The equivalent of Google GKE autopilot <https://cloud.google.com/kubernetes-engine/docs/concepts/autopilot-overview> in AWS is AWS Fargate <https://aws.amazon.com/fargate/>
I have not used the AWS Fargate so I can only mension Google's GKE Autopilot. This is developed from the concept of containerization and microservices. In the standard mode of creating a GKE cluster users can customize their configurations based on the requirements, GKE manages the control plane and users manually provision and manage their node infrastructure. So you choose your hardware type and memory/CPU where your spark containers will be running and they will be shown as VM hosts in your account. In GKE Autopilot mode, GKE manages the nodes, pre-configures the cluster with adds-on for auto-scaling, auto-upgrades, maintenance, Day 2 operations and security hardening. So there is a lot there. You don't choose your nodes and their sizes. You are effectively paying for the pods you use. Within spark-submit, you still need to specify the number of executors, driver and executor memory plus cores for each driver and executor when doing spark-submit. The theory is that the k8s cluster will deploy suitable nodes and will create enough pods on those nodes. With the standard k8s cluster you choose your nodes and you ensure that one core on each node is reserved for the OS itself. Otherwise if you allocate all cores to spark with --conf spark.executor.cores, you will receive this error kubctl describe pods -n spark ... Events: Type Reason Age From Message ---- ------ ---- ---- ------- Warning FailedScheduling 9s (x17 over 15m) default-scheduler 0/3 nodes are available: 3 Insufficient cpu. So with the standard k8s you have a choice of selecting your core sizes. With autopilot this node selection is left to autopilot to deploy suitable nodes and this will be a trial and error at the start (to get the configuration right). You may be lucky if the history of executions are kept current and the same job can be repeated. However, in my experience, to procedure the driver pod in "running state" is expensive timewise and without an executor in running state, there is no chance of spark job doing anything NAME READY STATUS RESTARTS AGE randomdatabigquery-cebab77eea6de971-exec-1 0/1 Pending 0 31s randomdatabigquery-cebab77eea6de971-exec-2 0/1 Pending 0 31s randomdatabigquery-cebab77eea6de971-exec-3 0/1 Pending 0 31s randomdatabigquery-cebab77eea6de971-exec-4 0/1 Pending 0 31s randomdatabigquery-cebab77eea6de971-exec-5 0/1 Pending 0 31s randomdatabigquery-cebab77eea6de971-exec-6 0/1 Pending 0 31s sparkbq-37405a7eea6b9468-driver 1/1 Running 0 3m4s NAME READY STATUS RESTARTS AGE randomdatabigquery-cebab77eea6de971-exec-6 0/1 ContainerCreating 0 112s sparkbq-37405a7eea6b9468-driver 1/1 Running 0 4m25s NAME READY STATUS RESTARTS AGE randomdatabigquery-cebab77eea6de971-exec-6 1/1 Running 0 114s sparkbq-37405a7eea6b9468-driver 1/1 Running 0 4m27s Basically I told Spak to have 6 executors but could only bring into running state one executor after the driver pod spinning for 4 minutes. 22/02/11 20:16:18 INFO SparkKubernetesClientFactory: Auto-configuring K8S client using current context from users K8S config file 22/02/11 20:16:19 INFO Utils: Using initial executors = 6, max of spark.dynamicAllocation.initialExecutors, spark.dynamicAllocation.minExecutors and spark.executor.instances 22/02/11 20:16:19 INFO ExecutorPodsAllocator: Going to request 3 executors from Kubernetes for ResourceProfile Id: 0, target: 6 running: 0. 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:20 INFO Utils: Successfully started service 'org.apache.spark.network.netty.NettyBlockTransferService' on port 7079. 22/02/11 20:16:20 INFO NettyBlockTransferService: Server created on sparkbq-37405a7eea6b9468-driver-svc.spark.svc:7079 22/02/11 20:16:20 INFO BlockManager: Using org.apache.spark.storage.RandomBlockReplicationPolicy for block replication policy 22/02/11 20:16:20 INFO BlockManagerMaster: Registering BlockManager BlockManagerId(driver, sparkbq-37405a7eea6b9468-driver-svc.spark.svc, 7079, None) 22/02/11 20:16:20 INFO BlockManagerMasterEndpoint: Registering block manager sparkbq-37405a7eea6b9468-driver-svc.spark.svc:7079 with 366.3 MiB RAM, BlockManagerId(driver, sparkbq-37405a7eea6b9468-driver-svc.spark.svc, 7079, None) 22/02/11 20:16:20 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, sparkbq-37405a7eea6b9468-driver-svc.spark.svc, 7079, None) 22/02/11 20:16:20 INFO BlockManager: Initialized BlockManager: BlockManagerId(driver, sparkbq-37405a7eea6b9468-driver-svc.spark.svc, 7079, None) 22/02/11 20:16:20 INFO Utils: Using initial executors = 6, max of spark.dynamicAllocation.initialExecutors, spark.dynamicAllocation.minExecutors and spark.executor.instances 22/02/11 20:16:20 WARN ExecutorAllocationManager: Dynamic allocation without a shuffle service is an experimental feature. 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:20 INFO ExecutorPodsAllocator: Going to request 3 executors from Kubernetes for ResourceProfile Id: 0, target: 6 running: 3. 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:20 INFO BasicExecutorFeatureStep: Decommissioning not enabled, skipping shutdown script 22/02/11 20:16:49 INFO KubernetesClusterSchedulerBackend: SchedulerBackend is ready for scheduling beginning after waiting maxRegisteredResourcesWaitingTime: 30000000000(ns) 22/02/11 20:16:49 INFO SharedState: Setting hive.metastore.warehouse.dir ('null') to the value of spark.sql.warehouse.dir ('file:/opt/spark/work-dir/spark-warehouse'). 22/02/11 20:16:49 INFO SharedState: Warehouse path is 'file:/opt/spark/work-dir/spark-warehouse'. OK there is a lot to digest here and I appreciate feedback from other members that have experimented with GKE autopilot or AWS Fargate or are familiar with k8s. Thanks view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> *Disclaimer:* Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction.