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快速入门：使用客户端库或 REST API 自定义解决方案

项目
09/24/2024

重要

从 2023 年 9 月 20 日开始，将无法创建新的 Azure 指标顾问资源。指标顾问服务将于 2026 年 10 月 1 日停用。

开始使用指标顾问 REST API 或客户端库。请按照以下步骤安装程序包并试用基本任务的示例代码。

使用指标顾问执行以下操作：

从数据源添加数据馈送
检查引入状态
配置检测和警报
查询异常情况检测结果
诊断异常

参考文档 | 库源代码 | 包 (NuGet) | 示例

重要

Microsoft 建议使用最安全的可用身份验证流。本文所述的某些身份验证流需要极高度地信任应用程序，并且附带了其他更安全的流中不存在的风险。请仅在无法使用其他更安全的流（例如托管标识）时才使用此流。

先决条件

Azure 订阅 - 免费创建订阅
.NET Core 的当前版本。
获得 Azure 订阅后，在 Azure 门户中创建指标顾问资源，以部署指标顾问实例。
带有时序数据的 SQL 数据库。

提示

可以在 GitHub 上找到 .NET 指标顾问示例。
指标顾问资源可能需要 10 到 30 分钟才能部署一个服务实例供你使用。部署成功后，选择“转到资源”。部署完成后，可以通过 Web 门户和 REST API 这两种方式开始使用指标顾问实例。
可以在 Azure 门户的资源“概述”部分中找到 REST API 的 URL。它将如下所示：
https://<instance-name>.cognitiveservices.azure.com/

设置

安装客户端库

创建新项目后，右键单击“解决方案资源管理器”中的项目解决方案，然后选择“管理 NuGet 包”，以安装客户端库。在打开的包管理器中，选择“浏览”，选中“包括预发行版”并搜索 Azure.AI.MetricsAdvisor。选择版本 1.0.0，然后选择“安装”。

在控制台窗口（例如 cmd、PowerShell 或 Bash）中，使用 dotnet new 命令创建名为 metrics-advisor-quickstart 的新控制台应用。此命令将创建包含单个源文件的简单“Hello World”C# 项目：program.cs。

dotnet new console -n metrics-advisor-quickstart

将目录更改为新创建的应用文件夹。可使用以下代码生成应用程序：

dotnet build

生成输出不应包含警告或错误。

...
Build succeeded.
 0 Warning(s)
 0 Error(s)
...

如果你使用的 IDE 而不是 Visual Studio，则可通过以下命令安装适用于 .NET 的指标顾问客户端库：

dotnet add package Azure.AI.MetricsAdvisor --version 1.1.0

环境变量

若要成功对异常检测器服务发出调用，需要使用以下值：

变量名称	值
`METRICS_ADVISOR_ENDPOINT`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到此值。终结点示例：`https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/`
`METRICS_ADVISOR_KEY`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到键值。可以使用 `KEY1` 或 `KEY2`。
`METRICS_ADVISOR_API_KEY`	从指标顾问门户检查资源时，可以在“设置”>“API 密钥”下找到密钥值。可以使用 `KEY1` 或 `KEY2`。
`SQL_CONNECTION_STRING`	本快速入门要求你拥有自己的 SQL 数据库 + 连接字符串。示例连接字符串类似于以下示例：`Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password>`有关构造 SQL 连接字符串的详细信息，请参阅 SQL 文档。
`SQL_QUERY`	特定于数据集的唯一查询。

创建环境变量

为密钥和终结点创建和分配持久环境变量。

重要

如果使用 API 密钥，请将其安全地存储在某个其他位置，例如 Azure Key Vault 中。请不要直接在代码中包含 API 密钥，并且切勿公开发布该密钥。

有关 Azure AI 服务安全性的详细信息，请参阅对 Azure AI 服务的请求进行身份验证。

setx METRICS_ADVISOR_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
setx METRICS_ADVISOR_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx METRICS_ADVISOR_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx SQL_CONNECTION_STRING "REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" 
setx SQL_QUERY "REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA"

[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_CONNECTION_STRING', 'REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_QUERY', 'REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA', 'User')

echo export METRICS_ADVISOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export SQL_CONNECTION_STRING="REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" >> /etc/environment && source /etc/environment
echo export SQL_QUERY="REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA" >> /etc/environment && source /etc/environment

创建应用程序

编辑 program.cs 文件并替换为以下内容：

// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

using System;
using System.Threading.Tasks;
using Azure.AI.MetricsAdvisor.Administration;
using Azure.AI.MetricsAdvisor.Models;
using Azure.AI.MetricsAdvisor.Tests;
using Azure.Core.TestFramework;
using NUnit.Framework;
using static System.Environment;

namespace Azure.AI.MetricsAdvisor.Samples
{
    [LiveOnly]
    public partial class MetricsAdvisorSamples : MetricsAdvisorTestEnvironment
    {
        [Test]
        public async Task CreateAndDeleteDataFeedAsync()
        {
            string endpoint =  GetEnvironmentVariable("METRICS_ADVISOR_ENDPOINT");
            string subscriptionKey = GetEnvironmentVariable("METRICS_ADVISOR_KEY");
            string apiKey = GetEnvironmentVariable("METRICS_ADVISOR_API_KEY");
            var credential = new MetricsAdvisorKeyCredential(subscriptionKey, apiKey);

            var adminClient = new MetricsAdvisorAdministrationClient(new Uri(endpoint), credential);

            #region Snippet:CreateDataFeedAsync
#if SNIPPET
            string sqlServerConnectionString = GetEnvironmentVariable("SQL_CONNECTION_STRING");
            string sqlServerQuery = GetEnvironmentVariable("SQL_QUERY");
#else
            string sqlServerConnectionString = SqlServerConnectionString;
            string sqlServerQuery = SqlServerQuery;
#endif

            var dataFeed = new DataFeed();

#if SNIPPET
            dataFeed.Name = "<dataFeedName>";
#else
            dataFeed.Name = GetUniqueName();
#endif
            dataFeed.DataSource = new SqlServerDataFeedSource(sqlServerConnectionString, sqlServerQuery);
            dataFeed.Granularity = new DataFeedGranularity(DataFeedGranularityType.Daily);

            dataFeed.Schema = new DataFeedSchema();
            dataFeed.Schema.MetricColumns.Add(new DataFeedMetric("cost"));
            dataFeed.Schema.MetricColumns.Add(new DataFeedMetric("revenue"));
            dataFeed.Schema.DimensionColumns.Add(new DataFeedDimension("category"));
            dataFeed.Schema.DimensionColumns.Add(new DataFeedDimension("region"));

            dataFeed.IngestionSettings = new DataFeedIngestionSettings(DateTimeOffset.Parse("2020-01-01T00:00:00Z"));

            Response<DataFeed> response = await adminClient.CreateDataFeedAsync(dataFeed);

            DataFeed createdDataFeed = response.Value;

            Console.WriteLine($"Data feed ID: {createdDataFeed.Id}");
            Console.WriteLine($"Data feed status: {createdDataFeed.Status.Value}");
            Console.WriteLine($"Data feed created time: {createdDataFeed.CreatedOn.Value}");

            Console.WriteLine($"Data feed administrators:");
            foreach (string admin in createdDataFeed.Administrators)
            {
                Console.WriteLine($" - {admin}");
            }

