Autoscaling monitoring

  • 2 minutes

In this unit, we look at autoscale monitoring concepts.

Monitoring

Similar to other Azure resources, Azure Spring Apps autoscale actions create logs. There are two categories of logs it can create:

  • Autoscale Evaluations: The autoscale engine records log entries for every single condition evaluation every time it does a check. The entry includes details on the observed values of the metrics, the rules evaluated, and if the evaluation resulted in a scale action or not.

  • Autoscale Scale Actions: The engine records scale action events started by the autoscale service and the results of those scales actions (success, failure, and how much scaling occurred as seen by the autoscale service).

Understanding autoscale events

In the autoscale setting screen, you can go to the Run history tab to see the most recent scale actions. The tab also shows the change in Observed Capacity over time. It also shows more details about all autoscale actions, including operations such as updating and deleting autoscale settings. The Setting screen also shows you the activity log and allows you to filter by autoscale operations.

Autoscale posts to the Activity Log if any of the following conditions occur:

  • Autoscale issues a scale operation.
  • Autoscale service successfully completes a scale action.
  • Autoscale service fails to take a scale action.
  • Autoscale detects flapping and aborts the scale attempt. You see a log type of Flapping in this situation. If you see Flapping, consider whether your thresholds are too narrow.
  • Autoscale detects flapping, but is still able to successfully scale. You see a log type of FlappingOccurred in this situation. If you see FlappingOccurred, the autoscale engine attempted to scale (for example, from four instances to two), but determined that this action would cause flapping. Instead, the autoscale engine scaled to a different number of instances (for example, using three instances instead of two), which no longer causes flapping, so it scaled to this number of instances.

Monitor the application's autoscale with Log Analytics

As with any Azure Monitor supported service, you can use Diagnostic Settings to route these logs:

  • To your Azure Log Analytics workspace for detailed analytics.
  • To Azure Event Hubs and then to non-Azure tools.
  • To your Azure storage account, for archiving.

You can validate the evaluations and scale actions better using Log Analytics. In your sample application, we routed your autoscale logs to Azure Monitor Logs (Log Analytics) through a workspace when you created the autoscale setting.

Data is retrieved from a Log Analytics workspace using a log query, which is a read-only request to process data and return results. Log queries are written in Kusto Query Language (KQL), which is the same query language used by Azure Data Explorer.

Note

For more information on KQL syntax, see the Summary unit at the end of this module.

In the next exercise, you'll use log analytics to find out more about the autoscale events.