Manage cluster vertical scaling (scale up) in Azure Data Explorer to accommodate changing demand
Sizing a cluster appropriately is critical to the performance of Azure Data Explorer. A static cluster size can lead to under-utilization or over-utilization, neither of which is ideal.
Since demand on a cluster can’t be predicted with absolute accuracy, a better approach is to scale a cluster, adding and removing capacity and CPU resources with changing demand.
There are two workflows for scaling an Azure Data Explorer cluster:
- Horizontal scaling, also called scaling in and out.
- Vertical scaling, also called scaling up and down.
This article explains the vertical scaling workflow:
Configure vertical scaling
In the Azure portal, go to your Azure Data Explorer cluster resource. Under Settings, select Scale up.
In the Scale up window, you will see a list of available SKUs for your cluster. For example, in the following figure, only four SKUs are available.
The SKUs are disabled because they're the current SKU, or they aren't available in the region where the cluster is located.
To change your SKU, select a new SKU and click Select.
- The vertical scaling process can take a few minutes, and during that time your cluster will be suspended.
- Scaling down can harm your cluster performance.
- The price is an estimate of the cluster's virtual machines and Azure Data Explorer service costs. Other costs are not included. See Azure Data Explorer cost estimator page for an estimate and the Azure Data Explorer pricing page for full pricing information.
You've now configured vertical scaling for your Azure Data Explorer cluster. Add another rule for a horizontal scaling. If you need assistance with cluster-scaling issues, open a support request in the Azure portal.
Manage cluster horizontal scaling to dynamically scale out the instance count based on metrics that you specify.
Monitor your resource usage by following this article: Monitor Azure Data Explorer performance, health, and usage with metrics.