Kubernetes Setup

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.

Apache Flink also provides a Kubernetes operator for managing Flink clusters on Kubernetes. It supports both standalone and native deployment mode and greatly simplifies deployment, configuration and the life cycle management of Flink resources on Kubernetes.

For more information, please refer to the Flink Kubernetes Operator documentation

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.

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-non-ha.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 ./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-non-ha.yaml

Deployment Modes

Application Mode

For high-level intuition behind the application mode, please refer to the deployment mode overview.

A Flink Application cluster is a dedicated cluster which runs a single application, which needs to be available at deployment time.

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 could be provided by these way：

  • The job artifacts could 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.
  • You can build a custom image which already contains the artifacts instead.
  • You can pass artifacts via the –jars option that are stored locally, on remote DFS, or accessible via an HTTP(S) endpoint.

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

Session Mode

For high-level intuition behind the session mode, please refer to the deployment mode overview.

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

Configuration

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

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

  • kubectl proxy:

    1. Run kubectl proxy in a terminal.
    2. Navigate to http://localhost:8001/api/v1/namespaces/default/services/flink-jobmanager:webui/proxy in your browser.

  • kubectl port-forward:

    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 can use the following command below to submit jobs to the cluster:
    1. $ ./bin/flink run -m localhost:8081 ./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> ./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.

Kubernetes High-Availability Services

Session Mode and Application Mode clusters support using the Kubernetes high availability service. You need to add the following Flink config options to flink-configuration-configmap.yaml.

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.   config.yaml: |+
  9.   ...
  10.     kubernetes.cluster-id: <cluster-id>
  11.     high-availability.type: kubernetes
  12.     high-availability.storageDir: hdfs:///flink/recovery
  13.     restart-strategy.type: fixed-delay
  14.     restart-strategy.fixed-delay.attempts: 10
  15.   ...

Moreover, you have to start the JobManager and TaskManager pods with a service account which has the permissions to create, edit, delete ConfigMaps. See how to configure service accounts for pods for more information.

When High-Availability is enabled, Flink will use its own HA-services for service discovery. Therefore, JobManager pods should be started with their IP address instead of a Kubernetes service as its jobmanager.rpc.address. Refer to the appendix for full configuration.

Standby JobManagers

Usually, it is enough to only start a single JobManager pod, because Kubernetes will restart it once the pod crashes. If you want to achieve faster recovery, configure the replicas in jobmanager-session-deployment-ha.yaml or parallelism in jobmanager-application-ha.yaml to a value greater than 1 to start standby JobManagers.

Using Standalone Kubernetes with Reactive Mode

Reactive Mode allows to run Flink in a mode, where the Application Cluster is always adjusting the job parallelism to the available resources. In combination with Kubernetes, the replica count of the TaskManager deployment determines the available resources. Increasing the replica count will scale up the job, reducing it will trigger a scale down. This can also be done automatically by using a Horizontal Pod Autoscaler.

To use Reactive Mode on Kubernetes, follow the same steps as for deploying a job using an Application Cluster. But instead of flink-configuration-configmap.yaml use this config map: flink-reactive-mode-configuration-configmap.yaml. It contains the scheduler-mode: reactive setting for Flink.

Once you have deployed the Application Cluster, you can scale your job up or down by changing the replica count in the flink-taskmanager deployment.

Enabling Local Recovery Across Pod Restarts

In order to speed up recoveries in case of pod failures, you can leverage Flink’s working directory feature together with local recovery. If the working directory is configured to reside on a persistent volume that gets remounted to a restarted TaskManager pod, then Flink is able to recover state locally. With the StatefulSet, Kubernetes gives you the exact tool you need to map a pod to a persistent volume.

Deploying TaskManagers as a StatefulSet, allows you to configure a volume claim template that is used to mount persistent volumes to the TaskManagers. Additionally, you need to configure a deterministic taskmanager.resource-id. A suitable value is the pod name, that you expose using environment variables. For an example StatefulSet configuration take a look at the appendix.

