Azure Metrics Advisor client library for Java - version 1.2.3

Článek
10/28/2024

Azure Metrics Advisor is a new Cognitive Service that uses time series based decision AI to identify and assist troubleshooting the incidents of online services, and monitor the business health by automating the slice and dice of business dataFeedMetrics.

Source code | Package (Maven) | API reference documentation | Product Documentation | Samples

Getting started

Prerequisites

Java Development Kit (JDK) version 8 or later
- Here are details about Java 8 client compatibility with Azure Certificate Authority.
Azure Subscription
Cognitive Services or Metrics Advisor account to use this package.

Include the package

Include the BOM file

Please include the azure-sdk-bom to your project to take dependency on the General Availability (GA) version of the library. In the following snippet, replace the {bom_version_to_target} placeholder with the version number. To learn more about the BOM, see the AZURE SDK BOM README.

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-sdk-bom</artifactId>
            <version>{bom_version_to_target}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

and then include the direct dependency in the dependencies section without the version tag as shown below.

<dependencies>
    <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-metricsadvisor</artifactId>
    </dependency>
</dependencies>

Include direct dependency

If you want to take dependency on a particular version of the library that is not present in the BOM, add the direct dependency to your project as follows. Note: This version targets Azure Metrics Advisor service API version v1.0.

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-metricsadvisor</artifactId>
    <version>1.2.3</version>
</dependency>

Create a Metrics Advisor resource

Authenticate the client

In order to interact with the Metrics Advisor service, you will need to create an instance of the Metrics Advisor client. Both the asynchronous and synchronous clients can be created by using MetricsAdvisorClientBuilder. Invoking buildClient() will create the synchronous client, while invoking buildAsyncClient will create its asynchronous counterpart.

Looking up the endpoint

You can find the endpoint for your Metric Advisor resource in the Azure Portal, or Azure CLI.

# Get the endpoint for the resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "endpoint"

Create a MetricsAdvisor client using MetricsAdvisorKeyCredential

You will need two keys to authenticate the client:

The subscription key to your Metrics Advisor resource. You can find this in the Keys and Endpoint section of your resource in the Azure portal.
The API key for your Metrics Advisor instance. You can find this on the web portal for Metrics Advisor, in API keys on the left navigation menu.

Once you have the two keys and endpoint, you can use the MetricsAdvisorKeyCredential class to authenticate the clients as follows:

Create a Metrics Advisor client using MetricsAdvisorKeyCredential

MetricsAdvisorKeyCredential credential = new MetricsAdvisorKeyCredential("subscription_key", "api_key");
MetricsAdvisorClient metricsAdvisorClient = new MetricsAdvisorClientBuilder()
    .endpoint("{endpoint}")
    .credential(credential)
    .buildClient();

Create a Metrics Administration client using MetricsAdvisorKeyCredential

MetricsAdvisorKeyCredential credential = new MetricsAdvisorKeyCredential("subscription_key", "api_key");
MetricsAdvisorAdministrationClient metricsAdvisorAdminClient =
    new MetricsAdvisorAdministrationClientBuilder()
        .endpoint("{endpoint}")
        .credential(credential)
        .buildClient();

Create a MetricsAdvisor client using Azure Service Directory

Azure SDK for Java supports an Azure Identity package, making it easy to get credentials from Microsoft identity platform.

Authentication with AAD requires some initial setup:

Add the Azure Identity package

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-identity</artifactId>
    <version>1.13.1</version>
</dependency>

Register a new Azure Active Directory application
Grant access to Metrics Advisor by assigning the "Cognitive Services User" role to your service principal.

After the setup, you can choose which type of credential from azure.identity to use. As an example, DefaultAzureCredential can be used to authenticate the client: Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

Authorization is easiest using DefaultAzureCredential. It finds the best credential to use in its running environment. For more information about using Azure Active Directory authorization with Metrics Advisor, please refer to the associated documentation.

Create a Metrics Advisor client using AAD authentication

TokenCredential credential = new DefaultAzureCredentialBuilder().build();
MetricsAdvisorClient metricsAdvisorClient = new MetricsAdvisorClientBuilder()
    .endpoint("{endpoint}")
    .credential(credential)
    .buildClient();

Create a Metrics Administration client using AAD authentication

TokenCredential credential = new DefaultAzureCredentialBuilder().build();
MetricsAdvisorAdministrationClient metricsAdvisorAdminClient =
    new MetricsAdvisorAdministrationClientBuilder()
        .endpoint("{endpoint}")
        .credential(credential)
        .buildClient();

Key concepts

MetricsAdvisorClient

MetricsAdvisorClient helps with:

Diagnose anomalies and incidents and help with root cause analysis of incidents.
Retrieve original time series data and time series data enriched by the service.
Send real time alerts through multiple notification hooks.
Adjust anomaly/incident detection using feedback to tune your model.