            Console.WriteLine($"Metric IDs:");
            foreach (DataFeedMetric metric in createdDataFeed.Schema.MetricColumns)
            {
                Console.WriteLine($" - {metric.Name}: {metric.Id}");
            }

            Console.WriteLine($"Dimensions:");
            foreach (DataFeedDimension dimension in createdDataFeed.Schema.DimensionColumns)
            {
                Console.WriteLine($" - {dimension.Name}");
            }
            #endregion

            // Delete the created data feed to clean up the Metrics Advisor resource. Do not perform this
            // step if you intend to keep using the data feed.

            await adminClient.DeleteDataFeedAsync(createdDataFeed.Id);
        }

        [Test]
        public async Task GetDataFeedAsync()
        {
            string endpoint = GetEnvironmentVariable("METRICS_ADVISOR_ENDPOINT");
            string subscriptionKey = GetEnvironmentVariable("METRICS_ADVISOR_KEY");
            string apiKey = GetEnvironmentVariable("METRICS_ADVISOR_API_KEY");
            var credential = new MetricsAdvisorKeyCredential(subscriptionKey, apiKey);

            var adminClient = new MetricsAdvisorAdministrationClient(new Uri(endpoint), credential);

            string dataFeedId = DataFeedId;

            Response<DataFeed> response = await adminClient.GetDataFeedAsync(dataFeedId);

            DataFeed dataFeed = response.Value;

            Console.WriteLine($"Data feed status: {dataFeed.Status.Value}");
            Console.WriteLine($"Data feed created time: {dataFeed.CreatedOn.Value}");

            Console.WriteLine($"Data feed administrators:");
            foreach (string admin in dataFeed.Administrators)
            {
                Console.WriteLine($" - {admin}");
            }

            Console.WriteLine($"Metric IDs:");
            foreach (DataFeedMetric metric in dataFeed.Schema.MetricColumns)
            {
                Console.WriteLine($" - {metric.Name}: {metric.Id}");
            }

            Console.WriteLine($"Dimensions:");
            foreach (DataFeedDimension dimension in dataFeed.Schema.DimensionColumns)
            {
                Console.WriteLine($" - {dimension.Name}");
            }
        }

        [Test]
        public async Task UpdateDataFeedAsync()
        {
            string endpoint = GetEnvironmentVariable("METRICS_ADVISOR_ENDPOINT");
            string subscriptionKey = GetEnvironmentVariable("METRICS_ADVISOR_KEY");
            string apiKey = GetEnvironmentVariable("METRICS_ADVISOR_API_KEY");
            var credential = new MetricsAdvisorKeyCredential(subscriptionKey, apiKey);

            var adminClient = new MetricsAdvisorAdministrationClient(new Uri(endpoint), credential);

            string dataFeedId = DataFeedId;

            Response<DataFeed> response = await adminClient.GetDataFeedAsync(dataFeedId);
            DataFeed dataFeed = response.Value;

            string originalDescription = dataFeed.Description;
            dataFeed.Description = "This description was generated by a sample.";

            // Some properties, such as IngestionStartOffset, can be reset to their default value
            // when set to null during an Update operation. Check the API documentation to verify
            // when a property supports this feature.

            TimeSpan? originalStartOffset = dataFeed.IngestionSettings.IngestionStartOffset;
            dataFeed.IngestionSettings.IngestionStartOffset = null;

            response = await adminClient.UpdateDataFeedAsync(dataFeed);
            DataFeed updatedDataFeed = response.Value;

            Console.WriteLine($"Updated description: {updatedDataFeed.Description}");
            Console.WriteLine($"Updated ingestion start offset: {updatedDataFeed.IngestionSettings.IngestionStartOffset}");

            // Undo the changes to leave the data feed unaltered. Skip this step if you intend to keep
            // the changes.

            dataFeed.Description = originalDescription;
            dataFeed.IngestionSettings.IngestionStartOffset = originalStartOffset;

            await adminClient.UpdateDataFeedAsync(dataFeed);
        }

        [Test]
        public async Task GetDataFeedsAsync()
        {
            string endpoint = GetEnvironmentVariable("METRICS_ADVISOR_ENDPOINT");
            string subscriptionKey = GetEnvironmentVariable("METRICS_ADVISOR_KEY");
            string apiKey = GetEnvironmentVariable("METRICS_ADVISOR_API_KEY");
            var credential = new MetricsAdvisorKeyCredential(subscriptionKey, apiKey);

            var adminClient = new MetricsAdvisorAdministrationClient(new Uri(endpoint), credential);

            var filter = new DataFeedFilter()
            {
                Status = DataFeedStatus.Active,
                GranularityType = DataFeedGranularityType.Daily
            };
            var options = new GetDataFeedsOptions()
            {
                Filter = filter,
                MaxPageSize = 5
            };

            int dataFeedCount = 0;

            await foreach (DataFeed dataFeed in adminClient.GetDataFeedsAsync(options))
            {
                Console.WriteLine($"Data feed ID: {dataFeed.Id}");
                Console.WriteLine($"Name: {dataFeed.Name}");
                Console.WriteLine($"Description: {dataFeed.Description}");
                Console.WriteLine();

                // Print at most 5 data feeds.
                if (++dataFeedCount >= 5)
                {
                    break;
                }
            }
        }
    }
}

运行应用程序

从应用程序目录使用 dotnet run 命令运行应用程序。

dotnet run

参考文档 | 库源代码 | 项目 (Maven) | 示例

重要

先决条件

Azure 订阅 - 免费创建订阅
最新版本的 Java 开发工具包 (JDK)
Gradle 生成工具，或其他依赖项管理器。
获得 Azure 订阅后，在 Azure 门户中创建指标顾问资源，以部署指标顾问实例。
带有时序数据的 SQL 数据库。

提示

可以在 GitHub 上找到 Java 指标顾问示例。
指标顾问资源可能需要 10 到 30 分钟才能部署一个服务实例供你使用。部署成功后，选择“转到资源”。部署完成后，可以通过 Web 门户和 REST API 这两种方式开始使用指标顾问实例。
可以在 Azure 门户的资源“概述”部分中找到 REST API 的 URL。它将如下所示：
- https://<instance-name>.cognitiveservices.azure.com/

设置

创建新的 Gradle 项目

本快速入门使用 Gradle 依赖项管理器。可在 Maven 中央存储库中找到有关客户端库的详细信息。

在控制台窗口（例如 cmd、PowerShell 或 Bash）中，为应用创建一个新目录并导航到该目录。

mkdir myapp && cd myapp

从工作目录运行 gradle init 命令。此命令将创建 Gradle 的基本生成文件，包括 build.gradle.kts，在运行时将使用该文件创建并配置应用程序。

gradle init --type basic

当提示你选择一个 DSL 时，选择 Kotlin。

安装客户端库

找到 build.gradle.kts，并使用喜好的 IDE 或文本编辑器将其打开。然后将以下生成配置复制到其中。请确保包含项目依赖项。

dependencies {
    compile("com.azure:azure-ai-metricsadvisor:1.1.8")
}

创建 Java 文件

为示例应用创建一个文件夹。在工作目录中运行以下命令：

mkdir -p src/main/java

导航到新文件夹，并创建名为 MetricsAdvisorQuickstarts.java 的文件。在喜好的编辑器或 IDE 中打开该文件并添加以下 import 语句：

