CfnConfiguredTable
- class aws_cdk.aws_cleanrooms.CfnConfiguredTable(scope, id, *, allowed_columns, analysis_method, name, table_reference, analysis_rules=None, description=None, tags=None)
Bases:
CfnResource
Creates a new configured table resource.
- See:
- CloudformationResource:
AWS::CleanRooms::ConfiguredTable
- 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_cleanrooms as cleanrooms cfn_configured_table = cleanrooms.CfnConfiguredTable(self, "MyCfnConfiguredTable", allowed_columns=["allowedColumns"], analysis_method="analysisMethod", name="name", table_reference=cleanrooms.CfnConfiguredTable.TableReferenceProperty( glue=cleanrooms.CfnConfiguredTable.GlueTableReferenceProperty( database_name="databaseName", table_name="tableName" ) ), # the properties below are optional analysis_rules=[cleanrooms.CfnConfiguredTable.AnalysisRuleProperty( policy=cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyProperty( v1=cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyV1Property( aggregation=cleanrooms.CfnConfiguredTable.AnalysisRuleAggregationProperty( aggregate_columns=[cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )], dimension_columns=["dimensionColumns"], join_columns=["joinColumns"], output_constraints=[cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )], scalar_functions=["scalarFunctions"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"], join_required="joinRequired" ), custom=cleanrooms.CfnConfiguredTable.AnalysisRuleCustomProperty( allowed_analyses=["allowedAnalyses"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_analysis_providers=["allowedAnalysisProviders"], differential_privacy=cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] ), disallowed_output_columns=["disallowedOutputColumns"] ), list=cleanrooms.CfnConfiguredTable.AnalysisRuleListProperty( join_columns=["joinColumns"], list_columns=["listColumns"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"] ) ) ), type="type" )], description="description", tags=[CfnTag( key="key", value="value" )] )
- Parameters:
scope (
Construct
) – Scope in which this resource is defined.id (
str
) – Construct identifier for this resource (unique in its scope).allowed_columns (
Sequence
[str
]) – The columns within the underlying AWS Glue table that can be utilized within collaborations.analysis_method (
str
) – The analysis method for the configured table. The only valid value is currentlyDIRECT_QUERY
.name (
str
) – A name for the configured table.table_reference (
Union
[IResolvable
,TableReferenceProperty
,Dict
[str
,Any
]]) – The AWS Glue table that this configured table represents.analysis_rules (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,AnalysisRuleProperty
,Dict
[str
,Any
]]],None
]) – The analysis rule that was created for the configured table.description (
Optional
[str
]) – A description for the configured table.tags (
Optional
[Sequence
[Union
[CfnTag
,Dict
[str
,Any
]]]]) – An optional label that you can assign to a resource when you create it. Each tag consists of a key and an optional value, both of which you define. When you use tagging, you can also use tag-based access control in IAM policies to control access to this resource.
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 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
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::CleanRooms::ConfiguredTable'
- allowed_columns
The columns within the underlying AWS Glue table that can be utilized within collaborations.
- analysis_method
The analysis method for the configured table.
- analysis_rules
The analysis rule that was created for the configured table.
- attr_arn
Returns the Amazon Resource Name (ARN) of the specified configured table.
Example:
arn:aws:cleanrooms:us-east-1:111122223333:configuredtable/a1b2c3d4-5678-90ab-cdef-EXAMPLE11111
- CloudformationAttribute:
Arn
- attr_configured_table_identifier
Returns the unique identifier of the specified configured table.
Example:
a1b2c3d4-5678-90ab-cdef-EXAMPLE33333
- CloudformationAttribute:
ConfiguredTableIdentifier
- 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.
- 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.
- description
A description for the configured table.
- 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.
- name
A name for the configured table.
- 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).
- table_reference
The AWS Glue table that this configured table represents.
- tags
An optional label that you can assign to a resource when you create it.
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
.
AggregateColumnProperty
- class CfnConfiguredTable.AggregateColumnProperty(*, column_names, function)
Bases:
object
Column in configured table that can be used in aggregate function in query.
