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Flink Application Custom Resource Definition

The flinkapplication is a kubernetes custom resource. Once the flinkapplication custom resource is created in Kubernetes, the FlinkK8sOperator watches the resource and tries to move it through a series of states until the desired state is reached.

FlinkApplication Custom Resource Example

The type information is available here FlinkApplication Type.

Below is the list of fields in the custom resource and their description:

  • spec type:FlinkApplicationSpec required=True Contains the entire specification of the flink application.

    • image type:string required=True The image name format should be registry/repository[:tag] to pull by tag, or registry/repository[@digest] to pull by digest.

    • imagePullPolicy type:v1.PullPolicy The default pull policy is IfNotPresent which causes the Kubelet to skip pulling an image if it already exists.

    • imagePullSecrets type:[]v1.LocalObjectReference Indicates name of Secrets, Kubernetes should get the credentials from.

    • serviceAccountName type:string Pods created for this Flink application will run with the provided service account (which must already exist in the namespace).

    • securityContext type:v1.PodSecurityContext This allows you to specify pod-level security attributes which will be applied to both job manager and task manager pods created for this Flink application. More information can be found in the Kubernetes documentation or the API spec.

    • taskManagerConfig type:TaskManagerConfig required=true Configuration for the Flink task manager.

      • resources type:ResourceRequirements Resources for the task manager. This includes cpu, memory, storage, and ephemeral-storage. If empty the operator will use a default value for cpu and memory.

      • envConfig type:EnvironmentConfig Configuration for setting environment variables in the task manager.

      • taskSlots type:int32 required=true Number of task slots per task manager.

      • offHeapMemoryFraction type:float64 For Flink 1.10 and below A value between 0 and 1 that represents % of container memory dedicated to system / off heap. The remaining memory is given to the taskmanager. Note that Flink may further reserve some of this memory for off-heap uses like network buffers, so you may see the JVM heap size configured to a lower amount. This configures taskmanager.heap.size.

      • systemMemoryFraction type:float64 For Flink 1.11 and above A value between 0 and 1 that represents % of container memory dedicated to the system. The remaining memory is given to the taskmanager process. This configures taskmanger.memory.process.size.

      • nodeSelector type:map[string]string Configuration for the node selectors used for the task manager.

      • tolerations type:[]v1.Toleration Array of node tolerations for the taskmanager pods.

      • affinity type:Affinity Configuration for setting affinity for the task manager pods.

    • jobManagerConfig type:JobManagerConfig Configuration for the Flink job manager.

      • resources type:ResourceRequirements Resources for the job manager. This includes cpu, memory, storage, and ephemeral-storage. If empty the operator will use a default value for cpu and memory.

      • envConfig type:EnvironmentConfig Configuration for setting environment variables in the job manager.

      • replicas type:int32 required=true Number of job managers for the flink cluster. If multiple job managers are provided, the user has to ensure that correct environment variables are set for High availability mode.

      • offHeapMemoryFraction type:float64 For Flink 1.10 and below A value between 0 and 1 that represents % of container memory dedicated to system / off heap. The remaining memory is given to the jobmanager. Note that Flink may further reserve some of this memory for off-heap uses like network buffers, so you may see the JVM heap size configured to a lower amount. This configures jobmanager.heap.size.

      • systemMemoryFraction type:float64 For Flink 1.11 and above A value between 0 and 1 that represents % of container memory dedicated to the system. The remaining memory is given to the jobmanager process. This configures jobmanager.memory.process.size.

      • nodeSelector type:map[string]string Configuration for the node selectors used for the job manager.

      • tolerations []v1.Toleration Array of node tolerations for the jobmanager pods.

      • affinity type:Affinity Configuration for setting affinity for the job manager pods.

    • jarName type:string required=true Name of the jar file to be run. The application image needs to ensure that the jar file is present at the right location, as the operator uses the Web API to submit jobs.

    • parallelism type:int32 required=true Job level parallelism for the Flink Job.

    • entryClass type:string Entry point for the Flink job.

    • programArgs type:string External configuration parameters to be passed as arguments to the job like input and output sources, etc.

    • savepointPath type:string If specified, the application state will be restored from this savepoint.

