azure.ai.metricsadvisor package¶

class azure.ai.metricsadvisor.MetricsAdvisorClient(endpoint: str, credential: azure.ai.metricsadvisor._metrics_advisor_key_credential.MetricsAdvisorKeyCredential, **kwargs: Any)[source]¶

Represents an client that calls restful API of Azure Metrics Advisor service.

Parameters

endpoint (str) – Supported Cognitive Services endpoints (protocol and hostname, for example: https://<resource-name>.cognitiveservices.azure.com).
credential (MetricsAdvisorKeyCredential) – An instance of ~azure.ai.metricsadvisor.MetricsAdvisorKeyCredential. Requires both subscription key and API key.

Keyword Arguments

pipeline (Pipeline) – If omitted, the standard pipeline is used.
transport (HttpTransport) – If omitted, the standard pipeline is used.
policies (list[HTTPPolicy]) – If omitted, the standard pipeline is used.

add_feedback(feedback: Union[AnomalyFeedback, ChangePointFeedback, CommentFeedback, PeriodFeedback], **kwargs: Any) → None [source]¶

Create a new metric feedback.

Parameters: feedback (AnomalyFeedback or ChangePointFeedback or CommentFeedback or PeriodFeedback) – metric feedback.
Raises: HttpResponseError –

Example:

Add new feedback.¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorClient
from azure.ai.metricsadvisor.models import AnomalyFeedback, ChangePointFeedback, CommentFeedback, PeriodFeedback

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

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

anomaly_feedback = AnomalyFeedback(metric_id=metric_id,
                                   dimension_key={"Dim1": "Common Lime"},
                                   start_time=datetime.datetime(2020, 8, 5),
                                   end_time=datetime.datetime(2020, 8, 7),
                                   value="NotAnomaly")
client.add_feedback(anomaly_feedback)

change_point_feedback = ChangePointFeedback(metric_id=metric_id,
                                            dimension_key={"Dim1": "Common Lime"},
                                            start_time=datetime.datetime(2020, 8, 5),
                                            end_time=datetime.datetime(2020, 8, 7),
                                            value="NotChangePoint")
client.add_feedback(change_point_feedback)

comment_feedback = CommentFeedback(metric_id=metric_id,
                                   dimension_key={"Dim1": "Common Lime"},
                                   start_time=datetime.datetime(2020, 8, 5),
                                   end_time=datetime.datetime(2020, 8, 7),
                                   value="comment")
client.add_feedback(comment_feedback)

period_feedback = PeriodFeedback(metric_id=metric_id,
                                 dimension_key={"Dim1": "Common Lime"},
                                 start_time=datetime.datetime(2020, 8, 5),
                                 end_time=datetime.datetime(2020, 8, 7),
                                 period_type="AssignValue",
                                 value=2)
client.add_feedback(period_feedback)

close() → None [source]¶: Close the MetricsAdvisorClient session.

get_feedback(feedback_id: str, **kwargs: Any) → Union[AnomalyFeedback, ChangePointFeedback, CommentFeedback, PeriodFeedback][source]¶

Get a metric feedback by its id.

Parameters: feedback_id (str) – the id of the feedback.
Returns: The feedback object
Return type: AnomalyFeedback or ChangePointFeedback or CommentFeedback or PeriodFeedback
Raises: HttpResponseError –

Example:

Get a metric feedback by its id.¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorClient

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

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

result = client.get_feedback(feedback_id=feedback_id)
print("Type: {}; Id: {}".format(result.feedback_type, result.id))

list_alerts_for_alert_configuration(alert_configuration_id: str, start_time: datetime.datetime, end_time: datetime.datetime, time_mode: Union[str, TimeMode], **kwargs: Any) → ItemPaged[Alert][source]¶

Query alerts under anomaly alert configuration.

Parameters

alert_configuration_id (str) – anomaly alert configuration unique id.
start_time (datetime) – start time.
end_time (datetime) – end time.
time_mode (str or TimeMode) – time mode. Possible values include: “AnomalyTime”, “CreatedTime”, “ModifiedTime”.

Keyword Arguments

skip (int) –

Returns

Alerts under anomaly alert configuration.

Return type

ItemPaged[Alert]

Raises

HttpResponseError –

Example:

Query anomaly detection results.¶

import datetime
from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorClient

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

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

results = client.list_alerts_for_alert_configuration(
    alert_configuration_id=alert_config_id,
    start_time=datetime.datetime(2020, 1, 1),
    end_time=datetime.datetime(2020, 9, 9),
    time_mode="AnomalyTime",
)
for result in results:
    print("Alert id: {}".format(result.id))
    print("Create on: {}".format(result.created_on))
return results

list_anomalies_for_alert(alert_configuration_id: str, alert_id: str, **kwargs: Any) → ItemPaged[Anomaly][source]¶

Query anomalies under a specific alert.

Parameters

alert_configuration_id (str) – anomaly alert configuration unique id.
alert_id (str) – alert id.

Keyword Arguments

skip (int) –

Returns

Anomalies under a specific alert.

Return type

ItemPaged[Anomaly]

Raises

HttpResponseError –

Example:

Query anomalies using alert id.¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorClient

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

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

results = client.list_anomalies_for_alert(
        alert_configuration_id=alert_config_id,
        alert_id=alert_id,
    )
for result in results:
    print("Create on: {}".format(result.created_on))
    print("Severity: {}".format(result.severity))
    print("Status: {}".format(result.status))

list_anomalies_for_detection_configuration(detection_configuration_id: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[Anomaly][source]¶

Query anomalies under anomaly detection configuration.

Parameters

detection_configuration_id (str) – anomaly detection configuration unique id.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.

Keyword Arguments

skip (int) –
filter (DetectionAnomalyFilterCondition) –

Returns

Anomalies under anomaly detection configuration.

