CfnAnomalyDetector
- class aws_cdk.aws_cloudwatch.CfnAnomalyDetector(scope, id, *, configuration=None, dimensions=None, metric_characteristics=None, metric_math_anomaly_detector=None, metric_name=None, namespace=None, single_metric_anomaly_detector=None, stat=None)
Bases:
CfnResource
The
AWS::CloudWatch::AnomalyDetector
type specifies an anomaly detection band for a certain metric and statistic.The band represents the expected “normal” range for the metric values. Anomaly detection bands can be used for visualization of a metric’s expected values, and for alarms.
For more information see Using CloudWatch anomaly detection. .
- See:
- CloudformationResource:
AWS::CloudWatch::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_cloudwatch as cloudwatch cfn_anomaly_detector = cloudwatch.CfnAnomalyDetector(self, "MyCfnAnomalyDetector", configuration=cloudwatch.CfnAnomalyDetector.ConfigurationProperty( excluded_time_ranges=[cloudwatch.CfnAnomalyDetector.RangeProperty( end_time="endTime", start_time="startTime" )], metric_time_zone="metricTimeZone" ), dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )], metric_characteristics=cloudwatch.CfnAnomalyDetector.MetricCharacteristicsProperty( periodic_spikes=False ), metric_math_anomaly_detector=cloudwatch.CfnAnomalyDetector.MetricMathAnomalyDetectorProperty( metric_data_queries=[cloudwatch.CfnAnomalyDetector.MetricDataQueryProperty( id="id", # the properties below are optional account_id="accountId", expression="expression", label="label", metric_stat=cloudwatch.CfnAnomalyDetector.MetricStatProperty( metric=cloudwatch.CfnAnomalyDetector.MetricProperty( metric_name="metricName", namespace="namespace", # the properties below are optional dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )] ), period=123, stat="stat", # the properties below are optional unit="unit" ), period=123, return_data=False )] ), metric_name="metricName", namespace="namespace", single_metric_anomaly_detector=cloudwatch.CfnAnomalyDetector.SingleMetricAnomalyDetectorProperty( account_id="accountId", dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )], metric_name="metricName", namespace="namespace", stat="stat" ), stat="stat" )
- Parameters:
scope (
Construct
) – Scope in which this resource is defined.id (
str
) – Construct identifier for this resource (unique in its scope).configuration (
Union
[IResolvable
,ConfigurationProperty
,Dict
[str
,Any
],None
]) – Specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model. The configuration can also include the time zone to use for the metric.dimensions (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,DimensionProperty
,Dict
[str
,Any
]]],None
]) – The dimensions of the metric associated with the anomaly detection band.metric_characteristics (
Union
[IResolvable
,MetricCharacteristicsProperty
,Dict
[str
,Any
],None
]) – Use this object to include parameters to provide information about your metric to CloudWatch to help it build more accurate anomaly detection models. Currently, it includes thePeriodicSpikes
parameter.metric_math_anomaly_detector (
Union
[IResolvable
,MetricMathAnomalyDetectorProperty
,Dict
[str
,Any
],None
]) – The CloudWatch metric math expression for this anomaly detector.metric_name (
Optional
[str
]) – The name of the metric associated with the anomaly detection band.namespace (
Optional
[str
]) – The namespace of the metric associated with the anomaly detection band.single_metric_anomaly_detector (
Union
[IResolvable
,SingleMetricAnomalyDetectorProperty
,Dict
[str
,Any
],None
]) – The CloudWatch metric and statistic for this anomaly detector.stat (
Optional
[str
]) – The statistic of the metric associated with the anomaly detection band.
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
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 prefixpath
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 toaddOverride
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: truedefault (
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:
- 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:
- get_metadata(key)
Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters:
key (
str
) –- See:
- Return type:
Any
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::CloudWatch::AnomalyDetector'
- attr_id
Id
- Type:
cloudformationAttribute
- cfn_options
Options for this resource, such as condition, update policy etc.
- cfn_resource_type
AWS resource type.
- configuration
Specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model.
- 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.
- dimensions
The dimensions of the metric associated with the anomaly detection band.
