CfnAnomalyDetector

class aws_cdk.aws_aps.CfnAnomalyDetector(scope, id, *, alias, configuration, workspace, evaluation_interval_in_seconds=None, labels=None, missing_data_action=None, tags=None)

Bases: CfnResource

Anomaly detection uses the Random Cut Forest algorithm for time-series analysis.

The anomaly detector analyzes Amazon Managed Service for Prometheus metrics to identify unusual patterns and behaviors.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-aps-anomalydetector.html
CloudformationResource:: AWS::APS::AnomalyDetector
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

cfn_anomaly_detector = aps.CfnAnomalyDetector(self, "MyCfnAnomalyDetector",
    alias="alias",
    configuration=aps.CfnAnomalyDetector.AnomalyDetectorConfigurationProperty(
        random_cut_forest=aps.CfnAnomalyDetector.RandomCutForestConfigurationProperty(
            query="query",

            # the properties below are optional
            ignore_near_expected_from_above=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
                amount=123,
                ratio=123
            ),
            ignore_near_expected_from_below=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
                amount=123,
                ratio=123
            ),
            sample_size=123,
            shingle_size=123
        )
    ),
    workspace="workspace",

    # the properties below are optional
    evaluation_interval_in_seconds=123,
    labels=[aps.CfnAnomalyDetector.LabelProperty(
        key="key",
        value="value"
    )],
    missing_data_action=aps.CfnAnomalyDetector.MissingDataActionProperty(
        mark_as_anomaly=False,
        skip=False
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Create a new AWS::APS::AnomalyDetector.

Parameters:

scope (Construct) – Scope in which this resource is defined.
id (str) – Construct identifier for this resource (unique in its scope).
alias (str) – The user-friendly name of the anomaly detector.
configuration (Union[IResolvable, AnomalyDetectorConfigurationProperty, Dict[str, Any]]) – The algorithm configuration of the anomaly detector.
workspace (str) – An Amazon Managed Service for Prometheus workspace is a logical and isolated Prometheus server dedicated to ingesting, storing, and querying your Prometheus-compatible metrics.
evaluation_interval_in_seconds (Union[int, float, None]) – The frequency, in seconds, at which the anomaly detector evaluates metrics. Default: - 60
labels (Union[IResolvable, Sequence[Union[IResolvable, LabelProperty, Dict[str, Any]]], None]) – The Amazon Managed Service for Prometheus metric labels associated with the anomaly detector.
missing_data_action (Union[IResolvable, MissingDataActionProperty, Dict[str, Any], None]) – The action taken when data is missing during evaluation.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – The tags applied to the anomaly detector.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:: path (str) – The path of the value to delete.
Return type:: None

add_dependency(target)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

Parameters:: target (CfnResource)
Return type:: None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

Parameters:: target (CfnResource)
Deprecated:: use addDependency
Stability:: deprecated
Return type:: None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

key (str)
value (Any)

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

path (str) –
- The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.
value (Any) –
- The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:: property_path (str) – The path to the property.
Return type:: None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

property_path (str) – The path of the property.
value (Any) – The value.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

policy (Optional[RemovalPolicy])
apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true
default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

attribute_name (str) – The name of the attribute.
type_hint (Optional[ResolutionTypeHint])

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:: key (str)
See:
Return type:: Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:: inspector (TreeInspector) – tree inspector to collect and process attributes.
Return type:: None

obtain_dependencies()

Retrieves an array of resources this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:: List[Union[Stack, CfnResource]]

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:: List[CfnResource]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:: new_logical_id (str) – The new logical ID to use for this stack element.
Return type:: None

remove_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:: target (CfnResource)
Return type:: None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

target (CfnResource) – The dependency to replace.
new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:: str
Returns:: a string representation of this resource

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::APS::AnomalyDetector'

alias: The user-friendly name of the anomaly detector.

anomaly_detector_ref: A reference to a AnomalyDetector resource.

attr_arn

The Amazon Resource Name (ARN) of the anomaly detector.

