piotr-szuberski commented on a change in pull request #12297:
URL: https://github.com/apache/beam/pull/12297#discussion_r468391828



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File path: sdks/python/apache_beam/io/kinesis.py
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@@ -0,0 +1,317 @@
+#
+# 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.
+#
+
+"""PTransforms for supporting Kinesis streaming in Python pipelines.
+
+  These transforms are currently supported by Beam Flink and Spark portable
+  runners.
+
+  **Setup**
+
+  Transforms provided in this module are cross-language transforms
+  implemented in the Beam Java SDK. During the pipeline construction, Python 
SDK
+  will connect to a Java expansion service to expand these transforms.
+  To facilitate this, a small amount of setup is needed before using these
+  transforms in a Beam Python pipeline.
+
+  There are several ways to setup cross-language Kinesis transforms.
+
+  * Option 1: use the default expansion service
+  * Option 2: specify a custom expansion service
+
+  See below for details regarding each of these options.
+
+  *Option 1: Use the default expansion service*
+
+  This is the recommended and easiest setup option for using Python Kinesis
+  transforms. This option is only available for Beam 2.24.0 and later.
+
+  This option requires following pre-requisites before running the Beam
+  pipeline.
+
+  * Install Java runtime in the computer from where the pipeline is constructed
+    and make sure that 'java' command is available.
+
+  In this option, Python SDK will either download (for released Beam version) 
or
+  build (when running from a Beam Git clone) a expansion service jar and use
+  that to expand transforms. Currently Kinesis transforms use the
+  'beam-sdks-java-io-kinesis-expansion-service' jar for this purpose.
+
+  *Option 2: specify a custom expansion service*
+
+  In this option, you startup your own expansion service and provide that as
+  a parameter when using the transforms provided in this module.
+
+  This option requires following pre-requisites before running the Beam
+  pipeline.
+
+  * Startup your own expansion service.
+  * Update your pipeline to provide the expansion service address when
+    initiating Kinesis transforms provided in this module.
+
+  Flink Users can use the built-in Expansion Service of the Flink Runner's
+  Job Server. If you start Flink's Job Server, the expansion service will be
+  started on port 8097. For a different address, please set the
+  expansion_service parameter.
+
+  **More information**
+
+  For more information regarding cross-language transforms see:
+  - https://beam.apache.org/roadmap/portability/
+
+  For more information specific to Flink runner see:
+  - https://beam.apache.org/documentation/runners/flink/
+"""
+
+# pytype: skip-file
+
+from __future__ import absolute_import
+
+import time
+from typing import List
+from typing import NamedTuple
+from typing import Optional
+from typing import Tuple
+
+from past.builtins import unicode
+
+from apache_beam import BeamJarExpansionService
+from apache_beam import ExternalTransform
+from apache_beam import NamedTupleBasedPayloadBuilder
+
+__all__ = [
+    'WriteToKinesis',
+    'ReadDataFromKinesis',
+    'InitialPositionInStream',
+    'WatermarkPolicy',
+]
+
+
+def default_io_expansion_service():
+  return BeamJarExpansionService(
+      'sdks:java:io:kinesis:expansion-service:shadowJar')
+
+
+WriteToKinesisSchema = NamedTuple(
+    'WriteToKinesisSchema',
+    [
+        ('stream_name', unicode),
+        ('aws_access_key', unicode),
+        ('aws_secret_key', unicode),
+        ('region', unicode),
+        ('partition_key', unicode),
+        ('service_endpoint', Optional[unicode]),
+        ('verify_certificate', Optional[bool]),
+        ('producer_properties', Optional[List[Tuple[unicode, unicode]]]),

Review comment:
       Great, Mapping will at least be there! I'll follow this PR.




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