CfnAnomalyDetector¶
-
class
aws_cdk.aws_lookoutmetrics.
CfnAnomalyDetector
(scope, id, *, anomaly_detector_config, metric_set_list, anomaly_detector_description=None, anomaly_detector_name=None, kms_key_arn=None)¶ Bases:
aws_cdk.core.CfnResource
A CloudFormation
AWS::LookoutMetrics::AnomalyDetector
.The
AWS::LookoutMetrics::AnomalyDetector
type creates an anomaly detector.- CloudformationResource
AWS::LookoutMetrics::AnomalyDetector
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics cfn_anomaly_detector = lookoutmetrics.CfnAnomalyDetector(self, "MyCfnAnomalyDetector", anomaly_detector_config=lookoutmetrics.CfnAnomalyDetector.AnomalyDetectorConfigProperty( anomaly_detector_frequency="anomalyDetectorFrequency" ), metric_set_list=[lookoutmetrics.CfnAnomalyDetector.MetricSetProperty( metric_list=[lookoutmetrics.CfnAnomalyDetector.MetricProperty( aggregation_function="aggregationFunction", metric_name="metricName", # the properties below are optional namespace="namespace" )], metric_set_name="metricSetName", metric_source=lookoutmetrics.CfnAnomalyDetector.MetricSourceProperty( app_flow_config=lookoutmetrics.CfnAnomalyDetector.AppFlowConfigProperty( flow_name="flowName", role_arn="roleArn" ), cloudwatch_config=lookoutmetrics.CfnAnomalyDetector.CloudwatchConfigProperty( role_arn="roleArn" ), rds_source_config=lookoutmetrics.CfnAnomalyDetector.RDSSourceConfigProperty( database_host="databaseHost", database_name="databaseName", database_port=123, db_instance_identifier="dbInstanceIdentifier", role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), redshift_source_config=lookoutmetrics.CfnAnomalyDetector.RedshiftSourceConfigProperty( cluster_identifier="clusterIdentifier", database_host="databaseHost", database_name="databaseName", database_port=123, role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), s3_source_config=lookoutmetrics.CfnAnomalyDetector.S3SourceConfigProperty( file_format_descriptor=lookoutmetrics.CfnAnomalyDetector.FileFormatDescriptorProperty( csv_format_descriptor=lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" ), json_format_descriptor=lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" ) ), role_arn="roleArn", # the properties below are optional historical_data_path_list=["historicalDataPathList"], templated_path_list=["templatedPathList"] ) ), # the properties below are optional dimension_list=["dimensionList"], metric_set_description="metricSetDescription", metric_set_frequency="metricSetFrequency", offset=123, timestamp_column=lookoutmetrics.CfnAnomalyDetector.TimestampColumnProperty( column_format="columnFormat", column_name="columnName" ), timezone="timezone" )], # the properties below are optional anomaly_detector_description="anomalyDetectorDescription", anomaly_detector_name="anomalyDetectorName", kms_key_arn="kmsKeyArn" )
Create a new
AWS::LookoutMetrics::AnomalyDetector
.- Parameters
scope (
Construct
) –scope in which this resource is defined.
id (
str
) –scoped id of the resource.
anomaly_detector_config (
Union
[IResolvable
,AnomalyDetectorConfigProperty
]) – Contains information about the configuration of the anomaly detector.metric_set_list (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,MetricSetProperty
]]]) – The detector’s dataset.anomaly_detector_description (
Optional
[str
]) – A description of the detector.anomaly_detector_name (
Optional
[str
]) – The name of the detector.kms_key_arn (
Optional
[str
]) – The ARN of the KMS key to use to encrypt your data.
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_depends_on
(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_metadata
(key, value)¶ Add a value to the CloudFormation Resource Metadata.
- Parameters
key (
str
) –value (
Any
) –
- See
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.
- Return type
None
-
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 intermdediate 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
).- 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 resoure, please consult that specific resource’s documentation.
- Return type
None
-
get_att
(attribute_name)¶ 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.- Return type
-
get_metadata
(key)¶ Retrieve a value value from the CloudFormation Resource Metadata.
- Parameters
key (
str
) –- See
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.
- Return type
Any
-
inspect
(inspector)¶ Examines the CloudFormation resource and discloses attributes.