CloudformationAttribute:: Arn

cdk_tag_manager: Tag Manager which manages the tags for this resource.

cfn_options: Options for this resource, such as condition, update policy etc.

cfn_resource_type: AWS resource type.

configuration: The algorithm configuration of the anomaly detector.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

env

evaluation_interval_in_seconds: The frequency, in seconds, at which the anomaly detector evaluates metrics.

labels: The Amazon Managed Service for Prometheus metric labels associated with the anomaly detector.

logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

missing_data_action: The action taken when data is missing during evaluation.

node: The tree node.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

tags: The tags applied to the anomaly detector.

workspace: An Amazon Managed Service for Prometheus workspace is a logical and isolated Prometheus server dedicated to ingesting, storing, and querying your Prometheus-compatible metrics.

Static Methods

classmethod arn_for_anomaly_detector(resource)

Parameters:: resource (IAnomalyDetectorRef)
Return type:: str

classmethod is_cfn_anomaly_detector(x)

Checks whether the given object is a CfnAnomalyDetector.

Parameters:: x (Any)
Return type:: bool

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:: x (Any)
Return type:: bool
Returns:: The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:: x (Any)
Return type:: bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Construct.

AnomalyDetectorConfigurationProperty

class CfnAnomalyDetector.AnomalyDetectorConfigurationProperty(*, random_cut_forest)

Bases: object

The configuration for the anomaly detection algorithm.

Parameters:: random_cut_forest (Union[IResolvable, RandomCutForestConfigurationProperty, Dict[str, Any]]) – The Random Cut Forest algorithm configuration for anomaly detection.
See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-anomalydetectorconfiguration.html
ExampleMetadata:: fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

anomaly_detector_configuration_property = aps.CfnAnomalyDetector.AnomalyDetectorConfigurationProperty(
    random_cut_forest=aps.CfnAnomalyDetector.RandomCutForestConfigurationProperty(
        query="query",

        # the properties below are optional
        ignore_near_expected_from_above=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
            amount=123,
            ratio=123
        ),
        ignore_near_expected_from_below=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
            amount=123,
            ratio=123
        ),
        sample_size=123,
        shingle_size=123
    )
)

Attributes

random_cut_forest

The Random Cut Forest algorithm configuration for anomaly detection.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-anomalydetectorconfiguration.html#cfn-aps-anomalydetector-anomalydetectorconfiguration-randomcutforest

IgnoreNearExpectedProperty

class CfnAnomalyDetector.IgnoreNearExpectedProperty(*, amount=None, ratio=None)

Bases: object

Configuration for threshold settings that determine when values near expected values should be ignored during anomaly detection.

Parameters:

amount (Union[int, float, None]) – The absolute amount by which values can differ from expected values before being considered anomalous.
ratio (Union[int, float, None]) – The ratio by which values can differ from expected values before being considered anomalous.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-ignorenearexpected.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

ignore_near_expected_property = aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
    amount=123,
    ratio=123
)

Attributes

amount

The absolute amount by which values can differ from expected values before being considered anomalous.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-ignorenearexpected.html#cfn-aps-anomalydetector-ignorenearexpected-amount

ratio

The ratio by which values can differ from expected values before being considered anomalous.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-ignorenearexpected.html#cfn-aps-anomalydetector-ignorenearexpected-ratio

LabelProperty

class CfnAnomalyDetector.LabelProperty(*, key, value)

Bases: object

The Amazon Managed Service for Prometheus metric labels associated with the anomaly detector.

Parameters:

key (str) – The key of the label.
value (str) – The value for this label.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-label.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

label_property = aps.CfnAnomalyDetector.LabelProperty(
    key="key",
    value="value"
)

Attributes

key

The key of the label.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-label.html#cfn-aps-anomalydetector-label-key

value

The value for this label.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-label.html#cfn-aps-anomalydetector-label-value

MissingDataActionProperty

class CfnAnomalyDetector.MissingDataActionProperty(*, mark_as_anomaly=None, skip=None)

Bases: object

Specifies the action to take when data is missing during anomaly detection evaluation.

