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Mulai cepat: Gunakan pustaka klien atau REST API untuk mengkustomisasi solusi Anda

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Penting

Mulai tanggal 20 September 2023, Anda tidak akan dapat membuat sumber daya Metrics Advisor baru. Layanan Metrics Advisor dihentikan pada 1 Oktober 2026.

Mulai menggunakan Metrics Advisor REST API atau pustaka klien. Ikuti langkah-langkah berikut untuk memasang paket dan mencoba contoh kode untuk tugas-tugas dasar.

Gunakan Advisor Metrik untuk melakukan:

  • Menambahkan umpan data dari sumber data
  • Periksa status penyerapan
  • Mengonfigurasikan deteksi dan pemberitahuan
  • Mengkueri hasil deteksi anomali
  • Diagnosa anomali

Dokumentasi referensi | Kode sumber pustaka | Paket (NuGet) | Sampel

Prasyarat

Tip

  • Anda dapat menemukan sampel JavaScript Metrics Advisor di GitHub.
  • Mungkin perlu waktu 10 hingga 30 menit hingga sumber daya Metrics Advisor Anda menyebarkan instans layanan untuk Anda gunakan. Pilih Buka sumber daya setelah berhasil disebarkan. Setelah penyebaran, Anda dapat mulai menggunakan instans Metrics Advisor dengan portal web dan REST API.
  • Anda dapat menemukan URL untuk REST API di portal Microsoft Azure, di bagian Gambaran umum sumber daya Anda. Ini akan terlihat seperti ini:
  • https://<instance-name>.cognitiveservices.azure.com/

Penyiapan

Memasang pustaka klien

Setelah Anda membuat proyek baru, pasang pustaka klien dengan mengklik kanan solusi proyek di Penjelajah Solusi, lalu pilih Kelola Paket NuGet. Di manajer paket yang terbuka, pilih Telusuri, centang Sertakan prarilis, dan cari Azure.AI.MetricsAdvisor. Pilih versi 1.0.0, lalu Pasang.

Di jendela konsol (seperti cmd, PowerShell, atau Bash), gunakan perintah dotnet new untuk membuat aplikasi konsol baru dengan nama metrics-advisor-quickstart. Perintah ini membuat proyek C# "Halo Dunia" sederhana dengan satu file sumber: program.cs.

dotnet new console -n metrics-advisor-quickstart

Ubah direktori Anda ke folder aplikasi yang baru dibuat. Anda dapat membangun aplikasi dengan:

dotnet build

Output build tidak boleh berisi peringatan atau kesalahan.

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

Jika Anda menggunakan IDE selain Visual Studio, Anda dapat memasang pustaka klien Metrics Advisor untuk .NET dengan perintah berikut:

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

Variabel lingkungan

Agar berhasil melakukan panggilan terhadap layanan Detektor Anomali, Anda memerlukan nilai berikut:

Nama variabel Nilai
METRICS_ADVISOR_ENDPOINT Nilai ini dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Microsoft Azure. Contoh titik akhir: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY Nilai kunci dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Azure. Anda dapat menggunakan KEY1 atau KEY2.
METRICS_ADVISOR_API_KEY Nilai kunci dapat ditemukan di bawah kunci Pengaturan> API saat memeriksa sumber daya Anda dari portal Metrics Advisor. Anda dapat menggunakan KEY1 atau KEY2.
SQL_CONNECTION_STRING Mulai cepat ini mengharuskan Anda memiliki SQL Database + string koneksi Anda sendiri. Contoh string koneksi akan terlihat mirip dengan contoh berikut:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> untuk informasi selengkapnya tentang membangun string koneksi SQL, lihat dokumentasi SQL.
SQL_QUERY Kueri unik khusus untuk himpunan data Anda.

Membuat variabel lingkungan

Buat dan tetapkan variabel lingkungan persisten untuk kunci dan titik akhir Anda.

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"

Membuat aplikasi

Edit file program.cs Anda dan ganti dengan yang berikut:

// 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;
                }
            }
        }
    }
}

Jalankan aplikasi

Menjalankan aplikasi dari direktori aplikasi Anda dengan perintah dotnet run.

dotnet run

Dokumentasi referensi | Kode sumber pustaka | Artefak (Maven) | Sampel

Prasyarat

Tip

  • Anda dapat menemukan sampel Java Metrics Advisor di GitHub.
  • Mungkin perlu waktu 10 hingga 30 menit hingga sumber daya Metrics Advisor Anda menyebarkan instans layanan untuk Anda gunakan. Pilih Buka sumber daya setelah berhasil disebarkan. Setelah penyebaran, Anda dapat mulai menggunakan instans Metrics Advisor dengan portal web dan REST API.
  • Anda dapat menemukan URL untuk REST API di portal Microsoft Azure, di bagian Gambaran umum sumber daya Anda. Ini akan terlihat seperti ini:
    • https://<instance-name>.cognitiveservices.azure.com/

Penyiapan

Buat proyek Gradle baru

Mulai cepat ini menggunakan pengelola dependensi Gradle. Anda dapat menemukan informasi pustaka klien lainnya di Repositori Maven Central.

Di jendela konsol (seperti cmd, PowerShell, atau Bash), buat direktori baru untuk aplikasi Anda, dan buka direktori tersebut.

mkdir myapp && cd myapp

Jalankan perintah gradle init dari direktori yang berfungsi. Perintah ini akan membuat file build penting untuk Gradle, termasuk build.gradle.kts yang digunakan saat runtime untuk membuat dan mengonfigurasi aplikasi Anda.

gradle init --type basic

Saat diminta untuk memilih DSL, pilih Kotlin.

Memasang pustaka klien

Temukan build.gradle.kts lalu buka dengan IDE atau editor teks pilihan Anda. Kemudian salin dalam konfigurasi build ini. Pastikan untuk menyertakan dependensi proyek.

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

Membuat file Java

Buat folder untuk aplikasi sampel Anda. Dari direktori kerja, jalankan perintah berikut:

mkdir -p src/main/java

Navigasi ke folder baru dan buat file yang disebut MetricsAdvisorQuickstarts.java. Buka di editor atau IDE pilihan Anda, lalu tambahkan pernyataan import berikut:

Tip

Ingin menampilkan seluruh file kode mulai cepat sekaligus? Anda dapat menemukannya di GitHub, yang berisi contoh kode dalam mulai cepat ini.

Variabel lingkungan

Agar berhasil melakukan panggilan terhadap layanan Detektor Anomali, Anda memerlukan nilai berikut:

Nama variabel Nilai
METRICS_ADVISOR_ENDPOINT Nilai ini dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Microsoft Azure. Contoh titik akhir: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY Nilai kunci dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Azure. Anda dapat menggunakan KEY1 atau KEY2.
METRICS_ADVISOR_API_KEY Nilai kunci dapat ditemukan di bawah kunci Pengaturan> API saat memeriksa sumber daya Anda dari portal Metrics Advisor. Anda dapat menggunakan KEY1 atau KEY2.
SQL_CONNECTION_STRING Mulai cepat ini mengharuskan Anda memiliki SQL Database + string koneksi Anda sendiri. Contoh string koneksi akan terlihat mirip dengan contoh berikut:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> untuk informasi selengkapnya tentang membangun string koneksi SQL, lihat dokumentasi SQL.
SQL_QUERY Kueri unik khusus untuk himpunan data Anda.

Membuat variabel lingkungan

Buat dan tetapkan variabel lingkungan persisten untuk kunci dan titik akhir Anda.

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"

Membuat aplikasi

Ganti konten file .java Anda dengan yang berikut ini:

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());
        });
    }
}

Anda dapat membuat aplikasi dengan:

gradle build

Jalankan aplikasi

Jalankan aplikasi dengan tujuan run:

gradle run

Dokumentasi referensi | Kode sumber pustaka | Paket (npm) | Sampel

Prasyarat

Tip

  • Anda dapat menemukan sampel JavaScript Metrics Advisor di GitHub.
  • Mungkin perlu waktu 10 hingga 30 menit hingga sumber daya Metrics Advisor Anda menyebarkan instans layanan untuk Anda gunakan. Pilih Buka sumber daya setelah berhasil disebarkan. Setelah penyebaran, Anda dapat mulai menggunakan instans Metrics Advisor dengan portal web dan REST API.
  • Anda dapat menemukan URL untuk REST API di portal Microsoft Azure, di bagian Gambaran umum sumber daya Anda. Ini akan terlihat seperti ini:
  • https://<instance-name>.cognitiveservices.azure.com/

Penyiapan

Membuat aplikasi Node.js baru

Di jendela konsol (seperti cmd, PowerShell, atau Bash), buat direktori baru untuk aplikasi Anda, dan buka direktori tersebut.

mkdir myapp && cd myapp

Jalankan perintah npm init untuk membuat aplikasi node dengan file package.json.

npm init

Memasang pustaka klien

Instal paket npm @azure/ai-metrics-advisor:

npm install @azure/ai-metrics-advisor

File package.json aplikasi Anda akan diperbarui dengan dependensi.

Variabel lingkungan

Agar berhasil melakukan panggilan terhadap layanan Detektor Anomali, Anda memerlukan nilai berikut:

Nama variabel Nilai
METRICS_ADVISOR_ENDPOINT Nilai ini dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Microsoft Azure. Contoh titik akhir: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY Nilai kunci dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Azure. Anda dapat menggunakan KEY1 atau KEY2.
METRICS_ADVISOR_API_KEY Nilai kunci dapat ditemukan di bawah kunci Pengaturan> API saat memeriksa sumber daya Anda dari portal Metrics Advisor. Anda dapat menggunakan KEY1 atau KEY2.
SQL_CONNECTION_STRING Mulai cepat ini mengharuskan Anda memiliki SQL Database + string koneksi Anda sendiri. Contoh string koneksi akan terlihat mirip dengan contoh berikut:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> untuk informasi selengkapnya tentang membangun string koneksi SQL, lihat dokumentasi SQL.
SQL_QUERY Kueri unik khusus untuk himpunan data Anda.

