Predictive Metric - Get

服務:

Monitor

API 版本:

2022-10-01

取得預測性自動調整計量未來數據

GET https://management.azure.com/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Insights/autoscalesettings/{autoscaleSettingName}/predictiveMetrics?timespan={timespan}&interval={interval}&metricNamespace={metricNamespace}&metricName={metricName}&aggregation={aggregation}&api-version=2022-10-01

URI 參數

名稱	位於	必要	類型	Description
autoscaleSettingName	path	True	string	自動調整設定名稱。
resourceGroupName	path	True	string	資源群組的名稱。 名稱不區分大小寫。
subscriptionId	path	True	string	目標訂用帳戶的標識碼。
aggregation	query	True	string	要擷取的匯總類型清單 (逗號分隔) 。
api-version	query	True	string	用於此作業的 API 版本。
interval	query	True	string duration	間隔 (亦即查詢的 timegrain) 。
metricName	query	True	string	要擷取的計量名稱 (逗號分隔) 。 特殊案例：如果 metricname 本身有逗號，請使用 %2 來指出它。 例如：『Metric，Name1』 應該是 『Metric%2Name1』
metricNamespace	query	True	string	要查詢計量定義的計量命名空間。
timespan	query	True	string	查詢的時間範圍。 它是格式為 『startDateTime_ISO/endDateTime_ISO』 的字串。

回應

名稱	類型	Description
200 OK	PredictiveResponse	成功要求以取得計量值清單。
Other Status Codes	AutoscaleErrorResponse	描述作業失敗原因的錯誤回應。

安全性

azure_auth

Azure Active Directory OAuth2 Flow

類型: oauth2
Flow: implicit
授權 URL: https://login.microsoftonline.com/common/oauth2/authorize

範圍

名稱	Description
user_impersonation	模擬您的用戶帳戶

範例

Get Metric for data

範例要求

HTTP

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

Java

import java.time.Duration;

/**
 * Samples for PredictiveMetric Get.
 */
public final class Main {
    /*
     * x-ms-original-file:
     * specification/monitor/resource-manager/Microsoft.Insights/stable/2022-10-01/examples/GetPredictiveMetric.json
     */
    /**
     * Sample code: Get Metric for data.
     * 
     * @param azure The entry point for accessing resource management APIs in Azure.
     */
    public static void getMetricForData(com.azure.resourcemanager.AzureResourceManager azure) {
        azure.diagnosticSettings().manager().serviceClient().getPredictiveMetrics().getWithResponse("myRG",
            "vmss1-Autoscale-775", "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z", Duration.parse("PT1H"),
            "Microsoft.Compute/virtualMachineScaleSets", "PercentageCPU", "Total", com.azure.core.util.Context.NONE);
    }
}

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

Go

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: https://github.com/Azure/azure-rest-api-specs/blob/969fd0c2634fbcc1975d7abe3749330a5145a97c/specification/monitor/resource-manager/Microsoft.Insights/stable/2022-10-01/examples/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("<subscription-id>", 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.PredictiveResponse = armmonitor.PredictiveResponse{
	// 	Data: []*armmonitor.PredictiveValue{
	// 		{
	// 			TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:00:00.000Z"); return t}()),
	// 			Value: to.Ptr[float64](123),
	// 		},
	// 		{
	// 			TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:02:00.000Z"); return t}()),
	// 			Value: to.Ptr[float64](120),
	// 		},
	// 		{
	// 			TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:02:00.000Z"); return t}()),
	// 			Value: to.Ptr[float64](88),
	// 		},
	// 		{
	// 			TimeStamp: to.Ptr(func() time.Time { t, _ := time.Parse(time.RFC3339Nano, "2021-10-14T22:03:00.000Z"); 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

JavaScript

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: specification/monitor/resource-manager/Microsoft.Insights/stable/2022-10-01/examples/GetPredictiveMetric.json
 */
async function getMetricForData() {
  const subscriptionId =
    process.env["MONITOR_SUBSCRIPTION_ID"] || "00000000-0000-0000-0000-000000000000";
  const resourceGroupName = process.env["MONITOR_RESOURCE_GROUP"] || "myRG";
  const autoscaleSettingName = "vmss1-Autoscale-775";
  const timespan = "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z";
  const interval = "PT1H";
  const metricNamespace = "Microsoft.Compute/virtualMachineScaleSets";
  const metricName = "PercentageCPU";
  const aggregation = "Total";
  const credential = new DefaultAzureCredential();
  const client = new MonitorClient(credential, subscriptionId);
  const result = await client.predictiveMetric.get(
    resourceGroupName,
    autoscaleSettingName,
    timespan,
    interval,
    metricNamespace,
    metricName,
    aggregation,
  );
  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

dotnet

using Azure;
using Azure.ResourceManager;
using System;
using System.Threading.Tasks;
using System.Xml;
using Azure.Core;
using Azure.Identity;
using Azure.ResourceManager.Monitor.Models;
using Azure.ResourceManager.Resources;
using Azure.ResourceManager.Monitor;

// Generated from example definition: specification/monitor/resource-manager/Microsoft.Insights/stable/2022-10-01/examples/GetPredictiveMetric.json
// this example is just showing the usage of "PredictiveMetric_Get" operation, for the dependent resources, they will have to be created separately.

