[GitHub] [flink] dianfu commented on a diff in pull request #19869: [FLINK-21996][python] Support Kinesis connector in Python DataStream API

[GitBox <gi...@apache.org>]

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?



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