- Parameters:
column_names (
Sequence
[str
]) – Column names in configured table of aggregate columns.function (
str
) – Aggregation function that can be applied to aggregate column in query.
- 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_cleanrooms as cleanrooms aggregate_column_property = cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )
Attributes
- column_names
Column names in configured table of aggregate columns.
- function
Aggregation function that can be applied to aggregate column in query.
AggregationConstraintProperty
- class CfnConfiguredTable.AggregationConstraintProperty(*, column_name, minimum, type)
Bases:
object
Constraint on query output removing output rows that do not meet a minimum number of distinct values of a specified column.
- Parameters:
column_name (
str
) – Column in aggregation constraint for which there must be a minimum number of distinct values in an output row for it to be in the query output.minimum (
Union
[int
,float
]) – The minimum number of distinct values that an output row must be an aggregation of. Minimum threshold of distinct values for a specified column that must exist in an output row for it to be in the query output.type (
str
) – The type of aggregation the constraint allows. The only valid value is currentlyCOUNT_DISTINCT
.
- 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_cleanrooms as cleanrooms aggregation_constraint_property = cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )
Attributes
- column_name
Column in aggregation constraint for which there must be a minimum number of distinct values in an output row for it to be in the query output.
- minimum
The minimum number of distinct values that an output row must be an aggregation of.
Minimum threshold of distinct values for a specified column that must exist in an output row for it to be in the query output.
- type
The type of aggregation the constraint allows.
The only valid value is currently
COUNT_DISTINCT
.
AnalysisRuleAggregationProperty
- class CfnConfiguredTable.AnalysisRuleAggregationProperty(*, aggregate_columns, dimension_columns, join_columns, output_constraints, scalar_functions, additional_analyses=None, allowed_join_operators=None, join_required=None)
Bases:
object
A type of analysis rule that enables query structure and specified queries that produce aggregate statistics.
- Parameters:
aggregate_columns (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,AggregateColumnProperty
,Dict
[str
,Any
]]]]) – The columns that query runners are allowed to use in aggregation queries.dimension_columns (
Sequence
[str
]) – The columns that query runners are allowed to select, group by, or filter by.join_columns (
Sequence
[str
]) – Columns in configured table that can be used in join statements and/or as aggregate columns. They can never be outputted directly.output_constraints (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,AggregationConstraintProperty
,Dict
[str
,Any
]]]]) – Columns that must meet a specific threshold value (after an aggregation function is applied to it) for each output row to be returned.scalar_functions (
Sequence
[str
]) – Set of scalar functions that are allowed to be used on dimension columns and the output of aggregation of metrics.additional_analyses (
Optional
[str
]) – An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query. TheadditionalAnalyses
parameter is currently supported for the list analysis rule (AnalysisRuleList
) and the custom analysis rule (AnalysisRuleCustom
).allowed_join_operators (
Optional
[Sequence
[str
]]) – Which logical operators (if any) are to be used in an INNER JOIN match condition. Default isAND
.join_required (
Optional
[str
]) – Control that requires member who runs query to do a join with their configured table and/or other configured table in query.
- 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_cleanrooms as cleanrooms analysis_rule_aggregation_property = cleanrooms.CfnConfiguredTable.AnalysisRuleAggregationProperty( aggregate_columns=[cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )], dimension_columns=["dimensionColumns"], join_columns=["joinColumns"], output_constraints=[cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )], scalar_functions=["scalarFunctions"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"], join_required="joinRequired" )
Attributes
- additional_analyses
An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query.
The
additionalAnalyses
parameter is currently supported for the list analysis rule (AnalysisRuleList
) and the custom analysis rule (AnalysisRuleCustom
).
- aggregate_columns
The columns that query runners are allowed to use in aggregation queries.
- allowed_join_operators
Which logical operators (if any) are to be used in an INNER JOIN match condition.
Default is
AND
.
- dimension_columns
The columns that query runners are allowed to select, group by, or filter by.
