apache_beam.ml.gcp.cloud_dlp module¶

PTransforms that implement Google Cloud Data Loss Prevention functionality.

class apache_beam.ml.gcp.cloud_dlp.MaskDetectedDetails(project=None, deidentification_template_name=None, deidentification_config=None, inspection_template_name=None, inspection_config=None, timeout=None)[source]¶

Bases: apache_beam.transforms.ptransform.PTransform

Scrubs sensitive information detected in text. The PTransform returns a PCollection of str Example usage:

pipeline | MaskDetectedDetails(project='example-gcp-project',
  deidentification_config={
      'info_type_transformations: {
          'transformations': [{
              'primitive_transformation': {
                  'character_mask_config': {
                      'masking_character': '#'
                  }
              }
          }]
      }
  }, inspection_config={'info_types': [{'name': 'EMAIL_ADDRESS'}]})

Initializes a MaskDetectedDetails transform.

Parameters:

project – Optional. GCP project name in which inspection will be performed
deidentification_template_name (str) – Either this or deidentification_config required. Name of deidentification template to be used on detected sensitive information instances in text.
deidentification_config – (Union[dict, google.cloud.dlp_v2.types.DeidentifyConfig]): Configuration for the de-identification of the content item. If both template name and config are supplied, config is more important.
inspection_template_name (str) – This or inspection_config required. Name of inspection template to be used to detect sensitive data in text.
inspection_config – (Union[dict, google.cloud.dlp_v2.types.InspectConfig]): Configuration for the inspector used to detect sensitive data in text. If both template name and config are supplied, config takes precedence.
timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.

expand(pcoll)[source]¶

annotations() → Dict[str, Union[bytes, str, google.protobuf.message.Message]]¶

default_label()¶

default_type_hints()¶

display_data()¶

Returns the display data associated to a pipeline component.

It should be reimplemented in pipeline components that wish to have static display data.

Returns:

A dictionary containing key:value pairs. The value might be an integer, float or string value; a DisplayDataItem for values that have more data (e.g. short value, label, url); or a HasDisplayData instance that has more display data that should be picked up. For example:

{
  'key1': 'string_value',
  'key2': 1234,
  'key3': 3.14159265,
  'key4': DisplayDataItem('apache.org', url='http://apache.org'),
  'key5': subComponent
}

Return type: Dict[str, Any]

classmethod from_runner_api(proto, context)¶

get_type_hints()¶

Gets and/or initializes type hints for this object.

If type hints have not been set, attempts to initialize type hints in this order: - Using self.default_type_hints(). - Using self.__class__ type hints.

get_windowing(inputs)¶

Returns the window function to be associated with transform’s output.

By default most transforms just return the windowing function associated with the input PCollection (or the first input if several).

infer_output_type(unused_input_type)¶

label¶

pipeline = None¶

classmethod register_urn(urn, parameter_type, constructor=None)¶

runner_api_requires_keyed_input()¶

side_inputs = ()¶

to_runner_api(context, has_parts=False, **extra_kwargs)¶

to_runner_api_parameter(unused_context)¶

to_runner_api_pickled(unused_context)¶

type_check_inputs(pvalueish)¶

type_check_inputs_or_outputs(pvalueish, input_or_output)¶

type_check_outputs(pvalueish)¶

with_input_types(input_type_hint)¶

Annotates the input type of a PTransform with a type-hint.

Parameters:	input_type_hint (type) – An instance of an allowed built-in type, a custom class, or an instance of a `TypeConstraint`.
Raises:	`TypeError` – If input_type_hint is not a valid type-hint. See `apache_beam.typehints.typehints.validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

with_output_types(type_hint)¶

Annotates the output type of a PTransform with a type-hint.

Parameters:	type_hint (type) – An instance of an allowed built-in type, a custom class, or a `TypeConstraint`.
Raises:	`TypeError` – If type_hint is not a valid type-hint. See `validate_composite_type_param()` for further details.
Returns:	A reference to the instance of this particular `PTransform` object. This allows chaining type-hinting related methods.
Return type:	PTransform

class apache_beam.ml.gcp.cloud_dlp.InspectForDetails(project=None, inspection_template_name=None, inspection_config=None, timeout=None)[source]¶