A Spark pool in Fabric with Medium nodes kept warm in advance, so sessions start quickly without waiting for provisioning; the capacity SKU sets its autoscale and dynamic executor allocation limits. Quick start-up isn't guaranteed, and a custom pool is the option when you need finer control.
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Terms in this definition
- Capacity
A reserved block of compute for a tenant whose size is fixed by its SKU; Fabric F SKUs express it in capacity units (CUs). Workspaces assigned to it unlock features like Copilot, and from F64 upwards people with free licences can view Power BI content.
- SKU
The size or tier of a service, for example a VM size or the Premium tier of ACR.
- Autoscale
Azure Monitor capability that adds or removes instances on a schedule or once a metric rule has been true for its full duration. The maximum instance count merely sets a ceiling and never triggers scaling itself.
- Dynamic allocation
Lets a Spark application grow and shrink its executor count with demand, adding them when busy and giving them up when idle, up to a ceiling set by the workspace admin. In Spark pools the option is named Dynamically allocate executors and is enabled by default together with Autoscale.
- CONTROL
Granting this on a securable gives all other permissions on it too, making it the most powerful SQL permission. At database scope that includes UNMASK and ALTER ANY MASK. Warehouse access through the Admin, Member or Contributor workspace roles carries it.
Related terms
- Capacity pools
Custom Spark pools defined by a capacity admin in the Spark Compute settings of a capacity, which then show up as choices for workspaces and environments and take roughly two to three minutes to start. From the same place the admin can stop workspaces customising pools or using the starter pool.
- Default pool
Unless an environment says otherwise, this is where a workspace's notebooks and Spark job definitions get their Spark compute. It starts out as the starter pool, and an Admin can point it at a custom pool through Workspace settings.