- join_columns
Columns in configured table that can be used in join statements and/or as aggregate columns.
They can never be outputted directly.
- join_required
Control that requires member who runs query to do a join with their configured table and/or other configured table in query.
- output_constraints
Columns that must meet a specific threshold value (after an aggregation function is applied to it) for each output row to be returned.
- scalar_functions
Set of scalar functions that are allowed to be used on dimension columns and the output of aggregation of metrics.
AnalysisRuleCustomProperty
- class CfnConfiguredTable.AnalysisRuleCustomProperty(*, allowed_analyses, additional_analyses=None, allowed_analysis_providers=None, differential_privacy=None, disallowed_output_columns=None)
Bases:
object
A type of analysis rule that enables the table owner to approve custom SQL queries on their configured tables.
It supports differential privacy.
- Parameters:
allowed_analyses (
Sequence
[str
]) – The ARN of the analysis templates that are allowed by the custom analysis rule.additional_analyses (
Optional
[str
]) – An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query.allowed_analysis_providers (
Optional
[Sequence
[str
]]) – The IDs of the AWS accounts that are allowed to query by the custom analysis rule. Required whenallowedAnalyses
isANY_QUERY
.differential_privacy (
Union
[IResolvable
,DifferentialPrivacyProperty
,Dict
[str
,Any
],None
]) – The differential privacy configuration.disallowed_output_columns (
Optional
[Sequence
[str
]]) – A list of columns that aren’t allowed to be shown in the query output.
- 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_cleanrooms as cleanrooms analysis_rule_custom_property = cleanrooms.CfnConfiguredTable.AnalysisRuleCustomProperty( allowed_analyses=["allowedAnalyses"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_analysis_providers=["allowedAnalysisProviders"], differential_privacy=cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] ), disallowed_output_columns=["disallowedOutputColumns"] )
Attributes
- additional_analyses
An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query.
- allowed_analyses
The ARN of the analysis templates that are allowed by the custom analysis rule.
- allowed_analysis_providers
The IDs of the AWS accounts that are allowed to query by the custom analysis rule.
Required when
allowedAnalyses
isANY_QUERY
.
- differential_privacy
The differential privacy configuration.
- disallowed_output_columns
A list of columns that aren’t allowed to be shown in the query output.
AnalysisRuleListProperty
- class CfnConfiguredTable.AnalysisRuleListProperty(*, join_columns, list_columns, additional_analyses=None, allowed_join_operators=None)
Bases:
object
A type of analysis rule that enables row-level analysis.
- Parameters:
join_columns (
Sequence
[str
]) – Columns that can be used to join a configured table with the table of the member who can query and other members’ configured tables.list_columns (
Sequence
[str
]) – Columns that can be listed in the output.additional_analyses (
Optional
[str
]) – An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query.allowed_join_operators (
Optional
[Sequence
[str
]]) – The logical operators (if any) that are to be used in an INNER JOIN match condition. Default isAND
.
- 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_cleanrooms as cleanrooms analysis_rule_list_property = cleanrooms.CfnConfiguredTable.AnalysisRuleListProperty( join_columns=["joinColumns"], list_columns=["listColumns"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"] )
Attributes
- additional_analyses
An indicator as to whether additional analyses (such as AWS Clean Rooms ML) can be applied to the output of the direct query.
- allowed_join_operators
The logical operators (if any) that are to be used in an INNER JOIN match condition.
Default is
AND
.
- join_columns
Columns that can be used to join a configured table with the table of the member who can query and other members’ configured tables.
- list_columns
Columns that can be listed in the output.
AnalysisRuleProperty
- class CfnConfiguredTable.AnalysisRuleProperty(*, policy, type)
Bases:
object
A specification about how data from the configured table can be used in a query.
- Parameters:
policy (
Union
[IResolvable
,ConfiguredTableAnalysisRulePolicyProperty
,Dict
[str
,Any
]]) – A policy that describes the associated data usage limitations.type (
str
) – The type of analysis rule.