Return type

ItemPaged[Anomaly]

Raises

HttpResponseError –

list_dimension_values_for_detection_configuration(detection_configuration_id: str, dimension_name: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[str][source]¶

Query dimension values of anomalies.

Parameters

detection_configuration_id (str) – anomaly detection configuration unique id.
dimension_name (str) – dimension to query.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.

Keyword Arguments

skip (int) –
dimension_name (str) – The dimension name to query.

Returns

Dimension values of anomalies.

Return type

ItemPaged[str]

Raises

HttpResponseError –

list_feedbacks(metric_id: str, **kwargs: Any) → ItemPaged[Union[AnomalyFeedback, ChangePointFeedback, CommentFeedback, PeriodFeedback]][source]¶

List feedback on the given metric.

Parameters

metric_id (str) – filter feedbacks by metric id

Keyword Arguments

skip (int) –
dimension_key (dict[str, str]) – filter specfic dimension name and values
feedback_type (str or FeedbackType) – filter feedbacks by type. Possible values include: “Anomaly”, “ChangePoint”, “Period”, “Comment”.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.
time_mode (str or FeedbackQueryTimeMode) – time mode to filter feedback. Possible values include: “MetricTimestamp”, “FeedbackCreatedTime”.

Returns

Pageable list of MetricFeedback

Return type

ItemPaged[ Union[AnomalyFeedback, ChangePointFeedback, CommentFeedback, PeriodFeedback]]

Raises

HttpResponseError –

Example:

List feedback on the given metric.¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorClient

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

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

results = client.list_feedbacks(metric_id=metric_id)
for result in results:
    print("Type: {}; Id: {}".format(result.feedback_type, result.id))

list_incident_root_causes(detection_configuration_id: str, incident_id: str, **kwargs: Any) → ItemPaged[IncidentRootCause][source]¶

Query root cause for incident.

Parameters

detection_configuration_id (str) – anomaly detection configuration unique id.
incident_id (str) – incident id.

Returns

Pageable of root cause for incident

Return type

ItemPaged[IncidentRootCause]

Raises

HttpResponseError –

list_incidents_for_alert(alert_configuration_id: str, alert_id: str, **kwargs: Any) → ItemPaged[Incident][source]¶

Query incidents under a specific alert.

Parameters

alert_configuration_id (str) – anomaly alerting configuration unique id.
alert_id (str) – alert id.

Keyword Arguments

skip (int) –

Returns

Incidents under a specific alert.

Return type

ItemPaged[Incident]

Raises

HttpResponseError –

list_incidents_for_detection_configuration(detection_configuration_id: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[Incident][source]¶

Query incidents under a specific alert.

Parameters

detection_configuration_id (str) – anomaly detection configuration unique id.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.

Keyword Arguments

filter (DetectionIncidentFilterCondition) –

Returns

Incidents under a specific alert.

Return type

ItemPaged[Incident]

Raises

HttpResponseError –

list_metric_dimension_values(metric_id: str, dimension_name: str, **kwargs: Any) → ItemPaged[str][source]¶

List dimension from certain metric.

Parameters

metric_id (str) – metric unique id.
dimension_name (str) – the dimension name

Keyword Arguments

skip (int) –
dimension_value_filter (str) – dimension value to be filtered.

Returns

Dimension from certain metric.

Return type

ItemPaged[str]

Raises

HttpResponseError –

list_metric_enriched_series_data(detection_configuration_id: str, series: Union[List[SeriesIdentity], List[Dict[str, str]]], start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[SeriesResult][source]¶

Query series enriched by anomaly detection.

Parameters

detection_configuration_id (str) – anomaly alerting configuration unique id.
series (SeriesIdentity or list[dict[str, str]]) – List of dimensions specified for series.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.

Returns

Pageable of SeriesResult

Return type

ItemPaged[SeriesResult]

Raises

HttpResponseError –

list_metric_enrichment_status(metric_id: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[EnrichmentStatus][source]¶

Query anomaly detection status.

Parameters

metric_id (str) – filter feedbacks by metric id.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.

Keyword Arguments

skip (int) –

Returns

Anomaly detection status.

Return type

ItemPaged[EnrichmentStatus]

Raises

HttpResponseError –

list_metric_series_definitions(metric_id: str, active_since: datetime.datetime, **kwargs: Any) → ItemPaged[MetricSeriesDefinition][source]¶

List series (dimension combinations) from metric.

Parameters

metric_id (str) – metric unique id.
active_since (datetime) – Required. query series ingested after this time, the format should be yyyy-MM-ddTHH:mm:ssZ.

Keyword Arguments

skip (int) –
active_since (datetime) – query series ingested after this time, the format should be yyyy-MM-ddTHH:mm:ssZ.
dimension_filter (dict[str, list[str]]) – filter specfic dimension name and values.

Returns

Series (dimension combinations) from metric.

Return type

ItemPaged[MetricSeriesDefinition]

Raises

HttpResponseError –

list_metrics_series_data(metric_id: str, start_time: datetime.datetime, end_time: datetime.datetime, series_to_filter: List[Dict[str, str]], **kwargs: Any) → ItemPaged[MetricSeriesData][source]¶

Get time series data from metric.

Parameters

metric_id (str) – metric unique id.
start_time (datetime) – start time filter under chosen time mode.
end_time (datetime) – end time filter under chosen time mode.
series_to_filter (list[dict[str, str]]) – query specific series.

Returns

Time series data from metric.

Return type

ItemPaged[MetricSeriesData]

Raises

HttpResponseError –

class azure.ai.metricsadvisor.MetricsAdvisorAdministrationClient(endpoint: str, credential: MetricsAdvisorKeyCredential, **kwargs: Any)[source]¶

MetricsAdvisorAdministrationClient is used to create and manage data feeds.