MetricsAdvisorAdministrationClient

MetricsAdvisorAdministrationClient allows you to

Manage data feeds
List available metrics and their detection configurations
Fine tune anomaly detection configurations
Configure anomaly alerting configurations
Manage notification hooks

Data feed

A data feed is what Metrics Advisor ingests from the user-specified data source such as Cosmos structure stream, SQL query result, and so on. It contains rows of timestamps, zero or more dimensions, one or more Metrics. Therefore, multiple metrics could share the same data source and even the same data feed.

Data Feed Metric

A metric is a quantifiable measure that is used to track and assess the status of a specific business process. It can be a combination of multiple time series values divided by dimensions, for example user count for a web vertical and en-us market.

Data Feed Dimension

A dimension is one or more categorical values of the provided data feed. The combination of those values identifies a particular univariate time series, for example: country, language, tenant, and so on.

Metric series

Metric series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Therefore, it is a sequence of discrete-time data.

Anomaly Detection Configuration

An anomaly detection configuration is a configuration supplied for a time series to identify if the data point is detected as an Anomaly. A metric can apply one or more detecting configurations. While a default detection configuration is automatically applied to each metric (named "Default"), we can tune the detection modes used on our data by creating a customized anomaly detection configuration.

Anomaly Incident

Incidents are generated for series when it has an anomaly depending on the applied Anomaly detection configurations. Metrics Advisor service groups series of anomalies within a metric into an incident.

Anomaly Alert

Anomaly Alerts can be configured to be triggered when certain anomalies are met. You can set multiple alerts with different settings. For example, you could create an anomalyAlert for anomalies with lower business impact, and another for more important alerts.

Notification Hook

A notification hook is the entry point that allows the users to subscribe to real-time alerts. These alerts are sent over the internet, using a Hook.

Examples

Add a data feed from a sample or data source
Check ingestion status
Configure anomaly detection configuration
Add hooks for receiving anomaly alerts
Configure an anomaly alert configuration
Query anomaly detection results

Add a data feed from a sample or data source

This example ingests the user specified SQLServerDataFeedSource data feed source data to the service.

DataFeed dataFeed = new DataFeed()
    .setName("dataFeedName")
    .setSource(new MySqlDataFeedSource("conn-string", "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());
}

Check ingestion status

This example checks the ingestion status of a previously provided data feed source.

String dataFeedId = "3d48er30-6e6e-4391-b78f-b00dfee1e6f5";

metricsAdvisorAdminClient.listDataFeedIngestionStatus(
    dataFeedId,
    new ListDataFeedIngestionOptions(
        OffsetDateTime.parse("2020-01-01T00:00:00Z"),
        OffsetDateTime.parse("2020-09-09T00:00:00Z"))
).forEach(dataFeedIngestionStatus -> {
    System.out.printf("Message : %s%n", dataFeedIngestionStatus.getMessage());
    System.out.printf("Timestamp value : %s%n", dataFeedIngestionStatus.getTimestamp());
    System.out.printf("Status : %s%n", dataFeedIngestionStatus.getStatus());
});

Configure anomaly detection configuration

This example demonstrates how a user can configure an anomaly detection configuration for their data.

String metricId = "3d48er30-6e6e-4391-b78f-b00dfee1e6f5";

ChangeThresholdCondition changeThresholdCondition = new ChangeThresholdCondition(
    20,
    10,
    true,
    AnomalyDetectorDirection.BOTH,
    new SuppressCondition(1, 2));

HardThresholdCondition hardThresholdCondition = new HardThresholdCondition(
    AnomalyDetectorDirection.DOWN,
    new SuppressCondition(1, 1))
    .setLowerBound(5.0);

SmartDetectionCondition smartDetectionCondition = new SmartDetectionCondition(
    10.0,
    AnomalyDetectorDirection.UP,
    new SuppressCondition(1, 2));

final AnomalyDetectionConfiguration anomalyDetectionConfiguration =
    metricsAdvisorAdminClient.createDetectionConfig(
        metricId,
        new AnomalyDetectionConfiguration("My dataPoint anomaly detection configuration")
            .setDescription("anomaly detection config description")
            .setWholeSeriesDetectionCondition(
                new MetricWholeSeriesDetectionCondition()
                    .setChangeThresholdCondition(changeThresholdCondition)
                    .setHardThresholdCondition(hardThresholdCondition)
                    .setSmartDetectionCondition(smartDetectionCondition)
                    .setConditionOperator(DetectionConditionOperator.OR))
    );

Add hooks for receiving anomaly alerts

This example creates an email hook that receives anomaly incident alerts.