提示

想要立即查看整个快速入门代码文件？可以在 GitHub 上找到它，其中包含此快速入门中的代码示例。

环境变量

若要成功对异常检测器服务发出调用，需要使用以下值：

变量名称	值
`METRICS_ADVISOR_ENDPOINT`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到此值。终结点示例：`https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/`
`METRICS_ADVISOR_KEY`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到键值。可以使用 `KEY1` 或 `KEY2`。
`METRICS_ADVISOR_API_KEY`	从指标顾问门户检查资源时，可以在“设置”>“API 密钥”下找到密钥值。可以使用 `KEY1` 或 `KEY2`。
`SQL_CONNECTION_STRING`	本快速入门要求你拥有自己的 SQL 数据库 + 连接字符串。示例连接字符串类似于以下示例：`Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password>`有关构造 SQL 连接字符串的详细信息，请参阅 SQL 文档。
`SQL_QUERY`	特定于数据集的唯一查询。

创建环境变量

为密钥和终结点创建和分配持久环境变量。

重要

如果使用 API 密钥，请将其安全地存储在某个其他位置，例如 Azure Key Vault 中。请不要直接在代码中包含 API 密钥，并且切勿公开发布该密钥。

有关 Azure AI 服务安全性的详细信息，请参阅对 Azure AI 服务的请求进行身份验证。

setx METRICS_ADVISOR_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
setx METRICS_ADVISOR_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx METRICS_ADVISOR_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx SQL_CONNECTION_STRING "REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" 
setx SQL_QUERY "REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA"

[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_CONNECTION_STRING', 'REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_QUERY', 'REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA', 'User')

echo export METRICS_ADVISOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export SQL_CONNECTION_STRING="REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" >> /etc/environment && source /etc/environment
echo export SQL_QUERY="REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA" >> /etc/environment && source /etc/environment

创建应用程序

将 .java 文件的内容替换为以下内容：

package com.azure.ai.metricsadvisor.administration;

import com.azure.ai.metricsadvisor.administration.models.AzureAppInsightsDataFeedSource;
import com.azure.ai.metricsadvisor.administration.models.DataFeed;
import com.azure.ai.metricsadvisor.administration.models.DataFeedDimension;
import com.azure.ai.metricsadvisor.administration.models.DataFeedGranularity;
import com.azure.ai.metricsadvisor.administration.models.DataFeedGranularityType;
import com.azure.ai.metricsadvisor.administration.models.DataFeedIngestionSettings;
import com.azure.ai.metricsadvisor.administration.models.DataFeedMetric;
import com.azure.ai.metricsadvisor.administration.models.DataFeedOptions;
import com.azure.ai.metricsadvisor.administration.models.DataFeedSchema;
import com.azure.ai.metricsadvisor.administration.models.DataFeedSourceType;
import com.azure.ai.metricsadvisor.models.MetricsAdvisorKeyCredential;

import java.time.OffsetDateTime;
import java.util.Arrays;
import java.util.Collections;

/**
 * Sample demonstrates how to create, get, update, delete and list datafeed.
 */
public class DatafeedSample {
    private static String subscription_key = System.getenv("METRICS_ADVISOR_KEY");
    private static String api_key = System.getenv("METRICS_ADVISOR_API_KEY");
    private static String endpoint = System.getenv("METRICS_ADVISOR_ENDPOINT");
    private static String connection_string = System.getenv("SQL_CONNECTION_STRING");
    private static String sql_query = System.getenv("SQL_QUERY");

    public static void main(String[] args) {
        final MetricsAdvisorAdministrationClient advisorAdministrationClient =
            new MetricsAdvisorAdministrationClientBuilder()
                .endpoint("https://{endpoint}.cognitiveservices.azure.com/")
                .credential(new MetricsAdvisorKeyCredential("subscription_key", "api_key"))
                .buildClient();

        // Create Data feed
DataFeed dataFeed = new DataFeed()
    .setName("dataFeedName")
    .setSource(new MySqlDataFeedSource(connection_string, sql_query))
    .setGranularity(new DataFeedGranularity().setGranularityType(DataFeedGranularityType.DAILY))
    .setSchema(new DataFeedSchema(
        Arrays.asList(
            new DataFeedMetric("cost"),
            new DataFeedMetric("revenue")
        )).setDimensions(
        Arrays.asList(
            new DataFeedDimension("city"),
            new DataFeedDimension("category")
        ))
    )
    .setIngestionSettings(new DataFeedIngestionSettings(OffsetDateTime.parse("2020-01-01T00:00:00Z")))
    .setOptions(new DataFeedOptions()
        .setDescription("data feed description")
        .setRollupSettings(new DataFeedRollupSettings()
            .setRollupType(DataFeedRollupType.AUTO_ROLLUP)));
final DataFeed createdSqlDataFeed = metricsAdvisorAdminClient.createDataFeed(dataFeed);

System.out.printf("Data feed Id : %s%n", createdSqlDataFeed.getId());
System.out.printf("Data feed name : %s%n", createdSqlDataFeed.getName());
System.out.printf("Is the query user is one of data feed administrator : %s%n", createdSqlDataFeed.isAdmin());
System.out.printf("Data feed created time : %s%n", createdSqlDataFeed.getCreatedTime());
System.out.printf("Data feed granularity type : %s%n",
    createdSqlDataFeed.getGranularity().getGranularityType());
System.out.printf("Data feed granularity value : %d%n",
    createdSqlDataFeed.getGranularity().getCustomGranularityValue());
System.out.println("Data feed related metric Ids:");
dataFeed.getMetricIds().forEach((metricId, metricName)
    -> System.out.printf("Metric Id : %s, Metric Name: %s%n", metricId, metricName));
System.out.printf("Data feed source type: %s%n", createdSqlDataFeed.getSourceType());

if (SQL_SERVER_DB == createdSqlDataFeed.getSourceType()) {
    System.out.printf("Data feed sql server query: %s%n",
        ((SqlServerDataFeedSource) createdSqlDataFeed.getSource()).getQuery());
}
        // Update the data feed.
        System.out.printf("Updating data feed: %s%n", dataFeed.getId());
        dataFeed = advisorAdministrationClient.updateDataFeed(dataFeed.setOptions(new DataFeedOptions()
            .setAdmins(Collections.singletonList("admin1@admin.com"))
        ));
        System.out.printf("Updated data feed admin list: %s%n",
            String.join(",", dataFeed.getOptions().getAdmins()));

        // Delete the data feed.
        System.out.printf("Deleting data feed: %s%n", dataFeed.getId());
        advisorAdministrationClient.deleteDataFeed(dataFeed.getId());
        System.out.printf("Deleted data feed%n");

        // List data feeds.
        System.out.printf("Listing data feeds%n");
        advisorAdministrationClient.listDataFeeds().forEach(dataFeedItem -> {
            System.out.printf("Data feed Id : %s%n", dataFeedItem.getId());
            System.out.printf("Data feed name : %s%n", dataFeedItem.getName());
            System.out.printf("Is the query user is one of data feed administrator : %s%n", dataFeedItem.isAdmin());
            System.out.printf("Data feed created time : %s%n", dataFeedItem.getCreatedTime());
            System.out.printf("Data feed granularity type : %s%n", dataFeedItem.getGranularity().getGranularityType());
            System.out.printf("Data feed granularity value : %d%n",
                dataFeedItem.getGranularity().getCustomGranularityValue());
            System.out.println("Data feed related metric Id's:");
            dataFeedItem.getMetricIds().forEach((metricId, metricName)
                -> System.out.printf("Metric Id : %s, Metric Name: %s%n", metricId, metricName));
            System.out.printf("Data feed source type: %s%n", dataFeedItem.getSourceType());
        });
    }
}