Parameters

endpoint (str) – Supported Cognitive Services endpoints (protocol and hostname, for example: https://<resource-name>.cognitiveservices.azure.com).
credential (MetricsAdvisorKeyCredential) – An instance of ~azure.ai.metricsadvisor.MetricsAdvisorKeyCredential. Requires both subscription key and API key.

Example:

Authenticate MetricsAdvisorAdministrationClient with a MetricsAdvisorKeyCredential¶

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))

close() → None [source]¶: Close the MetricsAdvisorAdministrationClient session.

create_anomaly_alert_configuration(name: str, metric_alert_configurations: List[MetricAlertConfiguration], hook_ids: List[str], **kwargs: Any) → AnomalyAlertConfiguration[source]¶

Create an anomaly alert configuration.

Parameters

name (str) – Name for the anomaly alert configuration.
metric_alert_configurations (list[MetricAlertConfiguration]) – Anomaly alert configurations.
hook_ids (list[str]) – Unique hook IDs.

Keyword Arguments

cross_metrics_operator (str or MetricAnomalyAlertConfigurationsOperator) – Cross metrics operator should be specified when setting up multiple metric alert configurations. Possible values include: “AND”, “OR”, “XOR”.
description (str) – Anomaly alert configuration description.

Returns

AnomalyAlertConfiguration

Return type

AnomalyAlertConfiguration

Raises

HttpResponseError –

Example:

Create an anomaly alert configuration¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import (
    MetricAlertConfiguration,
    MetricAnomalyAlertScope,
    TopNGroupScope,
    MetricAnomalyAlertConditions,
    SeverityCondition,
    MetricBoundaryCondition,
    MetricAnomalyAlertSnoozeCondition
)
service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
subscription_key = os.getenv("METRICS_ADVISOR_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")
anomaly_detection_configuration_id = os.getenv("METRICS_ADVISOR_DETECTION_CONFIGURATION_ID")
hook_id = os.getenv("METRICS_ADVISOR_HOOK_ID")

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

alert_config = client.create_anomaly_alert_configuration(
    name="my alert config",
    description="alert config description",
    cross_metrics_operator="AND",
    metric_alert_configurations=[
        MetricAlertConfiguration(
            detection_configuration_id=anomaly_detection_configuration_id,
            alert_scope=MetricAnomalyAlertScope(
                scope_type="WholeSeries"
            ),
            alert_conditions=MetricAnomalyAlertConditions(
                severity_condition=SeverityCondition(
                    min_alert_severity="Low",
                    max_alert_severity="High"
                )
            )
        ),
        MetricAlertConfiguration(
            detection_configuration_id=anomaly_detection_configuration_id,
            alert_scope=MetricAnomalyAlertScope(
                scope_type="TopN",
                top_n_group_in_scope=TopNGroupScope(
                    top=10,
                    period=5,
                    min_top_count=5
                )
            ),
            alert_conditions=MetricAnomalyAlertConditions(
                metric_boundary_condition=MetricBoundaryCondition(
                    direction="Up",
                    upper=50
                )
            ),
            alert_snooze_condition=MetricAnomalyAlertSnoozeCondition(
                auto_snooze=2,
                snooze_scope="Metric",
                only_for_successive=True
            )
        ),
    ],
    hook_ids=[hook_id]
)

return alert_config

create_data_feed(name: str, source: DataFeedSourceUnion, granularity: Union[str, DataFeedGranularityType, DataFeedGranularity], schema: Union[List[str], DataFeedSchema], ingestion_settings: Union[datetime.datetime, DataFeedIngestionSettings], **kwargs: Any) → DataFeed[source]¶

Create a new data feed.

Parameters

name (str) – Name for the data feed.
source (Union[AzureApplicationInsightsDataFeed, AzureBlobDataFeed, AzureCosmosDBDataFeed, AzureDataExplorerDataFeed, AzureDataLakeStorageGen2DataFeed, AzureTableDataFeed, HttpRequestDataFeed, InfluxDBDataFeed, MySqlDataFeed, PostgreSqlDataFeed, SQLServerDataFeed, MongoDBDataFeed, ElasticsearchDataFeed]) – The source of the data feed
granularity (Union[str, DataFeedGranularityType, DataFeedGranularity]) – Granularity type. If using custom granularity, you must instantiate a DataFeedGranularity.
schema (Union[list[str], DataFeedSchema]) – Data feed schema. Can be passed as a list of metric names as strings or as a DataFeedSchema object if additional configuration is needed.
ingestion_settings (Union[datetime, DataFeedIngestionSettings]) – The data feed ingestions settings. Can be passed as a datetime to use for the ingestion begin time or as a DataFeedIngestionSettings object if additional configuration is needed.

Keyword Arguments

options (DataFeedOptions) – Data feed options.

Returns

DataFeed

Return type

DataFeed

Raises

HttpResponseError –

Example:

Create a data feed¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import (
    SQLServerDataFeed,
    DataFeedSchema,
    Metric,
    Dimension,
    DataFeedOptions,
    DataFeedRollupSettings,
    DataFeedMissingDataPointFillSettings
)

service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
subscription_key = os.getenv("METRICS_ADVISOR_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")
sql_server_connection_string = os.getenv("METRICS_ADVISOR_SQL_SERVER_CONNECTION_STRING")
query = os.getenv("METRICS_ADVISOR_SQL_SERVER_QUERY")

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

data_feed = client.create_data_feed(
    name="My data feed",
    source=SQLServerDataFeed(
        connection_string=sql_server_connection_string,
        query=query,
    ),
    granularity="Daily",
    schema=DataFeedSchema(
        metrics=[
            Metric(name="cost", display_name="Cost"),
            Metric(name="revenue", display_name="Revenue")
        ],
        dimensions=[
            Dimension(name="category", display_name="Category"),
            Dimension(name="city", display_name="City")
        ],
        timestamp_column="Timestamp"
    ),
    ingestion_settings=datetime.datetime(2019, 10, 1),
    options=DataFeedOptions(
        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

create_hook(name: str, hook: Union[EmailHook, WebHook], **kwargs: Any) → Union[Hook, EmailHook, WebHook][source]¶

Create a new email or web hook.