Membuat variabel lingkungan

Buat dan tetapkan variabel lingkungan persisten untuk kunci dan titik akhir Anda.

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"

Membuat aplikasi

Buat file bernama index.js dan salin kode berikut:

/**
 *  @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);
  });

Jalankan aplikasi

Jalankan aplikasi dengan perintah node pada file mulai cepat Anda.

node index.js

Dokumentasi referensi | Kode sumber pustaka | Paket (PiPy) | Sampel

Prasyarat

Tip

  • Anda dapat menemukan sampel Phyton Metrics Advisor di GitHub.
  • Mungkin perlu waktu 10 hingga 30 menit hingga sumber daya Metrics Advisor Anda menyebarkan instans layanan untuk Anda gunakan. Pilih Buka sumber daya setelah berhasil disebarkan. Setelah penyebaran, Anda dapat mulai menggunakan instans Metrics Advisor dengan portal web dan REST API.
  • Anda dapat menemukan URL untuk REST API di portal Microsoft Azure, di bagian Gambaran umum sumber daya Anda. Ini akan terlihat seperti ini:
  • https://<instance-name>.cognitiveservices.azure.com/

Penyiapan

Memasang pustaka klien

Menginstal pustaka klien. Anda dapat memasang pustaka klien dengan:

pip install azure-ai-metricsadvisor --pre

Variabel lingkungan

Agar berhasil melakukan panggilan terhadap layanan Detektor Anomali, Anda memerlukan nilai berikut:

Nama variabel Nilai
METRICS_ADVISOR_ENDPOINT Nilai ini dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Microsoft Azure. Contoh titik akhir: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY Nilai kunci dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Azure. Anda dapat menggunakan KEY1 atau KEY2.
METRICS_ADVISOR_API_KEY Nilai kunci dapat ditemukan di bawah kunci Pengaturan> API saat memeriksa sumber daya Anda dari portal Metrics Advisor. Anda dapat menggunakan KEY1 atau KEY2.
SQL_CONNECTION_STRING Mulai cepat ini mengharuskan Anda memiliki SQL Database + string koneksi Anda sendiri. Contoh string koneksi akan terlihat mirip dengan contoh berikut:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> untuk informasi selengkapnya tentang membangun string koneksi SQL, lihat dokumentasi SQL.
SQL_QUERY Kueri unik khusus untuk himpunan data Anda.

Membuat variabel lingkungan

Buat dan tetapkan variabel lingkungan persisten untuk kunci dan titik akhir Anda.

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"

Membuat aplikasi

Buat aplikasi python berdasarkan kode berikut:

"""
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)

Jalankan aplikasi

Jalankan aplikasi dengan perintah python pada file mulai cepat Anda.

python quickstart-file.py

Prasyarat

Penyiapan

Contoh kode untuk mulai cepat ini akan menunjukkan kepada Anda cara memanggil REST API menggunakan Python. Untuk panggilan REST API tertentu, lihat Sampel GitHub

Tip

  • Mungkin perlu waktu 10 hingga 30 menit hingga sumber daya Metrics Advisor Anda menyebarkan instans layanan yang bisa digunakan. Pilih Buka sumber daya setelah berhasil disebarkan. Setelah penyebaran, Anda dapat mulai menggunakan instans Metrics Advisor dengan portal web dan REST API.
  • Anda dapat menemukan URL untuk REST API di portal Microsoft Azure, di bagian Gambaran umum sumber daya Anda. Lalu akan terlihat seperti ini:
  • https://<instance-name>.cognitiveservices.azure.com/

Variabel lingkungan

Agar berhasil melakukan panggilan terhadap layanan Detektor Anomali, Anda memerlukan nilai berikut:

Nama variabel Nilai
METRICS_ADVISOR_ENDPOINT Nilai ini dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Microsoft Azure. Contoh titik akhir: https://YOUR_RESOURCE_NAME.cognitiveservices.azure.com/
METRICS_ADVISOR_KEY Nilai kunci dapat ditemukan di bagian Kunci & Titik Akhir saat memeriksa sumber daya Anda dari portal Azure. Anda dapat menggunakan KEY1 atau KEY2.
METRICS_ADVISOR_API_KEY Nilai kunci dapat ditemukan di bawah kunci Pengaturan> API saat memeriksa sumber daya Anda dari portal Metrics Advisor. Anda dapat menggunakan KEY1 atau KEY2.
SQL_CONNECTION_STRING Mulai cepat ini mengharuskan Anda memiliki SQL Database + string koneksi Anda sendiri. Contoh string koneksi akan terlihat mirip dengan contoh berikut:Data Source=<Server>;Initial Catalog=<db-name>;User ID=<user-name>;Password=<password> untuk informasi selengkapnya tentang membangun string koneksi SQL, lihat dokumentasi SQL.
SQL_QUERY Kueri unik khusus untuk himpunan data Anda.

Membuat variabel lingkungan

Buat dan tetapkan variabel lingkungan persisten untuk kunci dan titik akhir Anda.

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"

Membuat aplikasi

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']

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