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Predictive Metric - Get

服务:

Monitor

API 版本:

2022-10-01

获取预测性自动缩放指标的未来数据

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 参数

名称	在	必需	类型	说明
autoscaleSettingName	path	True	string	自动缩放设置的名称。
resourceGroupName	path	True	string	资源组的名称。 此名称不区分大小写。
subscriptionId	path	True	string	目标订阅的 ID。
aggregation	query	True	string	要检索的聚合类型列表 (逗号分隔) 。
api-version	query	True	string	要用于此操作的 API 版本。
interval	query	True	string duration	间隔 (即查询的 timegrain) 。
metricName	query	True	string	要检索的指标名称 (逗号分隔) 。 特殊情况：如果指标名称本身包含逗号，则使用 %2 来指示它。 例如：“Metric，Name1”应为 “Metric%2Name1”
metricNamespace	query	True	string	要查询其指标定义的指标命名空间。
timespan	query	True	string	查询的时间跨度。 它是格式为“startDateTime_ISO/endDateTime_ISO”的字符串。

响应

名称	类型	说明
200 OK	PredictiveResponse	成功请求获取指标值列表。
Other Status Codes	AutoscaleErrorResponse	描述操作失败原因的错误响应。

安全性

azure_auth

Azure Active Directory OAuth2 流

类型: oauth2
流向: implicit
授权 URL: https://login.microsoftonline.com/common/oauth2/authorize

作用域

名称	说明
user_impersonation	模拟用户帐户

示例

Get Metric for data

示例请求

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

示例响应

状态代码:

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
    }
  ]
}

定义

名称	说明
AutoscaleErrorResponse	描述错误响应的格式。
createdByType	创建资源的标识类型。
Error	错误对象。
PredictiveResponse	对指标查询的响应。
PredictiveValue	表示给定存储桶中的预测指标值。
systemData	与资源的创建和上次修改相关的元数据。

AutoscaleErrorResponse

描述错误响应的格式。

名称	类型	说明
error	Error	错误对象。
systemData	systemData	与响应相关的系统元数据。

createdByType

创建资源的标识类型。

名称	类型	说明
Application	string
Key	string
ManagedIdentity	string
User	string

Error

错误对象。

名称	类型	说明
code	string	服务器定义的一组错误代码中的一个。
details	string	错误详细信息的可读表示形式。
message	string	错误的用户可读表示形式。
target	string	特定错误的目标。

PredictiveResponse

对指标查询的响应。

名称	类型	说明
data	PredictiveValue[]	集合的值。
interval	string	返回指标数据的间隔 (窗口大小) 。 将来可能会对此进行调整，并从最初请求的内容返回。 如果发出了元数据请求，则不存在此请求。
metricName	string	正在查询的指标
targetResourceId	string	预测指标的资源。
timespan	string	检索数据的时间跨度。 其值由两个连接在一起的日期/时间组成，以“/”分隔。 将来可能会对此进行调整，并从最初请求的内容返回。

PredictiveValue

表示给定存储桶中的预测指标值。

名称	类型	说明
timeStamp	string	ISO 8601 格式的指标值的时间戳。
value	number	此时间存储桶中的预测值。

systemData

与资源的创建和上次修改相关的元数据。

名称	类型	说明
createdAt	string	资源创建时间戳 (UTC) 。
createdBy	string	创建资源的标识。
createdByType	createdByType	创建资源的标识类型。
lastModifiedAt	string	资源上次修改的时间戳 (UTC)
lastModifiedBy	string	上次修改资源的标识。
lastModifiedByType	createdByType	上次修改资源的标识类型。