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.   config.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.     jobmanager.memory.process.size: 1600m
  15.     taskmanager.memory.process.size: 1728m
  16.     parallelism.default: 2    
  17.   log4j-console.properties: |+
  18.     # This affects logging for both user code and Flink
  19.     rootLogger.level = INFO
  20.     rootLogger.appenderRef.console.ref = ConsoleAppender
  21.     rootLogger.appenderRef.rolling.ref = RollingFileAppender
  23.     # Uncomment this if you want to _only_ change Flink's logging
  24.     #logger.flink.name = org.apache.flink
  25.     #logger.flink.level = INFO
  27.     # The following lines keep the log level of common libraries/connectors on
  28.     # log level INFO. The root logger does not override this. You have to manually
  29.     # change the log levels here.
  30.     logger.pekko.name = org.apache.pekko
  31.     logger.pekko.level = INFO
  32.     logger.kafka.name= org.apache.kafka
  33.     logger.kafka.level = INFO
  34.     logger.hadoop.name = org.apache.hadoop
  35.     logger.hadoop.level = INFO
  36.     logger.zookeeper.name = org.apache.zookeeper
  37.     logger.zookeeper.level = INFO
  39.     # Log all infos to the console
  40.     appender.console.name = ConsoleAppender
  41.     appender.console.type = CONSOLE
  42.     appender.console.layout.type = PatternLayout
  43.     appender.console.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
  45.     # Log all infos in the given rolling file
  46.     appender.rolling.name = RollingFileAppender
  47.     appender.rolling.type = RollingFile
  48.     appender.rolling.append = false
  49.     appender.rolling.fileName = ${sys:log.file}
  50.     appender.rolling.filePattern = ${sys:log.file}.%i
  51.     appender.rolling.layout.type = PatternLayout
  52.     appender.rolling.layout.pattern = %d{yyyy-MM-dd HH:mm:ss,SSS} %-5p %-60c %x - %m%n
  53.     appender.rolling.policies.type = Policies
  54.     appender.rolling.policies.size.type = SizeBasedTriggeringPolicy
  55.     appender.rolling.policies.size.size=100MB
  56.     appender.rolling.strategy.type = DefaultRolloverStrategy
  57.     appender.rolling.strategy.max = 10
  59.     # Suppress the irrelevant (wrong) warnings from the Netty channel handler
  60.     logger.netty.name = org.jboss.netty.channel.DefaultChannelPipeline
  61.     logger.netty.level = OFF

flink-reactive-mode-configuration-configmap.yaml

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

jobmanager-service.yaml Optional service, which is only necessary for non-HA mode.

  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

Session cluster resource definitions

jobmanager-session-deployment-non-ha.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: apache/flink:1.19.0-scala_2.12
  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: config.yaml
  44.             path: config.yaml
  45.           - key: log4j-console.properties
  46.             path: log4j-console.properties

jobmanager-session-deployment-ha.yaml

  1. apiVersion: apps/v1
  2. kind: Deployment
  3. metadata:
  4.   name: flink-jobmanager
  5. spec:
  6.   replicas: 1 # Set the value to greater than 1 to start standby JobManagers
  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: apache/flink:1.19.0-scala_2.12
  20.         env:
  21.         - name: POD_IP
  22.           valueFrom:
  23.             fieldRef:
  24.               apiVersion: v1
  25.               fieldPath: status.podIP
  26.         # The following args overwrite the value of jobmanager.rpc.address configured in the configuration config map to POD_IP.
  27.         args: ["jobmanager", "$(POD_IP)"]
  28.         ports:
  29.         - containerPort: 6123
  30.           name: rpc
  31.         - containerPort: 6124
  32.           name: blob-server
  33.         - containerPort: 8081
  34.           name: webui
  35.         livenessProbe:
  36.           tcpSocket:
  37.             port: 6123
  38.           initialDelaySeconds: 30
  39.           periodSeconds: 60
  40.         volumeMounts:
  41.         - name: flink-config-volume
  42.           mountPath: /opt/flink/conf
  43.         securityContext:
  44.           runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  45.       serviceAccountName: flink-service-account # Service account which has the permissions to create, edit, delete ConfigMaps
  46.       volumes:
  47.       - name: flink-config-volume
  48.         configMap:
  49.           name: flink-config
  50.           items:
  51.           - key: config.yaml
  52.             path: config.yaml
  53.           - key: log4j-console.properties
  54.             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: apache/flink:1.19.0-scala_2.12
  20.         args: ["taskmanager"]
  21.         ports:
  22.         - containerPort: 6122
  23.           name: rpc
  24.         livenessProbe:
  25.           tcpSocket:
  26.             port: 6122
  27.           initialDelaySeconds: 30
  28.           periodSeconds: 60
  29.         volumeMounts:
  30.         - name: flink-config-volume
  31.           mountPath: /opt/flink/conf/
  32.         securityContext:
  33.           runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  34.       volumes:
  35.       - name: flink-config-volume
  36.         configMap:
  37.           name: flink-config
  38.           items:
  39.           - key: config.yaml
  40.             path: config.yaml
  41.           - key: log4j-console.properties
  42.             path: log4j-console.properties