- 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.
- metric_characteristics
Use this object to include parameters to provide information about your metric to CloudWatch to help it build more accurate anomaly detection models.
- metric_math_anomaly_detector
The CloudWatch metric math expression for this anomaly detector.
- metric_name
The name of the metric associated with the anomaly detection band.
- namespace
The namespace of the metric associated with the anomaly detection band.
- 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 })
.
- single_metric_anomaly_detector
The CloudWatch metric and statistic for this anomaly detector.
- stack
The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- stat
The statistic of the metric associated with the anomaly detection band.
Static Methods
- 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 detectConstruct
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 classConstruct
in each copy of theconstructs
library is seen as a different class, and an instance of one class will not test asinstanceof
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 theconstructs
library can be accidentally installed, andinstanceof
will behave unpredictably. It is safest to avoid usinginstanceof
, 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 extendsConstruct
.
ConfigurationProperty
- class CfnAnomalyDetector.ConfigurationProperty(*, excluded_time_ranges=None, metric_time_zone=None)
Bases:
object
Specifies details about how the anomaly detection model is to be trained, including time ranges to exclude when training and updating the model.
The configuration can also include the time zone to use for the metric.
- Parameters:
excluded_time_ranges (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,RangeProperty
,Dict
[str
,Any
]]],None
]) – Specifies an array of time ranges to exclude from use when the anomaly detection model is trained and updated. Use this to make sure that events that could cause unusual values for the metric, such as deployments, aren’t used when CloudWatch creates or updates the model.metric_time_zone (
Optional
[str
]) – The time zone to use for the metric. This is useful to enable the model to automatically account for daylight savings time changes if the metric is sensitive to such time changes. To specify a time zone, use the name of the time zone as specified in the standard tz database. For more information, see tz database .
- See:
- 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_cloudwatch as cloudwatch configuration_property = cloudwatch.CfnAnomalyDetector.ConfigurationProperty( excluded_time_ranges=[cloudwatch.CfnAnomalyDetector.RangeProperty( end_time="endTime", start_time="startTime" )], metric_time_zone="metricTimeZone" )
Attributes
- excluded_time_ranges
Specifies an array of time ranges to exclude from use when the anomaly detection model is trained and updated.
Use this to make sure that events that could cause unusual values for the metric, such as deployments, aren’t used when CloudWatch creates or updates the model.
- metric_time_zone
The time zone to use for the metric.
This is useful to enable the model to automatically account for daylight savings time changes if the metric is sensitive to such time changes.
To specify a time zone, use the name of the time zone as specified in the standard tz database. For more information, see tz database .
DimensionProperty
- class CfnAnomalyDetector.DimensionProperty(*, name, value)
Bases:
object
A dimension is a name/value pair that is part of the identity of a metric.
Because dimensions are part of the unique identifier for a metric, whenever you add a unique name/value pair to one of your metrics, you are creating a new variation of that metric. For example, many Amazon EC2 metrics publish
InstanceId
as a dimension name, and the actual instance ID as the value for that dimension.You can assign up to 30 dimensions to a metric.
- Parameters:
name (
str
) – The name of the dimension.value (
str
) – The value of the dimension. Dimension values must contain only ASCII characters and must include at least one non-whitespace character. ASCII control characters are not supported as part of dimension values.
- See:
- 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_cloudwatch as cloudwatch dimension_property = cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )
Attributes
- name
The name of the dimension.
- value
The value of the dimension.
Dimension values must contain only ASCII characters and must include at least one non-whitespace character. ASCII control characters are not supported as part of dimension values.
MetricCharacteristicsProperty
- class CfnAnomalyDetector.MetricCharacteristicsProperty(*, periodic_spikes=None)
Bases:
object
This object includes parameters that you can use to provide information to CloudWatch to help it build more accurate anomaly detection models.
