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Quickstart: Use the client libraries or REST APIs to customize your solution

  • Artikkeli

Important

Starting on the 20th of September, 2023 you won’t be able to create new Metrics Advisor resources. The Metrics Advisor service is being retired on the 1st of October, 2026.

Get started with the Metrics Advisor REST API or client libraries. Follow these steps to install the package and try out the example code for basic tasks.

Use Metrics Advisor to perform:

  • Add a data feed from a data source
  • Check ingestion status
  • Configure detection and alerts
  • Query the anomaly detection results
  • Diagnose anomalies

Reference documentation | Library source code | Package (NuGet) | Samples

Important

Microsoft recommends that you use the most secure authentication flow available. Some of the authentication flows described in this article require a very high degree of trust in the application, and carries risks that are not present in other more secure flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Prerequisites

Tip

  • You can find .NET Metrics Advisor samples on GitHub.
  • It may take 10 to 30 minutes for your Metrics Advisor resource to deploy a service instance for you to use. Select Go to resource once it successfully deploys. After deployment, you can start using your Metrics Advisor instance with both the web portal and REST API.
  • You can find the URL for the REST API in Azure portal, in the Overview section of your resource. It will look like this:
  • https://<instance-name>.cognitiveservices.azure.com/

Set up

Install the client library

Once you've created a new project, install the client library by right-clicking on the project solution in the Solution Explorer and selecting Manage NuGet Packages. In the package manager that opens select Browse, check Include prerelease, and search for Azure.AI.MetricsAdvisor. Select version 1.0.0, and then Install.

In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name metrics-advisor-quickstart. This command creates a simple "Hello World" C# project with a single source file: program.cs.

dotnet new console -n metrics-advisor-quickstart

Change your directory to the newly created app folder. You can build the application with:

dotnet build

The build output should contain no warnings or errors.

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

If you are using an IDE other than Visual Studio you can install the Metrics Advisor client library for .NET with the following command:

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

Environment variables

To successfully make a call against the Anomaly Detector service, you'll need the following values:

Variable name Value
METRICS_ADVISOR_ENDPOINT This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Example endpoint: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY The key value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
METRICS_ADVISOR_API_KEY The key value can be found under Settings > API keys when examining your resource from the Metrics Advisor portal. You can use either KEY1 or KEY2.
SQL_CONNECTION_STRING This quickstart requires you to have your own SQL Database + connection string. An example connection string would look similar to the following example:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> for more information on constructing SQL connection strings, see the SQL documentation.
SQL_QUERY Unique query specific to your dataset.

Create environment variables

Create and assign persistent environment variables for your key and endpoint.

Important

If you use an API key, store it securely somewhere else, such as in Azure Key Vault. Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services.

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"

Create your application

Edit your program.cs file and replace with the following:

// 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;
                }
            }
        }
    }
}

Run the application

Run the application from your application directory with the dotnet run command.

dotnet run

Reference documentation | Library source code | Artifact (Maven) | Samples

Important

Microsoft recommends that you use the most secure authentication flow available. Some of the authentication flows described in this article require a very high degree of trust in the application, and carries risks that are not present in other more secure flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Prerequisites

Tip

  • You can find Java Metrics Advisor samples on GitHub.
  • It may take 10 to 30 minutes for your Metrics Advisor resource to deploy a service instance for you to use. Select Go to resource once it successfully deploys. After deployment, you can start using your Metrics Advisor instance with both the web portal and REST API.
  • You can find the URL for the REST API in Azure portal, in the Overview section of your resource. It will look like this:
    • https://<instance-name>.cognitiveservices.azure.com/

Set up

Create a new Gradle project

This quickstart uses the Gradle dependency manager. You can find more client library information on the Maven Central Repository.

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

mkdir myapp && cd myapp

Run the gradle init command from your working directory. This command will create essential build files for Gradle, including build.gradle.kts which is used at runtime to create and configure your application.

gradle init --type basic

When prompted to choose a DSL, select Kotlin.

Install the client library

Locate build.gradle.kts and open it with your preferred IDE or text editor. Then copy in this build configuration. Be sure to include the project dependencies.

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

Create a Java file

Create a folder for your sample app. From your working directory, run the following command:

mkdir -p src/main/java

Navigate to the new folder and create a file called MetricsAdvisorQuickstarts.java. Open it in your preferred editor or IDE and add the following import statements:

Tip

Want to view the whole quickstart code file at once? You can find it on GitHub, which contains the code examples in this quickstart.

Environment variables

To successfully make a call against the Anomaly Detector service, you'll need the following values:

Variable name Value
METRICS_ADVISOR_ENDPOINT This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Example endpoint: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY The key value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
METRICS_ADVISOR_API_KEY The key value can be found under Settings > API keys when examining your resource from the Metrics Advisor portal. You can use either KEY1 or KEY2.
SQL_CONNECTION_STRING This quickstart requires you to have your own SQL Database + connection string. An example connection string would look similar to the following example:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> for more information on constructing SQL connection strings, see the SQL documentation.
SQL_QUERY Unique query specific to your dataset.

Create environment variables

Create and assign persistent environment variables for your key and endpoint.

Important

If you use an API key, store it securely somewhere else, such as in Azure Key Vault. Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services.

