io errors on aws ephemeral storage volume

Note that in the completed state, the driver pod does not use any computational or memory resources.

Logs and remains in “completed” state in the Kubernetes API until it’s eventually garbage collected or manually cleaned up. When the application completes, the executor pods terminate and are cleaned up, but the driver pod persists.The driver creates executors which are also running within Kubernetes pods and connects to them, and executes application code.Spark creates a Spark driver running within a Kubernetes pod.The submission mechanism works as follows: Spark-submit can be directly used to submit a Spark application to a Kubernetes cluster. You must have Kubernetes DNS configured in your cluster.The service account credentials used by the driver pods must be allowed to create pods, services and configmaps.You can verify that you can list these resources You must have appropriate permissions to list, create, edit and delete.Check kubernetes-client library’s version of your Spark environment, and its compatibility with your Kubernetes cluster’s version.We recommend 3 CPUs and 4g of memory to be able to start a simple Spark application with a single Be aware that the default minikube configuration is not enough for running Spark applications.We recommend using the latest release of minikube with the DNS addon enabled.You may set up a test cluster on your local machine using If you do not already have a working Kubernetes cluster, A running Kubernetes cluster at version >= 1.24 with access configured to it using.In particular it allows for hostPath volumes which as described in the Kubernetes documentation have known security vulnerabilities.Ĭluster administrators should use Pod Security Policies to limit the ability to mount hostPath volumes appropriately for their environments. Volume MountsĪs described later in this document under Using Kubernetes Volumes Spark on K8S provides configuration options that allow for mounting certain volume types into the driver and executor pods. Cluster administrators should use Pod Security Policies if they wish to limit the users that pods may run as. Please bear in mind that this requires cooperation from your users and as such may not be a suitable solution for shared environments. This can be used to override the USER directives in the images themselves. Users building their own images with the provided docker-image-tool.sh script can use the -u option to specify the desired UID.Īlternatively the Pod Template feature can be used to add a Security Context with a runAsUser to the pods that Spark submits. The resulting UID should include the root group in its supplementary groups in order to be able to run the Spark executables. Security conscious deployments should consider providing custom images with USER directives specifying their desired unprivileged UID and GID. This means that the resulting images will be running the Spark processes as this UID inside the container. Images built from the project provided Dockerfiles contain a default USER directive with a default UID of 185. Please see Spark Security and the specific security sections in this doc before running Spark. Or an untrusted network, it’s important to secure access to the cluster to prevent unauthorized applications When deploying a cluster that is open to the internet Security features like authentication are not enabled by default. Kubernetes scheduler that has been added to Spark.

Spark can run on clusters managed by Kubernetes.

Using Apache YuniKorn as Customized Scheduler for Spark on Kubernetes.

Using Volcano as Customized Scheduler for Spark on Kubernetes.

Customized Kubernetes Schedulers for Spark on Kubernetes.

Resource Allocation and Configuration Overview.Client Mode Executor Pod Garbage Collection.Warning FailedScheduling 11m default-scheduler 0/7 nodes are available: 1 Insufficient cpu, 2 Insufficient memory, 2 node(s) had taint serviceAccount: thanos serviceAccountName: thanos terminationGracePeriodSeconds: 30 tolerations: Warning FailedScheduling 11m default-scheduler 0/7 nodes are available: 7 persistentvolumeclaim 'thanos-compact-eks-euc1-prod-05-72vk5-data' not found.

Io errors on aws ephemeral storage volume