可使用以下命令生成应用：

gradle build

运行应用程序

使用 run 目标运行应用程序：

gradle run

参考文档 | 库源代码 | 包 (npm) | 示例

重要

先决条件

Azure 订阅 - 免费创建订阅
最新版本的 Node.js
获得 Azure 订阅后，在 Azure 门户中创建指标顾问资源，以部署指标顾问实例。
带有时序数据的 SQL 数据库。

提示

可以在 GitHub 上找到 JavaScript 指标顾问示例。
指标顾问资源可能需要 10 到 30 分钟才能部署一个服务实例供你使用。部署成功后，选择“转到资源”。部署完成后，可以通过 Web 门户和 REST API 这两种方式开始使用指标顾问实例。
可以在 Azure 门户的资源“概述”部分中找到 REST API 的 URL。它将如下所示：
https://<instance-name>.cognitiveservices.azure.com/

设置

创建新的 Node.js 应用程序

在控制台窗口（例如 cmd、PowerShell 或 Bash）中，为应用创建一个新目录并导航到该目录。

mkdir myapp && cd myapp

运行 npm init 命令以使用 package.json 文件创建一个 node 应用程序。

npm init

安装客户端库

安装 @azure/ai-metrics-advisor npm 包：

npm install @azure/ai-metrics-advisor

应用的 package.json 文件将使用依赖项进行更新。

环境变量

若要成功对异常检测器服务发出调用，需要使用以下值：

变量名称	值
`METRICS_ADVISOR_ENDPOINT`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到此值。终结点示例：`https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/`
`METRICS_ADVISOR_KEY`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到键值。可以使用 `KEY1` 或 `KEY2`。
`METRICS_ADVISOR_API_KEY`	从指标顾问门户检查资源时，可以在“设置”>“API 密钥”下找到密钥值。可以使用 `KEY1` 或 `KEY2`。
`SQL_CONNECTION_STRING`	本快速入门要求你拥有自己的 SQL 数据库 + 连接字符串。示例连接字符串类似于以下示例：`Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password>`有关构造 SQL 连接字符串的详细信息，请参阅 SQL 文档。
`SQL_QUERY`	特定于数据集的唯一查询。

创建环境变量

为密钥和终结点创建和分配持久环境变量。

重要

如果使用 API 密钥，请将其安全地存储在某个其他位置，例如 Azure Key Vault 中。请不要直接在代码中包含 API 密钥，并且切勿公开发布该密钥。

有关 Azure AI 服务安全性的详细信息，请参阅对 Azure AI 服务的请求进行身份验证。

setx METRICS_ADVISOR_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
setx METRICS_ADVISOR_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx METRICS_ADVISOR_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx SQL_CONNECTION_STRING "REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" 
setx SQL_QUERY "REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA"

[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_CONNECTION_STRING', 'REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_QUERY', 'REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA', 'User')

echo export METRICS_ADVISOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export SQL_CONNECTION_STRING="REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" >> /etc/environment && source /etc/environment
echo export SQL_QUERY="REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA" >> /etc/environment && source /etc/environment

创建应用程序

创建一个名为 index.js 的文件，并复制以下代码：

/**
 *  @summary This sample demonstrates how to get started by creating a data feed, checking ingestion status,
 * creating detection and alerting configurations, and querying for alerts and anomalies.
 */

// Load the .env file if it exists
const dotenv = require("dotenv");
dotenv.config();

const {
  MetricsAdvisorKeyCredential,
  MetricsAdvisorAdministrationClient,
  MetricsAdvisorClient
} = require("@azure/ai-metrics-advisor");

async function main() {
  // You will need to set these environment variables or edit the following values
  const endpoint = process.env["METRICS_ADVISOR_ENDPOINT"] || "<service endpoint>";
  const subscriptionKey = process.env["METRICS_ADVISOR_KEY"] || "<subscription key>";
  const apiKey = process.env["METRICS_ADVISOR_API_KEY"] || "<api key>";
  const sqlServerConnectionString =
    process.env["SQL_SERVER_CONNECTION_STRING"] ||
    "<connection string to SQL Server>";
  const sqlServerQuery =
    process.env["SQL_SERVER_QUERY"] || "<SQL Server query to retrive data>";

  const credential = new MetricsAdvisorKeyCredential(subscriptionKey, apiKey);

  const client = new MetricsAdvisorClient(endpoint, credential);
  const adminClient = new MetricsAdvisorAdministrationClient(endpoint, credential);

  const created = await createDataFeed(adminClient, sqlServerConnectionString, sqlServerQuery);
  console.log(`Data feed created: ${created.id}`);
  console.log("  metrics: ");
  console.log(created.schema.metrics);

  console.log("Waiting for a minute before checking ingestion status...");
  await delay(60 * 1000);

  try {
    await checkIngestionStatus(
      adminClient,
      created.id,
      new Date(Date.UTC(2020, 8, 1)),
      new Date(Date.UTC(2020, 8, 12))
    );

    const metricId = created.schema.metrics[0].id;
    const detectionConfig = await configureAnomalyDetectionConfiguration(adminClient, metricId);
    console.log(`Detection configuration created: ${detectionConfig.id}`);

    const hook = await createWebhookHook(adminClient);
    console.log(`Webhook hook created: ${hook.id}`);

    const alertConfig = await configureAlertConfiguration(adminClient, detectionConfig.id, [
      hook.id
    ]);
    console.log(`Alert configuration created: ${alertConfig.id}`);

    // you can use alert configuration created in above step to query the alert.
    const alerts = await queryAlerts(
      client,
      alertConfig.id,
      new Date(Date.UTC(2020, 8, 1)),
      new Date(Date.UTC(2020, 8, 12))
    );

    if (alerts.length > 1) {
      // query anomalies using an alert id.
      await queryAnomaliesByAlert(client, alerts[0]);
    } else {
      console.log("No alerts during the time period");
    }
  } finally {
    console.log(`Deleting the data feed '${created.id}`);
    await adminClient.deleteDataFeed(created.id);
  }
}

async function createDataFeed(adminClient, sqlServerConnectionString, sqlServerQuery) {
  console.log("Creating Datafeed...");
  const dataFeed = {
    name: "test_datafeed_" + new Date().getTime().toString(),
    source: {
      dataSourceType: "SqlServer",
      connectionString: sqlServerConnectionString,
      query: sqlServerQuery,
      authenticationType: "Basic"
    },
    granularity: {
      granularityType: "Daily"
    },
    schema: {
      metrics: [
        {
          name: "revenue",
          displayName: "revenue",
          description: "Metric1 description"
        },
        {
          name: "cost",
          displayName: "cost",
          description: "Metric2 description"
        }
      ],
      dimensions: [
        { name: "city", displayName: "city display" },
        { name: "category", displayName: "category display" }
      ],
      timestampColumn: undefined
    },
    ingestionSettings: {
      ingestionStartTime: new Date(Date.UTC(2020, 5, 1)),
      ingestionStartOffsetInSeconds: 0,
      dataSourceRequestConcurrency: -1,
      ingestionRetryDelayInSeconds: -1,
      stopRetryAfterInSeconds: -1
    },
    rollupSettings: {
      rollupType: "AutoRollup",
      rollupMethod: "Sum",
      rollupIdentificationValue: "__SUM__"
    },
    missingDataPointFillSettings: {
      fillType: "SmartFilling"
    },
    accessMode: "Private",
    admins: ["xyz@microsoft.com"]
  };
  const result = await adminClient.createDataFeed(dataFeed);

