dianfu commented on code in PR #19869: URL: https://github.com/apache/flink/pull/19869#discussion_r888480774
########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, Review Comment: Rename field `_j_kinesis_deserialization_schema` of KinesisDeserializationSchema into `_j_deserialization_schema`? Then we could make the logic here simple. ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( Review Comment: I guess it will fail for the following case: `KinesisConsumer(str, DeserializationSchema, Dict)`. Could convert streams to list if it's a string, deserializer of type DeserializationSchema to KinesisDeserializationSchema and always use the following constructor to address it. ``` public FlinkKinesisConsumer( List<String> streams, KinesisDeserializationSchema<T> deserializer, Properties configProps) { ``` ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + FixedKinesisPartitionKeyGenerator()) + + @staticmethod + def random_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RandomKinesisPartitionKeyGenerator()) + + @staticmethod + def row_data_fields_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RowDataFieldsKinesisPartitionKeyGenerator()) + + +class KinesisSink(Sink): + """ + A Kinesis Data Streams (KDS) Sink that performs async requests against a destination stream + using the buffering protocol specified in AsyncSinkBase. + + The sink internally uses a software.amazon.awssdk.services.kinesis.KinesisAsyncClient to + communicate with the AWS endpoint. + + The behaviour of the buffering may be specified by providing configuration during the sink + build time. + + - maxBatchSize: the maximum size of a batch of entries that may be sent to KDS + - maxInFlightRequests: the maximum number of in flight requests that may exist, if any more in + flight requests need to be initiated once the maximum has been reached, then it will be + blocked until some have completed + - maxBufferedRequests: the maximum number of elements held in the buffer, requests to add + elements will be blocked while the number of elements in the buffer is at the maximum + - maxBatchSizeInBytes: the maximum size of a batch of entries that may be sent to KDS + measured in bytes + - maxTimeInBufferMS: the maximum amount of time an entry is allowed to live in the buffer, + if any element reaches this age, the entire buffer will be flushed immediately + - maxRecordSizeInBytes: the maximum size of a record the sink will accept into the buffer, + a record of size larger than this will be rejected when passed to the sink + - failOnError: when an exception is encountered while persisting to Kinesis Data Streams, + the job will fail immediately if failOnError is set + """ + + def __init__(self, j_kinesis_sink): + super(KinesisSink, self).__init__(sink=j_kinesis_sink) + + @staticmethod + def builder() -> 'KinesisSinkBuilder': + return KinesisSinkBuilder() + + +class KinesisSinkBuilder(object): + """ + Builder to construct KinesisStreamsSink. + + The following example shows the minimum setup to create a KinesisStreamsSink that writes String + values to a Kinesis Data Streams stream named your_stream_here. + + Example: + :: + + >>> sink = KinesisSink.builder() \\ + ... .set_kinesis_client_properties(SINK_PROPERTIES) \\ + ... .set_stream_name(STREAM_NAME) \\ + ... .set_serialization_schema(SimpleStringSchema()) \\ + ... .build() + + If the following parameters are not set in this builder, the following defaults will be used: + + - maxBatchSize will be 500 + - maxInFlightRequests will be 50 + - maxBufferedRequests will be 10000 + - maxBatchSizeInBytes will be 5 MB i.e. 5 * 1024 * 1024 + - maxTimeInBufferMS will be 5000ms + - maxRecordSizeInBytes will be 1 MB i.e. 1 * 1024 * 1024 + - failOnError will be false + """ + + def __init__(self): + super().