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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2020/08/17 05:16:06 UTC
[GitHub] [flink] hequn8128 commented on a change in pull request #13161: [FLINK-18886][python] Support Kafka connectors for Python DataStream …
hequn8128 commented on a change in pull request #13161:
URL: https://github.com/apache/flink/pull/13161#discussion_r471206526
##########
File path: flink-python/pyflink/datastream/tests/test_connectors.py
##########
@@ -0,0 +1,97 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+
+from pyflink.common import typeinfo
+from pyflink.common.serialization_schemas import JsonRowDeserializationSchema, \
+ JsonRowSerializationSchema
+from pyflink.common.typeinfo import Types
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.connectors import FlinkKafkaConsumer010, FlinkKafkaProducer010, \
+ FlinkKafkaConsumer011, FlinkKafkaProducer011, FlinkKafkaConsumer, FlinkKafkaProducer
+from pyflink.java_gateway import get_gateway
+from pyflink.testing.test_case_utils import PyFlinkTestCase, _load_specific_flink_module_jars, \
+ get_private_field, invoke_java_object_method
+
+
+class FlinkKafkaTest(PyFlinkTestCase):
+
+ def setUp(self) -> None:
+ self.env = StreamExecutionEnvironment.get_execution_environment()
+ self._cxt_clz_loader = get_gateway().jvm.Thread.currentThread().getContextClassLoader()
Review comment:
Add comments for the self._cxt_clz_loader during setUp and tearDown.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
+
+
+class FlinkKafkaConsumer010(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
+ j_flink_kafka_consumer_010 = \
+ JFlinkKafkaConsumer010(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer010, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer_010)
+
+
+class FlinkKafkaConsumer011(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer011 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
+ j_flink_kafka_consumer_011 = \
+ JFlinkKafkaConsumer011(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer011, self).__init__(j_flink_kafka_consumer_011)
+
+
+class FlinkKafkaConsumer(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
+ j_flink_kafka_consumer = \
+ JFlinkKafkaConsumer(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer)
+
+
+class FlinkKafkaProducerBase(SinkFunction):
+ """
+ Flink Sink to produce data into a Kafka topic.
+
+ Please note that this producer provides at-least-once reliability guarantees when checkpoints
+ are enabled and set_flush_on_checkpoint(True) is set. Otherwise, the producer doesn;t provid any
+ reliability guarantees.
+ """
+
+ def __init__(self, j_flink_kafka_producer):
+ super(FlinkKafkaProducerBase, self).__init__(sink_func=j_flink_kafka_producer)
+
+ def set_log_failures_only(self, log_failures_only: bool):
+ """
+ Defines whether the producer should fail on errors, or only log them. If this is set to
+ true, then exceptions will be only logged, if set to false, exceptions will be eventually
+ thrown and cause the streaming program to fail (and enter recovery).
+
+ :param log_failures_only: The flag to indicate logging-only on exceptions.
+ """
+ self._j_function.setLogFailuresOnly(log_failures_only)
+
+ def set_flush_on_checkpoint(self, flush_on_checkpoint: bool):
+ """
+ If set to true, the Flink producer will wait for all outstanding messages in the Kafka
+ buffers to be acknowledged by the Kafka producer on a checkpoint.
+
+ This way, the producer can guarantee that messages in the Kafka buffers are part of the
+ checkpoint.
+
+ :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint)
+ """
+ self._j_function.setFlushOnCheckpoint(flush_on_checkpoint)
+
+
+class FlinkKafkaProducer010(FlinkKafkaProducerBase):
+ """
+ Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.10.x.
+ """
+
+ def __init__(self, topic: str, serialization_schema: SerializationSchema,
+ producer_config: Dict):
+ """
+ Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic.
+
+ Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner.
+ This default partitioner maps each sink subtask to a single Kafka partition (i.e. all
+ records received by a sink subtask will end up in the same Kafka partition).
+
+ :param topic: ID of the Kafka topic.
+ :param serialization_schema: User defined key-less serialization schema.
+ :param producer_config: Properties with the producer configuration.
+ """
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in producer_config.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaProducer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010
+ j_flink_kafka_producer = JFlinkKafkaProducer010(
+ topic, serialization_schema._j_serialization_schema, j_properties)
+ super(FlinkKafkaProducer010, self).__init__(j_flink_kafka_producer=j_flink_kafka_producer)
+
+ def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool):
+ """
+ If set to true, Flink will write the (event time) timestamp attached to each record into
+ Kafka. Timestamps must be positive for Kafka to accept them.
+
+ :param write_timestamp_to_kafka: Flag indicating if Flink's internal timestamps are written
+ to Kafka.
