apache_beam.runners.direct.helper_transforms module

class apache_beam.runners.direct.helper_transforms.LiftedCombinePerKey(combine_fn, args, kwargs)

Bases: apache_beam.transforms.ptransform.PTransform

An implementation of CombinePerKey that does mapper-side pre-combining.

expand(pcoll)

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.runners.direct.helper_transforms.PartialGroupByKeyCombiningValues(combine_fn)

Bases: apache_beam.transforms.core.DoFn

Aggregates values into a per-key-window cache.

As bundles are in-memory-sized, we don’t bother flushing until the very end.

setup()

start_bundle()

process(element, window=WindowParam)

finish_bundle()

teardown()

default_type_hints()

BundleFinalizerParam

alias of apache_beam.transforms.core._BundleFinalizerParam

DoFnProcessParams = [ElementParam, SideInputParam, TimestampParam, WindowParam, <class 'apache_beam.transforms.core._WatermarkEstimatorParam'>, PaneInfoParam, <class 'apache_beam.transforms.core._BundleFinalizerParam'>, KeyParam, <class 'apache_beam.transforms.core._StateDoFnParam'>, <class 'apache_beam.transforms.core._TimerDoFnParam'>]

DynamicTimerTagParam = DynamicTimerTagParam

ElementParam = ElementParam

KeyParam = KeyParam

PaneInfoParam = PaneInfoParam

RestrictionParam

alias of apache_beam.transforms.core._RestrictionDoFnParam

SideInputParam = SideInputParam

StateParam

alias of apache_beam.transforms.core._StateDoFnParam

TimerParam

alias of apache_beam.transforms.core._TimerDoFnParam

TimestampParam = TimestampParam

WatermarkEstimatorParam

alias of apache_beam.transforms.core._WatermarkEstimatorParam

WindowParam = WindowParam

default_label()

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]

static from_callable(fn)

classmethod from_runner_api(fn_proto, context)

Converts from an FunctionSpec to a Fn object.

Prefer registering a urn with its parameter type and constructor.

get_function_arguments(func)

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.

infer_output_type(input_type)

classmethod register_pickle_urn(pickle_urn)

Registers and implements the given urn via pickling.

classmethod register_urn(urn, parameter_type, fn=None)

Registers a urn with a constructor.

For example, if ‘beam:fn:foo’ had parameter type FooPayload, one could write RunnerApiFn.register_urn(‘bean:fn:foo’, FooPayload, foo_from_proto) where foo_from_proto took as arguments a FooPayload and a PipelineContext. This function can also be used as a decorator rather than passing the callable in as the final parameter.

A corresponding to_runner_api_parameter method would be expected that returns the tuple (‘beam:fn:foo’, FooPayload)

to_runner_api(context)

Returns an FunctionSpec encoding this Fn.

Prefer overriding self.to_runner_api_parameter.

to_runner_api_parameter(context)

static unbounded_per_element()

A decorator on process fn specifying that the fn performs an unbounded amount of work per input element.

with_input_types(*arg_hints, **kwarg_hints)

with_output_types(*arg_hints, **kwarg_hints)

class apache_beam.runners.direct.helper_transforms.FinishCombine(combine_fn)

Bases: apache_beam.transforms.core.DoFn

Merges partially combined results.

setup()

process(element)

teardown()

default_type_hints()

BundleFinalizerParam

alias of apache_beam.transforms.core._BundleFinalizerParam

DoFnProcessParams = [ElementParam, SideInputParam, TimestampParam, WindowParam, <class 'apache_beam.transforms.core._WatermarkEstimatorParam'>, PaneInfoParam, <class 'apache_beam.transforms.core._BundleFinalizerParam'>, KeyParam, <class 'apache_beam.transforms.core._StateDoFnParam'>, <class 'apache_beam.transforms.core._TimerDoFnParam'>]

DynamicTimerTagParam = DynamicTimerTagParam

ElementParam = ElementParam

KeyParam = KeyParam

PaneInfoParam = PaneInfoParam

RestrictionParam

alias of apache_beam.transforms.core._RestrictionDoFnParam

SideInputParam = SideInputParam

StateParam

alias of apache_beam.transforms.core._StateDoFnParam

TimerParam

alias of apache_beam.transforms.core._TimerDoFnParam

TimestampParam = TimestampParam

WatermarkEstimatorParam

alias of apache_beam.transforms.core._WatermarkEstimatorParam

WindowParam = WindowParam

default_label()

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]

finish_bundle()

Called after a bundle of elements is processed on a worker.

static from_callable(fn)

classmethod from_runner_api(fn_proto, context)

Converts from an FunctionSpec to a Fn object.

Prefer registering a urn with its parameter type and constructor.

get_function_arguments(func)

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.

infer_output_type(input_type)

classmethod register_pickle_urn(pickle_urn)

Registers and implements the given urn via pickling.

classmethod register_urn(urn, parameter_type, fn=None)

Registers a urn with a constructor.

For example, if ‘beam:fn:foo’ had parameter type FooPayload, one could write RunnerApiFn.register_urn(‘bean:fn:foo’, FooPayload, foo_from_proto) where foo_from_proto took as arguments a FooPayload and a PipelineContext. This function can also be used as a decorator rather than passing the callable in as the final parameter.

A corresponding to_runner_api_parameter method would be expected that returns the tuple (‘beam:fn:foo’, FooPayload)

start_bundle()

Called before a bundle of elements is processed on a worker.

Elements to be processed are split into bundles and distributed to workers. Before a worker calls process() on the first element of its bundle, it calls this method.

to_runner_api(context)

Returns an FunctionSpec encoding this Fn.

Prefer overriding self.to_runner_api_parameter.

to_runner_api_parameter(context)

static unbounded_per_element()

A decorator on process fn specifying that the fn performs an unbounded amount of work per input element.

with_input_types(*arg_hints, **kwarg_hints)

with_output_types(*arg_hints, **kwarg_hints)