I’d like to get clarification on my understanding of schema evolution of state for FlinkSQL jobs and the implications. From reading https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/fault-tolerance/serialization/schema_evolution/, I’d conclude that FlinkSQL currently doesn’t support state schema evolution. If I have that correct, would that mean that if I take a save point for a job with windowing, add a new field and then attempt to restore the job from that save point it would fail?

I think the definition of schema evolution is different in the context from the provided link compared to your use case. Schema evolution in the DataStream API is all about evolution of the state schema that you have defined yourself in the DataStream API. Your use case is all about Flink SQL, which is explained in more depth at https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/concepts/overview/#stateful-upgrades-and-evolution. I would also recommend to check out https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=191336489