- Parameters:
periodic_spikes (
Union
[bool
,IResolvable
,None
]) – Set this parameter to true if values for this metric consistently include spikes that should not be considered to be anomalies. With this set to true, CloudWatch will expect to see spikes that occurred consistently during the model training period, and won’t flag future similar spikes as anomalies.- See:
- 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_cloudwatch as cloudwatch metric_characteristics_property = cloudwatch.CfnAnomalyDetector.MetricCharacteristicsProperty( periodic_spikes=False )
Attributes
- periodic_spikes
Set this parameter to true if values for this metric consistently include spikes that should not be considered to be anomalies.
With this set to true, CloudWatch will expect to see spikes that occurred consistently during the model training period, and won’t flag future similar spikes as anomalies.
MetricDataQueryProperty
- class CfnAnomalyDetector.MetricDataQueryProperty(*, id, account_id=None, expression=None, label=None, metric_stat=None, period=None, return_data=None)
Bases:
object
This structure is used in both
GetMetricData
andPutMetricAlarm
.The supported use of this structure is different for those two operations.
When used in
GetMetricData
, it indicates the metric data to return, and whether this call is just retrieving a batch set of data for one metric, or is performing a Metrics Insights query or a math expression. A singleGetMetricData
call can include up to 500MetricDataQuery
structures.When used in
PutMetricAlarm
, it enables you to create an alarm based on a metric math expression. EachMetricDataQuery
in the array specifies either a metric to retrieve, or a math expression to be performed on retrieved metrics. A singlePutMetricAlarm
call can include up to 20MetricDataQuery
structures in the array. The 20 structures can include as many as 10 structures that contain aMetricStat
parameter to retrieve a metric, and as many as 10 structures that contain theExpression
parameter to perform a math expression. Of thoseExpression
structures, one must havetrue
as the value forReturnData
. The result of this expression is the value the alarm watches.Any expression used in a
PutMetricAlarm
operation must return a single time series. For more information, see Metric Math Syntax and Functions in the Amazon CloudWatch User Guide .Some of the parameters of this structure also have different uses whether you are using this structure in a
GetMetricData
operation or aPutMetricAlarm
operation. These differences are explained in the following parameter list.- Parameters:
id (
str
) – A short name used to tie this object to the results in the response. This name must be unique within a single call toGetMetricData
. If you are performing math expressions on this set of data, this name represents that data and can serve as a variable in the mathematical expression. The valid characters are letters, numbers, and underscore. The first character must be a lowercase letter.account_id (
Optional
[str
]) – The ID of the account where the metrics are located. If you are performing aGetMetricData
operation in a monitoring account, use this to specify which account to retrieve this metric from. If you are performing aPutMetricAlarm
operation, use this to specify which account contains the metric that the alarm is watching.expression (
Optional
[str
]) –This field can contain either a Metrics Insights query, or a metric math expression to be performed on the returned data. For more information about Metrics Insights queries, see Metrics Insights query components and syntax in the Amazon CloudWatch User Guide . A math expression can use the
Id
of the other metrics or queries to refer to those metrics, and can also use theId
of other expressions to use the result of those expressions. For more information about metric math expressions, see Metric Math Syntax and Functions in the Amazon CloudWatch User Guide . Within each MetricDataQuery object, you must specify eitherExpression
orMetricStat
but not both.label (
Optional
[str
]) – A human-readable label for this metric or expression. This is especially useful if this is an expression, so that you know what the value represents. If the metric or expression is shown in a CloudWatch dashboard widget, the label is shown. If Label is omitted, CloudWatch generates a default. You can put dynamic expressions into a label, so that it is more descriptive. For more information, see Using Dynamic Labels .metric_stat (
Union
[IResolvable
,MetricStatProperty
,Dict
[str
,Any
],None
]) – The metric to be returned, along with statistics, period, and units. Use this parameter only if this object is retrieving a metric and not performing a math expression on returned data. Within one MetricDataQuery object, you must specify eitherExpression
orMetricStat
but not both.period (
Union
[int
,float
,None
]) – The granularity, in seconds, of the returned data points. For metrics with regular resolution, a period can be as short as one minute (60 seconds) and must be a multiple of 60. For high-resolution metrics that are collected at intervals of less than one minute, the period can be 1, 5, 10, 30, 60, or any multiple of 60. High-resolution metrics are those metrics stored by aPutMetricData
operation that includes aStorageResolution of 1 second
.return_data (
Union
[bool
,IResolvable
,None
]) – When used inGetMetricData
, this option indicates whether to return the timestamps and raw data values of this metric. If you are performing this call just to do math expressions and do not also need the raw data returned, you can specifyfalse
. If you omit this, the default oftrue
is used. When used inPutMetricAlarm
, specifytrue
for the one expression result to use as the alarm. For all other metrics and expressions in the samePutMetricAlarm
operation, specifyReturnData
as False.
