           initialDelaySeconds: 30
  32.           periodSeconds: 60
  33.         volumeMounts:
  34.         - name: flink-config-volume
  35.           mountPath: /opt/flink/conf
  36.         securityContext:
  37.           runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  38.       volumes:
  39.       - name: flink-config-volume
  40.         configMap:
  41.           name: flink-config
  42.           items:
  43.           - key: flink-conf.yaml
  44.             path: flink-conf.yaml
  45.           - key: log4j-console.properties
  46.             path: log4j-console.properties

taskmanager-session-deployment.yaml

  1. apiVersion: apps/v1
  2. kind: Deployment
  3. metadata:
  4.   name: flink-taskmanager
  5. spec:
  6.   replicas: 2
  7.   selector:
  8.     matchLabels:
  9.       app: flink
  10.       component: taskmanager
  11.   template:
  12.     metadata:
  13.       labels:
  14.         app: flink
  15.         component: taskmanager
  16.     spec:
  17.       containers:
  18.       - name: taskmanager
  19.         image: flink:1.12.0-scala_2.11
  20.         args: ["taskmanager"]
  21.         ports:
  22.         - containerPort: 6122
  23.           name: rpc
  24.         - containerPort: 6125
  25.           name: query-state
  26.         livenessProbe:
  27.           tcpSocket:
  28.             port: 6122
  29.           initialDelaySeconds: 30
  30.           periodSeconds: 60
  31.         volumeMounts:
  32.         - name: flink-config-volume
  33.           mountPath: /opt/flink/conf/
  34.         securityContext:
  35.           runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  36.       volumes:
  37.       - name: flink-config-volume
  38.         configMap:
  39.           name: flink-config
  40.           items:
  41.           - key: flink-conf.yaml
  42.             path: flink-conf.yaml
  43.           - key: log4j-console.properties
  44.             path: log4j-console.properties

Application cluster resource definitions

jobmanager-application.yaml

  1. apiVersion: batch/v1
  2. kind: Job
  3. metadata:
  4.   name: flink-jobmanager
  5. spec:
  6.   template:
  7.     metadata:
  8.       labels:
  9.         app: flink
  10.         component: jobmanager
  11.     spec:
  12.       restartPolicy: OnFailure
  13.       containers:
  14.         - name: jobmanager
  15.           image: flink:1.12.0-scala_2.11
  16.           env:
  17.           args: ["standalone-job", "--job-classname", "com.job.ClassName", <optional arguments>, <job arguments>] # optional arguments: ["--job-id", "<job id>", "--fromSavepoint", "/path/to/savepoint", "--allowNonRestoredState"]
  18.           ports:
  19.             - containerPort: 6123
  20.               name: rpc
  21.             - containerPort: 6124
  22.               name: blob-server
  23.             - containerPort: 8081
  24.               name: webui
  25.           livenessProbe:
  26.             tcpSocket:
  27.               port: 6123
  28.             initialDelaySeconds: 30
  29.             periodSeconds: 60
  30.           volumeMounts:
  31.             - name: flink-config-volume
  32.               mountPath: /opt/flink/conf
  33.             - name: job-artifacts-volume
  34.               mountPath: /opt/flink/usrlib
  35.           securityContext:
  36.             runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  37.       volumes:
  38.         - name: flink-config-volume
  39.           configMap:
  40.             name: flink-config
  41.             items:
  42.               - key: flink-conf.yaml
  43.                 path: flink-conf.yaml
  44.               - key: log4j-console.properties
  45.                 path: log4j-console.properties
  46.         - name: job-artifacts-volume
  47.           hostPath:
  48.             path: /host/path/to/job/artifacts

taskmanager-job-deployment.yaml

  1. apiVersion: apps/v1
  2. kind: Deployment
  3. metadata:
  4.   name: flink-taskmanager
  5. spec:
  6.   replicas: 2
  7.   selector:
  8.     matchLabels:
  9.       app: flink
  10.       component: taskmanager
  11.   template:
  12.     metadata:
  13.       labels:
  14.         app: flink
  15.         component: taskmanager
  16.     spec:
  17.       containers:
  18.       - name: taskmanager
  19.         image: flink:1.12.0-scala_2.11
  20.         env:
  21.         args: ["taskmanager"]
  22.         ports:
  23.         - containerPort: 6122
  24.           name: rpc
  25.         - containerPort: 6125
  26.           name: query-state
  27.         livenessProbe:
  28.           tcpSocket:
  29.             port: 6122
  30.           initialDelaySeconds: 30
  31.           periodSeconds: 60
  32.         volumeMounts:
  33.         - name: flink-config-volume
  34.           mountPath: /opt/flink/conf/
  35.         - name: job-artifacts-volume
  36.           mountPath: /opt/flink/usrlib
  37.         securityContext:
  38.           runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  39.       volumes:
  40.       - name: flink-config-volume
  41.         configMap:
  42.           name: flink-config
  43.           items:
  44.           - key: flink-conf.yaml
  45.             path: flink-conf.yaml
  46.           - key: log4j-console.properties
  47.             path: log4j-console.properties
  48.       - name: job-artifacts-volume
  49.         hostPath:
  50.           path: /host/path/to/job/artifacts