Application cluster resource definitions

jobmanager-application-non-ha.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: apache/flink:1.19.0-scala_2.12
  16.           env:
  17.           args: ["standalone-job", "--job-classname", "com.job.ClassName", <optional arguments>, <job arguments>] # optional arguments: ["--job-id", "<job id>", "--jars", "/path/to/artifact1,/path/to/artifact2", "--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: config.yaml
  43.                 path: config.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

jobmanager-application-ha.yaml

  1. apiVersion: batch/v1
  2. kind: Job
  3. metadata:
  4.   name: flink-jobmanager
  5. spec:
  6.   parallelism: 1 # Set the value to greater than 1 to start standby JobManagers
  7.   template:
  8.     metadata:
  9.       labels:
  10.         app: flink
  11.         component: jobmanager
  12.     spec:
  13.       restartPolicy: OnFailure
  14.       containers:
  15.         - name: jobmanager
  16.           image: apache/flink:1.19.0-scala_2.12
  17.           env:
  18.           - name: POD_IP
  19.             valueFrom:
  20.               fieldRef:
  21.                 apiVersion: v1
  22.                 fieldPath: status.podIP
  23.           # The following args overwrite the value of jobmanager.rpc.address configured in the configuration config map to POD_IP.
  24.           args: ["standalone-job", "--host", "$(POD_IP)", "--job-classname", "com.job.ClassName", <optional arguments>, <job arguments>] # optional arguments: ["--job-id", "<job id>", "--jars", "/path/to/artifact1,/path/to/artifact2", "--fromSavepoint", "/path/to/savepoint", "--allowNonRestoredState"]
  25.           ports:
  26.             - containerPort: 6123
  27.               name: rpc
  28.             - containerPort: 6124
  29.               name: blob-server
  30.             - containerPort: 8081
  31.               name: webui
  32.           livenessProbe:
  33.             tcpSocket:
  34.               port: 6123
  35.             initialDelaySeconds: 30
  36.             periodSeconds: 60
  37.           volumeMounts:
  38.             - name: flink-config-volume
  39.               mountPath: /opt/flink/conf
  40.             - name: job-artifacts-volume
  41.               mountPath: /opt/flink/usrlib
  42.           securityContext:
  43.             runAsUser: 9999  # refers to user _flink_ from official flink image, change if necessary
  44.       serviceAccountName: flink-service-account # Service account which has the permissions to create, edit, delete ConfigMaps
  45.       volumes:
  46.         - name: flink-config-volume
  47.           configMap:
  48.             name: flink-config
  49.             items:
  50.               - key: config.yaml
  51.                 path: config.yaml
  52.               - key: log4j-console.properties
  53.                 path: log4j-console.properties
  54.         - name: job-artifacts-volume
  55.           hostPath:
  56.             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: apache/flink:1.19.0-scala_2.12
  20.         env:
  21.         args: ["taskmanager"]
  22.         ports:
  23.         - containerPort: 6122
  24.           name: rpc
  25.         livenessProbe:
  26.           tcpSocket:
  27.             port: 6122
  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: config.yaml
  43.             path: config.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

Local Recovery Enabled TaskManager StatefulSet

  1. apiVersion: v1
  2. kind: ConfigMap
  3. metadata:
  4.   name: flink-config
  5.   labels:
  6.     app: flink
  7. data:
  8.   config.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.     state.backend.local-recovery: true
  15.     process.taskmanager.working-dir: /pv    
  16. ---
  17. apiVersion: v1
  18. kind: Service
  19. metadata:
  20.   name: taskmanager-hl
  21. spec:
  22.   clusterIP: None
  23.   selector:
  24.     app: flink
  25.     component: taskmanager
  26. ---
  27. apiVersion: apps/v1
  28. kind: StatefulSet
  29. metadata:
  30.   name: flink-taskmanager
  31. spec:
  32.   serviceName: taskmanager-hl
  33.   replicas: 2
  34.   selector:
  35.     matchLabels:
  36.       app: flink
  37.       component: taskmanager
  38.   template:
  39.     metadata:
  40.       labels:
  41.         app: flink
  42.         component: taskmanager
  43.     spec:
  44.       securityContext:
  45.         runAsUser: 9999
  46.         fsGroup: 9999
  47.       containers:
  48.       - name: taskmanager
  49.         image: apache/flink:1.19.0-scala_2.12
  50.         env:
  51.           - name: POD_NAME
  52.             valueFrom:
  53.               fieldRef:
  54.                 fieldPath: metadata.name
  55.         args: ["taskmanager", "-Dtaskmanager.resource-id=$(POD_NAME)"]
  56.         ports:
  57.         - containerPort: 6122
  58.           name: rpc
  59.         - containerPort: 6121
  60.           name: metrics
  61.         livenessProbe:
  62.           tcpSocket:
  63.             port: 6122
  64.           initialDelaySeconds: 30
  65.           periodSeconds: 60
  66.         volumeMounts:
  67.         - name: flink-config-volume
  68.           mountPath: /opt/flink/conf/
  69.         - name: pv
  70.           mountPath: /pv
  71.       volumes:
  72.       - name: flink-config-volume
  73.         configMap:
  74.           name: flink-config
  75.           items:
  76.           - key: config.yaml
  77.             path: config.yaml
  78.           - key: log4j-console.properties
  79.             path: log4j-console.properties
  80.   volumeClaimTemplates:
  81.   - metadata:
  82.       name: pv
  83.     spec:
  84.       accessModes: [ "ReadWriteOnce" ]
  85.       resources:
  86.         requests:
  87.           storage: 50Gi