Parameters

name (str) – The name for the hook.
hook (Union[EmailHook, WebHook]) – An email or web hook

Returns

EmailHook or WebHook

Return type

Union[Hook, EmailHook, WebHook]

Raises

HttpResponseError –

Example:

Create a hook¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import EmailHook

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))

hook = client.create_hook(
    name="email hook",
    hook=EmailHook(
        description="my email hook",
        emails_to_alert=["alertme@alertme.com"],
        external_link="https://adwiki.azurewebsites.net/articles/howto/alerts/create-hooks.html"
    )
)

return hook

create_metric_anomaly_detection_configuration(name: str, metric_id: str, whole_series_detection_condition: MetricDetectionCondition, **kwargs: Any) → AnomalyDetectionConfiguration[source]¶

Create anomaly detection configuration.

Parameters

name (str) – The name for the anomaly detection configuration
metric_id (str) – Required. metric unique id.
whole_series_detection_condition (MetricDetectionCondition) – Required. Conditions to detect anomalies in all time series of a metric.

Keyword Arguments

description (str) – anomaly detection configuration description.
series_group_detection_conditions (list[MetricSeriesGroupDetectionCondition]) – detection configuration for series group.
series_detection_conditions (list[MetricSingleSeriesDetectionCondition]) – detection configuration for specific series.

Returns

AnomalyDetectionConfiguration

Return type

AnomalyDetectionConfiguration

Raises

HttpResponseError –

Example:

Create an anomaly detection configuration¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import (
    ChangeThresholdCondition,
    HardThresholdCondition,
    SmartDetectionCondition,
    SuppressCondition,
    MetricDetectionCondition,
)

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

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

change_threshold_condition = ChangeThresholdCondition(
    anomaly_detector_direction="Both",
    change_percentage=20,
    shift_point=10,
    within_range=True,
    suppress_condition=SuppressCondition(
        min_number=5,
        min_ratio=2
    )
)
hard_threshold_condition = HardThresholdCondition(
    anomaly_detector_direction="Up",
    upper_bound=100,
    suppress_condition=SuppressCondition(
        min_number=2,
        min_ratio=2
    )
)
smart_detection_condition = SmartDetectionCondition(
    anomaly_detector_direction="Up",
    sensitivity=10,
    suppress_condition=SuppressCondition(
        min_number=2,
        min_ratio=2
    )
)

detection_config = client.create_metric_anomaly_detection_configuration(
    name="my_detection_config",
    metric_id=metric_id,
    description="anomaly detection config for metric",
    whole_series_detection_condition=MetricDetectionCondition(
        cross_conditions_operator="OR",
        change_threshold_condition=change_threshold_condition,
        hard_threshold_condition=hard_threshold_condition,
        smart_detection_condition=smart_detection_condition
    )
)

return detection_config

delete_anomaly_alert_configuration(alert_configuration_id: str, **kwargs: Any) → None [source]¶

Delete an anomaly alert configuration by its ID.

Parameters: alert_configuration_id (str) – anomaly alert configuration unique id.
Returns: None
Return type: None
Raises: HttpResponseError –

Example:

Delete an anomaly alert configuration by its 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_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")

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

client.delete_anomaly_alert_configuration(alert_config_id)

try:
    client.get_anomaly_alert_configuration(alert_config_id)
except ResourceNotFoundError:
    print("Alert configuration successfully deleted.")

delete_data_feed(data_feed_id: str, **kwargs: Any) → None [source]¶

Delete a data feed by its ID.

Parameters: data_feed_id (str) – The data feed unique id.
Returns: None
Return type: None
Raises: HttpResponseError –

Example:

Delete a data feed by its 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_SUBSCRIPTION_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.")

delete_hook(hook_id: str, **kwargs: Any) → None [source]¶

Delete a web or email hook by its ID.

Parameters: hook_id (str) – Hook unique ID.
Returns: None
Return type: None
Raises: HttpResponseError –

Example:

Delete a hook by its 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_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")

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

client.delete_hook(hook_id)

try:
    client.get_hook(hook_id)
except ResourceNotFoundError:
    print("Hook successfully deleted.")

delete_metric_anomaly_detection_configuration(detection_configuration_id: str, **kwargs: Any) → None [source]¶

Delete an anomaly detection configuration by its ID.

Parameters: detection_configuration_id (str) – anomaly detection configuration unique id.
Returns: None
Return type: None
Raises: HttpResponseError –

Example:

Delete an anomaly detection configuration by its 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_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")

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

client.delete_metric_anomaly_detection_configuration(detection_config_id)

try:
    client.get_metric_anomaly_detection_configuration(detection_config_id)
except ResourceNotFoundError:
    print("Detection configuration successfully deleted.")

get_anomaly_alert_configuration(alert_configuration_id: str, **kwargs: Any) → azure.ai.metricsadvisor.models._models.AnomalyAlertConfiguration[source]¶

Get a single anomaly alert configuration.