- See:
- 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_cloudwatch as cloudwatch metric_data_query_property = cloudwatch.CfnAnomalyDetector.MetricDataQueryProperty( id="id", # the properties below are optional account_id="accountId", expression="expression", label="label", metric_stat=cloudwatch.CfnAnomalyDetector.MetricStatProperty( metric=cloudwatch.CfnAnomalyDetector.MetricProperty( metric_name="metricName", namespace="namespace", # the properties below are optional dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )] ), period=123, stat="stat", # the properties below are optional unit="unit" ), period=123, return_data=False )
Attributes
- account_id
The ID of the account where the metrics are located.
If you are performing a
GetMetricData
operation in a monitoring account, use this to specify which account to retrieve this metric from.If you are performing a
PutMetricAlarm
operation, use this to specify which account contains the metric that the alarm is watching.
- expression
This field can contain either a Metrics Insights query, or a metric math expression to be performed on the returned data.
For more information about Metrics Insights queries, see Metrics Insights query components and syntax in the Amazon CloudWatch User Guide .
A math expression can use the
Id
of the other metrics or queries to refer to those metrics, and can also use theId
of other expressions to use the result of those expressions. For more information about metric math expressions, see Metric Math Syntax and Functions in the Amazon CloudWatch User Guide .Within each MetricDataQuery object, you must specify either
Expression
orMetricStat
but not both.
- id
A short name used to tie this object to the results in the response.
This name must be unique within a single call to
GetMetricData
. If you are performing math expressions on this set of data, this name represents that data and can serve as a variable in the mathematical expression. The valid characters are letters, numbers, and underscore. The first character must be a lowercase letter.
- label
A human-readable label for this metric or expression.
This is especially useful if this is an expression, so that you know what the value represents. If the metric or expression is shown in a CloudWatch dashboard widget, the label is shown. If Label is omitted, CloudWatch generates a default.
You can put dynamic expressions into a label, so that it is more descriptive. For more information, see Using Dynamic Labels .
- metric_stat
The metric to be returned, along with statistics, period, and units.
Use this parameter only if this object is retrieving a metric and not performing a math expression on returned data.
Within one MetricDataQuery object, you must specify either
Expression
orMetricStat
but not both.
- period
The granularity, in seconds, of the returned data points.
For metrics with regular resolution, a period can be as short as one minute (60 seconds) and must be a multiple of 60. For high-resolution metrics that are collected at intervals of less than one minute, the period can be 1, 5, 10, 30, 60, or any multiple of 60. High-resolution metrics are those metrics stored by a
PutMetricData
operation that includes aStorageResolution of 1 second
.
- return_data
When used in
GetMetricData
, this option indicates whether to return the timestamps and raw data values of this metric.If you are performing this call just to do math expressions and do not also need the raw data returned, you can specify
false
. If you omit this, the default oftrue
is used.When used in
PutMetricAlarm
, specifytrue
for the one expression result to use as the alarm. For all other metrics and expressions in the samePutMetricAlarm
operation, specifyReturnData
as False.
MetricMathAnomalyDetectorProperty
- class CfnAnomalyDetector.MetricMathAnomalyDetectorProperty(*, metric_data_queries=None)
Bases:
object
Indicates the CloudWatch math expression that provides the time series the anomaly detector uses as input.
The designated math expression must return a single time series.
