Github user koeninger commented on the issue:
https://github.com/apache/spark/pull/15102
Ok, so this kind of thing is why I was concerned about the copy, paste,
randomly change things approach to developing this module.
> (5) Topics are deleted when a Spark job is runinng, which may cause
OffsetOutOfRangeException. (I'm not sure if there are more types of exceptions,
may need to investigate) Solution: log a warning. Note: if a Spark job fails,
then the query will fail as well.
OffsetOutOfRangeException basically means you asked Kafka for an offset,
and it wasn't there. The most common reason this happens isn't because a topic
got deleted, it's because messages expired out of retention before they got
read.
Just logging at warning level and continuing in this situation is
catastrophically, someone-loses-their-paying-job-not-their-spark-job, bad.
The existing kafka DStream integrations that have been around for 7 spark
versions will just let that exception be thrown, resulting in errors / failed
tasks, which make it pretty obvious that something is really wrong.
If you think that behavior is incorrect, let's figure out a unified
behavior for how to deal with exceptional situations that break fundamental
assumptions, and make it reallllly obvious to users how to get the behavior
they need across both modules. But having the structured stream behave in
significantly different ways seems like a recipe for trouble.
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