Parameters: alert_configuration_id (str) – anomaly alert configuration unique id.
Returns: AnomalyAlertConfiguration
Return type: AnomalyAlertConfiguration
Raises: HttpResponseError –

Example:

Get a single anomaly alert configuration by its ID¶

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))

config = client.get_anomaly_alert_configuration(alert_config_id)

print("Alert config ID: {}".format(config.id))
print("Alert config name: {}".format(config.name))
print("Description: {}".format(config.description))
print("Ids of hooks associated with alert: {}".format(config.hook_ids))
print("Use {} operator for multiple alert conditions\n".format(config.cross_metrics_operator))

print("Alert uses detection configuration ID: {}".format(
    config.metric_alert_configurations[0].detection_configuration_id
))
print("Alert scope type: {}".format(config.metric_alert_configurations[0].alert_scope.scope_type))
print("Alert severity condition: min- {}, max- {}".format(
    config.metric_alert_configurations[0].alert_conditions.severity_condition.min_alert_severity,
    config.metric_alert_configurations[0].alert_conditions.severity_condition.max_alert_severity,
))
print("\nAlert uses detection configuration ID: {}".format(
    config.metric_alert_configurations[1].detection_configuration_id
))
print("Alert scope type: {}".format(config.metric_alert_configurations[1].alert_scope.scope_type))
print("Top N: {}".format(config.metric_alert_configurations[1].alert_scope.top_n_group_in_scope.top))
print("Point count used to look back: {}".format(
    config.metric_alert_configurations[1].alert_scope.top_n_group_in_scope.period
))
print("Min top count: {}".format(
    config.metric_alert_configurations[1].alert_scope.top_n_group_in_scope.min_top_count
))
print("Alert metric boundary condition direction: {}, upper bound: {}".format(
    config.metric_alert_configurations[1].alert_conditions.metric_boundary_condition.direction,
    config.metric_alert_configurations[1].alert_conditions.metric_boundary_condition.upper,
))
print("Alert snooze condition, snooze point count: {}".format(
    config.metric_alert_configurations[1].alert_snooze_condition.auto_snooze,
))
print("Alert snooze scope: {}".format(
    config.metric_alert_configurations[1].alert_snooze_condition.snooze_scope,
))
print("Snooze only for successive anomalies?: {}".format(
    config.metric_alert_configurations[1].alert_snooze_condition.only_for_successive,
))

get_data_feed(data_feed_id: str, **kwargs: Any) → azure.ai.metricsadvisor.models._models.DataFeed[source]¶

Get a data feed by its id.

Parameters: data_feed_id (str) – The data feed unique id.
Returns: DataFeed
Return type: DataFeed
Raises: HttpResponseError –

Example:

Get a single data feed by its ID¶

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_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.options.data_feed_description))
print("Data feed rollup type: {}".format(data_feed.options.rollup_settings.rollup_type))
print("Data feed rollup method: {}".format(data_feed.options.rollup_settings.rollup_method))
print("Data feed fill setting: {}".format(data_feed.options.missing_data_point_fill_settings.fill_type))
print("Data feed access mode: {}".format(data_feed.options.access_mode))

get_data_feed_ingestion_progress(data_feed_id: str, **kwargs: Any) → azure.ai.metricsadvisor.models._models.DataFeedIngestionProgress[source]¶

Get last successful data ingestion job timestamp by data feed.

Parameters: data_feed_id (str) – The data feed unique id.
Returns: DataFeedIngestionProgress, containing latest_success_timestamp and latest_active_timestamp
Return type: DataFeedIngestionProgress
Raises: HttpResponseError –

Example:

Get the progress of data feed ingestion¶

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")
data_feed_id = os.getenv("METRICS_ADVISOR_DATA_FEED_ID")

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

progress = client.get_data_feed_ingestion_progress(data_feed_id)

print("Lastest active timestamp: {}".format(progress.latest_active_timestamp))
print("Latest successful timestamp: {}".format(progress.latest_success_timestamp))

get_hook(hook_id: str, **kwargs: Any) → Union[Hook, EmailHook, WebHook][source]¶

Get a web or email hook by its id.

Parameters: hook_id (str) – Hook unique ID.
Returns: EmailHook or Webhook
Return type: Union[Hook, EmailHook, WebHook]
Raises: HttpResponseError –

Example:

Get a hook by its ID¶

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))

hook = client.get_hook(hook_id)

print("Hook name: {}".format(hook.name))
print("Description: {}".format(hook.description))
print("Emails to alert: {}".format(hook.emails_to_alert))
print("External link: {}".format(hook.external_link))
print("Admins: {}".format(hook.admins))

get_metric_anomaly_detection_configuration(detection_configuration_id: str, **kwargs: Any) → azure.ai.metricsadvisor.models._models.AnomalyDetectionConfiguration[source]¶

Get a single anomaly detection configuration.

Parameters: detection_configuration_id (str) – anomaly detection configuration unique id.
Returns: AnomalyDetectionConfiguration
Return type: AnomalyDetectionConfiguration
Raises: HttpResponseError –

Example:

Get a single anomaly detection configuration by its ID¶

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))

config = client.get_metric_anomaly_detection_configuration(detection_config_id)

print("Detection config name: {}".format(config.name))
print("Description: {}".format(config.description))
print("Metric ID: {}".format(config.metric_id))

print("\nDetection conditions specified for configuration...")
print("\nWhole Series Detection Conditions:\n")
conditions = config.whole_series_detection_condition

print("Use {} operator for multiple detection conditions".format(conditions.cross_conditions_operator))

print("Smart Detection Condition:")
print("- Sensitivity: {}".format(conditions.smart_detection_condition.sensitivity))
print("- Detection direction: {}".format(conditions.smart_detection_condition.anomaly_detector_direction))
print("- Suppress conditions: minimum number: {}; minimum ratio: {}".format(
    conditions.smart_detection_condition.suppress_condition.min_number,
    conditions.smart_detection_condition.suppress_condition.min_ratio
))

