apache_beam.io.kafka module¶

Unbounded source and sink transforms for: Kafka.

These transforms are currently supported by Beam portable runners (for example, portable Flink and Spark) as well as Dataflow runner.

Setup

Transforms provided in this module are cross-language transforms implemented in the Beam Java SDK. During the pipeline construction, Python SDK will connect to a Java expansion service to expand these transforms. To facilitate this, a small amount of setup is needed before using these transforms in a Beam Python pipeline.

There are several ways to setup cross-language Kafka transforms.

Option 1: use the default expansion service
Option 2: specify a custom expansion service

See below for details regarding each of these options.

Option 1: Use the default expansion service

This is the recommended and easiest setup option for using Python Kafka transforms. This option is only available for Beam 2.22.0 and later.

This option requires following pre-requisites before running the Beam pipeline.

Install Java runtime in the computer from where the pipeline is constructed and make sure that ‘java’ command is available.

In this option, Python SDK will either download (for released Beam version) or build (when running from a Beam Git clone) a expansion service jar and use that to expand transforms. Currently Kafka transforms use the ‘beam-sdks-java-io-expansion-service’ jar for this purpose.

Option 2: specify a custom expansion service

In this option, you startup your own expansion service and provide that as a parameter when using the transforms provided in this module.

This option requires following pre-requisites before running the Beam pipeline.

Startup your own expansion service.
Update your pipeline to provide the expansion service address when initiating Kafka transforms provided in this module.

Flink Users can use the built-in Expansion Service of the Flink Runner’s Job Server. If you start Flink’s Job Server, the expansion service will be started on port 8097. For a different address, please set the expansion_service parameter.

More information

For more information regarding cross-language transforms see: - https://beam.apache.org/roadmap/portability/

For more information specific to Flink runner see: - https://beam.apache.org/documentation/runners/flink/

class apache_beam.io.kafka.ReadFromKafkaSchema(consumer_config, topics, key_deserializer, value_deserializer, start_read_time, max_num_records, max_read_time, commit_offset_in_finalize, timestamp_policy)¶

Bases: tuple

Create new instance of ReadFromKafkaSchema(consumer_config, topics, key_deserializer, value_deserializer, start_read_time, max_num_records, max_read_time, commit_offset_in_finalize, timestamp_policy)

commit_offset_in_finalize¶: Alias for field number 7

consumer_config¶: Alias for field number 0

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

index()¶

Return first index of value.

Raises ValueError if the value is not present.

key_deserializer¶: Alias for field number 2

max_num_records¶: Alias for field number 5

max_read_time¶: Alias for field number 6

start_read_time¶: Alias for field number 4

timestamp_policy¶: Alias for field number 8

topics¶: Alias for field number 1

value_deserializer¶: Alias for field number 3

apache_beam.io.kafka.default_io_expansion_service()[source]¶

class apache_beam.io.kafka.ReadFromKafka(consumer_config, topics, key_deserializer='org.apache.kafka.common.serialization.ByteArrayDeserializer', value_deserializer='org.apache.kafka.common.serialization.ByteArrayDeserializer', start_read_time=None, max_num_records=None, max_read_time=None, commit_offset_in_finalize=False, timestamp_policy='ProcessingTime', expansion_service=None)[source]¶

Bases: apache_beam.transforms.external.ExternalTransform

An external PTransform which reads from Kafka and returns a KV pair for each item in the specified Kafka topics. If no Kafka Deserializer for key/value is provided, then the data will be returned as a raw byte array.

Experimental; no backwards compatibility guarantees.

Initializes a read operation from Kafka.

Parameters:

consumer_config – A dictionary containing the consumer configuration.
topics – A list of topic strings.
key_deserializer – A fully-qualified Java class name of a Kafka Deserializer for the topic’s key, e.g. ‘org.apache.kafka.common.serialization.LongDeserializer’. Default: ‘org.apache.kafka.common.serialization.ByteArrayDeserializer’.
value_deserializer – A fully-qualified Java class name of a Kafka Deserializer for the topic’s value, e.g. ‘org.apache.kafka.common.serialization.LongDeserializer’. Default: ‘org.apache.kafka.common.serialization.ByteArrayDeserializer’.
start_read_time – Use timestamp to set up start offset in milliseconds epoch.
max_num_records – Maximum amount of records to be read. Mainly used for tests and demo applications.
max_read_time – Maximum amount of time in seconds the transform executes. Mainly used for tests and demo applications.
commit_offset_in_finalize – Whether to commit offsets when finalizing.
timestamp_policy – The built-in timestamp policy which is used for extracting timestamp from KafkaRecord.
expansion_service – The address (host:port) of the ExpansionService.

byte_array_deserializer = 'org.apache.kafka.common.serialization.ByteArrayDeserializer'¶

processing_time_policy = 'ProcessingTime'¶

create_time_policy = 'CreateTime'¶

log_append_time = 'LogAppendTime'¶

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

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]

expand(pvalueish)¶

classmethod from_runner_api(proto, context)¶

classmethod get_local_namespace()¶

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¶

classmethod outer_namespace(namespace)¶

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)¶

to_runner_api_transform(context, full_label)¶

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.kafka.WriteToKafkaSchema(producer_config, topic, key_serializer, value_serializer)¶

Bases: tuple

Create new instance of WriteToKafkaSchema(producer_config, topic, key_serializer, value_serializer)

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

index()¶

Return first index of value.

Raises ValueError if the value is not present.

key_serializer¶: Alias for field number 2

producer_config¶: Alias for field number 0

topic¶: Alias for field number 1

value_serializer¶: Alias for field number 3

class apache_beam.io.kafka.WriteToKafka(producer_config, topic, key_serializer='org.apache.kafka.common.serialization.ByteArraySerializer', value_serializer='org.apache.kafka.common.serialization.ByteArraySerializer', expansion_service=None)[source]¶

Bases: apache_beam.transforms.external.ExternalTransform

An external PTransform which writes KV data to a specified Kafka topic. If no Kafka Serializer for key/value is provided, then key/value are assumed to be byte arrays.

Experimental; no backwards compatibility guarantees.

Initializes a write operation to Kafka.

Parameters:

producer_config – A dictionary containing the producer configuration.
topic – A Kafka topic name.
key_deserializer – A fully-qualified Java class name of a Kafka Serializer for the topic’s key, e.g. ‘org.apache.kafka.common.serialization.LongSerializer’. Default: ‘org.apache.kafka.common.serialization.ByteArraySerializer’.
value_deserializer – A fully-qualified Java class name of a Kafka Serializer for the topic’s value, e.g. ‘org.apache.kafka.common.serialization.LongSerializer’. Default: ‘org.apache.kafka.common.serialization.ByteArraySerializer’.
expansion_service – The address (host:port) of the ExpansionService.

byte_array_serializer = 'org.apache.kafka.common.serialization.ByteArraySerializer'¶

URN = 'beam:external:java:kafka:write:v1'¶

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]

expand(pvalueish)¶

classmethod from_runner_api(proto, context)¶

classmethod get_local_namespace()¶

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¶

classmethod outer_namespace(namespace)¶

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)¶

to_runner_api_transform(context, full_label)¶

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