Thanks Mich. Very insightful. AK On Monday, 14 February 2022, 11:18:19 GMT, Mich Talebzadeh <mich.talebza...@gmail.com> wrote: Good question. However, we ought to look at what options we have so to speak. Let us consider Spark on Dataproc, Spark on Kubernetes and Spark on Dataflow
Spark on DataProc is proven and it is in useat many organizations, I have deployed it extensively. It is infrastructure asa service provided including Spark, Hadoop and other artefacts. You have tomanage cluster creation, automate cluster creation and tear down, submittingjobs etc. However, it is another stack that needs to be managed.It now has autoscaling(enables cluster worker VM autoscaling ) policy as well. Spark on GKEis something newer. Worth adding that the Spark DEV team are working hard to improve the performanceof Spark on Kubernetes, for example, through Support forCustomized Kubernetes Scheduler. As I explained in the first thread, Spark on Kubernetes relies on containerisation.Containers make applications more portable. Moreover, they simplify thepackaging of dependencies, especially with PySpark and enable repeatable andreliable build workflows which is cost effective. They also reduce the overalldevops load and allow one to iterate on the code faster. From a purely costperspective it would be cheaper with Docker as you can share resourceswith your other services. You can create Spark docker with different versionsof Spark, Scala, Java, OS etc. That docker file is portable. Can be used onPrem, AWS, GCP etc in container registries and devops and data science peoplecan share it as well. Built once used by many. Kuberneteswith autopilot helps scale the nodes of the Kubernetes cluster depending on theload. That is what I am currently looking into. With regard to Dataflow, which I believe issimilar to AWSGlue, it is a managed service for executing data processing patterns. Patternsor pipelines are built with the Apache Beam SDK,which is an open source programming model that supports Java, Python and GO. Itenables batch and streaming pipelines. You create your pipelines with an ApacheBeam program and then run them on the Dataflow service. TheApache Spark Runner can be used to execute Beam pipelines using Spark. When you run a job on Dataflow,it spins up a cluster of virtual machines, distributes the tasks in the job tothe VMs, and dynamically scales the cluster based on how the job is performing.As I understand both iterative processing and notebooks plus Machine learning withSpark ML are not currently supported by Dataflow So we have three choiceshere. If you are migrating from on-prem Hadoop/spark/YARN set-up, you may gofor Dataproc which will provide the same look and feel. If you want to usemicroservices and containers in your event driven architecture, you can adopt dockerimages that run on Kubernetes clusters, including Multi-Cloud KubernetesCluster. Dataflow is probably best suited for green-field projects. Lessoperational overhead, unified approach for batch and streaming pipelines. So as ever your mileage varies. If you want to migratefrom your existing Hadoop/Spark cluster to GCP, or take advantage of yourexisting workforce, choose Dataproc or GKE. In many cases, a bigconsideration is that one already has a codebase written against a particularframework, and one just wants to deploy it on the GCP, so even if, say, theBeam programming mode/dataflow is superior to Hadoop, someone with a lot ofHadoop code might still choose Dataproc or GDE for the time being, rather thanrewriting their code on Beam to run on Dataflow. HTH view my Linkedin profile https://en.everybodywiki.com/Mich_Talebzadeh Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destructionof 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 suchloss, damage or destruction. On Mon, 14 Feb 2022 at 05:46, Gourav Sengupta <gourav.sengu...@gmail.com> wrote: Hi,may be this is useful in case someone is testing SPARK in containers for developing SPARK. >From a production scale work point of view:But if I am in AWS, I will just use >GLUE if I want to use containers for SPARK, without massively increasing my >costs for operations unnecessarily. Also, in case I am not wrong, GCP already has SPARK running in serverless mode. Personally I would never create the overhead of additional costs and issues to my clients of deploying SPARK when those solutions are already available by Cloud vendors. Infact, that is one of the precise reasons why people use cloud - to reduce operational costs. Sorry, just trying to understand what is the scope of this work. Regards,Gourav Sengupta On Fri, Feb 11, 2022 at 8:35 PM Mich Talebzadeh <mich.talebza...@gmail.com> wrote: The equivalent of Google GKE autopilot in AWS is AWS 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 userscan customize their configurations based on the requirements, GKE manages thecontrol plane and users manually provision and manage their nodeinfrastructure. 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, Day2 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 Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destructionof 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 suchloss, damage or destruction.