+ """
+ self._j_function.setWriteTimestampToKafka(write_timestamp_to_kafka)
+
+
+class Semantic(object):
Review comment:
This class has never been used.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
+
+
+class FlinkKafkaConsumer010(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
+ j_flink_kafka_consumer_010 = \
+ JFlinkKafkaConsumer010(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer010, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer_010)
+
+
+class FlinkKafkaConsumer011(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer011 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
+ j_flink_kafka_consumer_011 = \
+ JFlinkKafkaConsumer011(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer011, self).__init__(j_flink_kafka_consumer_011)
+
+
+class FlinkKafkaConsumer(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
+ j_flink_kafka_consumer = \
+ JFlinkKafkaConsumer(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer)
+
+
+class FlinkKafkaProducerBase(SinkFunction):
Review comment:
Also extends from abc.ABC indicating that this is a abstract class.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
Review comment:
Also extends from abc.ABC indicating that this is a abstract class.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
+
+
+class FlinkKafkaConsumer010(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
+ j_flink_kafka_consumer_010 = \
+ JFlinkKafkaConsumer010(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
Review comment:
Can we extract a method for these logics so that can be reused by all three sub-classes.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
Review comment:
Can't return a java typeinfo.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
+
+
+class FlinkKafkaConsumer010(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
+ j_flink_kafka_consumer_010 = \
+ JFlinkKafkaConsumer010(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer010, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer_010)
+
+
+class FlinkKafkaConsumer011(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer011 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
+ j_flink_kafka_consumer_011 = \
+ JFlinkKafkaConsumer011(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer011, self).__init__(j_flink_kafka_consumer_011)
+
+
+class FlinkKafkaConsumer(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
+ j_flink_kafka_consumer = \
+ JFlinkKafkaConsumer(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer)
+
+
+class FlinkKafkaProducerBase(SinkFunction):
+ """
+ Flink Sink to produce data into a Kafka topic.
+
+ Please note that this producer provides at-least-once reliability guarantees when checkpoints
+ are enabled and set_flush_on_checkpoint(True) is set. Otherwise, the producer doesn;t provid any
+ reliability guarantees.
+ """
+
+ def __init__(self, j_flink_kafka_producer):
+ super(FlinkKafkaProducerBase, self).__init__(sink_func=j_flink_kafka_producer)
+
+ def set_log_failures_only(self, log_failures_only: bool):
+ """
+ Defines whether the producer should fail on errors, or only log them. If this is set to
+ true, then exceptions will be only logged, if set to false, exceptions will be eventually
+ thrown and cause the streaming program to fail (and enter recovery).
+
+ :param log_failures_only: The flag to indicate logging-only on exceptions.
+ """
+ self._j_function.setLogFailuresOnly(log_failures_only)
+
+ def set_flush_on_checkpoint(self, flush_on_checkpoint: bool):
+ """
+ If set to true, the Flink producer will wait for all outstanding messages in the Kafka
+ buffers to be acknowledged by the Kafka producer on a checkpoint.
+
+ This way, the producer can guarantee that messages in the Kafka buffers are part of the
+ checkpoint.
+
+ :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint)
+ """
+ self._j_function.setFlushOnCheckpoint(flush_on_checkpoint)
+
+
+class FlinkKafkaProducer010(FlinkKafkaProducerBase):
+ """
+ Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.10.x.
+ """
+
+ def __init__(self, topic: str, serialization_schema: SerializationSchema,
+ producer_config: Dict):
+ """
+ Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic.
+
+ Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner.
+ This default partitioner maps each sink subtask to a single Kafka partition (i.e. all
+ records received by a sink subtask will end up in the same Kafka partition).
+
+ :param topic: ID of the Kafka topic.
+ :param serialization_schema: User defined key-less serialization schema.
+ :param producer_config: Properties with the producer configuration.
+ """
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in producer_config.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaProducer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010
+ j_flink_kafka_producer = JFlinkKafkaProducer010(
+ topic, serialization_schema._j_serialization_schema, j_properties)
+ super(FlinkKafkaProducer010, self).__init__(j_flink_kafka_producer=j_flink_kafka_producer)
+
+ def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool):
+ """
+ If set to true, Flink will write the (event time) timestamp attached to each record into
+ Kafka. Timestamps must be positive for Kafka to accept them.
+
+ :param write_timestamp_to_kafka: Flag indicating if Flink's internal timestamps are written
+ to Kafka.
+ """
+ self._j_function.setWriteTimestampToKafka(write_timestamp_to_kafka)
+
+
+class Semantic(object):
+ """
+ Semantics that can be chosen.