NotificationHook emailNotificationHook = new EmailNotificationHook("email Hook")
    .setDescription("my email Hook")
    .setEmailsToAlert(Collections.singletonList("alertme@alertme.com"))
    .setExternalLink("https://adwiki.azurewebsites.net/articles/howto/alerts/create-hooks.html");

final NotificationHook notificationHook = metricsAdvisorAdminClient.createHook(emailNotificationHook);
EmailNotificationHook createdEmailHook = (EmailNotificationHook) notificationHook;
System.out.printf("Email Hook Id: %s%n", createdEmailHook.getId());
System.out.printf("Email Hook name: %s%n", createdEmailHook.getName());
System.out.printf("Email Hook description: %s%n", createdEmailHook.getDescription());
System.out.printf("Email Hook external Link: %s%n", createdEmailHook.getExternalLink());
System.out.printf("Email Hook emails to alert: %s%n",
    String.join(",", createdEmailHook.getEmailsToAlert()));

Configure an anomaly alert configuration

This example demonstrates how a user can configure an alerting configuration for detected anomalies in their data.

String detectionConfigurationId1 = "9ol48er30-6e6e-4391-b78f-b00dfee1e6f5";
String detectionConfigurationId2 = "3e58er30-6e6e-4391-b78f-b00dfee1e6f5";
String hookId1 = "5f48er30-6e6e-4391-b78f-b00dfee1e6f5";
String hookId2 = "8i48er30-6e6e-4391-b78f-b00dfee1e6f5";

final AnomalyAlertConfiguration anomalyAlertConfiguration
    = metricsAdvisorAdminClient.createAlertConfig(
        new AnomalyAlertConfiguration("My anomaly alert config name")
            .setDescription("alert config description")
            .setMetricAlertConfigurations(
                Arrays.asList(
                    new MetricAlertConfiguration(detectionConfigurationId1,
                        MetricAnomalyAlertScope.forWholeSeries()),
                    new MetricAlertConfiguration(detectionConfigurationId2,
                        MetricAnomalyAlertScope.forWholeSeries())
                        .setAlertConditions(new MetricAnomalyAlertConditions()
                            .setSeverityRangeCondition(new SeverityCondition(AnomalySeverity.HIGH,
                                AnomalySeverity.HIGH)))
                ))
            .setCrossMetricsOperator(MetricAlertConfigurationsOperator.AND)
            .setHookIdsToAlert(Arrays.asList(hookId1, hookId2)));

Query anomaly detection results

This example demonstrates how a user can query alerts triggered for an anomaly detection configuration and get anomalies for that anomalyAlert.

String alertConfigurationId = "9ol48er30-6e6e-4391-b78f-b00dfee1e6f5";
final OffsetDateTime startTime = OffsetDateTime.parse("2020-01-01T00:00:00Z");
final OffsetDateTime endTime = OffsetDateTime.parse("2020-09-09T00:00:00Z");
metricsAdvisorClient.listAlerts(
    alertConfigurationId,
        startTime, endTime)
    .forEach(alert -> {
        System.out.printf("AnomalyAlert Id: %s%n", alert.getId());
        System.out.printf("AnomalyAlert created on: %s%n", alert.getCreatedTime());

        // List anomalies for returned alerts
        metricsAdvisorClient.listAnomaliesForAlert(
            alertConfigurationId,
            alert.getId())
            .forEach(anomaly -> {
                System.out.printf("DataPoint Anomaly was created on: %s%n", anomaly.getCreatedTime());
                System.out.printf("DataPoint Anomaly severity: %s%n", anomaly.getSeverity().toString());
                System.out.printf("DataPoint Anomaly status: %s%n", anomaly.getStatus());
                System.out.printf("DataPoint Anomaly related series key: %s%n", anomaly.getSeriesKey().asMap());
            });
    });

Troubleshooting

General

Metrics Advisor clients raises HttpResponseException [exceptions][http_response_exception]. For example, if you try to provide a non existing feedback Id an HttpResponseException would be raised with an error indicating the failure cause. In the following code snippet, the error is handled gracefully by catching the exception and display the additional information about the error.

try {
    metricsAdvisorClient.getFeedback("non_existing_feedback_id");
} catch (HttpResponseException e) {
    System.out.println(e.getMessage());
}

Enable client logging

Azure SDKs for Java offer a consistent logging story to help aid in troubleshooting application errors and expedite their resolution. The logs produced will capture the flow of an application before reaching the terminal state to help locate the root issue. View the logging wiki for guidance about enabling logging.

Default HTTP Client

All client libraries by default use the Netty HTTP client. Adding the above dependency will automatically configure the client library to use the Netty HTTP client. Configuring or changing the HTTP client is detailed in the HTTP clients wiki.

Next steps

For more details see the samples README.

Async APIs

All the examples shown so far have been using synchronous APIs, but we provide full support for async APIs as well. You'll need to use MetricsAdvisorAsyncClient

MetricsAdvisorKeyCredential credential = new MetricsAdvisorKeyCredential("subscription_key", "api_key");
MetricsAdvisorAsyncClient metricsAdvisorAsyncClient = new MetricsAdvisorClientBuilder()
    .credential(credential)
    .endpoint("{endpoint}")
    .buildAsyncClient();

Additional documentation

For more extensive documentation on Azure Cognitive Services Metrics Advisor, see the Metrics Advisor documentation.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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