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"

Create your application

Replace the contents of your .java file with the following:

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());
        });
    }
}

You can build the app with:

gradle build

Run the application

Run the application with the run goal:

gradle run

Reference documentation | Library source code | Package (npm) | Samples

Important

Microsoft recommends that you use the most secure authentication flow available. Some of the authentication flows described in this article require a very high degree of trust in the application, and carries risks that are not present in other more secure flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Prerequisites

Tip

  • You can find JavaScript Metrics Advisor samples on GitHub.
  • It may take 10 to 30 minutes for your Metrics Advisor resource to deploy a service instance for you to use. Select Go to resource once it successfully deploys. After deployment, you can start using your Metrics Advisor instance with both the web portal and REST API.
  • You can find the URL for the REST API in Azure portal, in the Overview section of your resource. It will look like this:
  • https://<instance-name>.cognitiveservices.azure.com/

Set up

Create a new Node.js application

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

mkdir myapp && cd myapp

Run the npm init command to create a node application with a package.json file.

npm init

Install the client library

Install the @azure/ai-metrics-advisor npm package:

npm install @azure/ai-metrics-advisor

Your app's package.json file will be updated with the dependencies.

Environment variables

To successfully make a call against the Anomaly Detector service, you'll need the following values:

Variable name Value
METRICS_ADVISOR_ENDPOINT This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Example endpoint: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY The key value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
METRICS_ADVISOR_API_KEY The key value can be found under Settings > API keys when examining your resource from the Metrics Advisor portal. You can use either KEY1 or KEY2.
SQL_CONNECTION_STRING This quickstart requires you to have your own SQL Database + connection string. An example connection string would look similar to the following example:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> for more information on constructing SQL connection strings, see the SQL documentation.
SQL_QUERY Unique query specific to your dataset.

Create environment variables

Create and assign persistent environment variables for your key and endpoint.

Important

If you use an API key, store it securely somewhere else, such as in Azure Key Vault. Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services.

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"

Create your application

Create a file named index.js and copy the following code:

/**
 *  @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);
  });

Run the application

Run the application with the node command on your quickstart file.

node index.js

Reference documentation | Library source code | Package (PiPy) | Samples

Important

Microsoft recommends that you use the most secure authentication flow available. Some of the authentication flows described in this article require a very high degree of trust in the application, and carries risks that are not present in other more secure flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Prerequisites

Tip

  • You can find Python Metrics Advisor samples on GitHub.
  • It may take 10 to 30 minutes for your Metrics Advisor resource to deploy a service instance for you to use. Select Go to resource once it successfully deploys. After deployment, you can start using your Metrics Advisor instance with both the web portal and REST API.
  • You can find the URL for the REST API in Azure portal, in the Overview section of your resource. It will look like this:
  • https://<instance-name>.cognitiveservices.azure.com/

Set up

Install the client library

Install the client library. You can install the client library with:

pip install azure-ai-metricsadvisor --pre

Environment variables

To successfully make a call against the Anomaly Detector service, you'll need the following values:

Variable name Value
METRICS_ADVISOR_ENDPOINT This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Example endpoint: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY The key value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
METRICS_ADVISOR_API_KEY The key value can be found under Settings > API keys when examining your resource from the Metrics Advisor portal. You can use either KEY1 or KEY2.
SQL_CONNECTION_STRING This quickstart requires you to have your own SQL Database + connection string. An example connection string would look similar to the following example:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> for more information on constructing SQL connection strings, see the SQL documentation.
SQL_QUERY Unique query specific to your dataset.

Create environment variables

Create and assign persistent environment variables for your key and endpoint.

Important

If you use an API key, store it securely somewhere else, such as in Azure Key Vault. Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services.

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"

Create your application

Create a python application based off the follow code:

"""
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)

Run the application

Run the application with the python command on your quickstart file.

python quickstart-file.py

Prerequisites

Important

Microsoft recommends that you use the most secure authentication flow available. Some of the authentication flows described in this article require a very high degree of trust in the application, and carries risks that are not present in other more secure flows. You should only use this flow when other more secure flows, such as managed identities, aren't viable.

Set up

The example code for this quickstart will show you how to call the REST API using Python. For specific REST API calls, consult GitHub Samples

Tip

  • It may 10 to 30 minutes for your Metrics Advisor resource to deploy a service instance for you to use. Select Go to resource once it successfully deploys. After deployment, you can start using your Metrics Advisor instance with both the web portal and REST API.
  • You can find the URL for the REST API in Azure portal, in the Overview section of your resource. it will look like this:
  • https://<instance-name>.cognitiveservices.azure.com/

Environment variables

To successfully make a call against the Anomaly Detector service, you'll need the following values:

Variable name Value
METRICS_ADVISOR_ENDPOINT This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Example endpoint: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY The key value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.
METRICS_ADVISOR_API_KEY The key value can be found under Settings > API keys when examining your resource from the Metrics Advisor portal. You can use either KEY1 or KEY2.
SQL_CONNECTION_STRING This quickstart requires you to have your own SQL Database + connection string. An example connection string would look similar to the following example:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> for more information on constructing SQL connection strings, see the SQL documentation.
SQL_QUERY Unique query specific to your dataset.

Create environment variables

Create and assign persistent environment variables for your key and endpoint.

Important

If you use an API key, store it securely somewhere else, such as in Azure Key Vault. Don't include the API key directly in your code, and never post it publicly.

For more information about AI services security, see Authenticate requests to Azure AI services.

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"

Create your application

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']

Clean up resources

If you want to clean up and remove an Azure AI services subscription, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps