Can I avoid network shuffle when creating a KeyedStream from a pre-partitioned Kinesis Data Stream in Apache Flink?

Is it possible to create a KeyedStream from a pre-sharded/pre-partitioned Kinesis Data Stream without the need for a network shuffle (i.e. using reinterpretAsKeyedStream or something similar)?

• If that is not possible (i.e. the only reliable is to consume from Kinesis and then use keyBy), then is network shuffling at least minimized by doing a keyBy on a the field that the source is sharded by (e.g. env.addSource(source).keyBy(pojo -> pojo.getTransactionId()), where the source is a kinesis data stream that is sharded by transactionId)
• If the above is possible, what are the limitations?

What I've Learned so Far

• The functionality I am describing is already implemented by reinterpretAsKeyedStream, but this feature is experimental and seems to have significant drawbacks (as per discussions in the stackoverflow posts below)
  • reinterpretAsStream docs: https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/experimental/
  • https://stackoverflow.com/questions/72629086/using-keyby-vs-reinterpretaskeyedstream-when-reading-from-kafka
  • https://stackoverflow.com/questions/73278930/apache-flink-how-to-align-flink-and-kafka-sharding
• In addition to the above, all the discussions related to reinterpretAsKeyedStream that I've found are in the context of Kafka, so I'm not sure how the outcomes differ for a Kinesis Data Stream

Context of my Application

• Re. configurations: both the Kinesis Data Stream and Flink will be hosted serverlessly, and automatically scale up/down depending on load (which as I understand it, means that reinterpretAsKeyedStream cannot be used)

Any help/insight is much appreciated, thanks!

I don't believe there's any way to easily do what you want, at least not in a way that's resilient to changes in the parallelism of your source and your cluster. I have used helicopter stunts to achieve something similar to this, but it involved fragile code (depends on exactly how Flink handles partitioning).