A big difference between Kinesis shards and Kafka partitions is that Kinesis users can choose to "merge" and "split" shards at any time for adjustable stream throughput capacity. This article explains this quite clearly: https://brandur.org/kinesis-by-example.
This will break the static shard-to-task mapping implemented in the basic version of the Kinesis consumer (https://issues.apache.org/jira/browse/FLINK-3229). The static shard-to-task mapping is done in a simple round-robin-like distribution which can be locally determined at each Flink consumer task (Flink Kafka consumer does this too).
To handle Kinesis resharding, we will need some way to let the Flink consumer tasks coordinate which shards they are currently handling, and allow the tasks to ask the coordinator for a shards reassignment when the task finds out it has found a closed shard at runtime (shards will be closed by Kinesis when it is merged and split).
We need a centralized coordinator state store which is visible to all Flink consumer tasks. Tasks can use this state store to locally determine what shards it can be reassigned. Amazon KCL uses a DynamoDB table for the coordination, but as described in https://issues.apache.org/jira/browse/FLINK-3211, we unfortunately can't use KCL for the implementation of the consumer if we want to leverage Flink's checkpointing mechanics. For our own implementation, Zookeeper can be used for this state store, but that means it would require the user to set up ZK to work.
Since this feature introduces extensive work, it is opened as a separate sub-task from the basic implementation https://issues.apache.org/jira/browse/FLINK-3229.