print("Hard Threshold Condition:")
print("- Lower bound: {}".format(conditions.hard_threshold_condition.lower_bound))
print("- Upper bound: {}".format(conditions.hard_threshold_condition.upper_bound))
print("- Detection direction: {}".format(conditions.smart_detection_condition.anomaly_detector_direction))
print("- Suppress conditions: minimum number: {}; minimum ratio: {}".format(
    conditions.smart_detection_condition.suppress_condition.min_number,
    conditions.smart_detection_condition.suppress_condition.min_ratio
))

print("Change Threshold Condition:")
print("- Change percentage: {}".format(conditions.change_threshold_condition.change_percentage))
print("- Shift point: {}".format(conditions.change_threshold_condition.shift_point))
print("- Detect anomaly if within range: {}".format(conditions.change_threshold_condition.within_range))
print("- Detection direction: {}".format(conditions.smart_detection_condition.anomaly_detector_direction))
print("- Suppress conditions: minimum number: {}; minimum ratio: {}".format(
    conditions.smart_detection_condition.suppress_condition.min_number,
    conditions.smart_detection_condition.suppress_condition.min_ratio
))

list_anomaly_alert_configurations(detection_configuration_id: str, **kwargs: Any) → ItemPaged[AnomalyAlertConfiguration][source]¶

Query all anomaly alert configurations for specific anomaly detection configuration.

Parameters: detection_configuration_id (str) – anomaly detection configuration unique id.
Returns: Pageable of AnomalyAlertConfiguration
Return type: ItemPaged[AnomalyAlertConfiguration]
Raises: HttpResponseError –

Example:

List all anomaly alert configurations for specific anomaly detection configuration¶

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")
anomaly_detection_configuration_id = os.getenv("METRICS_ADVISOR_DETECTION_CONFIGURATION_ID")

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

configs = client.list_anomaly_alert_configurations(anomaly_detection_configuration_id)
for config in configs:
    print("Alert config name: {}".format(config.name))
    print("Alert description: {}".format(config.description))
    print("Ids of hooks associated with alert: {}\n".format(config.hook_ids))

list_data_feed_ingestion_status(data_feed_id: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → ItemPaged[DataFeedIngestionStatus][source]¶

Get data ingestion status by data feed.

Parameters

data_feed_id (str) – The data feed unique id.
start_time (datetime) – Required. the start point of time range to query data ingestion status.
end_time (datetime) – Required. the end point of time range to query data ingestion status.

Keyword Arguments

skip (int) –

Returns

Pageable of DataFeedIngestionStatus

Return type

ItemPaged[DataFeedIngestionStatus]

Raises

HttpResponseError –

Example:

List the data feed ingestion statuses by data feed ID¶

import datetime
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")
data_feed_id = os.getenv("METRICS_ADVISOR_DATA_FEED_ID")

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

ingestion_status = client.list_data_feed_ingestion_status(
    data_feed_id,
    datetime.datetime(2020, 9, 20),
    datetime.datetime(2020, 9, 25)
)
for status in ingestion_status:
    print("Timestamp: {}".format(status.timestamp))
    print("Status: {}".format(status.status))
    print("Message: {}\n".format(status.message))

list_data_feeds(**kwargs: Any) → ItemPaged[DataFeed][source]¶

List all data feeds.

Keyword Arguments

data_feed_name (str) – filter data feed by its name.
data_source_type (str or DataSourceType) – filter data feed by its source type.
granularity_type (str or DataFeedGranularityType) – filter data feed by its granularity.
status (str or DataFeedStatus) – filter data feed by its status.
creator (str) – filter data feed by its creator.
skip (int) –

Returns

Pageable of DataFeed

Return type

ItemPaged[DataFeed]

Raises

HttpResponseError –

Example:

List data feeds under an account.¶

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)

list_hooks(**kwargs: Any) → ItemPaged[Union[Hook, EmailHook, WebHook]][source]¶

List all hooks.

Keyword Arguments

hook_name (str) – filter hook by its name.
skip (int) –

Returns

Pageable containing EmailHook and WebHook

Return type

ItemPaged[Union[Hook, EmailHook, WebHook]]

Raises

HttpResponseError –

Example:

List all of the hooks under the account¶

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))

hooks = client.list_hooks()
for hook in hooks:
    print("Hook type: {}".format(hook.hook_type))
    print("Hook name: {}".format(hook.name))
    print("Description: {}\n".format(hook.description))

list_metric_anomaly_detection_configurations(metric_id: str, **kwargs: Any) → ItemPaged[AnomalyDetectionConfiguration][source]¶

Query all anomaly detection configurations for specific metric.

Parameters: metric_id (str) – metric unique id.
Returns: Pageable of AnomalyDetectionConfiguration
Return type: ItemPaged[AnomalyDetectionConfiguration]
Raises: HttpResponseError –

Example:

List all anomaly detection configurations for a specific metric¶

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")
metric_id = os.getenv("METRICS_ADVISOR_METRIC_ID")

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

configs = client.list_metric_anomaly_detection_configurations(metric_id=metric_id)
for config in configs:
    print("Detection config name: {}".format(config.name))
    print("Description: {}".format(config.description))
    print("Metric ID: {}\n".format(config.metric_id))

refresh_data_feed_ingestion(data_feed_id: str, start_time: datetime.datetime, end_time: datetime.datetime, **kwargs: Any) → None [source]¶

Refreshes data ingestion by data feed to backfill data.

Parameters

data_feed_id (str) – The data feed unique id.
start_time (datetime) – The start point of time range to refresh data ingestion.
end_time (datetime) – The end point of time range to refresh data ingestion.