Parameters:

mark_as_anomaly (Union[bool, IResolvable, None]) – Marks missing data points as anomalies.
skip (Union[bool, IResolvable, None]) – Skips evaluation when data is missing.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-missingdataaction.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

missing_data_action_property = aps.CfnAnomalyDetector.MissingDataActionProperty(
    mark_as_anomaly=False,
    skip=False
)

Attributes

mark_as_anomaly

Marks missing data points as anomalies.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-missingdataaction.html#cfn-aps-anomalydetector-missingdataaction-markasanomaly

skip

Skips evaluation when data is missing.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-missingdataaction.html#cfn-aps-anomalydetector-missingdataaction-skip

RandomCutForestConfigurationProperty

class CfnAnomalyDetector.RandomCutForestConfigurationProperty(*, query, ignore_near_expected_from_above=None, ignore_near_expected_from_below=None, sample_size=None, shingle_size=None)

Bases: object

Configuration for the Random Cut Forest algorithm used for anomaly detection in time-series data.

Parameters:

query (str) – The Prometheus query used to retrieve the time-series data for anomaly detection. .. epigraph:: Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see Aggregation operators on the Prometheus docs website. Supported PromQL aggregation operators : avg , count , group , max , min , quantile , stddev , stdvar , and sum .
ignore_near_expected_from_above (Union[IResolvable, IgnoreNearExpectedProperty, Dict[str, Any], None]) – Configuration for ignoring values that are near expected values from above during anomaly detection.
ignore_near_expected_from_below (Union[IResolvable, IgnoreNearExpectedProperty, Dict[str, Any], None]) – Configuration for ignoring values that are near expected values from below during anomaly detection.
sample_size (Union[int, float, None]) – The number of data points sampled from the input stream for the Random Cut Forest algorithm. The default number is 256 consecutive data points. Default: - 256
shingle_size (Union[int, float, None]) – The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm. The default number is 8 consecutive data points. Default: - 8

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_aps as aps

random_cut_forest_configuration_property = aps.CfnAnomalyDetector.RandomCutForestConfigurationProperty(
    query="query",

    # the properties below are optional
    ignore_near_expected_from_above=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
        amount=123,
        ratio=123
    ),
    ignore_near_expected_from_below=aps.CfnAnomalyDetector.IgnoreNearExpectedProperty(
        amount=123,
        ratio=123
    ),
    sample_size=123,
    shingle_size=123
)

Attributes

ignore_near_expected_from_above

Configuration for ignoring values that are near expected values from above during anomaly detection.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html#cfn-aps-anomalydetector-randomcutforestconfiguration-ignorenearexpectedfromabove

ignore_near_expected_from_below

Configuration for ignoring values that are near expected values from below during anomaly detection.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html#cfn-aps-anomalydetector-randomcutforestconfiguration-ignorenearexpectedfrombelow

query

The Prometheus query used to retrieve the time-series data for anomaly detection.

Random Cut Forest queries must be wrapped by a supported PromQL aggregation operator. For more information, see Aggregation operators on the Prometheus docs website.

Supported PromQL aggregation operators : avg , count , group , max , min , quantile , stddev , stdvar , and sum .

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html#cfn-aps-anomalydetector-randomcutforestconfiguration-query

sample_size

The number of data points sampled from the input stream for the Random Cut Forest algorithm.

The default number is 256 consecutive data points.

Default:

256

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html#cfn-aps-anomalydetector-randomcutforestconfiguration-samplesize

shingle_size

The number of consecutive data points used to create a shingle for the Random Cut Forest algorithm.

The default number is 8 consecutive data points.

Default:

8

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-aps-anomalydetector-randomcutforestconfiguration.html#cfn-aps-anomalydetector-randomcutforestconfiguration-shinglesize