- 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_cleanrooms as cleanrooms analysis_rule_property = cleanrooms.CfnConfiguredTable.AnalysisRuleProperty( policy=cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyProperty( v1=cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyV1Property( aggregation=cleanrooms.CfnConfiguredTable.AnalysisRuleAggregationProperty( aggregate_columns=[cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )], dimension_columns=["dimensionColumns"], join_columns=["joinColumns"], output_constraints=[cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )], scalar_functions=["scalarFunctions"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"], join_required="joinRequired" ), custom=cleanrooms.CfnConfiguredTable.AnalysisRuleCustomProperty( allowed_analyses=["allowedAnalyses"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_analysis_providers=["allowedAnalysisProviders"], differential_privacy=cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] ), disallowed_output_columns=["disallowedOutputColumns"] ), list=cleanrooms.CfnConfiguredTable.AnalysisRuleListProperty( join_columns=["joinColumns"], list_columns=["listColumns"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"] ) ) ), type="type" )
Attributes
- policy
A policy that describes the associated data usage limitations.
ConfiguredTableAnalysisRulePolicyProperty
- class CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyProperty(*, v1)
Bases:
object
Controls on the query specifications that can be run on a configured table.
- Parameters:
v1 (
Union
[IResolvable
,ConfiguredTableAnalysisRulePolicyV1Property
,Dict
[str
,Any
]]) – Controls on the query specifications that can be run on a configured table.- 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_cleanrooms as cleanrooms configured_table_analysis_rule_policy_property = cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyProperty( v1=cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyV1Property( aggregation=cleanrooms.CfnConfiguredTable.AnalysisRuleAggregationProperty( aggregate_columns=[cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )], dimension_columns=["dimensionColumns"], join_columns=["joinColumns"], output_constraints=[cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )], scalar_functions=["scalarFunctions"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"], join_required="joinRequired" ), custom=cleanrooms.CfnConfiguredTable.AnalysisRuleCustomProperty( allowed_analyses=["allowedAnalyses"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_analysis_providers=["allowedAnalysisProviders"], differential_privacy=cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] ), disallowed_output_columns=["disallowedOutputColumns"] ), list=cleanrooms.CfnConfiguredTable.AnalysisRuleListProperty( join_columns=["joinColumns"], list_columns=["listColumns"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"] ) ) )
Attributes
- v1
Controls on the query specifications that can be run on a configured table.
ConfiguredTableAnalysisRulePolicyV1Property
- class CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyV1Property(*, aggregation=None, custom=None, list=None)
Bases:
object
Controls on the query specifications that can be run on a configured table.
- Parameters:
aggregation (
Union
[IResolvable
,AnalysisRuleAggregationProperty
,Dict
[str
,Any
],None
]) – Analysis rule type that enables only aggregation queries on a configured table.custom (
Union
[IResolvable
,AnalysisRuleCustomProperty
,Dict
[str
,Any
],None
]) – Analysis rule type that enables custom SQL queries on a configured table.list (
Union
[IResolvable
,AnalysisRuleListProperty
,Dict
[str
,Any
],None
]) – Analysis rule type that enables only list queries on a configured table.
- 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_cleanrooms as cleanrooms configured_table_analysis_rule_policy_v1_property = cleanrooms.CfnConfiguredTable.ConfiguredTableAnalysisRulePolicyV1Property( aggregation=cleanrooms.CfnConfiguredTable.AnalysisRuleAggregationProperty( aggregate_columns=[cleanrooms.CfnConfiguredTable.AggregateColumnProperty( column_names=["columnNames"], function="function" )], dimension_columns=["dimensionColumns"], join_columns=["joinColumns"], output_constraints=[cleanrooms.CfnConfiguredTable.AggregationConstraintProperty( column_name="columnName", minimum=123, type="type" )], scalar_functions=["scalarFunctions"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"], join_required="joinRequired" ), custom=cleanrooms.CfnConfiguredTable.AnalysisRuleCustomProperty( allowed_analyses=["allowedAnalyses"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_analysis_providers=["allowedAnalysisProviders"], differential_privacy=cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] ), disallowed_output_columns=["disallowedOutputColumns"] ), list=cleanrooms.CfnConfiguredTable.AnalysisRuleListProperty( join_columns=["joinColumns"], list_columns=["listColumns"], # the properties below are optional additional_analyses="additionalAnalyses", allowed_join_operators=["allowedJoinOperators"] ) )
Attributes
- aggregation
Analysis rule type that enables only aggregation queries on a configured table.
