apache_beam.io.gcp.bigtableio module

BigTable connector

This module implements writing to BigTable tables. The default mode is to set row data to write to BigTable tables. The syntax supported is described here: https://cloud.google.com/bigtable/docs/quickstart-cbt

BigTable connector can be used as main outputs. A main output (common case) is expected to be massive and will be split into manageable chunks and processed in parallel. In the example below we created a list of rows then passed to the GeneratedDirectRows DoFn to set the Cells and then we call the BigTableWriteFn to insert those generated rows in the table.

main_table = (p

beam.Create(self._generate())

WriteToBigTable(project_id, instance_id, table_id))

class apache_beam.io.gcp.bigtableio.WriteToBigTable(project_id=None, instance_id=None, table_id=None)

Bases: apache_beam.transforms.ptransform.PTransform

A transform to write to the Bigtable Table.

A PTransform that write a list of DirectRow into the Bigtable Table

The PTransform to access the Bigtable Write connector :param project_id: GCP Project of to write the Rows :type project_id: str :param instance_id: GCP Instance to write the Rows :type instance_id: str :param table_id: GCP Table to write the DirectRows :type table_id: str

expand(pvalue)

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