__init__() + JKinesisSink = get_gateway().jvm.org.apache.flink.connector.kinesis.sink.KinesisStreamsSink + self._j_kinesis_sink_builder = JKinesisSink.builder() + + def set_stream_name(self, stream_name: Union[str, List[str]]) -> 'KinesisSinkBuilder': + """ + Sets the name of the KDS stream that the sink will connect to. There is no default for this + parameter, therefore, this must be provided at sink creation time otherwise the build will + fail. + """ + self._j_kinesis_sink_builder.setStreamName(stream_name) + return self + + def set_serialization_schema(self, serialization_schema: SerializationSchema) \ + -> 'KinesisSinkBuilder': + """ + Sets the SerializationSchema of the KinesisSinkBuilder. + """ + self._j_kinesis_sink_builder.setSerializationSchema( + serialization_schema._j_serialization_schema) + return self + + def set_partition_key_generator(self, partition_key_generator: PartitionKeyGenerator) \ + -> 'KinesisSinkBuilder': + """ + Sets the PartitionKeyGenerator of the KinesisSinkBuilder. + """ + self._j_kinesis_sink_builder.setPartitionKeyGenerator( + partition_key_generator.j_partition_key_generator) + return self + + def set_fail_on_error(self, fail_on_error: bool) -> 'KinesisSinkBuilder': + """ + Sets the failOnError of the KinesisSinkBuilder. If failOnError is on, then a runtime + exception will be raised. Otherwise, those records will be requested in the buffer for + retry. + """ + self._j_kinesis_sink_builder.setFailOnError(fail_on_error) + return self + + def set_kinesis_client_properties(self, kinesis_client_properties: Dict) \ + -> 'KinesisSinkBuilder': + """ + Sets the kinesisClientProperties of the KinesisSinkBuilder. + """ + j_properties = get_gateway().jvm.java.util.Properties() + for key, value in kinesis_client_properties.items(): + j_properties.setProperty(key, value) + self._j_kinesis_sink_builder.setKinesisClientProperties(j_properties) + return self + + def set_max_batch_size(self, max_batch_size: int) -> 'KinesisSinkBuilder': + """ + Maximum number of elements that may be passed in a list to be written downstream. + """ + self._j_kinesis_sink_builder.setMaxBatchSize(max_batch_size) + return self + + def set_max_in_flight_requests(self, max_in_flight_requests): Review Comment: ```suggestion def set_max_in_flight_requests(self, max_in_flight_requests: int): ``` Also check the below methods. ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': Review Comment: ```suggestion def fixed() -> 'PartitionKeyGenerator': ``` ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ Review Comment: Add static method job_manager_watermark_tracker which returns JobManagerWatermarkTracker? Otherwise, however users use it? ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + FixedKinesisPartitionKeyGenerator()) + + @staticmethod + def random_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': Review Comment: ```suggestion def random() -> 'PartitionKeyGenerator': ``` ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. Review Comment: ``` See assign(StreamShardHandle, int) for details. ``` This documentation doesn't really make sense for Python users. However, you could document that users could provide a Java KinesisShardAssigner and use it in Python if they want to provide custom shared assigner. ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): Review Comment: ```suggestion class FlinkKinesisConsumer(SourceFunction): ``` Keep the naming convention consistent with the Java API ########## flink-python/pyflink/datastream/tests/test_connectors.py: ########## @@ -517,3 +519,65 @@ def test_seq_source(self): to_field = seq_source_clz.getDeclaredField("to") to_field.setAccessible(True) self.assertEqual(10, to_field.get(seq_source.get_java_function())) + + +class FlinkKinesisTest(ConnectorTestBase): + @classmethod + def _get_jars_relative_path(cls): + return '/flink-connectors/flink-sql-connector-kinesis' + + def test_kinesis_source(self): + consumer_config = dict() + consumer_config['aws.region'] = 'us-east-1' + consumer_config['aws.credentials.provider.basic.accesskeyid'] = 'aws_access_key_id' + consumer_config['aws.credentials.provider.basic.secretkey'] = 'aws_secret_access_key' + consumer_config['flink.stream.initpos'] = 'LATEST' + + JKinesisDeserializationSchema = get_gateway().jvm.org.apache.flink.streaming.connectors.\ + kinesis.serialization.KinesisDeserializationSchemaWrapper + schema = JKinesisDeserializationSchema(SimpleStringSchema()._j_deserialization_schema) + + kinesis_source = KinesisConsumer("stream-1", KinesisDeserializationSchema(schema), + consumer_config) + + watermark_tracker = job_manager_watermark_tracker("myKinesisSource") + kinesis_source.set_watermark_tracker(watermark_tracker) + + ds = self.env.add_source(source_func=kinesis_source, source_name="kinesis source") + ds.print() Review Comment: remove this line ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): Review Comment: Rename it to AssignerWithPeriodicWatermarksWrapper and move it to watermark_strategy.py? ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': Review Comment: Add documentation for it? ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + Review Comment: Also add the default_shard_assigner() ? Otherwise, I'm afraid that users may think that this is the only available shard assigner. ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + FixedKinesisPartitionKeyGenerator()) + + @staticmethod + def random_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RandomKinesisPartitionKeyGenerator()) + + @staticmethod + def row_data_fields_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RowDataFieldsKinesisPartitionKeyGenerator()) + + +class KinesisSink(Sink): Review Comment: ```suggestion class KinesisStreamsSink(Sink): ``` ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + FixedKinesisPartitionKeyGenerator()) + + @staticmethod + def random_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RandomKinesisPartitionKeyGenerator()) + + @staticmethod + def row_data_fields_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': Review Comment: It's used internally in the Table API and so could be removed. ########## docs/content/docs/connectors/datastream/kinesis.md: ########## @@ -112,6 +112,23 @@ val kinesis = env.addSource(new FlinkKinesisConsumer[String]( "kinesis_stream_name", new SimpleStringSchema, consumerConfig)) ``` {{< /tab >}} +{{< tab "python" >}} +```python +consumer_config= dict() +consumer_config['aws.region'] = 'us-east-1' +consumer_config['aws.credentials.provider.basic.accesskeyid'] = 'aws_access_key_id' +consumer_config['aws.credentials.provider.basic.secretkey'] = 'aws_secret_access_key' +consumer_config['flink.stream.initpos'] = 'LATEST' + +env = StreamExecutionEnvironment.get_execution_environment() + +JKinesisDeserializationSchema = get_gateway().jvm.org.apache.flink.streaming.connectors. + kinesis.serialization.KinesisDeserializationSchemaWrapper +schema = JKinesisDeserializationSchema(SimpleStringSchema()._j_deserialization_schema) + +kinesis = KinesisConsumer("stream-1", KinesisDeserializationSchema(schema), consumer_config) +``` +{{< /tab >}} Review Comment: Many examples in this file only have Java & Scala examples, why not update them? ########## docs/content/docs/connectors/datastream/kinesis.md: ########## @@ -112,6 +112,23 @@ val kinesis = env.addSource(new FlinkKinesisConsumer[String]( "kinesis_stream_name", new SimpleStringSchema, consumerConfig)) ``` {{< /tab >}} +{{< tab "python" >}} +```python +consumer_config= dict() +consumer_config['aws.region'] = 'us-east-1' +consumer_config['aws.credentials.provider.basic.accesskeyid'] = 'aws_access_key_id' +consumer_config['aws.credentials.provider.basic.secretkey'] = 'aws_secret_access_key' +consumer_config['flink.stream.initpos'] = 'LATEST' + +env = StreamExecutionEnvironment.get_execution_environment() + +JKinesisDeserializationSchema = get_gateway().jvm.org.apache.flink.streaming.connectors. + kinesis.serialization.KinesisDeserializationSchemaWrapper +schema = JKinesisDeserializationSchema(SimpleStringSchema()._j_deserialization_schema) + +kinesis = KinesisConsumer("stream-1", KinesisDeserializationSchema(schema), consumer_config) Review Comment: Why not `KinesisConsumer("stream-1", SimpleStringSchema(), consumer_config)` ########## docs/content/docs/connectors/datastream/kinesis.md: ########## @@ -112,6 +112,23 @@ val kinesis = env.addSource(new FlinkKinesisConsumer[String]( "kinesis_stream_name", new SimpleStringSchema, consumerConfig)) ``` {{< /tab >}} +{{< tab "python" >}} Review Comment: ```suggestion {{< tab "Python" >}} ``` ########## docs/content/docs/connectors/datastream/kinesis.md: ########## @@ -112,6 +112,23 @@ val kinesis = env.addSource(new FlinkKinesisConsumer[String]( "kinesis_stream_name", new SimpleStringSchema, consumerConfig)) ``` {{< /tab >}} +{{< tab "python" >}} +```python +consumer_config= dict() +consumer_config['aws.region'] = 