- Parameters
inspector (
TreeInspector
) –tree inspector to collect and process attributes.
- Return type
None
-
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
-
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::LookoutMetrics::AnomalyDetector'¶
-
anomaly_detector_config
¶ Contains information about the configuration of the anomaly detector.
-
anomaly_detector_description
¶ A description of the detector.
-
anomaly_detector_name
¶ The name of the detector.
-
attr_arn
¶ The Amazon Resource Name (ARN) of the detector.
For example,
arn:aws:lookoutmetrics:us-east-2:123456789012:AnomalyDetector:my-detector
- CloudformationAttribute
Arn
- Return type
str
-
cfn_options
¶ Options for this resource, such as condition, update policy etc.
- Return type
-
cfn_resource_type
¶ AWS resource type.
- Return type
str
-
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.
- Return type
List
[str
]
-
kms_key_arn
¶ The ARN of the KMS key to use to encrypt your data.
-
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)
.- Return type
str
- Returns
the logical ID as a stringified token. This value will only get resolved during synthesis.
-
metric_set_list
¶ The detector’s dataset.
-
node
¶ The construct tree node associated with this construct.
- Return type
-
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 })
.- Return type
str
-
stack
¶ The stack in which this element is defined.
CfnElements must be defined within a stack scope (directly or indirectly).
- Return type
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
(construct)¶ Check whether the given construct is a CfnResource.
- Parameters
construct (
IConstruct
) –- Return type
bool
-
classmethod
is_construct
(x)¶ Return whether the given object is a Construct.
- Parameters
x (
Any
) –- Return type
bool
AnomalyDetectorConfigProperty¶
-
class
CfnAnomalyDetector.
AnomalyDetectorConfigProperty
(*, anomaly_detector_frequency)¶ Bases:
object
Contains information about a detector’s configuration.
- Parameters
anomaly_detector_frequency (
str
) – The frequency at which the detector analyzes its source data.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics anomaly_detector_config_property = lookoutmetrics.CfnAnomalyDetector.AnomalyDetectorConfigProperty( anomaly_detector_frequency="anomalyDetectorFrequency" )
Attributes
-
anomaly_detector_frequency
¶ The frequency at which the detector analyzes its source data.
AppFlowConfigProperty¶
-
class
CfnAnomalyDetector.
AppFlowConfigProperty
(*, flow_name, role_arn)¶ Bases:
object
Details about an Amazon AppFlow flow datasource.
- Parameters
flow_name (
str
) – name of the flow.role_arn (
str
) – An IAM role that gives Amazon Lookout for Metrics permission to access the flow.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics app_flow_config_property = lookoutmetrics.CfnAnomalyDetector.AppFlowConfigProperty( flow_name="flowName", role_arn="roleArn" )
Attributes
-
flow_name
¶ name of the flow.
-
role_arn
¶ An IAM role that gives Amazon Lookout for Metrics permission to access the flow.
CloudwatchConfigProperty¶
-
class
CfnAnomalyDetector.
CloudwatchConfigProperty
(*, role_arn)¶ Bases:
object
Details about an Amazon CloudWatch datasource.
- Parameters
role_arn (
str
) – An IAM role that gives Amazon Lookout for Metrics permission to access data in Amazon CloudWatch.- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics cloudwatch_config_property = lookoutmetrics.CfnAnomalyDetector.CloudwatchConfigProperty( role_arn="roleArn" )
Attributes
-
role_arn
¶ An IAM role that gives Amazon Lookout for Metrics permission to access data in Amazon CloudWatch.
CsvFormatDescriptorProperty¶
-
class
CfnAnomalyDetector.
CsvFormatDescriptorProperty
(*, charset=None, contains_header=None, delimiter=None, file_compression=None, header_list=None, quote_symbol=None)¶ Bases:
object
Contains information about how a source CSV data file should be analyzed.
- Parameters
charset (
Optional
[str
]) – The character set in which the source CSV file is written.contains_header (
Union
[bool
,IResolvable
,None
]) – Whether or not the source CSV file contains a header.delimiter (
Optional
[str
]) – The character used to delimit the source CSV file.file_compression (
Optional
[str
]) – The level of compression of the source CSV file.header_list (
Optional
[Sequence
[str
]]) – A list of the source CSV file’s headers, if any.quote_symbol (
Optional
[str
]) – The character used as a quote character.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics csv_format_descriptor_property = lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" )
Attributes
-
charset
¶ The character set in which the source CSV file is written.