Returns

None

Return type

None

Raises

HttpResponseError –

Example:

Refresh data feed ingestion over a period of time¶

import datetime
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")
data_feed_id = os.getenv("METRICS_ADVISOR_DATA_FEED_ID")

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

client.refresh_data_feed_ingestion(
    data_feed_id,
    datetime.datetime(2020, 9, 20),
    datetime.datetime(2020, 9, 25)
)

update_anomaly_alert_configuration(alert_configuration: Union[str, azure.ai.metricsadvisor.models._models.AnomalyAlertConfiguration], **kwargs: Any) → azure.ai.metricsadvisor.models._models.AnomalyAlertConfiguration[source]¶

Update anomaly alerting configuration. Either pass the entire AnomalyAlertConfiguration object with the chosen updates or the ID to your alert configuration with updates passed via keyword arguments. If you pass both the AnomalyAlertConfiguration object and keyword arguments, the keyword arguments will take precedence.

Parameters

alert_configuration (str or AnomalyAlertConfiguration) – AnomalyAlertConfiguration object or the ID to the alert configuration.

Keyword Arguments

name (str) – Name for the anomaly alert configuration.
metric_alert_configurations (list[MetricAlertConfiguration]) – Anomaly alert configurations.
hook_ids (list[str]) – Unique hook IDs.
cross_metrics_operator (str or MetricAnomalyAlertConfigurationsOperator) – Cross metrics operator should be specified when setting up multiple metric alert configurations. Possible values include: “AND”, “OR”, “XOR”.
description (str) – Anomaly alert configuration description.

Returns

AnomalyAlertConfiguration

Return type

AnomalyAlertConfiguration

Raises

HttpResponseError –

Example:

Update an existing anomaly alert configuration¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import (
    MetricAlertConfiguration,
    MetricAnomalyAlertScope,
    MetricAnomalyAlertConditions,
    MetricBoundaryCondition
)
service_endpoint = os.getenv("METRICS_ADVISOR_ENDPOINT")
subscription_key = os.getenv("METRICS_ADVISOR_SUBSCRIPTION_KEY")
api_key = os.getenv("METRICS_ADVISOR_API_KEY")
anomaly_detection_configuration_id = os.getenv("METRICS_ADVISOR_DETECTION_CONFIGURATION_ID")

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

alert_config.name = "updated config name"
additional_alert = MetricAlertConfiguration(
    detection_configuration_id=anomaly_detection_configuration_id,
    alert_scope=MetricAnomalyAlertScope(
        scope_type="SeriesGroup",
        series_group_in_scope={'city': 'Shenzhen'}
    ),
    alert_conditions=MetricAnomalyAlertConditions(
        metric_boundary_condition=MetricBoundaryCondition(
            direction="Down",
            lower=5
        )
    )
)
alert_config.metric_alert_configurations.append(additional_alert)

updated = client.update_anomaly_alert_configuration(
    alert_config,
    cross_metrics_operator="OR",
    description="updated alert config"
)

print("Updated alert name: {}".format(updated.name))
print("Updated alert description: {}".format(updated.description))
print("Updated cross metrics operator: {}".format(updated.cross_metrics_operator))
print("Updated alert condition configuration scope type: {}".format(
    updated.metric_alert_configurations[2].alert_scope.scope_type
))

update_data_feed(data_feed: Union[str, azure.ai.metricsadvisor.models._models.DataFeed], **kwargs: Any) → azure.ai.metricsadvisor.models._models.DataFeed[source]¶

Update a data feed. Either pass the entire DataFeed object with the chosen updates or the ID to your data feed with updates passed via keyword arguments. If you pass both the DataFeed object and keyword arguments, the keyword arguments will take precedence.

Parameters

data_feed (DataFeed or str) – The data feed with updates or the data feed ID.

Keyword Arguments

name (str) – The name to update the data feed.
timestamp_column (str) – User-defined timestamp column name.
ingestion_begin_time (datetime) – Ingestion start time.
data_source_request_concurrency (int) – The max concurrency of data ingestion queries against user data source. Zero (0) means no limitation.
ingestion_retry_delay (int) – The min retry interval for failed data ingestion tasks, in seconds.
ingestion_start_offset (int) – The time that the beginning of data ingestion task will delay for every data slice according to this offset, in seconds.
stop_retry_after (int) – Stop retry data ingestion after the data slice first schedule time in seconds.
rollup_identification_value (str) – The identification value for the row of calculated all-up value.
rollup_type (str or DataFeedRollupType) – Mark if the data feed needs rollup. Possible values include: “NoRollup”, “AutoRollup”, “AlreadyRollup”. Default value: “AutoRollup”.
auto_rollup_group_by_column_names (list[str]) – Roll up columns.
rollup_method (str or DataFeedAutoRollupMethod) – Roll up method. Possible values include: “None”, “Sum”, “Max”, “Min”, “Avg”, “Count”.
fill_type (str or DataSourceMissingDataPointFillType) – The type of fill missing point for anomaly detection. Possible values include: “SmartFilling”, “PreviousValue”, “CustomValue”, “NoFilling”. Default value: “SmartFilling”.
custom_fill_value (float) – The value of fill missing point for anomaly detection if “CustomValue” fill type is specified.
admins (list[str]) – Data feed administrators.
data_feed_description (str) – Data feed description.
viewers (list[str]) – Data feed viewer.
access_mode (str or DataFeedAccessMode) – Data feed access mode. Possible values include: “Private”, “Public”. Default value: “Private”.
action_link_template (str) – action link for alert.
status (str or DataFeedStatus) – Data feed status. Possible values include: “Active”, “Paused”.
source (Union[AzureApplicationInsightsDataFeed, AzureBlobDataFeed, AzureCosmosDBDataFeed, AzureDataExplorerDataFeed, AzureDataLakeStorageGen2DataFeed, AzureTableDataFeed, HttpRequestDataFeed, InfluxDBDataFeed, MySqlDataFeed, PostgreSqlDataFeed, SQLServerDataFeed, MongoDBDataFeed, ElasticsearchDataFeed]) – The source of the data feed for update

Returns

DataFeed

Return type

DataFeed

Raises

HttpResponseError –

Example:

Update an existing data feed¶

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))

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

updated_data_feed = client.update_data_feed(
    data_feed,
    access_mode="Public",
    fill_type="CustomValue",
    custom_fill_value=1
)

print("Updated name: {}".format(updated_data_feed.name))
print("Updated description: {}".format(updated_data_feed.options.data_feed_description))
print("Updated access mode: {}".format(updated_data_feed.options.access_mode))
print("Updated fill setting, value: {}, {}".format(
    updated_data_feed.options.missing_data_point_fill_settings.fill_type,
    updated_data_feed.options.missing_data_point_fill_settings.custom_fill_value,
))

update_hook(hook: Union[str, EmailHook, WebHook], **kwargs: Any) → Union[Hook, EmailHook, WebHook][source]¶

Update a hook. Either pass the entire EmailHook or WebHook object with the chosen updates, or the ID to your hook configuration with the updates passed via keyword arguments. If you pass both the hook object and keyword arguments, the keyword arguments will take precedence.

Parameters

hook (Union[str, EmailHook, WebHook]) – An email or web hook or the ID to the hook. If an ID is passed, you must pass hook_type.

Keyword Arguments

hook_type (str) – The hook type. Possible values are “Email” or “Web”. Must be passed if only the hook ID is provided.
name (str) – Hook unique name.
description (str) – Hook description.
external_link (str) – Hook external link.
emails_to_alert (list[str]) – Email TO: list. Only should be passed to update EmailHook.
endpoint (str) – API address, will be called when alert is triggered, only support POST method via SSL. Only should be passed to update WebHook.
username (str) – basic authentication. Only should be passed to update WebHook.
password (str) – basic authentication. Only should be passed to update WebHook.
certificate_key (str) – client certificate. Only should be passed to update WebHook.
certificate_password (str) – client certificate password. Only should be passed to update WebHook.

Returns

EmailHook or WebHook

Return type

Union[Hook, EmailHook, WebHook]

Raises

HttpResponseError –

Example:

Update an existing hook¶

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))

hook.name = "updated hook name"
hook.description = "updated hook description"

updated = client.update_hook(
    hook,
    emails_to_alert=["newemail@alertme.com"]
)
print("Updated name: {}".format(updated.name))
print("Updated description: {}".format(updated.description))
print("Updated emails: {}".format(updated.emails_to_alert))

update_metric_anomaly_detection_configuration(detection_configuration: Union[str, azure.ai.metricsadvisor.models._models.AnomalyDetectionConfiguration], **kwargs: Any) → azure.ai.metricsadvisor.models._models.AnomalyDetectionConfiguration[source]¶

Update anomaly metric detection configuration. Either pass the entire AnomalyDetectionConfiguration object with the chosen updates or the ID to your detection configuration with updates passed via keyword arguments. If you pass both the AnomalyDetectionConfiguration object and keyword arguments, the keyword arguments will take precedence.

Parameters

detection_configuration (str or AnomalyDetectionConfiguration) – AnomalyDetectionConfiguration object or the ID to the detection configuration.

Keyword Arguments

name (str) – The name for the anomaly detection configuration
metric_id (str) – metric unique id.
whole_series_detection_condition (MetricDetectionCondition) – Required. Conditions to detect anomalies in all time series of a metric.
description (str) – anomaly detection configuration description.
series_group_detection_conditions (list[MetricSeriesGroupDetectionCondition]) – detection configuration for series group.
series_detection_conditions (list[MetricSingleSeriesDetectionCondition]) – detection configuration for specific series.

Returns

AnomalyDetectionConfiguration

Return type

AnomalyDetectionConfiguration

Raises

HttpResponseError –

Example:

Update an existing anomaly detection configuration¶

from azure.ai.metricsadvisor import MetricsAdvisorKeyCredential, MetricsAdvisorAdministrationClient
from azure.ai.metricsadvisor.models import (
    MetricSeriesGroupDetectionCondition,
    MetricSingleSeriesDetectionCondition,
    SmartDetectionCondition,
    SuppressCondition
)

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))

detection_config.name = "updated config name"
detection_config.description = "updated with more detection conditions"
smart_detection_condition = SmartDetectionCondition(
    anomaly_detector_direction="Up",
    sensitivity=10,
    suppress_condition=SuppressCondition(
        min_number=2,
        min_ratio=2
    )
)

updated = client.update_metric_anomaly_detection_configuration(
    detection_config,
    series_group_detection_conditions=[
        MetricSeriesGroupDetectionCondition(
            series_group_key={"city": "Seoul"},
            smart_detection_condition=smart_detection_condition
        )
    ],
    series_detection_conditions=[
        MetricSingleSeriesDetectionCondition(
            series_key={"city": "Osaka", "category": "Cell Phones"},
            smart_detection_condition=smart_detection_condition
        )
    ]
)
print("Updated detection name: {}".format(updated.name))
print("Updated detection description: {}".format(updated.description))
print("Updated detection condition for series group: {}".format(
    updated.series_group_detection_conditions[0].series_group_key
))
print("Updated detection condition for series: {}".format(
    updated.series_detection_conditions[0].series_key
))

class azure.ai.metricsadvisor.MetricsAdvisorKeyCredential(subscription_key: str, api_key: str)[source]¶

Credential type used for authenticating to an Azure Metrics Advisor service.

Parameters

subscription_key (str) – The subscription key
api_key (str) – The api key

Raises

TypeError

azure.ai.metricsadvisor package¶

Subpackages¶