// get your azure access token, for more details of how Azure SDK get your access token, please refer to https://learn.microsoft.com/en-us/dotnet/azure/sdk/authentication?tabs=command-line
TokenCredential cred = new DefaultAzureCredential();
// authenticate your client
ArmClient client = new ArmClient(cred);

// this example assumes you already have this AutoscaleSettingResource created on azure
// for more information of creating AutoscaleSettingResource, please refer to the document of AutoscaleSettingResource
string subscriptionId = "00000000-0000-0000-0000-000000000000";
string resourceGroupName = "myRG";
string autoscaleSettingName = "vmss1-Autoscale-775";
ResourceIdentifier autoscaleSettingResourceId = AutoscaleSettingResource.CreateResourceIdentifier(subscriptionId, resourceGroupName, autoscaleSettingName);
AutoscaleSettingResource autoscaleSetting = client.GetAutoscaleSettingResource(autoscaleSettingResourceId);

// invoke the operation
string timespan = "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z";
TimeSpan interval = XmlConvert.ToTimeSpan("PT1H");
string metricNamespace = "Microsoft.Compute/virtualMachineScaleSets";
string metricName = "PercentageCPU";
string aggregation = "Total";
AutoscaleSettingPredicativeResult result = await autoscaleSetting.GetPredictiveMetricAsync(timespan, interval, metricNamespace, metricName, aggregation);

Console.WriteLine($"Succeeded: {result}");

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

範例回覆

狀態碼:

200

{
  "timespan": "2021-10-14T22:00:00.000Z/2021-10-16T22:00:00.000Z",
  "interval": "PT1H",
  "metricName": "PercentageCPU",
  "targetResourceId": "/subscriptions/00000000-0000-0000-0000-000000000000/resourceGroups/myRG/providers/Microsoft.Compute/virtualMachineScaleSets/vmss1",
  "data": [
    {
      "timeStamp": "2021-10-14T22:00:00Z",
      "value": 123
    },
    {
      "timeStamp": "2021-10-14T22:02:00Z",
      "value": 120
    },
    {
      "timeStamp": "2021-10-14T22:02:00Z",
      "value": 88
    },
    {
      "timeStamp": "2021-10-14T22:03:00Z",
      "value": 100
    }
  ]
}

定義

名稱	Description
AutoscaleErrorResponse	描述錯誤回應的格式。
createdByType	建立資源的身分識別類型。
Error	error 物件。
PredictiveResponse	計量查詢的回應。
PredictiveValue	表示指定貯體中的預測計量值。
systemData	與建立和上次修改資源相關的元數據。

AutoscaleErrorResponse

描述錯誤回應的格式。

名稱	類型	Description
error	Error	error 物件。
systemData	systemData	與回應相關的系統元數據。

createdByType

建立資源的身分識別類型。

名稱	類型	Description
Application	string
Key	string
ManagedIdentity	string
User	string

Error

error 物件。

名稱	類型	Description
code	string	其中一組伺服器定義的錯誤碼。
details	string	人類可讀取的錯誤詳細數據表示法。
message	string	人類可讀取的錯誤表示法。
target	string	特定錯誤的目標。

PredictiveResponse

計量查詢的回應。

名稱	類型	Description
data	PredictiveValue[]	集合的值。
interval	string	傳回計量數據的間隔 (視窗大小) 。 這可能會在未來進行調整，並從原本要求的內容傳回。 如果已提出元數據要求，則不存在此狀況。
metricName	string	正在查詢的計量
targetResourceId	string	預測計量的資源。
timespan	string	擷取數據的時間範圍。 其值是由兩個串連的日期時間所組成，並以 『/』 分隔。 這可能會在未來進行調整，並從原本要求的內容傳回。

PredictiveValue

表示指定貯體中的預測計量值。

名稱	類型	Description
timeStamp	string	ISO 8601 格式計量值的時間戳。
value	number	此時間貯體中的預測值。

systemData

與建立和上次修改資源相關的元數據。

名稱	類型	Description
createdAt	string	資源建立的時間戳 (UTC) 。
createdBy	string	建立資源的身分識別。
createdByType	createdByType	建立資源的身分識別類型。
lastModifiedAt	string	上次修改的資源時間戳 (UTC)
lastModifiedBy	string	上次修改資源的身分識別。
lastModifiedByType	createdByType	上次修改資源的身分識別類型。