-
contains_header
¶ Whether or not the source CSV file contains a header.
-
delimiter
¶ The character used to delimit the source CSV file.
-
file_compression
¶ The level of compression of the source CSV file.
-
header_list
¶ A list of the source CSV file’s headers, if any.
-
quote_symbol
¶ The character used as a quote character.
FileFormatDescriptorProperty¶
-
class
CfnAnomalyDetector.
FileFormatDescriptorProperty
(*, csv_format_descriptor=None, json_format_descriptor=None)¶ Bases:
object
Contains information about a source file’s formatting.
- Parameters
csv_format_descriptor (
Union
[IResolvable
,CsvFormatDescriptorProperty
,None
]) – Contains information about how a source CSV data file should be analyzed.json_format_descriptor (
Union
[IResolvable
,JsonFormatDescriptorProperty
,None
]) – Contains information about how a source JSON data file should be analyzed.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics file_format_descriptor_property = lookoutmetrics.CfnAnomalyDetector.FileFormatDescriptorProperty( csv_format_descriptor=lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" ), json_format_descriptor=lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" ) )
Attributes
-
csv_format_descriptor
¶ Contains information about how a source CSV data file should be analyzed.
-
json_format_descriptor
¶ Contains information about how a source JSON data file should be analyzed.
JsonFormatDescriptorProperty¶
-
class
CfnAnomalyDetector.
JsonFormatDescriptorProperty
(*, charset=None, file_compression=None)¶ Bases:
object
Contains information about how a source JSON data file should be analyzed.
- Parameters
charset (
Optional
[str
]) – The character set in which the source JSON file is written.file_compression (
Optional
[str
]) – The level of compression of the source CSV file.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics json_format_descriptor_property = lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" )
Attributes
-
charset
¶ The character set in which the source JSON file is written.
-
file_compression
¶ The level of compression of the source CSV file.
MetricProperty¶
-
class
CfnAnomalyDetector.
MetricProperty
(*, aggregation_function, metric_name, namespace=None)¶ Bases:
object
A calculation made by contrasting a measure and a dimension from your source data.
- Parameters
aggregation_function (
str
) – The function with which the metric is calculated.metric_name (
str
) – The name of the metric.namespace (
Optional
[str
]) – The namespace for the metric.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics metric_property = lookoutmetrics.CfnAnomalyDetector.MetricProperty( aggregation_function="aggregationFunction", metric_name="metricName", # the properties below are optional namespace="namespace" )
Attributes
-
aggregation_function
¶ The function with which the metric is calculated.
-
metric_name
¶ The name of the metric.
-
namespace
¶ The namespace for the metric.
MetricSetProperty¶
-
class
CfnAnomalyDetector.
MetricSetProperty
(*, metric_list, metric_set_name, metric_source, dimension_list=None, metric_set_description=None, metric_set_frequency=None, offset=None, timestamp_column=None, timezone=None)¶ Bases:
object
Contains information about a dataset.
