apache_beam.io.gcp.pubsub module¶

Google Cloud PubSub sources and sinks.

Cloud Pub/Sub sources and sinks are currently supported only in streaming pipelines, during remote execution.

This API is currently under development and is subject to change.

class apache_beam.io.gcp.pubsub.PubsubMessage(data, attributes)[source]¶

Bases: object

Represents a Cloud Pub/Sub message.

Message payload includes the data and attributes fields. For the payload to be valid, at least one of its fields must be non-empty.

data¶: (bytes) Message data. May be None.

attributes¶: (dict) Key-value map of str to str, containing both user-defined and service generated attributes (such as id_label and timestamp_attribute). May be None.

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

Bases: apache_beam.transforms.ptransform.PTransform

A PTransform for reading from Cloud Pub/Sub.

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.

expand(pvalue)[source]¶

to_runner_api_parameter(context)[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_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

apache_beam.io.gcp.pubsub.ReadStringsFromPubSub(topic=None, subscription=None, id_label=None)[source]¶

apache_beam.io.gcp.pubsub.WriteStringsToPubSub(topic)[source]¶

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

Bases: apache_beam.transforms.ptransform.PTransform

A PTransform for writing messages to Cloud Pub/Sub.

Initializes WriteToPubSub.

Parameters:

topic – Cloud Pub/Sub topic in the form “/topics/<project>/<topic>”.
with_attributes – True - input elements will be PubsubMessage objects. False - input elements will be of type bytes (message data only).
id_label – If set, will set an attribute for each Cloud Pub/Sub message with the given name and a unique value. This attribute can then be used in a ReadFromPubSub PTransform to deduplicate messages.
timestamp_attribute – If set, will set an attribute for each Cloud Pub/Sub message with the given name and the message’s publish time as the value.

static message_to_proto_str(element)[source]¶

static bytes_to_proto_str(element)[source]¶

expand(pcoll)[source]¶

to_runner_api_parameter(context)[source]¶

display_data()[source]¶

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

default_label()¶

default_type_hints()¶

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_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.gcp.pubsub.PubSubSourceDescriptor[source]¶

Bases: tuple

A PubSub source descriptor for MultipleReadFromPubSub`

source¶: Existing Cloud Pub/Sub topic or subscription to use in the form “projects/<project>/topics/<topic>” or “projects/<project>/subscriptions/<subscription>”

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.

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.

Create new instance of PubSubSourceDescriptor(source, id_label, timestamp_attribute)

source: Alias for field number 0

id_label: Alias for field number 1

timestamp_attribute: Alias for field number 2

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

index()¶

Return first index of value.

Raises ValueError if the value is not present.

class apache_beam.io.gcp.pubsub.MultipleReadFromPubSub(pubsub_source_descriptors, with_attributes=False)[source]¶

Bases: apache_beam.transforms.ptransform.PTransform

A PTransform that expands ReadFromPubSub to read from multiple PubSubSourceDescriptor.

The MultipleReadFromPubSub transform allows you to read multiple topics and/or subscriptions using just one transform. It is the recommended transform to read multiple Pub/Sub sources when the output PCollection are going to be flattened. The transform takes a list of PubSubSourceDescriptor and organize them by type (topic / subscription) and project::

topic_1 = PubSubSourceDescriptor('projects/myproject/topics/a_topic')
topic_2 = PubSubSourceDescriptor(
            'projects/myproject2/topics/b_topic',
            'my_label',
            'my_timestamp_attribute')
subscription_1 = PubSubSourceDescriptor(
            'projects/myproject/subscriptions/a_subscription')

results = pipeline | MultipleReadFromPubSub(
            [topic_1, topic_2, subscription_1])

Initializes PubSubMultipleReader.

Parameters:	pubsub_source_descriptors – List of Cloud Pub/Sub topics or subscriptions of type ~PubSubSourceDescriptor. with_attributes – True - input elements will be `PubsubMessage` objects. False - input elements will be of type `bytes` (message data only).

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

expand(pcol)[source]¶