...
When the usage pattern of a cluster is not fixed, load-based scaling is recommended. This type of scaling automatically adjusts the number of query engines allows you to automatically scale up or scale down the Query Engine instances based on the resource utilization of the Query Engine instances. By configuring cluster scaling in this way, you can optimize the utilization of your cloud cluster and reduce compute costs.
Setting Load-based cluster scaling rules
From Kyvos 2024.2 onwards, load based scaling is implemented based on the CPU and Memory usage of the Query Engine instances. System resources will be are monitored for all BI Servers and Query Engines every 30 seconds, and Query Engines’ scaling will be performed based on this data.
...
On the Cluster Scaling page, the Load option is selected by default.
To set the scaling mode, select one of the following:
Managed: Select the required capacity from the list to start Query Engine when any query is fired.
Custom: Select this option to set configure the rules for resource utilizationcustom rules as per your cluster usage pattern.
To set scale up rules,
Select the required capacity from the list to start Query Engine when any query is fired.
Enter a percentage to scale up the cluster if CPU and or Memory utilization threshold goes above the specified percentage. Also, specify the number of data points and the total number of data points to set.
To set scale down rules,
Specify the BI Server and/or Query Engine from the list to shut down when no queries are fired for the specified period of time.
Enter a percentage to scale down the cluster if CPU and memory Memory utilization threshold remains below the specified percentage. Also, specify the number of data points and the total number of data points to set.
Click Save. The load-based scaling mode is set.
| Panel |
|---|
| panelIconId | atlassian-note |
|---|
| panelIcon | :note: |
|---|
| bgColor | #DEEBFF |
|---|
|
NOTE A data point is information on resource utilization captured every 30 seconds. |
Transitioning period
| Anchor |
|---|
| transioningperiod |
|---|
| transioningperiod |
|---|
|
The following tables specify the approximate time required to complete the process during the transitioning period.
| Aura tab collection |
|---|
| params | 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 |
|---|
|
| Aura tab |
|---|
| summary | AWS |
|---|
| params | JTdCJTIydGl0bGUlMjIlM0ElMjJBV1MlMjIlN0Q= |
|---|
|
The process with the number of Query Engine =10 | The time required for Load-based scaling |
|---|
scale up | 16 8 mins. | scale down | 12 7 mins. | startup | 4 3 mins. |
| Aura tab |
|---|
| summary | Azure |
|---|
| params | JTdCJTIydGl0bGUlMjIlM0ElMjJBenVyZSUyMiU3RA== |
|---|
|
The process with the number of Query Engine =7 | The previous time required for Load-based scalingNew time required for Load-based scaling |
|---|
Scale up | 15-19 minutes | 11-13 10 minutes | Scale down | 15-19 minutes | 11-13 9 minutes | Startup | 8-10 minutes | 8-10 4 minutes |
| Aura tab |
|---|
| summary | GCP |
|---|
| params | JTdCJTIydGl0bGUlMjIlM0ElMjJHQ1AlMjIlN0Q= |
|---|
|
The process with the number of Query Engine =10 | The time required for Load-based scaling |
|---|
scale up | 4 -5 mins. | scale down | 5-6 3 mins. | startup | 3 -4 mins. |
|
| Panel |
|---|
| panelIconId | atlassian-info |
|---|
| panelIcon | :info: |
|---|
| bgColor | #FFFAE6 |
|---|
|
Important If the cluster is down and a query is executed, the first query triggers the cluster startup process, and all queries fail until the cluster is up and running. In this case, the following messages are displayed: Message 1: "Could not serve the query as Query Engine Cluster is not available. Query Engine is launched. Please try after some time." Message 2: "Could not serve the query as Query Engine Cluster is starting. Please try after some time."
By default, the queries fail until Query Engines are started. If you want to hold the queries when query engines are down, you can specify the time (in minutes) for holding queries by using the QE_STARTUP_QUERY_HOLD_TIME property.
Ensure that if the query engines become active before the configured time, the query will be served; otherwise, it will fail. The Query Engines do not start for any ROLAP queries. During the transitioning period of Query Engines; such as scale-up, scale-down, or shut down; you can design and refine the semantic model because the Coordination Master is always up and running . The capacity of the BI Servers cannot be changed. All BI Servers can be shut down except the Coordination Master. If there is only one BI Server, this BI Server is treated as the Coordination Master. The Settings option and Add Schedule option are disabled on the Load screen. You can view on-screen notifications that provide you with timely information about the state of the cluster. When you scale down the Query Engines, you reduce the capacity of the node, including the number of cores and memory. Conversely, when you scale up the Query Engines, you increase the capacity of the node by adding more cores and memory. The Query Engines do not start for any ROLAP queries. During the transitioning period of Query Engines; such as scale-up, scale-down, or shut down; you can design and refine the semantic model because the Coordination Master is always up and running .
|