    • savepointDisabled type:boolean If specified, during an update, the current application (if existing) is cancelled without taking a savepoint.

    • allowNonRestoredState type:boolean Skips savepoint operator state that cannot be mapped to the new program version.

    • flinkVersion type:string required=true The version of Flink to be managed. This version must match the version in the image.

    • flinkConfig type:FlinkConfig Optional map of flink configuration, which passed on to the deployment as environment variable with OPERATOR_FLINK_CONFIG.

    • deploymentMode type:DeploymentMode Indicates the type of deployment that operator should perform if the custom resource is updated. Currently two deployment modes, Dual and BlueGreen are supported.

      Dual This deployment mode is intended for applications where downtime during deployment needs to be as minimal as possible. In this deployment mode, the operator brings up a second Flink cluster with the new image, while the original Flink cluster is still active. Once the pods and containers in the new flink cluster are ready, the Operator cancels the job in the first Cluster with savepoint, deletes the cluster and starts the job in the second cluster. (More information in the state machine section below). This mode is suitable for real time processing applications. BlueGreen This deployment mode is intended for applications where downtime during deployment needs to be zero. In this mode, the operator brings up a whole new flink job/cluster along side the original Flink job. The two versions of Flink jobs are differentiated by a color: blue/green. Once the new Flink application version is created, the application transitions to a DualRunning phase. To transition back from the DualRunning phase to a single application version, users must set a tearDownVersionHash that enables the operator to teardown the version corresponding to the hash specified.

    • deleteMode type:DeleteMode Indicates how Flink jobs are torn down when the FlinkApplication resource is deleted.

      Savepoint (default) The operator will take a final savepoint before cancelling the job, and will not tear down the cluster until a savepoint has succeeded.

      ForceCancel The operator will force cancel the job before tearing down the cluster.

      None The operator will immediately tear down the cluster.

    • scaleMode type:ScaleMode Indicates how the operator should respond to changes of application parallelism.

      NewCluster (default) The operator will treat scale-ups as normal deploys (consistent with any other change to the CRD), creating a new cluster with the desired scale.

      InPlace [Experimental] On scale-up operations, the operator will attempt to perform an in-place update of the cluster, by first increasing the size of the cluster to accommodate the newer, larger parallelism, then updating the job with the new parallelism. Note that for an in-place mode to occur, you must ensure that no fields of the FlinkApplication change, besides parallelism or a *Mode field (like ScaleMode). In particular, this means that you cannot use kubectl apply to make this change, as kubectl apply modifies annotations on the resource which, for FlinkApplications, are propagated down to pods.

      This configuration is not supported in combination with BlueGreen mode, and will be ignored if BlueGreen is set.

    • restartNonce type:string Can be set or modified to force a restart of the cluster.

    • volumes type:[]v1.Volume Represents a named volume in a pod that may be accessed by any container in the pod.

    • volumeMounts type:[]v1.VolumeMount Describes a mounting of a Volume within a container.

    • forceRollback type:bool Can be set to true to force rollback a deploy/update. The rollback is not performed when the application is in a RUNNING phase. If an application is successfully rolled back, it is moved to a DeployFailed phase. Un-setting or setting ForceRollback to False will allow updates to progress normally.

    • maxCheckpointRestoreAgeSeconds type:int32 If the operator is unable to successfully take a savepoint of the existing job when updating, it may fall back to instead using the job's most recent checkpoint. This parameter determines the maximum checkpoint age (in seconds) that is eligible for use during update; the operator won't automatically roll back the application state to a checkpoint older than this. This check is done prior to cancelling the existing job; if no savepoint or valid checkpoint is available, deploy will fail and leave the existing job running. It defaults to 1 hour (3600 seconds), to protect one from accidentally restarting the application using a very old checkpoint (which might put your application under huge load / result in lots of duplicate data). Note: This doesn't affect the flink application's checkpointing mechanism in anyway.

    • tearDownVersionHash type:string Used only with the BlueGreen deployment mode. This is set typically once a FlinkApplication successfully transitions to the DualRunning phase. Once set, the application version corresponding to the hash is torn down. On successful teardown, the FlinkApplication transitions to a Running phase.