Hello @panb    We hope your Query has been addressed by us & shall mark the Post as Resolved. In Summary,   Your Team needs to meet the requirement as stated in [1], which doesn't differentiate in Processor Type & I believe your Team is referring to Hygon Dhyana Processor. Note that we have shared the Hardware requirement is shared for CDP v7.1.8 as CDH isn't recommended now owing to End-Of-Life.  As a Best Practise, I shall suggest engaging with Cloudera Account Team associated with Customer to perform any due diligence with respect to Supportability & Best Practices prior to onboarding Use-Cases into any new Platform, wherein Supportability is doubted by your Team. Regards, Smarak   [1] Hardware Requirements | CDP Private Cloud (cloudera.com) ... View more

Hello @pankshiv1809    Thanks for using Cloudera Community. Based on your Post, Assuming [1] would help i.e. Using Dynamic Allocation to allow Spark adjust resources based on Workload requirement.    Regards, Smarak   [1] Job Scheduling - Spark 3.3.1 Documentation (apache.org) ... View more

Hello @Ryan_2002    Thanks for using Cloudera Community. To your Q, the Driver Cap is the Engine/Resource Profile & the Executor's Resource Usage is defined by the SparkSession or "spark-defaults.conf" file within the Project wherein the Workbench Session is being created.    Your Team can review the Pods in the User's Namespace & see the same i.e. upon a Workbench Session Creation, an Engine Pod is started with "Limits" set toEngine/Resource Profile Settings. After SparkSession is initialised, additional Pods are generated within the User's Namespace based on the Execution's Configs passed via SparkSession or "spark-defaults.conf" file.    You may configure the Executor's Configs as per your usage yet the same depends on the CML Workspace AutoScale Range & InstanceType. Say, an InstanceType supporting 8 vCPU & Executors requesting 8 vCPU won't work. Similarly, AutoScale Max of 5 yet requesting Executors collectively utilising the Resource Limit of 5 Nodes.    Hope the above helps answer your Post's queries. If Yes, Kindly mark the Post as Solved. If No, Feel free to share your concerns & we shall address accordingly.    Regards, Smarak ... View more

Hello @quangbilly79    Thanks for using Cloudera Community. Based on your Post, you may consider "Kafka Gateway" as the Client for Kafka, which are setup on the Hosts wherein the same is added as per Cloudera Manager "Assign Roles". A Client/Gateway is familiar with the Service (Kafka in this Case) & all Client/Service Configs are available for the Client/Gateway without any manual intervention. Any changes made to the Service or Client Configs is pushed to the Service/Client Configuration by Cloudera Manager.    Imagine a Scenario wherein you wish to run "hdfs dfs -ls" on a HDFS FileSystem. Simply running the Command won't work unless the Host wherein the Command "hdfs dfs -ls" is being run knows the Setup (HDFS FileSystem, NameNode, Port, Protocol). Review [1] for an Example. Adding an HDFS Gateway ensures User doesn't need to manually configure a Client/Gateway with Cloudera Manager doing the needful.    Similarly, Kafka Gateway operates. Else, Customer need to manually configure the Client/Gateway Setup. Hope the above answer your query concerning the Gateway Role.    Regards, Smarak   [1] https://www.ibm.com/docs/en/spectrum-scale-bda?topic=hdfs-clients-configuration ... View more