Forum Discussion

Anonymous's avatar
Anonymous
Not applicable
6 years ago

Premium Capacity

Is anyone familiar with the note below regarding the use of a single P2 Premium node in comparision to the use of two P1 Premium nodes that are provisioned as a single capacity?

https://docs.microsoft.com/en-us/power-bi/service-premium-what-is#capacity-nodes

"Note

Using a single larger SKU (e.g. one P2 SKU) can be preferable to combining smaller SKUs (e.g. two P1 SKUs). For example, you can use larger models and achieve better parallelism with the P2."

 

As I understood it per the below link, Premium nodes should just show up as an amount of vcores to allow for provisioning of capacities.

https://docs.microsoft.com/en-us/power-bi/service-admin-premium-purchase#purchase-additional-capacities

 

Is anyone familiar with the reasoning as to why this note suggests larger models and better parallelism would be better suited using larger sku's (P2 or P3) versus combining multiple P1s together as you scale?

5 Replies

  • Greg_Deckler's avatar
    Greg_Deckler
    Community Champion
    Think of it this way, and these are not exact stats, just demonstration

    2 boxes each with 32 GB of memory and 16 CPU's each

    or

    1 box with 64 GB of memory and 32 CPU's

    Same stats, the second box can fit large models in memory and thread across more CPU's at a time.
    • Anonymous's avatar
      Anonymous
      Not applicable

      Thanks Greg.

       

      That makes sense if Microsoft is provisioning those resources upfront based on the size of node purchased rather than based on the capacity size chosen by the Power BI admin during the provisioning of the capacity. I was under the impression that each node purchase just added additional vcores to your vcore pool which you can then use to provision capacities in the admin portal, similiar to the snapshot image below.

       

      It seems strange that the quantity and size of node (P1, P2, P3, etc.) is abstracted from the admin yet it could impact scalability and parallelism for data models that would exceed the memory capacity of a single P1 node.