- Parameters:
metric_data_queries (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,MetricDataQueryProperty
,Dict
[str
,Any
]]],None
]) – An array of metric data query structures that enables you to create an anomaly detector based on the result of a metric math expression. Each item inMetricDataQueries
gets a metric or performs a math expression. One item inMetricDataQueries
is the expression that provides the time series that the anomaly detector uses as input. Designate the expression by settingReturnData
totrue
for this object in the array. For all other expressions and metrics, setReturnData
tofalse
. The designated expression must return a single time series.- See:
- 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_cloudwatch as cloudwatch metric_math_anomaly_detector_property = cloudwatch.CfnAnomalyDetector.MetricMathAnomalyDetectorProperty( metric_data_queries=[cloudwatch.CfnAnomalyDetector.MetricDataQueryProperty( id="id", # the properties below are optional account_id="accountId", expression="expression", label="label", metric_stat=cloudwatch.CfnAnomalyDetector.MetricStatProperty( metric=cloudwatch.CfnAnomalyDetector.MetricProperty( metric_name="metricName", namespace="namespace", # the properties below are optional dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )] ), period=123, stat="stat", # the properties below are optional unit="unit" ), period=123, return_data=False )] )
Attributes
- metric_data_queries
An array of metric data query structures that enables you to create an anomaly detector based on the result of a metric math expression.
Each item in
MetricDataQueries
gets a metric or performs a math expression. One item inMetricDataQueries
is the expression that provides the time series that the anomaly detector uses as input. Designate the expression by settingReturnData
totrue
for this object in the array. For all other expressions and metrics, setReturnData
tofalse
. The designated expression must return a single time series.
MetricProperty
- class CfnAnomalyDetector.MetricProperty(*, metric_name, namespace, dimensions=None)
Bases:
object
Represents a specific metric.
- Parameters:
metric_name (
str
) – The name of the metric. This is a required field.namespace (
str
) – The namespace of the metric.dimensions (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,DimensionProperty
,Dict
[str
,Any
]]],None
]) – The dimensions for the metric.
- See:
- 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_cloudwatch as cloudwatch metric_property = cloudwatch.CfnAnomalyDetector.MetricProperty( metric_name="metricName", namespace="namespace", # the properties below are optional dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )] )
Attributes
- dimensions
The dimensions for the metric.
- metric_name
The name of the metric.
This is a required field.
- namespace
The namespace of the metric.
MetricStatProperty
- class CfnAnomalyDetector.MetricStatProperty(*, metric, period, stat, unit=None)
Bases:
object
This structure defines the metric to be returned, along with the statistics, period, and units.
- Parameters:
metric (
Union
[IResolvable
,MetricProperty
,Dict
[str
,Any
]]) – The metric to return, including the metric name, namespace, and dimensions.period (
Union
[int
,float
]) – The granularity, in seconds, of the returned data points. For metrics with regular resolution, a period can be as short as one minute (60 seconds) and must be a multiple of 60. For high-resolution metrics that are collected at intervals of less than one minute, the period can be 1, 5, 10, 30, 60, or any multiple of 60. High-resolution metrics are those metrics stored by aPutMetricData
call that includes aStorageResolution
of 1 second. If theStartTime
parameter specifies a time stamp that is greater than 3 hours ago, you must specify the period as follows or no data points in that time range is returned: - Start time between 3 hours and 15 days ago - Use a multiple of 60 seconds (1 minute). - Start time between 15 and 63 days ago - Use a multiple of 300 seconds (5 minutes). - Start time greater than 63 days ago - Use a multiple of 3600 seconds (1 hour).stat (
str
) – The statistic to return. It can include any CloudWatch statistic or extended statistic.unit (
Optional
[str
]) – When you are using aPut
operation, this defines what unit you want to use when storing the metric. In aGet
operation, if you omitUnit
then all data that was collected with any unit is returned, along with the corresponding units that were specified when the data was reported to CloudWatch. If you specify a unit, the operation returns only data that was collected with that unit specified. If you specify a unit that does not match the data collected, the results of the operation are null. CloudWatch does not perform unit conversions.
