Hi all,
Currently, we are exploring the various features of Flink and need some clarification on the below-mentioned questions. - I have a stateless Flink application where the source and sink are two different Kafka topics. Is there any benefit in adding checkpointing for this application?. will it help in some way for the rewind and replays while restarting from the failure? - I have a stateful use case where events are processed based on a set of dynamic rules provided by an external system, say a Kafka source. Also, the actual events are distinguishable based on a key.A broadcast function is used for broadcasting the dynamic rules and storing the same in Flink state. So my question is, processing the incoming streams based on these rules stored in Flink state per key is efficient or not ( i am using rocksdb as state-backend ) ? What about using an external cache for this? Is stateful function a good contender here? - Is there any benefit in using Apache camel along with Flink ? Thanks Jessy