'us-east-1' +consumer_config['aws.credentials.provider.basic.accesskeyid'] = 'aws_access_key_id' +consumer_config['aws.credentials.provider.basic.secretkey'] = 'aws_secret_access_key' +consumer_config['flink.stream.initpos'] = 'LATEST' Review Comment: What about change it to the following which seems more pythonic: ``` consumer_config = { 'aws.region': 'us-east-1', 'aws.credentials.provider.basic.accesskeyid': 'aws_access_key_id', 'aws.credentials.provider.basic.secretkey': 'aws_secret_access_key', 'flink.stream.initpos': 'LATEST' } ``` ########## flink-python/pyflink/datastream/connectors/kinesis.py: ########## @@ -0,0 +1,368 @@ +################################################################################ +# 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, Union, List + +from pyflink.common import SerializationSchema, DeserializationSchema +from pyflink.datastream.functions import SourceFunction +from pyflink.datastream.connectors import Sink +from pyflink.java_gateway import get_gateway + + +# ---- KinesisSource ---- + + +class KinesisShardAssigner(object): + """ + Utility to map Kinesis shards to Flink subtask indices. Users can implement this interface to + optimize distribution of shards over subtasks. See assign(StreamShardHandle, int) for details. + """ + + def __init__(self, _j_kinesis_shard_assigner): + self._j_kinesis_shard_assigner = _j_kinesis_shard_assigner + + @staticmethod + def uniform_shard_assigner() -> 'KinesisShardAssigner': + return get_gateway().jvm.org.apache.flink.streaming.connectors \ + .kinesis.util.UniformShardAssigner() + + +class KinesisDeserializationSchema(object): + """ + This is a deserialization schema specific for the Flink Kinesis Consumer. Different from the + basic DeserializationSchema, this schema offers additional Kinesis-specific information about + the record that may be useful to the user application. + """ + + def __init__(self, _j_kinesis_deserialization_schema): + if _j_kinesis_deserialization_schema is None: + self._j_kinesis_deserialization_schema = get_gateway().jvm.org.apache.flink \ + .streaming.connectors.kinesis.serialization.KinesisDeserializationSchema + else: + self._j_kinesis_deserialization_schema = _j_kinesis_deserialization_schema + + +class AssignerWithPeriodicWatermarks(object): + """ + The AssignerWithPeriodicWatermarks assigns event time timestamps to elements, and generates + low watermarks that signal event time progress within the stream. These timestamps and + watermarks are used by functions and operators that operate on event time, for example event + time windows. + """ + + def __init__(self, _j_assigner_with_periodic_watermarks): + self._j_assigner_with_periodic_watermarks = _j_assigner_with_periodic_watermarks + + +class WatermarkTracker(object): + """ + The watermark tracker is responsible for aggregating watermarks across distributed operators. + It can be used for sub tasks of a single Flink source as well as multiple heterogeneous sources + or other operators.The class essentially functions like a distributed hash table that enclosing + operators can use to adopt their processing / IO rates + """ + + +def job_manager_watermark_tracker(aggregate_name: str, + log_accumulator_interval_millis: int = -1) \ + -> 'WatermarkTracker': + return get_gateway().jvm.org.apache.flink.streaming.connectors.kinesis.util \ + .JobManagerWatermarkTracker(aggregate_name, log_accumulator_interval_millis) + + +class KinesisConsumer(SourceFunction): + """ + The Flink Kinesis Consumer is an exactly-once parallel streaming data source that subscribes to + multiple AWS Kinesis streams within the same AWS service region, and can handle resharding of + streams. Each subtask of the consumer is responsible for fetching data records from multiple + Kinesis shards. The number of shards fetched by each subtask will change as shards are closed + and created by Kinesis. + + To leverage Flink's checkpointing mechanics for exactly-once streaming processing guarantees, + the Flink Kinesis consumer is implemented with the AWS Java SDK, instead of the officially + recommended AWS Kinesis Client Library, for low-level control on the management of stream state. + The Flink Kinesis Connector also supports setting the initial starting points of