- Parameters
metric_list (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,MetricProperty
]]]) – A list of metrics that the dataset will contain.metric_set_name (
str
) – The name of the dataset.metric_source (
Union
[IResolvable
,MetricSourceProperty
]) – Contains information about how the source data should be interpreted.dimension_list (
Optional
[Sequence
[str
]]) – A list of the fields you want to treat as dimensions.metric_set_description (
Optional
[str
]) – A description of the dataset you are creating.metric_set_frequency (
Optional
[str
]) – The frequency with which the source data will be analyzed for anomalies.offset (
Union
[int
,float
,None
]) – After an interval ends, the amount of seconds that the detector waits before importing data. Offset is only supported for S3 and Redshift datasources.timestamp_column (
Union
[IResolvable
,TimestampColumnProperty
,None
]) – Contains information about the column used for tracking time in your source data.timezone (
Optional
[str
]) – The time zone in which your source data was recorded.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics metric_set_property = lookoutmetrics.CfnAnomalyDetector.MetricSetProperty( metric_list=[lookoutmetrics.CfnAnomalyDetector.MetricProperty( aggregation_function="aggregationFunction", metric_name="metricName", # the properties below are optional namespace="namespace" )], metric_set_name="metricSetName", metric_source=lookoutmetrics.CfnAnomalyDetector.MetricSourceProperty( app_flow_config=lookoutmetrics.CfnAnomalyDetector.AppFlowConfigProperty( flow_name="flowName", role_arn="roleArn" ), cloudwatch_config=lookoutmetrics.CfnAnomalyDetector.CloudwatchConfigProperty( role_arn="roleArn" ), rds_source_config=lookoutmetrics.CfnAnomalyDetector.RDSSourceConfigProperty( database_host="databaseHost", database_name="databaseName", database_port=123, db_instance_identifier="dbInstanceIdentifier", role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), redshift_source_config=lookoutmetrics.CfnAnomalyDetector.RedshiftSourceConfigProperty( cluster_identifier="clusterIdentifier", database_host="databaseHost", database_name="databaseName", database_port=123, role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), s3_source_config=lookoutmetrics.CfnAnomalyDetector.S3SourceConfigProperty( file_format_descriptor=lookoutmetrics.CfnAnomalyDetector.FileFormatDescriptorProperty( csv_format_descriptor=lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" ), json_format_descriptor=lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" ) ), role_arn="roleArn", # the properties below are optional historical_data_path_list=["historicalDataPathList"], templated_path_list=["templatedPathList"] ) ), # the properties below are optional dimension_list=["dimensionList"], metric_set_description="metricSetDescription", metric_set_frequency="metricSetFrequency", offset=123, timestamp_column=lookoutmetrics.CfnAnomalyDetector.TimestampColumnProperty( column_format="columnFormat", column_name="columnName" ), timezone="timezone" )
Attributes
-
dimension_list
¶ A list of the fields you want to treat as dimensions.
-
metric_list
¶ A list of metrics that the dataset will contain.
-
metric_set_description
¶ A description of the dataset you are creating.
-
metric_set_frequency
¶ The frequency with which the source data will be analyzed for anomalies.
-
metric_set_name
¶ The name of the dataset.
-
metric_source
¶ Contains information about how the source data should be interpreted.
-
offset
¶ After an interval ends, the amount of seconds that the detector waits before importing data.
Offset is only supported for S3 and Redshift datasources.
-
timestamp_column
¶ Contains information about the column used for tracking time in your source data.
-
timezone
¶ The time zone in which your source data was recorded.
MetricSourceProperty¶
-
class
CfnAnomalyDetector.
MetricSourceProperty
(*, app_flow_config=None, cloudwatch_config=None, rds_source_config=None, redshift_source_config=None, s3_source_config=None)¶ Bases:
object
Contains information about how the source data should be interpreted.
- Parameters
app_flow_config (
Union
[IResolvable
,AppFlowConfigProperty
,None
]) – An object containing information about the AppFlow configuration.cloudwatch_config (
Union
[IResolvable
,CloudwatchConfigProperty
,None
]) – An object containing information about the Amazon CloudWatch monitoring configuration.rds_source_config (
Union
[IResolvable
,RDSSourceConfigProperty
,None
]) – An object containing information about the Amazon Relational Database Service (RDS) configuration.redshift_source_config (
Union
[IResolvable
,RedshiftSourceConfigProperty
,None
]) – An object containing information about the Amazon Redshift database configuration.s3_source_config (
Union
[IResolvable
,S3SourceConfigProperty
,None
]) – Contains information about the configuration of the S3 bucket that contains source files.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics metric_source_property = lookoutmetrics.CfnAnomalyDetector.MetricSourceProperty( app_flow_config=lookoutmetrics.CfnAnomalyDetector.AppFlowConfigProperty( flow_name="flowName", role_arn="roleArn" ), cloudwatch_config=lookoutmetrics.CfnAnomalyDetector.CloudwatchConfigProperty( role_arn="roleArn" ), rds_source_config=lookoutmetrics.CfnAnomalyDetector.RDSSourceConfigProperty( database_host="databaseHost", database_name="databaseName", database_port=123, db_instance_identifier="dbInstanceIdentifier", role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), redshift_source_config=lookoutmetrics.CfnAnomalyDetector.RedshiftSourceConfigProperty( cluster_identifier="clusterIdentifier", database_host="databaseHost", database_name="databaseName", database_port=123, role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) ), s3_source_config=lookoutmetrics.CfnAnomalyDetector.S3SourceConfigProperty( file_format_descriptor=lookoutmetrics.CfnAnomalyDetector.FileFormatDescriptorProperty( csv_format_descriptor=lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" ), json_format_descriptor=lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" ) ), role_arn="roleArn", # the properties below are optional historical_data_path_list=["historicalDataPathList"], templated_path_list=["templatedPathList"] ) )
Attributes
-
app_flow_config
¶ An object containing information about the AppFlow configuration.