- See:
- 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_cloudwatch as cloudwatch metric_stat_property = cloudwatch.CfnAnomalyDetector.MetricStatProperty( metric=cloudwatch.CfnAnomalyDetector.MetricProperty( metric_name="metricName", namespace="namespace", # the properties below are optional dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )] ), period=123, stat="stat", # the properties below are optional unit="unit" )
Attributes
- metric
The metric to return, including the metric name, namespace, and dimensions.
- period
The granularity, in seconds, of the returned data points.
For metrics with regular resolution, a period can be as short as one minute (60 seconds) and must be a multiple of 60. For high-resolution metrics that are collected at intervals of less than one minute, the period can be 1, 5, 10, 30, 60, or any multiple of 60. High-resolution metrics are those metrics stored by a
PutMetricData
call that includes aStorageResolution
of 1 second.If the
StartTime
parameter specifies a time stamp that is greater than 3 hours ago, you must specify the period as follows or no data points in that time range is returned:Start time between 3 hours and 15 days ago - Use a multiple of 60 seconds (1 minute).
Start time between 15 and 63 days ago - Use a multiple of 300 seconds (5 minutes).
Start time greater than 63 days ago - Use a multiple of 3600 seconds (1 hour).
- stat
The statistic to return.
It can include any CloudWatch statistic or extended statistic.
- unit
When you are using a
Put
operation, this defines what unit you want to use when storing the metric.In a
Get
operation, if you omitUnit
then all data that was collected with any unit is returned, along with the corresponding units that were specified when the data was reported to CloudWatch. If you specify a unit, the operation returns only data that was collected with that unit specified. If you specify a unit that does not match the data collected, the results of the operation are null. CloudWatch does not perform unit conversions.
RangeProperty
- class CfnAnomalyDetector.RangeProperty(*, end_time, start_time)
Bases:
object
Each
Range
specifies one range of days or times to exclude from use for training or updating an anomaly detection model.- Parameters:
end_time (
str
) – The end time of the range to exclude. The format isyyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.start_time (
str
) – The start time of the range to exclude. The format isyyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.
- See:
- 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_cloudwatch as cloudwatch range_property = cloudwatch.CfnAnomalyDetector.RangeProperty( end_time="endTime", start_time="startTime" )
Attributes
- end_time
The end time of the range to exclude.
The format is
yyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.
- start_time
The start time of the range to exclude.
The format is
yyyy-MM-dd'T'HH:mm:ss
. For example,2019-07-01T23:59:59
.
SingleMetricAnomalyDetectorProperty
- class CfnAnomalyDetector.SingleMetricAnomalyDetectorProperty(*, account_id=None, dimensions=None, metric_name=None, namespace=None, stat=None)
Bases:
object
Designates the CloudWatch metric and statistic that provides the time series the anomaly detector uses as input.
If you have enabled unified cross-account observability, and this account is a monitoring account, the metric can be in the same account or a source account.
- Parameters:
account_id (
Optional
[str
]) – If the CloudWatch metric that provides the time series that the anomaly detector uses as input is in another account, specify that account ID here. If you omit this parameter, the current account is used.dimensions (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,DimensionProperty
,Dict
[str
,Any
]]],None
]) – The metric dimensions to create the anomaly detection model for.metric_name (
Optional
[str
]) – The name of the metric to create the anomaly detection model for.namespace (
Optional
[str
]) – The namespace of the metric to create the anomaly detection model for.stat (
Optional
[str
]) – The statistic to use for the metric and anomaly detection model.
- See:
- 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_cloudwatch as cloudwatch single_metric_anomaly_detector_property = cloudwatch.CfnAnomalyDetector.SingleMetricAnomalyDetectorProperty( account_id="accountId", dimensions=[cloudwatch.CfnAnomalyDetector.DimensionProperty( name="name", value="value" )], metric_name="metricName", namespace="namespace", stat="stat" )
Attributes
- account_id
If the CloudWatch metric that provides the time series that the anomaly detector uses as input is in another account, specify that account ID here.
If you omit this parameter, the current account is used.
- dimensions
The metric dimensions to create the anomaly detection model for.
- metric_name
The name of the metric to create the anomaly detection model for.
- namespace
The namespace of the metric to create the anomaly detection model for.
- stat
The statistic to use for the metric and anomaly detection model.