  return result;
}

async function checkIngestionStatus(adminClient, datafeedId, startTime, endTime) {
  // This shows how to use for-await-of syntax to list status
  console.log("Checking ingestion status...");
  const listIterator = adminClient.listDataFeedIngestionStatus(datafeedId, startTime, endTime);
  for await (const status of listIterator) {
    console.log(`  [${status.timestamp}] ${status.status} - ${status.message}`);
  }
}

async function configureAnomalyDetectionConfiguration(adminClient, metricId) {
  console.log(`Creating an anomaly detection configuration on metric '${metricId}'...`);
  const anomalyConfig = {
    name: "test_detection_configuration" + new Date().getTime().toString(),
    metricId,
    wholeSeriesDetectionCondition: {
      smartDetectionCondition: {
        sensitivity: 100,
        anomalyDetectorDirection: "Both",
        suppressCondition: {
          minNumber: 1,
          minRatio: 1
        }
      }
    },
    description: "Detection configuration description"
  };
  return await adminClient.createDetectionConfig(anomalyConfig);
}

async function createWebhookHook(adminClient) {
  console.log("Creating a webhook hook");
  const hook = {
    hookType: "Webhook",
    name: "web hook " + new Date().getTime().toString(),
    description: "description",
    hookParameter: {
      endpoint: "https://httpbin.org/post",
      username: "user",
      password: "pass"
      // certificateKey: "k",
      // certificatePassword: "kp"
    }
  };

  return await adminClient.createHook(hook);
}

async function configureAlertConfiguration(adminClient, detectionConfigId, hookIds) {
  console.log("Creating a new alerting configuration...");
  const anomalyAlert = {
    name: "test_alert_config_" + new Date().getTime().toString(),
    crossMetricsOperator: "AND",
    metricAlertConfigurations: [
      {
        detectionConfigurationId: detectionConfigId,
        alertScope: {
          scopeType: "All"
        },
        alertConditions: {
          severityCondition: {
            minAlertSeverity: "Medium",
            maxAlertSeverity: "High"
          }
        },
        snoozeCondition: {
          autoSnooze: 0,
          snoozeScope: "Metric",
          onlyForSuccessive: true
        }
      }
    ],
    hookIds,
    description: "Alerting config description"
  };
  return await adminClient.createAlertConfig(anomalyAlert);
}

async function queryAlerts(client, alertConfigId, startTime, endTime) {
  console.log(`Listing alerts for alert configuration '${alertConfigId}'`);
  // This shows how to use `for-await-of` syntax to list alerts
  console.log("  using for-await-of syntax");
  let alerts = [];
  const listIterator = client.listAlerts(alertConfigId, startTime, endTime, "AnomalyTime");
  for await (const alert of listIterator) {
    alerts.push(alert);
    console.log("    Alert");
    console.log(`      id: ${alert.id}`);
    console.log(`      timestamp: ${alert.timestamp}`);
    console.log(`      created on: ${alert.createdOn}`);
  }
  // alternatively we could list results by pages
  console.log(`  by pages`);
  const iterator = client
    .listAlerts(alertConfigId, startTime, endTime, "AnomalyTime")
    .byPage({ maxPageSize: 2 });

  let result = await iterator.next();
  while (!result.done) {
    console.log("    -- Page -- ");
    for (const item of result.value) {
      console.log(`      id: ${item.id}`);
      console.log(`      timestamp: ${item.timestamp}`);
      console.log(`      created on: ${item.createdOn}`);
    }
    result = await iterator.next();
  }

  return alerts;
}

async function queryAnomaliesByAlert(client, alert) {
  console.log(
    `Listing anomalies for alert configuration '${alert.alertConfigId}' and alert '${alert.id}'`
  );
  const listIterator = client.listAnomaliesForAlert(alert);
  for await (const anomaly of listIterator) {
    console.log(
      `  Anomaly ${anomaly.severity} ${anomaly.status} ${anomaly.seriesKey.dimension} ${anomaly.timestamp}`
    );
  }
}

async function delay(milliseconds) {
  return new Promise((resolve) => setTimeout(resolve, milliseconds));
}

main()
  .then((_) => {
    console.log("Succeeded");
  })
  .catch((err) => {
    console.log("Error occurred:");
    console.log(err);
  });

运行应用程序

在快速入门文件中使用 node 命令运行应用程序。

node index.js

参考文档 | 库源代码 | 包 (PiPy) | 示例

重要

先决条件

Azure 订阅 - 免费创建订阅
Python 3.x
获得 Azure 订阅后，在 Azure 门户中创建指标顾问资源，以部署指标顾问实例。
带有时序数据的 SQL 数据库。

提示

可以在 GitHub 上找到 Python 指标顾问示例。
指标顾问资源可能需要 10 到 30 分钟才能部署一个服务实例供你使用。部署成功后，选择“转到资源”。部署完成后，可以通过 Web 门户和 REST API 这两种方式开始使用指标顾问实例。
可以在 Azure 门户的资源“概述”部分中找到 REST API 的 URL。它将如下所示：
https://<instance-name>.cognitiveservices.azure.com/

设置

安装客户端库

安装客户端库。可使用以下方式安装客户端库：

pip install azure-ai-metricsadvisor --pre

环境变量

若要成功对异常检测器服务发出调用，需要使用以下值：

变量名称	值
`METRICS_ADVISOR_ENDPOINT`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到此值。终结点示例：`https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/`
`METRICS_ADVISOR_KEY`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到键值。可以使用 `KEY1` 或 `KEY2`。
`METRICS_ADVISOR_API_KEY`	从指标顾问门户检查资源时，可以在“设置”>“API 密钥”下找到密钥值。可以使用 `KEY1` 或 `KEY2`。
`SQL_CONNECTION_STRING`	本快速入门要求你拥有自己的 SQL 数据库 + 连接字符串。示例连接字符串类似于以下示例：`Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password>`有关构造 SQL 连接字符串的详细信息，请参阅 SQL 文档。
`SQL_QUERY`	特定于数据集的唯一查询。

创建环境变量

为密钥和终结点创建和分配持久环境变量。

重要

如果使用 API 密钥，请将其安全地存储在某个其他位置，例如 Azure Key Vault 中。请不要直接在代码中包含 API 密钥，并且切勿公开发布该密钥。

有关 Azure AI 服务安全性的详细信息，请参阅对 Azure AI 服务的请求进行身份验证。

setx METRICS_ADVISOR_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
setx METRICS_ADVISOR_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx METRICS_ADVISOR_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx SQL_CONNECTION_STRING "REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" 
setx SQL_QUERY "REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA"

[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_CONNECTION_STRING', 'REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_QUERY', 'REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA', 'User')

echo export METRICS_ADVISOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export SQL_CONNECTION_STRING="REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" >> /etc/environment && source /etc/environment
echo export SQL_QUERY="REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA" >> /etc/environment && source /etc/environment