-
cloudwatch_config
¶ An object containing information about the Amazon CloudWatch monitoring configuration.
-
rds_source_config
¶ An object containing information about the Amazon Relational Database Service (RDS) configuration.
-
redshift_source_config
¶ An object containing information about the Amazon Redshift database configuration.
-
s3_source_config
¶ Contains information about the configuration of the S3 bucket that contains source files.
RDSSourceConfigProperty¶
-
class
CfnAnomalyDetector.
RDSSourceConfigProperty
(*, database_host, database_name, database_port, db_instance_identifier, role_arn, secret_manager_arn, table_name, vpc_configuration)¶ Bases:
object
Contains information about the Amazon Relational Database Service (RDS) configuration.
- Parameters
database_host (
str
) – The host name of the database.database_name (
str
) – The name of the RDS database.database_port (
Union
[int
,float
]) – The port number where the database can be accessed.db_instance_identifier (
str
) – A string identifying the database instance.role_arn (
str
) – The Amazon Resource Name (ARN) of the role.secret_manager_arn (
str
) – The Amazon Resource Name (ARN) of the AWS Secrets Manager role.table_name (
str
) – The name of the table in the database.vpc_configuration (
Union
[IResolvable
,VpcConfigurationProperty
]) – An object containing information about the Amazon Virtual Private Cloud (VPC) configuration.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics r_dSSource_config_property = lookoutmetrics.CfnAnomalyDetector.RDSSourceConfigProperty( database_host="databaseHost", database_name="databaseName", database_port=123, db_instance_identifier="dbInstanceIdentifier", role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) )
Attributes
-
database_host
¶ The host name of the database.
-
database_name
¶ The name of the RDS database.
-
database_port
¶ The port number where the database can be accessed.
-
db_instance_identifier
¶ A string identifying the database instance.
-
role_arn
¶ The Amazon Resource Name (ARN) of the role.
-
secret_manager_arn
¶ The Amazon Resource Name (ARN) of the AWS Secrets Manager role.
-
table_name
¶ The name of the table in the database.
-
vpc_configuration
¶ An object containing information about the Amazon Virtual Private Cloud (VPC) configuration.
RedshiftSourceConfigProperty¶
-
class
CfnAnomalyDetector.
RedshiftSourceConfigProperty
(*, cluster_identifier, database_host, database_name, database_port, role_arn, secret_manager_arn, table_name, vpc_configuration)¶ Bases:
object
Provides information about the Amazon Redshift database configuration.
- Parameters
cluster_identifier (
str
) – A string identifying the Redshift cluster.database_host (
str
) – The name of the database host.database_name (
str
) – The Redshift database name.database_port (
Union
[int
,float
]) – The port number where the database can be accessed.role_arn (
str
) – The Amazon Resource Name (ARN) of the role providing access to the database.secret_manager_arn (
str
) – The Amazon Resource Name (ARN) of the AWS Secrets Manager role.table_name (
str
) – The table name of the Redshift database.vpc_configuration (
Union
[IResolvable
,VpcConfigurationProperty
]) – Contains information about the Amazon Virtual Private Cloud (VPC) configuration.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics redshift_source_config_property = lookoutmetrics.CfnAnomalyDetector.RedshiftSourceConfigProperty( cluster_identifier="clusterIdentifier", database_host="databaseHost", database_name="databaseName", database_port=123, role_arn="roleArn", secret_manager_arn="secretManagerArn", table_name="tableName", vpc_configuration=lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] ) )
Attributes
-
cluster_identifier
¶ A string identifying the Redshift cluster.
