apache_beam.io.external.gcp.pubsub module¶

class apache_beam.io.external.gcp.pubsub.ReadFromPubsubSchema(topic, subscription, id_label, with_attributes, timestamp_attribute)¶

Bases: tuple

Create new instance of ReadFromPubsubSchema(topic, subscription, id_label, with_attributes, timestamp_attribute)

count()¶: Return number of occurrences of value.

id_label¶: Alias for field number 2

index()¶

Return first index of value.

Raises ValueError if the value is not present.

subscription¶: Alias for field number 1

timestamp_attribute¶: Alias for field number 4

topic¶: Alias for field number 0

with_attributes¶: Alias for field number 3

class apache_beam.io.external.gcp.pubsub.ReadFromPubSub(topic=None, subscription=None, id_label=None, with_attributes=False, timestamp_attribute=None, expansion_service=None)[source]¶

Bases: apache_beam.transforms.ptransform.PTransform

An external PTransform for reading from Cloud Pub/Sub.

Experimental; no backwards compatibility guarantees. It requires special preparation of the Java SDK. See BEAM-7870.

Initializes ReadFromPubSub.

Parameters:

topic – Cloud Pub/Sub topic in the form “projects/<project>/topics/<topic>”. If provided, subscription must be None.
subscription – Existing Cloud Pub/Sub subscription to use in the form “projects/<project>/subscriptions/<subscription>”. If not specified, a temporary subscription will be created from the specified topic. If provided, topic must be None.
id_label – The attribute on incoming Pub/Sub messages to use as a unique record identifier. When specified, the value of this attribute (which can be any string that uniquely identifies the record) will be used for deduplication of messages. If not provided, we cannot guarantee that no duplicate data will be delivered on the Pub/Sub stream. In this case, deduplication of the stream will be strictly best effort.
with_attributes – True - output elements will be PubsubMessage objects. False - output elements will be of type bytes (message data only).
timestamp_attribute –
Message value to use as element timestamp. If None, uses message publishing time as the timestamp.

Timestamp values should be in one of two formats:
- A numerical value representing the number of milliseconds since the Unix epoch.
- A string in RFC 3339 format, UTC timezone. Example: 2015-10-29T23:41:41.123Z. The sub-second component of the timestamp is optional, and digits beyond the first three (i.e., time units smaller than milliseconds) may be ignored.

URN = 'beam:external:java:pubsub:read:v1'¶

expand(pbegin)[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.io.external.gcp.pubsub.WriteToPubsubSchema(topic, id_label, timestamp_attribute)¶