创建应用程序

基于以下代码创建 Python 应用程序：

"""
FILE: sample_data_feeds.py
DESCRIPTION:
    This sample demonstrates how to create, get, list, update, and delete datafeeds under your Metrics Advisor account.
USAGE:
    python sample_data_feeds.py
    Set the environment variables with your own values before running the sample:
    1) METRICS_ADVISOR_ENDPOINT - the endpoint of your Azure AI Metrics Advisor service
    2) METRICS_ADVISOR_KEY - Metrics Advisor service subscription key
    3) METRICS_ADVISOR_API_KEY - Metrics Advisor service API key
    4) SQL_CONNECTION_STRING - Used in this sample for demonstration, but you should
       add your own credentials specific to the data source type you're using
    5) SQL_QUERY - Used in this sample for demonstration, but you should
       add your own query specific to the structure of the data in your datasource.
"""

import os
import datetime


def sample_create_data_feed():
    from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
    from azure.ai.metricsadvisor.models import (
        SqlServerDataFeedSource,
        DataFeedSchema,
        DataFeedMetric,
        DataFeedDimension,
        DataFeedRollupSettings,
        DataFeedMissingDataPointFillSettings,
    )

    service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
    subscription_key = os.getenv("METRICS_ADVISOR_KEY")
    api_key = os.getenv("METRICS_ADVISOR_API_KEY")
    sql_server_connection_string = os.getenv("SQL_CONNECTION_STRING")
    query = os.getenv("SQL_QUERY")

    client = MetricsAdvisorAdministrationClient(service_endpoint,
                                  MetricsAdvisorKeyCredential(subscription_key, api_key))

    data_feed = client.create_data_feed(
        name="My data feed",
        source=SqlServerDataFeedSource(
            connection_string=sql_server_connection_string,
            query=query,
        ),
        granularity="Daily",
        schema=DataFeedSchema(
            metrics=[
                DataFeedMetric(name="cost", display_name="Cost"),
                DataFeedMetric(name="revenue", display_name="Revenue")
            ],
            dimensions=[
                DataFeedDimension(name="category", display_name="Category"),
                DataFeedDimension(name="region", display_name="region")
            ],
            timestamp_column="Timestamp"
        ),
        ingestion_settings=datetime.datetime(2019, 10, 1),
        data_feed_description="cost/revenue data feed",
        rollup_settings=DataFeedRollupSettings(
            rollup_type="AutoRollup",
            rollup_method="Sum",
            rollup_identification_value="__CUSTOM_SUM__"
        ),
        missing_data_point_fill_settings=DataFeedMissingDataPointFillSettings(
            fill_type="SmartFilling"
        ),
        access_mode="Private"
    )

    return data_feed

def sample_get_data_feed(data_feed_id):
    from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient

    service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
    subscription_key = os.getenv("METRICS_ADVISOR_KEY")
    api_key = os.getenv("METRICS_ADVISOR_API_KEY")

    client = MetricsAdvisorAdministrationClient(service_endpoint,
                                  MetricsAdvisorKeyCredential(subscription_key, api_key))

    data_feed = client.get_data_feed(data_feed_id)

    print("ID: {}".format(data_feed.id))
    print("Data feed name: {}".format(data_feed.name))
    print("Created time: {}".format(data_feed.created_time))
    print("Status: {}".format(data_feed.status))
    print("Source type: {}".format(data_feed.source.data_source_type))
    print("Granularity type: {}".format(data_feed.granularity.granularity_type))
    print("Data feed metrics: {}".format([metric.name for metric in data_feed.schema.metrics]))
    print("Data feed dimensions: {}".format([dimension.name for dimension in data_feed.schema.dimensions]))
    print("Data feed timestamp column: {}".format(data_feed.schema.timestamp_column))
    print("Ingestion data starting on: {}".format(data_feed.ingestion_settings.ingestion_begin_time))
    print("Data feed description: {}".format(data_feed.data_feed_description))
    print("Data feed rollup type: {}".format(data_feed.rollup_settings.rollup_type))
    print("Data feed rollup method: {}".format(data_feed.rollup_settings.rollup_method))
    print("Data feed fill setting: {}".format(data_feed.missing_data_point_fill_settings.fill_type))
    print("Data feed access mode: {}".format(data_feed.access_mode))

def sample_list_data_feeds():
    from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient

    service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
    subscription_key = os.getenv("METRICS_ADVISOR_SUBSCRIPTION_KEY")
    api_key = os.getenv("METRICS_ADVISOR_API_KEY")

    client = MetricsAdvisorAdministrationClient(service_endpoint,
                                  MetricsAdvisorKeyCredential(subscription_key, api_key))

    data_feeds = client.list_data_feeds()

    for feed in data_feeds:
        print("Data feed name: {}".format(feed.name))
        print("ID: {}".format(feed.id))
        print("Created time: {}".format(feed.created_time))
        print("Status: {}".format(feed.status))
        print("Source type: {}".format(feed.source.data_source_type))
        print("Granularity type: {}".format(feed.granularity.granularity_type))

        print("\nFeed metrics:")
        for metric in feed.schema.metrics:
            print(metric.name)

        print("\nFeed dimensions:")
        for dimension in feed.schema.dimensions:
            print(dimension.name)

def sample_update_data_feed(data_feed):
    from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient

    service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
    subscription_key = os.getenv("METRICS_ADVISOR_KEY")
    api_key = os.getenv("METRICS_ADVISOR_API_KEY")

    client = MetricsAdvisorAdministrationClient(service_endpoint,
                                  MetricsAdvisorKeyCredential(subscription_key, api_key))

    # update data feed on the data feed itself or by using available keyword arguments
    data_feed.name = "updated name"
    data_feed.data_feed_description = "updated description for data feed"

    updated = client.update_data_feed(
        data_feed,
        access_mode="Public",
        fill_type="CustomValue",
        custom_fill_value=1
    )
    print("Updated name: {}".format(updated.name))
    print("Updated description: {}".format(updated.data_feed_description))
    print("Updated access mode: {}".format(updated.access_mode))
    print("Updated fill setting, value: {}, {}".format(
        updated.missing_data_point_fill_settings.fill_type,
        updated.missing_data_point_fill_settings.custom_fill_value,
    ))

def sample_delete_data_feed(data_feed_id):
    from azure.core.exceptions import ResourceNotFoundError
    from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient

    service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
    subscription_key = os.getenv("METRICS_ADVISOR_KEY")
    api_key = os.getenv("METRICS_ADVISOR_API_KEY")

    client = MetricsAdvisorAdministrationClient(service_endpoint,
                                  MetricsAdvisorKeyCredential(subscription_key, api_key))

    client.delete_data_feed(data_feed_id)

    try:
        client.get_data_feed(data_feed_id)
    except ResourceNotFoundError:
        print("Data feed successfully deleted.")

if __name__ == '__main__':
    print("---Creating data feed...")
    data_feed = sample_create_data_feed()
    print("Data feed successfully created...")
    print("\n---Get a data feed...")
    sample_get_data_feed(data_feed.id)
    print("\n---List data feeds...")
    sample_list_data_feeds()
    print("\n---Update a data feed...")
    sample_update_data_feed(data_feed)
    print("\n---Delete a data feed...")
    sample_delete_data_feed(data_feed.id)

运行应用程序

在快速入门文件中使用 python 命令运行应用程序。

python quickstart-file.py

先决条件

Azure 订阅 - 免费创建订阅
获得 Azure 订阅后，在 Azure 门户中创建“指标顾问”资源，以部署“指标顾问”实例。
Python 3.x
带有时序数据的 SQL 数据库。

