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"""Unbounded source and sink transforms for
`Kafka <href="http://kafka.apache.org/>`_.
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) an expansion service jar and use
that to expand transforms. Currently Kafka transforms use the
'beam-sdks-java-io-expansion-service' jar for this purpose.
Note that the KafkaIO read transform can be compiled in two modes
* `ReadFromKafkaViaUnbounded` (legacy)
* `ReadFromKafkaViaSDF` (default)
To use the legacy mode, the `use_deprecated_read` flag should be specified
within the IO expansion service. For example,
kafka.default_io_expansion_service(
append_args=["--experiments=use_deprecated_read"]
)
*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/
"""
# pytype: skip-file
import typing
import numpy as np
from apache_beam.transforms.external import BeamJarExpansionService
from apache_beam.transforms.external import ExternalTransform
from apache_beam.transforms.external import NamedTupleBasedPayloadBuilder
ReadFromKafkaSchema = typing.NamedTuple(
'ReadFromKafkaSchema',
[
('consumer_config', typing.Mapping[str, str]),
('topics', typing.List[str]),
('key_deserializer', str),
('value_deserializer', str),
('start_read_time', typing.Optional[int]),
('max_num_records', typing.Optional[int]),
('max_read_time', typing.Optional[int]),
('commit_offset_in_finalize', bool),
('timestamp_policy', str),
('consumer_polling_timeout', typing.Optional[int]),
('redistribute', typing.Optional[bool]),
('redistribute_num_keys', typing.Optional[np.int32]),
('allow_duplicates', typing.Optional[bool]),
])
[docs]
def default_io_expansion_service(append_args=None):
return BeamJarExpansionService(
'sdks:java:io:expansion-service:shadowJar', append_args=append_args)
[docs]
class ReadFromKafka(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.
"""
# Returns the key/value data as raw byte arrays
byte_array_deserializer = (
'org.apache.kafka.common.serialization.ByteArrayDeserializer')
processing_time_policy = 'ProcessingTime'
create_time_policy = 'CreateTime'
log_append_time = 'LogAppendTime'
URN_WITH_METADATA = (
'beam:transform:org.apache.beam:kafka_read_with_metadata:v1')
URN_WITHOUT_METADATA = (
'beam:transform:org.apache.beam:kafka_read_without_metadata:v1')
def __init__(
self,
consumer_config,
topics,
key_deserializer=byte_array_deserializer,
value_deserializer=byte_array_deserializer,
start_read_time=None,
max_num_records=None,
max_read_time=None,
commit_offset_in_finalize=False,
timestamp_policy=processing_time_policy,
consumer_polling_timeout=2,
with_metadata=False,
expansion_service=None,
redistribute=False,
redistribute_num_keys=np.int32(0),
allow_duplicates=False,
):
"""
Initializes a read operation from Kafka.
:param consumer_config: A dictionary containing the consumer configuration.
:param topics: A list of topic strings.
:param 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'.
:param 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'.
:param start_read_time: Use timestamp to set up start offset in milliseconds
epoch.
:param max_num_records: Maximum amount of records to be read. Mainly used
for tests and demo applications.
:param max_read_time: Maximum amount of time in seconds the transform
executes. Mainly used for tests and demo applications.
:param commit_offset_in_finalize: Whether to commit offsets when finalizing.
:param timestamp_policy: The built-in timestamp policy which is used for
extracting timestamp from KafkaRecord.
:param consumer_polling_timeout: Kafka client polling request
timeout time in seconds. A lower timeout optimizes for latency. Increase
the timeout if the consumer is not fetching any records. Default is 2
seconds.
:param with_metadata: whether the returned PCollection should contain
Kafka related metadata or not. If False (default), elements of the
returned PCollection will be of type 'bytes' if True, elements of the
returned PCollection will be of the type 'Row'. Note that, currently
this only works when using default key and value deserializers where
Java Kafka Reader reads keys and values as 'byte[]'.
:param expansion_service: The address (host:port) of the ExpansionService.
:param redistribute: whether a Redistribute transform should be applied
immediately after the read.
:param redistribute_num_keys: Configures how many keys the Redistribute
spreads the data across.
:param allow_duplicates: whether the Redistribute transform allows for
duplicates (this serves solely as a hint to the underlying runner).
"""
if timestamp_policy not in [ReadFromKafka.processing_time_policy,
ReadFromKafka.create_time_policy,
ReadFromKafka.log_append_time]:
raise ValueError(
'timestamp_policy should be one of '
'[ProcessingTime, CreateTime, LogAppendTime]')
super().__init__(
self.URN_WITH_METADATA if with_metadata else self.URN_WITHOUT_METADATA,
NamedTupleBasedPayloadBuilder(
ReadFromKafkaSchema(
consumer_config=consumer_config,
topics=topics,
key_deserializer=key_deserializer,
value_deserializer=value_deserializer,
max_num_records=max_num_records,
max_read_time=max_read_time,
start_read_time=start_read_time,
commit_offset_in_finalize=commit_offset_in_finalize,
timestamp_policy=timestamp_policy,
consumer_polling_timeout=consumer_polling_timeout,
redistribute=redistribute,
redistribute_num_keys=redistribute_num_keys,
allow_duplicates=allow_duplicates)),
expansion_service or default_io_expansion_service())
WriteToKafkaSchema = typing.NamedTuple(
'WriteToKafkaSchema',
[
('producer_config', typing.Mapping[str, str]),
('topic', str),
('key_serializer', str),
('value_serializer', str),
])
[docs]
class WriteToKafka(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.
"""
# Default serializer which passes raw bytes to Kafka
byte_array_serializer = (
'org.apache.kafka.common.serialization.ByteArraySerializer')
URN = 'beam:transform:org.apache.beam:kafka_write:v1'
def __init__(
self,
producer_config,
topic,
key_serializer=byte_array_serializer,
value_serializer=byte_array_serializer,
expansion_service=None):
"""
Initializes a write operation to Kafka.
:param producer_config: A dictionary containing the producer configuration.
:param topic: A Kafka topic name.
:param 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'.
:param 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'.
:param expansion_service: The address (host:port) of the ExpansionService.
"""
super().__init__(
self.URN,
NamedTupleBasedPayloadBuilder(
WriteToKafkaSchema(
producer_config=producer_config,
topic=topic,
key_serializer=key_serializer,
value_serializer=value_serializer,
)),
expansion_service or default_io_expansion_service())