Hi,Yes, if Flink does not recognize your registered state type, it will by default use Kryo for the serialization.And generally speaking, Kryo does not have good support for evolvable schemas compared to other serialization frameworks such as Avro or Protobuf.The reason why Flink defaults to Kryo for unrecognizable types has some historical reasons due to the original use of Flink's type serialization stack being used on the batch side, but IMO the short answer is that it would make sense to have a different default serializer (perhaps Avro) for snapshotting state in streaming programs.However, I believe this would be better suited as a separate discussion thread.The good news is that with Flink 1.7, state schema evolution is fully supported out of the box for Avro types, such as GenericRecord or code generated SpecificRecords.If you want to have evolvable schema for your state types, then it is recommended to use Avro as state types.Support for evolving schema of other data types such as POJOs and Scala case classes is also on the radar for future releases.Does this help answer your question?By the way, the slides your are looking at I would consider quite outdated for the topic, since Flink 1.7 was released with much smoother support for state schema evolution.An updated version of the slides is not yet publicly available, but if you want I can send you one privately.Otherwise, the Flink docs for 1.7 would also be equally helpful.Cheers,GordonOn Fri, Dec 21, 2018, 8:11 PM Padarn Wilson <padarn.wilson@xxxxxxxx wrote:Hi all,I am trying to understand the situation with state serialization in flink. I'm looking at a number of sources, but slide 35 from here crystalizes my confusion:https://www.slideshare.net/FlinkForward/flink-forward-berlin-2017-tzuli-gordon-tai-managing-state-in-apache-flinkSo, I understand that if 'Flink's own serialization stack' is unable to serialize a type you define, then it will fall back on Kryo generics. In this case, I believe what I'm being told is that state compatibility is difficult to ensure, and schema evolution in your jobs is not possible.However on this slide, they say"Kryo is generally not recommended ...Serialization frameworks with schema evolution support is recommended: Avro, Thrift"So is this implying that Flink's non-default serialization stack does not support schema evolution? In this case is it best practice to register custom serializers whenever possible.Thanks
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