Your Browser is Out of Date

Nytro.ai uses technology that works best in other browsers.
For a full experience use one of the browsers below
Building a Hybrid Database-as-a-Service Platform with Azure Stack HCI
Overview Solution Architecture Lab Setup Performance-Oriented Test Scenarios Functional Test Scenarios
Scale up Azure SQL Managed Instances
Scale out AKS-HCI workload cluster
Expand the Azure Stack HCI cluster
Full-stack life cycle management with Cluster-Aware Updating
Conclusion Appendix A: On-premises monitoring setup Appendix B: SQL MI and HammerDB Test Harness details Additional Resources

Scale out AKS-HCI workload cluster

Scale out AKS-HCI workload cluster

  • As we deployed more Azure Arc-enabled SQL Managed Instances, to meet demand, we had to scale out the aks-lab-workloads-1 cluster worker nodes. Azure Monitor Container Insights helped us determine the opportune time to scale out. Depending on the performance and capacity metrics reported by Azure Monitor, we chose to scale out the control-plane nodes and the worker nodes. For testing, to scale out only the aks-lab-workloads-1-linux node pool from two to four worker nodes, we used PowerShell commands run on one of the Azure Stack HCI cluster nodes.

    To see the existing worker nodes, we ran the following command:

    PS D:\Source\Lab> kubectl get nodes

    NAME              STATUS   ROLES                  AGE     VERSION

    moc-lcp9ks30ktb   Ready    <none>                 12d     v1.21.2

    moc-ljhfmw3lpwa   Ready    <none>                 12d     v1.21.2

    moc-lwdgzty53ri   Ready    control-plane,master   19d     v1.21.2

    The existing aks-lab-workloads-1-linux nodes, moc-lcp9ks30ktb and moc-ljhfmw3lpwa, were running on AXNode02 and AXNode04. The 10 SQL MIs we had deployed for the performance testing, sqlmi-01 – sqlmi-10, were evenly distributed across these two nodes.

    Then, we ran the PowerShell command on AXNode01 to expand the node pool:

    PS D:\Source\Lab> Set-AksHciNodePool -ClusterName aks-lab-workloads-1 -Name aks-lab-workloads-1-linux -Count 4

    We ran kubectl again to see the new nodes:

    PS D:\Source\Lab> kubectl get nodes

    NAME              STATUS   ROLES                  AGE     VERSION

    moc-l0v18yh90cy   Ready    <none>                 30m     v1.21.2

    moc-lcp9ks30ktb   Ready    <none>                 12d     v1.21.2

    moc-ljdqtqgo8kl   Ready    <none>                 30m     v1.21.2

    moc-ljhfmw3lpwa   Ready    <none>                 12d     v1.21.2

    moc-lwdgzty53ri   Ready    control-plane,master   19d     v1.21.2

    To evenly distribute the AKS-HCI load across the Azure Stack HCI cluster, the new worker nodes were automatically deployed onto AXNode01 and AXNode03. The scale-out procedure only took 2 minutes and 30 seconds to complete before all four nodes were online and ready for host new AKS-HCI resources. This time includes the installation of all new software agents on the nodes for monitoring and management. After the scale-up procedure finished, the existing SQL MIs running on the cluster did not get redistributed across all four nodes, as expected. 

    Then we created four SQL MIs (sqlmi-11 – sqlmi-14). They were automatically created and evenly distributed across the two new worker nodes, moc-l0v18yh90cy and moc-ljdqtqgo8kl.

    kubectl get pods --all-namespaces -o wide --field-selector spec.nodeName=moc-l0v18yh90cy

    NAMESPACE     NAME                   READY   STATUS      NODE          

    arc           sqlmi-13-0             3/3     Running     moc-l0v18yh90cy

    arc           sqlmi-14-0             3/3     Running     moc-l0v18yh90cy

    kubectl get pods --all-namespaces -o wide --field-selector spec.nodeName=moc-ljdqtqgo8kl

    NAMESPACE     NAME                   READY   STATUS      NODE          

    arc           sqlmi-11-0             3/3     Running     moc-ljdqtqgo8kl

    arc           sqlmi-12-0             3/3     Running     moc-ljdqtqgo8kl