- custom
Analysis rule type that enables custom SQL queries on a configured table.
- list
Analysis rule type that enables only list queries on a configured table.
DifferentialPrivacyColumnProperty
- class CfnConfiguredTable.DifferentialPrivacyColumnProperty(*, name)
Bases:
object
Specifies the name of the column that contains the unique identifier of your users, whose privacy you want to protect.
- Parameters:
name (
str
) – The name of the column, such as user_id, that contains the unique identifier of your users, whose privacy you want to protect. If you want to turn on differential privacy for two or more tables in a collaboration, you must configure the same column as the user identifier column in both analysis rules.- 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_cleanrooms as cleanrooms differential_privacy_column_property = cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )
Attributes
- name
The name of the column, such as user_id, that contains the unique identifier of your users, whose privacy you want to protect.
If you want to turn on differential privacy for two or more tables in a collaboration, you must configure the same column as the user identifier column in both analysis rules.
DifferentialPrivacyProperty
- class CfnConfiguredTable.DifferentialPrivacyProperty(*, columns)
Bases:
object
The analysis method for the configured tables.
The only valid value is currently
DIRECT_QUERY
.- Parameters:
columns (
Union
[IResolvable
,Sequence
[Union
[IResolvable
,DifferentialPrivacyColumnProperty
,Dict
[str
,Any
]]]]) – The name of the column, such as user_id, that contains the unique identifier of your users, whose privacy you want to protect. If you want to turn on differential privacy for two or more tables in a collaboration, you must configure the same column as the user identifier column in both analysis rules.- 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_cleanrooms as cleanrooms differential_privacy_property = cleanrooms.CfnConfiguredTable.DifferentialPrivacyProperty( columns=[cleanrooms.CfnConfiguredTable.DifferentialPrivacyColumnProperty( name="name" )] )
Attributes
- columns
The name of the column, such as user_id, that contains the unique identifier of your users, whose privacy you want to protect.
If you want to turn on differential privacy for two or more tables in a collaboration, you must configure the same column as the user identifier column in both analysis rules.
GlueTableReferenceProperty
- class CfnConfiguredTable.GlueTableReferenceProperty(*, database_name, table_name)
Bases:
object
A reference to a table within an AWS Glue data catalog.
- Parameters:
database_name (
str
) – The name of the database the AWS Glue table belongs to.table_name (
str
) – The name of the AWS Glue table.
- 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_cleanrooms as cleanrooms glue_table_reference_property = cleanrooms.CfnConfiguredTable.GlueTableReferenceProperty( database_name="databaseName", table_name="tableName" )
Attributes
- database_name
The name of the database the AWS Glue table belongs to.
- table_name
The name of the AWS Glue table.
TableReferenceProperty
- class CfnConfiguredTable.TableReferenceProperty(*, glue)
Bases:
object
A pointer to the dataset that underlies this table.
Currently, this can only be an AWS Glue table.
- Parameters:
glue (
Union
[IResolvable
,GlueTableReferenceProperty
,Dict
[str
,Any
]]) – If present, a reference to the AWS Glue table referred to by this table reference.- 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_cleanrooms as cleanrooms table_reference_property = cleanrooms.CfnConfiguredTable.TableReferenceProperty( glue=cleanrooms.CfnConfiguredTable.GlueTableReferenceProperty( database_name="databaseName", table_name="tableName" ) )
Attributes
- glue
If present, a reference to the AWS Glue table referred to by this table reference.