-
database_host
¶ The name of the database host.
-
database_name
¶ The Redshift database name.
-
database_port
¶ The port number where the database can be accessed.
-
role_arn
¶ The Amazon Resource Name (ARN) of the role providing access to the database.
-
secret_manager_arn
¶ The Amazon Resource Name (ARN) of the AWS Secrets Manager role.
-
table_name
¶ The table name of the Redshift database.
-
vpc_configuration
¶ Contains information about the Amazon Virtual Private Cloud (VPC) configuration.
S3SourceConfigProperty¶
-
class
CfnAnomalyDetector.
S3SourceConfigProperty
(*, file_format_descriptor, role_arn, historical_data_path_list=None, templated_path_list=None)¶ Bases:
object
Contains information about the configuration of the S3 bucket that contains source files.
- Parameters
file_format_descriptor (
Union
[IResolvable
,FileFormatDescriptorProperty
]) – Contains information about a source file’s formatting.role_arn (
str
) – The ARN of an IAM role that has read and write access permissions to the source S3 bucket.historical_data_path_list (
Optional
[Sequence
[str
]]) – A list of paths to the historical data files.templated_path_list (
Optional
[Sequence
[str
]]) – A list of templated paths to the source files.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics s3_source_config_property = lookoutmetrics.CfnAnomalyDetector.S3SourceConfigProperty( file_format_descriptor=lookoutmetrics.CfnAnomalyDetector.FileFormatDescriptorProperty( csv_format_descriptor=lookoutmetrics.CfnAnomalyDetector.CsvFormatDescriptorProperty( charset="charset", contains_header=False, delimiter="delimiter", file_compression="fileCompression", header_list=["headerList"], quote_symbol="quoteSymbol" ), json_format_descriptor=lookoutmetrics.CfnAnomalyDetector.JsonFormatDescriptorProperty( charset="charset", file_compression="fileCompression" ) ), role_arn="roleArn", # the properties below are optional historical_data_path_list=["historicalDataPathList"], templated_path_list=["templatedPathList"] )
Attributes
-
file_format_descriptor
¶ Contains information about a source file’s formatting.
-
historical_data_path_list
¶ A list of paths to the historical data files.
-
role_arn
¶ The ARN of an IAM role that has read and write access permissions to the source S3 bucket.
-
templated_path_list
¶ A list of templated paths to the source files.
TimestampColumnProperty¶
-
class
CfnAnomalyDetector.
TimestampColumnProperty
(*, column_format=None, column_name=None)¶ Bases:
object
Contains information about the column used to track time in a source data file.
- Parameters
column_format (
Optional
[str
]) – The format of the timestamp column.column_name (
Optional
[str
]) – The name of the timestamp column.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics timestamp_column_property = lookoutmetrics.CfnAnomalyDetector.TimestampColumnProperty( column_format="columnFormat", column_name="columnName" )
Attributes
-
column_format
¶ The format of the timestamp column.
-
column_name
¶ The name of the timestamp column.
VpcConfigurationProperty¶
-
class
CfnAnomalyDetector.
VpcConfigurationProperty
(*, security_group_id_list, subnet_id_list)¶ Bases:
object
Contains configuration information about the Amazon Virtual Private Cloud (VPC).
- Parameters
security_group_id_list (
Sequence
[str
]) – An array of strings containing the list of security groups.subnet_id_list (
Sequence
[str
]) – An array of strings containing the Amazon VPC subnet IDs (e.g.,subnet-0bb1c79de3EXAMPLE
.
- Link
- ExampleMetadata
fixture=_generated
Example:
# The code below shows an example of how to instantiate this type. # The values are placeholders you should change. import aws_cdk.aws_lookoutmetrics as lookoutmetrics vpc_configuration_property = lookoutmetrics.CfnAnomalyDetector.VpcConfigurationProperty( security_group_id_list=["securityGroupIdList"], subnet_id_list=["subnetIdList"] )
Attributes
-
security_group_id_list
¶ An array of strings containing the list of security groups.
-
subnet_id_list
¶ An array of strings containing the Amazon VPC subnet IDs (e.g.,
subnet-0bb1c79de3EXAMPLE
.