+ :data: `EXACTLY_ONCE`:
+ The Flink producer will write all messages in a Kafka transaction that will be committed to
+ the Kafka on a checkpoint. In this mode FlinkKafkaProducer011 sets up a pool of
+ FlinkKafkaProducer. Between each checkpoint there is created new Kafka transaction, which is
+ being committed on FlinkKafkaProducer011.notifyCheckpointComplete(long). If checkpoint
+ complete notifications are running late, FlinkKafkaProducer011 can run out of
+ FlinkKafkaProducers in the pool. In that case any subsequent FlinkKafkaProducer011.snapshot-
+ State() requests will fail and the FlinkKafkaProducer011 will keep using the
+ FlinkKafkaProducer from previous checkpoint. To decrease chances of failing checkpoints
+ there are four options:
+ 1. decrease number of max concurrent checkpoints
+ 2. make checkpoints mre reliable (so that they complete faster)
+ 3. increase delay between checkpoints
+ 4. increase size of FlinkKafkaProducers pool
+
+ :data: `AT_LEAST_ONCE`:
+ The Flink producer will wait for all outstanding messages in the Kafka buffers to be
+ acknowledged by the Kafka producer on a checkpoint.
+
+ :data: `NONE`:
+ Means that nothing will be guaranteed. Messages can be lost and/or duplicated in case of
+ failure.
+ """
+
+ EXACTLY_ONCE = 0,
+ AT_LEAST_ONCE = 1,
+ NONE = 2
+
+ @staticmethod
+ def _to_j_semantic(semantic):
+ gateway = get_gateway()
+ JSemantic = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011.Semantic
+ if semantic == Semantic.EXACTLY_ONCE:
+ return JSemantic.EXACTLY_ONCE
+ elif semantic == Semantic.AT_LEAST_ONCE:
+ return JSemantic.AT_LEAST_ONCE
+ elif semantic == Semantic.NONE:
+ return JSemantic.NONE
+ else:
+ raise TypeError("Unsupported semantic: %s, supported semantics are: "
+ "Semantic.EXACTLY_ONCE, Semantic.AT_LEAST_ONCE, Semantic.NONE"
+ % semantic)
+
+
+class FlinkKafkaProducer011(SinkFunction):
Review comment:
Maybe we can make this class extends from `FlinkKafkaProducerBase` so that methods can be reused.
##########
File path: flink-python/pyflink/datastream/connectors.py
##########
@@ -0,0 +1,546 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+from typing import Dict, List, Union
+
+from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema
+from pyflink.datastream.functions import SourceFunction, SinkFunction
+from pyflink.java_gateway import get_gateway
+
+
+class FlinkKafkaConsumerBase(SourceFunction):
+ """
+ Base class of all Flink Kafka Consumer data sources. This implements the common behavior across
+ all kafka versions.
+
+ The Kafka version specific behavior is defined mainly in the specific subclasses.
+ """
+
+ def __init__(self, j_flink_kafka_consumer):
+ super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer)
+
+ def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool):
+ """
+ Specifies whether or not the consumer should commit offsets back to kafka on checkpoints.
+ This setting will only have effect if checkpointing is enabled for the job. If checkpointing
+ isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+)
+ property settings will be used.
+ """
+ self._j_function = self._j_function \
+ .setCommitOffsetsOnCheckpoints(commit_on_checkpoints)
+ return self
+
+ def set_start_from_earliest(self):
+ """
+ Specifies the consumer to start reading from the earliest offset for all partitions. This
+ lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromEarliest()
+ return self
+
+ def set_start_from_latest(self):
+ """
+ Specifies the consuer to start reading from the latest offset for all partitions. This lets
+ the consumer ignore any committed group offsets in Zookeeper / Kafka brokers.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromLatest()
+ return self
+
+ def set_start_from_timestamp(self, startup_offsets_timestamp: int):
+ """
+ Specifies the consumer to start reading partitions from a specified timestamp. The specified
+ timestamp must be before the current timestamp. This lets the consumer ignore any committed
+ group offsets in Zookeeper / Kafka brokers.
+
+ The consumer will look up the earliest offset whose timestamp is greater than or equal to
+ the specific timestamp from Kafka. If there's no such offset, the consumer will use the
+ latest offset to read data from Kafka.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+
+ :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for
+ epoch.
+ """
+ self._j_function = self._j_function.setStartFromTimestamp(
+ startup_offsets_timestamp)
+ return self
+
+ def set_start_from_group_offsets(self):
+ """
+ Specifies the consumer to start reading from any committed group offsets found in Zookeeper/
+ Kafka brokers. The 'group.id' property must be set in the configuration properties. If no
+ offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the
+ configuration properties will be used for the partition.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function.setStartFromGroupOffsets()
+ return self
+
+ def disable_filter_restored_partitions_with_subscribed_topics(self):
+ """
+ By default, when restoring from a checkpoint / savepoint, the consumer always ignores
+ restored partitions that are no longer associated with the current specified topics or topic
+ pattern to subscribe to.
+
+ This method does not affect where partitions are read from when the consumer is restored
+ from a checkpoint or savepoint. When the consumer is restored from a checkpoint or
+ savepoint, only the offsets in the restored state will be used.