重要

设置

本快速入门的示例代码将演示如何使用 Python 调用 REST API。有关特定的 REST API 调用，请参阅 GitHub 示例

提示

指标顾问资源可能需要 10 到 30 分钟才能部署一个服务实例供你使用。部署成功后，选择“转到资源”。部署完成后，可以通过 Web 门户和 REST API 这两种方式开始使用指标顾问实例。
可以在 Azure 门户的资源“概述”部分中找到 REST API 的 URL。它将如下所示：
https://<instance-name>.cognitiveservices.azure.com/

环境变量

若要成功对异常检测器服务发出调用，需要使用以下值：

变量名称	值
`METRICS_ADVISOR_ENDPOINT`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到此值。终结点示例：`https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/`
`METRICS_ADVISOR_KEY`	从 Azure 门户检查资源时，可在“密钥和终结点”部分中找到键值。可以使用 `KEY1` 或 `KEY2`。
`METRICS_ADVISOR_API_KEY`	从指标顾问门户检查资源时，可以在“设置”>“API 密钥”下找到密钥值。可以使用 `KEY1` 或 `KEY2`。
`SQL_CONNECTION_STRING`	本快速入门要求你拥有自己的 SQL 数据库 + 连接字符串。示例连接字符串类似于以下示例：`Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password>`有关构造 SQL 连接字符串的详细信息，请参阅 SQL 文档。
`SQL_QUERY`	特定于数据集的唯一查询。

创建环境变量

为密钥和终结点创建和分配持久环境变量。

重要

如果使用 API 密钥，请将其安全地存储在某个其他位置，例如 Azure Key Vault 中。请不要直接在代码中包含 API 密钥，并且切勿公开发布该密钥。

有关 Azure AI 服务安全性的详细信息，请参阅对 Azure AI 服务的请求进行身份验证。

setx METRICS_ADVISOR_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE" 
setx METRICS_ADVISOR_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx METRICS_ADVISOR_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx SQL_CONNECTION_STRING "REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" 
setx SQL_QUERY "REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA"

[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_ENDPOINT', 'REPLACE_WITH_YOUR_ENDPOINT_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('METRICS_ADVISOR_API_KEY', 'REPLACE_WITH_YOUR_KEY_VALUE_HERE', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_CONNECTION_STRING', 'REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING', 'User')
[System.Environment]::SetEnvironmentVariable('SQL_QUERY', 'REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA', 'User')

echo export METRICS_ADVISOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export METRICS_ADVISOR_API_KEY="REPLACE_WITH_YOUR_KEY_VALUE_HERE" >> /etc/environment && source /etc/environment
echo export SQL_CONNECTION_STRING="REPLACE_WITH_YOUR_UNIQUE_SQL_CONNECTION_STRING" >> /etc/environment && source /etc/environment
echo export SQL_QUERY="REPLACE_WITH_YOUR_UNIQUE_SQL_QUERY_BASED_ON_THE_UNDERLYING_STRUCTURE_OF_YOUR_DATA" >> /etc/environment && source /etc/environment

创建应用程序

import requests
import json
import time


def add_data_feed(endpoint, subscription_key, api_key):
    url = endpoint + '/dataFeeds'
    data_feed_body = {
        "dataSourceType": "SqlServer",
        "dataFeedName": "test_data_feed_00000001",
        "dataFeedDescription": "",
        "dataSourceParameter": {
            "connectionString": os.environ['SQL_CONNECTION_STRING'],
            "query": os.environ['SQL_QUERY']
        },
        "granularityName": "Daily",
        "granularityAmount": 0,
        "metrics": [
            {
                "metricName": "revenue",
                "metricDisplayName": "revenue",
                "metricDescription": ""
            },
            {
                "metricName": "cost",
                "metricDisplayName": "cost",
                "metricDescription": ""
            }
        ],
        "dimension": [
            {
                "dimensionName": "city",
                "dimensionDisplayName": "city"
            },
            {
                "dimensionName": "category",
                "dimensionDisplayName": "category"
            }
        ],
        "timestampColumn": "timestamp",
        "dataStartFrom": "2020-06-01T00:00:00.000Z",
        "startOffsetInSeconds": 0,
        "maxConcurrency": -1,
        "minRetryIntervalInSeconds": -1,
        "stopRetryAfterInSeconds": -1,
        "allUpIdentification": "__SUM__",
        "needRollup": "AlreadyRollup",
        "fillMissingPointType": "SmartFilling",
        "fillMissingPointValue": 0,
        "viewMode": "Private",
        "admins": [
            "admin@contoso.com"
        ],
        "viewers": [
        ],
        "actionLinkTemplate": ""
    }
    res = requests.post(url, data=json.dumps(data_feed_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 201:
        raise RuntimeError("add_data_feed failed " + res.text)
    else:
        print("add_data_feed success " + res.text)
    return res.headers['Location']


def check_ingestion_latest_status(endpoint, subscription_key, api_key, datafeed_id):
    url = endpoint + '/dataFeeds/{}/ingestionProgress'.format(datafeed_id)
    res = requests.get(url, headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                     'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("check_ingestion_latest_status failed " + res.text)
    else:
        print("check_ingestion_latest_status success " + res.text)


def check_ingestion_detail_status(endpoint, subscription_key, api_key, datafeed_id, start_time, end_time):
    url = endpoint + '/dataFeeds/{}/ingestionStatus/query'.format(datafeed_id)
    ingestion_detail_status_body = {
      "startTime": start_time,
      "endTime": end_time
    }
    res = requests.post(url, data=json.dumps(ingestion_detail_status_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("check_ingestion_detail_status failed " + res.text)
    else:
        print("check_ingestion_detail_status success " + res.text)


def create_detection_config(endpoint, subscription_key, api_key, metric_id):
    url = endpoint + '/enrichment/anomalyDetection/configurations'
    detection_config_body = {
        "name": "test_detection_config0000000001",
        "description": "string",
        "metricId": metric_id,
        "wholeMetricConfiguration": {
            "smartDetectionCondition": {
                "sensitivity": 100,
                "anomalyDetectorDirection": "Both",
                "suppressCondition": {
                    "minNumber": 1,
                    "minRatio": 1
                }
            }
        },
        "dimensionGroupOverrideConfigurations": [
        ],
        "seriesOverrideConfigurations": [
        ]
    }
    res = requests.post(url, data=json.dumps(detection_config_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 201:
        raise RuntimeError("create_detection_config failed " + res.text)
    else:

        print("create_detection_config success " + res.text)
    return res.headers['Location']


def create_web_hook(endpoint, subscription_key, api_key):
    url = endpoint + '/hooks'
    web_hook_body = {
        "hookType": "Webhook",
        "hookName": "test_web_hook000001",
        "description": "",
        "externalLink": "",
        "hookParameter": {
            "endpoint": "https://www.contoso.com",
            "username": "",
            "password": ""
        }
    }
    res = requests.post(url, data=json.dumps(web_hook_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 201:
        raise RuntimeError("create_web_hook failed " + res.text)
    else:
        print("create_web_hook success " + res.text)
    return res.headers['Location']