Kinesis + streams, namely TRIM_HORIZON and LATEST. + + Kinesis and the Flink consumer support dynamic re-sharding and shard IDs, while sequential, + cannot be assumed to be consecutive. There is no perfect generic default assignment function. + Default shard to subtask assignment, which is based on hash code, may result in skew, with some + subtasks having many shards assigned and others none. + + It is recommended to monitor the shard distribution and adjust assignment appropriately. + A custom assigner implementation can be set via setShardAssigner(KinesisShardAssigner) to + optimize the hash function or use static overrides to limit skew. + + In order for the consumer to emit watermarks, a timestamp assigner needs to be set via + setPeriodicWatermarkAssigner(AssignerWithPeriodicWatermarks) and the auto watermark emit + interval configured via ExecutionConfig.setAutoWatermarkInterval(long). + + Watermarks can only advance when all shards of a subtask continuously deliver records. + To avoid an inactive or closed shard to block the watermark progress, the idle timeout should + be configured via configuration property ConsumerConfigConstants.SHARD_IDLE_INTERVAL_MILLIS. + By default, shards won't be considered idle and watermark calculation will wait for newer + records to arrive from all shards. + + Note that re-sharding of the Kinesis stream while an application (that relies on the Kinesis + records for watermarking) is running can lead to incorrect late events. This depends on how + shards are assigned to subtasks and applies regardless of whether watermarks are generated in + the source or a downstream operator. + """ + + def __init__(self, + streams: Union[str, List[str]], + deserializer: Union[DeserializationSchema, KinesisDeserializationSchema], + config_props: Dict + ): + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in config_props.items(): + j_properties.setProperty(key, value) + + JKinesisConsumer = gateway.jvm.org.apache.flink.streaming.connectors.kinesis. \ + FlinkKinesisConsumer + + self._j_kinesis_consumer = JKinesisConsumer( + streams, + deserializer._j_deserialization_schema + if isinstance(deserializer, DeserializationSchema) + else deserializer._j_kinesis_deserialization_schema, + j_properties) + + super(KinesisConsumer, self).__init__(self._j_kinesis_consumer) + + def set_shard_assigner(self, shard_assigner: KinesisShardAssigner) -> 'KinesisConsumer': + """ + Provide a custom assigner to influence how shards are distributed over subtasks. + """ + self._j_kinesis_consumer.setShardAssigner(shard_assigner._j_kinesis_shard_assigner) + return self + + def set_periodic_watermark_assigner(self, + periodic_watermark_assigner: + AssignerWithPeriodicWatermarks) -> 'KinesisConsumer': + """ + Set the assigner that will extract the timestamp from T and calculate the watermark. + """ + self._j_kinesis_consumer.setPeriodicWatermarkAssigner( + periodic_watermark_assigner._j_assigner_with_periodic_watermarks) + return self + + def set_watermark_tracker(self, watermark_tracker: WatermarkTracker) -> 'KinesisConsumer': + """ + Set the global watermark tracker. When set, it will be used by the fetcher to align the + shard consumers by event time. + """ + self._j_kinesis_consumer.setWatermarkTracker(watermark_tracker) + return self + + +# ---- KinesisSink ---- + +class PartitionKeyGenerator(object): + def __init__(self, j_partition_key_generator): + self.j_partition_key_generator = j_partition_key_generator + + @staticmethod + def fixed_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + FixedKinesisPartitionKeyGenerator()) + + @staticmethod + def random_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RandomKinesisPartitionKeyGenerator()) + + @staticmethod + def row_data_fields_kinesis_partition_key_generator() -> 'PartitionKeyGenerator': + return PartitionKeyGenerator(get_gateway().jvm.org.apache.flink.connector.kinesis.table. + RowDataFieldsKinesisPartitionKeyGenerator()) + + +class KinesisSink(Sink): + """ + A Kinesis Data Streams (KDS) Sink that performs async requests against a destination stream Review Comment: Also add KinesisFirehoseSink? -- This is an automated message from the Apache Git Service. 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