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- RecordTooLargeException:
- In transactions, the producer collects multiple records in batches. Then a RecordTooLargeException related to a single record leads to failing the entire batch. A custom exception handler in this case may decide on dropping the record and continuing the processing. See Example 1, please.
- More over, in this case, the producer does throw the exception to the user. The users using the producer directly can react to it. However, for Kafka Streams, a record that is too large is a poison pill record, and there is no easy way to skip over it. Currently, Kafka Streams treats this error as fatal and seeks to go back to the last commit of the input topic offset and retry to hit the same error again. It would require a major change inside Kafka Streams to add a bookkeeping code to track this error case correctly and to skip over this record when retrying. A handler would allow us to react to this error inside the producer, i.e., local to where the error happens, and thus simplify the overall code significantly.
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