def create_alert_config(endpoint, subscription_key, api_key, anomaly_detection_configuration_id, hook_id):
    url = endpoint + '/alert/anomaly/configurations'
    web_hook_body = {
        "name": "test_alert_config00000001",
        "description": "",
        "crossMetricsOperator": "AND",
        "hookIds": [
           hook_id
        ],
        "metricAlertingConfigurations": [
            {
                "anomalyDetectionConfigurationId": anomaly_detection_configuration_id,
                "anomalyScopeType": "All",
                "severityFilter": {
                    "minAlertSeverity": "Low",
                    "maxAlertSeverity": "High"
                },
                "snoozeFilter": {
                    "autoSnooze": 0,
                    "snoozeScope": "Metric",
                    "onlyForSuccessive": True
                },
            }
        ]
    }
    res = requests.post(url, data=json.dumps(web_hook_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 201:
        raise RuntimeError("create_alert_config failed " + res.text)
    else:
        print("create_alert_config success " + res.text)
    return res.headers['Location']


def query_alert_by_alert_config(endpoint, subscription_key, api_key, alert_config_id, start_time, end_time):
    url = endpoint + '/alert/anomaly/configurations/{}/alerts/query'.format(alert_config_id)
    alerts_body = {
        "startTime": start_time,
        "endTime": end_time,
        "timeMode": "AnomalyTime"
    }
    res = requests.post(url, data=json.dumps(alerts_body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_alert_by_alert_config failed " + res.text)
    else:
        print("query_alert_by_alert_config success " + res.text)
    return [item['alertId'] for item in json.loads(res.content)['value']]


def query_anomaly_by_alert(endpoint, subscription_key, api_key, alert_config_id, alert_id):
    url = endpoint + '/alert/anomaly/configurations/{}/alerts/{}/anomalies'.format(alert_config_id, alert_id)
    res = requests.get(url,
                       headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_anomaly_by_alert failed " + res.text)
    else:
        print("query_anomaly_by_alert success " + res.text)
    return json.loads(res.content)


def query_incident_by_alert(endpoint, subscription_key, api_key, alert_config_id, alert_id):
    url = endpoint + '/alert/anomaly/configurations/{}/alerts/{}/incidents'.format(alert_config_id, alert_id)
    res = requests.get(url,
                       headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_incident_by_alert failed " + res.text)
    else:
        print("query_incident_by_alert success " + res.text)
    return json.loads(res.content)


def query_root_cause_by_incident(endpoint, subscription_key, api_key, detection_config_id, incident_id):
    url = endpoint + '/enrichment/anomalyDetection/configurations/{}/incidents/{}/rootCause'.format(detection_config_id, incident_id)
    res = requests.get(url,
                       headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_root_cause_by_incident failed " + res.text)
    else:
        print("query_root_cause_by_incident success " + res.text)
    return json.loads(res.content)


def query_anomaly_by_detection_config(endpoint, subscription_key, api_key, detection_config_id, start_time, end_time):
    url = endpoint + '/enrichment/anomalyDetection/configurations/{}/anomalies/query'.format(detection_config_id)
    body = {
        "startTime": start_time,
        "endTime": end_time,
        "filter": {
            "dimensionFilter": [
            ],
            "severityFilter": {
              "min": "Low",
              "max": "High"
            }
          }
    }
    res = requests.post(url, data=json.dumps(body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_anomaly_by_detection_config failed " + res.text)
    else:
        print("query_anomaly_by_detection_config success " + res.text)
    return json.loads(res.content)


def query_incident_by_detection_config(endpoint, subscription_key, api_key, detection_config_id, start_time, end_time):
    url = endpoint + '/enrichment/anomalyDetection/configurations/{}/incidents/query'.format(detection_config_id)
    body = {
        "startTime": start_time,
        "endTime": end_time,
        "filter": {
            "dimensionFilter": [
            ],
        }
    }
    res = requests.post(url, data=json.dumps(body),
                        headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                 'x-api-key': api_key})
    if res.status_code != 200:
        raise RuntimeError("query_incident_by_detection_config failed " + res.text)
    else:
        print("query_incident_by_detection_config success " + res.text)
    return json.loads(res.content)


if __name__ == '__main__':
    # Example endpoint: https://[placeholder].cognitiveservices.azure.com/metricsadvisor/v1.0
    endpoint = os.environ['METRICS_ADVISOR_ENDPOINT'] + "metricsadvisor/v1.0"
    subscription_key = os.environ['METRICS_ADVISOR_KEY']
    api_key = os.environ['METRICS_ADVISOR_API_KEY']

    '''
    First part
    1.onboard datafeed
    2.check datafeed latest status
    3.check datafeed status details
    4.create detection config
    5.create webhook
    6.create alert config
    '''
    datafeed_resource_url = add_data_feed(endpoint, subscription_key, api_key)
    print(datafeed_resource_url)

    # datafeed_id and metrics_id can get from datafeed_resource_url
    datafeed_info = json.loads(requests.get(url=datafeed_resource_url,
                                            headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                                     'x-api-key': api_key}).content)
    print(datafeed_info)
    datafeed_id = datafeed_info['dataFeedId']
    metrics_id = []
    for metrics in datafeed_info['metrics']:
        metrics_id.append(metrics['metricId'])
    time.sleep(60)

    check_ingestion_latest_status(endpoint, subscription_key, api_key, datafeed_id)

    check_ingestion_detail_status(endpoint, subscription_key, api_key, datafeed_id,
                                  "2020-06-01T00:00:00Z", "2020-07-01T00:00:00Z")

    detection_config_resource_url = create_detection_config(endpoint, subscription_key, api_key, metrics_id[0])
    print(detection_config_resource_url)

    # anomaly_detection_configuration_id can get from detection_config_resource_url
    detection_config = json.loads(requests.get(url=detection_config_resource_url,
                                               headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                                        'x-api-key': api_key}).content)
    print(detection_config)
    anomaly_detection_configuration_id = detection_config['anomalyDetectionConfigurationId']

    webhook_resource_url = create_web_hook(endpoint, subscription_key, api_key)
    print(webhook_resource_url)

    # hook_id can get from webhook_resource_url
    webhook = json.loads(requests.get(url=webhook_resource_url,
                                      headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                               'x-api-key': api_key}).content)
    print(webhook)
    hook_id = webhook['hookId']

    alert_config_resource_url = create_alert_config(endpoint, subscription_key, api_key,
                                                    anomaly_detection_configuration_id, hook_id)

    # anomaly_alerting_configuration_id can get from alert_config_resource_url
    alert_config = json.loads(requests.get(url=alert_config_resource_url,
                                           headers={'Ocp-Apim-Subscription-Key': subscription_key,
                                                    'x-api-key': api_key}).content)
    print(alert_config)
    anomaly_alerting_configuration_id = alert_config['anomalyAlertingConfigurationId']

清理资源

如果想要清理并移除 Azure AI 服务订阅，可以删除资源或资源组。删除资源组同时也会删除与之相关联的任何其他资源。

通过

快速入门：使用客户端库或 REST API 自定义解决方案

先决条件

设置

安装客户端库

环境变量

创建环境变量

创建应用程序

运行应用程序

先决条件

设置

创建新的 Gradle 项目

安装客户端库

创建 Java 文件

环境变量

创建环境变量

创建应用程序

运行应用程序

先决条件

设置

创建新的 Node.js 应用程序

安装客户端库

环境变量

创建环境变量

创建应用程序

运行应用程序

先决条件

设置

安装客户端库

环境变量

创建环境变量

创建应用程序

运行应用程序

先决条件

设置

环境变量

创建环境变量

创建应用程序

清理资源

后续步骤

其他资源