+ """
+ self._j_function = self._j_function \
+ .disableFilterRestoredPartitionsWithSubscribedTopics()
+ return self
+
+ def get_produced_type(self):
+ return self._j_function.getProducedType()
+
+
+class FlinkKafkaConsumer010(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
+ j_flink_kafka_consumer_010 = \
+ JFlinkKafkaConsumer010(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer010, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer_010)
+
+
+class FlinkKafkaConsumer011(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer011 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011
+ j_flink_kafka_consumer_011 = \
+ JFlinkKafkaConsumer011(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer011, self).__init__(j_flink_kafka_consumer_011)
+
+
+class FlinkKafkaConsumer(FlinkKafkaConsumerBase):
+ """
+ The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from
+ Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will
+ pull data from one or more Kafka partitions.
+
+ The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost
+ during a failure, and taht the computation processes elements 'exactly once. (These guarantees
+ naturally assume that Kafka itself does not loose any data.)
+
+ Please note that Flink snapshots the offsets internally as part of its distributed checkpoints.
+ The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in
+ sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of
+ how far the Flink Kafka consumer has consumed a topic.
+
+ Please refer to Kafka's documentation for the available configuration properties:
+ http://kafka.apache.org/documentation.html#newconsumerconfigs
+ """
+
+ def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema,
+ properties: Dict):
+ """
+ Creates a new Kafka streaming source consumer for Kafka 0.10.x.
+
+ This constructor allows passing multiple topics to the consumer.
+
+ :param topics: The Kafka topics to read from.
+ :param deserialization_schema: The de-/serializer used to convert between Kafka's byte
+ messages and Flink's objects.
+ :param properties: The properties that are used to configure both the fetcher and the offset
+ handler.
+ """
+ if not isinstance(topics, list):
+ topics = [topics]
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in properties.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaConsumer = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer
+ j_flink_kafka_consumer = \
+ JFlinkKafkaConsumer(topics,
+ deserialization_schema._j_deserialization_schema,
+ j_properties)
+ super(FlinkKafkaConsumer, self).__init__(
+ j_flink_kafka_consumer=j_flink_kafka_consumer)
+
+
+class FlinkKafkaProducerBase(SinkFunction):
+ """
+ Flink Sink to produce data into a Kafka topic.
+
+ Please note that this producer provides at-least-once reliability guarantees when checkpoints
+ are enabled and set_flush_on_checkpoint(True) is set. Otherwise, the producer doesn;t provid any
+ reliability guarantees.
+ """
+
+ def __init__(self, j_flink_kafka_producer):
+ super(FlinkKafkaProducerBase, self).__init__(sink_func=j_flink_kafka_producer)
+
+ def set_log_failures_only(self, log_failures_only: bool):
+ """
+ Defines whether the producer should fail on errors, or only log them. If this is set to
+ true, then exceptions will be only logged, if set to false, exceptions will be eventually
+ thrown and cause the streaming program to fail (and enter recovery).
+
+ :param log_failures_only: The flag to indicate logging-only on exceptions.
+ """
+ self._j_function.setLogFailuresOnly(log_failures_only)
+
+ def set_flush_on_checkpoint(self, flush_on_checkpoint: bool):
+ """
+ If set to true, the Flink producer will wait for all outstanding messages in the Kafka
+ buffers to be acknowledged by the Kafka producer on a checkpoint.
+
+ This way, the producer can guarantee that messages in the Kafka buffers are part of the
+ checkpoint.
+
+ :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint)
+ """
+ self._j_function.setFlushOnCheckpoint(flush_on_checkpoint)
+
+
+class FlinkKafkaProducer010(FlinkKafkaProducerBase):
+ """
+ Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.10.x.
+ """
+
+ def __init__(self, topic: str, serialization_schema: SerializationSchema,
+ producer_config: Dict):
+ """
+ Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic.
+
+ Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner.
+ This default partitioner maps each sink subtask to a single Kafka partition (i.e. all
+ records received by a sink subtask will end up in the same Kafka partition).
+
+ :param topic: ID of the Kafka topic.
+ :param serialization_schema: User defined key-less serialization schema.
+ :param producer_config: Properties with the producer configuration.
+ """
+ gateway = get_gateway()
+ j_properties = gateway.jvm.java.util.Properties()
+ for key, value in producer_config.items():
+ j_properties.setProperty(key, value)
+
+ JFlinkKafkaProducer010 = gateway.jvm \
+ .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010
+ j_flink_kafka_producer = JFlinkKafkaProducer010(
+ topic, serialization_schema._j_serialization_schema, j_properties)
+ super(FlinkKafkaProducer010, self).__init__(j_flink_kafka_producer=j_flink_kafka_producer)
+
+ def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool):
Review comment:
Why not put this method into the base class?
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