Predictive Metric - Get

GET https://management.azure.com/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/myRG/providers/Microsoft.Insights/autoscalesettings/vmss1-Autoscale-775/predictiveMetrics?api-version=2022-10-01&timespan=2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z&interval=PT1H&metricNamespace=Microsoft.Compute/virtualMachineScaleSets&metricName=PercentageCPU&aggregation=Total

import isodate

from azure.identity import DefaultAzureCredential

from azure.mgmt.monitor import MonitorManagementClient

"""
# PREREQUISITES
    pip install azure-identity
    pip install azure-mgmt-monitor
# USAGE
    python get_predictive_metric.py

    Before run the sample, please set the values of the client ID, tenant ID and client secret
    of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID,
    AZURE_CLIENT_SECRET. For more info about how to get the value, please see:
    https://docs.microsoft.com/azure/active-directory/develop/howto-create-service-principal-portal
"""


def main():
    client = MonitorManagementClient(
        credential=DefaultAzureCredential(),
        subscription_id="SUBSCRIPTION_ID",
    )

    response = client.predictive_metric.get(
        resource_group_name="myRG",
        autoscale_setting_name="vmss1-Autoscale-775",
        timespan="2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z",
        interval=isodate.parse_duration("PT1H"),
        metric_namespace="Microsoft.Compute/virtualMachineScaleSets",
        metric_name="PercentageCPU",
        aggregation="Total",
    )
    print(response)


# x-ms-original-file: 2022-10-01/GetPredictiveMetric.json
if __name__ == "__main__":
    main()

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

package armmonitor_test

import (
	"context"
	"log"

	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
	"github.com/Azure/azure-sdk-for-go/sdk/resourcemanager/monitor/armmonitor"
)

// Generated from example definition: 2022-10-01/GetPredictiveMetric.json
func ExamplePredictiveMetricClient_Get() {
	cred, err := azidentity.NewDefaultAzureCredential(nil)
	if err != nil {
		log.Fatalf("failed to obtain a credential: %v", err)
	}
	ctx := context.Background()
	clientFactory, err := armmonitor.NewClientFactory("00000000-0000-0000-0000-000000000000", cred, nil)
	if err != nil {
		log.Fatalf("failed to create client: %v", err)
	}
	res, err := clientFactory.NewPredictiveMetricClient().Get(ctx, "myRG", "vmss1-Autoscale-775", "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z", "PT1H", "Microsoft.Compute/virtualMachineScaleSets", "PercentageCPU", "Total", nil)
	if err != nil {
		log.Fatalf("failed to finish the request: %v", err)
	}
	// You could use response here. We use blank identifier for just demo purposes.
	_ = res
	// If the HTTP response code is 200 as defined in example definition, your response structure would look as follows. Please pay attention that all the values in the output are fake values for just demo purposes.
	// res = armmonitor.PredictiveMetricClientGetResponse{
	// 	PredictiveResponse: armmonitor.PredictiveResponse{
	// 		Data: []*armmonitor.PredictiveValue{
	// 			{
	// 				TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:00:00Z"); return t}()),
	// 				Value: to.Ptr[float64](123),
	// 			},
	// 			{
	// 				TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:02:00Z"); return t}()),
	// 				Value: to.Ptr[float64](120),
	// 			},
	// 			{
	// 				TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:02:00Z"); return t}()),
	// 				Value: to.Ptr[float64](88),
	// 			},
	// 			{
	// 				TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:03:00Z"); return t}()),
	// 				Value: to.Ptr[float64](100),
	// 			},
	// 		},
	// 		Interval: to.Ptr("PT1H"),
	// 		MetricName: to.Ptr("PercentageCPU"),
	// 		TargetResourceID: to.Ptr("/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/myRG/providers/Microsoft.Compute/virtualMachineScaleSets/vmss1"),
	// 		Timespan: to.Ptr("2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z"),
	// 	},
	// }
}

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue

const { MonitorClient } = require("@azure/arm-monitor");
const { DefaultAzureCredential } = require("@azure/identity");

/**
 * This sample demonstrates how to get predictive autoscale metric future data
 *
 * @summary get predictive autoscale metric future data
 * x-ms-original-file: 2022-10-01/GetPredictiveMetric.json
 */
async function getMetricForData() {
  const credential = new DefaultAzureCredential();
  const subscriptionId = "00000000-0000-0000-0000-000000000000";
  const client = new MonitorClient(credential, subscriptionId);
  const result = await client.predictiveMetric.get(
    "myRG",
    "vmss1-Autoscale-775",
    "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z",
    "PT1H",
    "Microsoft.Compute/virtualMachineScaleSets",
    "PercentageCPU",
    "Total",
  );
  console.log(result);
}

To use the Azure SDK library in your project, see this documentation. To provide feedback on this code sample, open a GitHub issue