Github user viktorklang commented on the issue:
https://github.com/apache/spark/pull/21342
Scala 2.13.0-M5 and forward has improved handling of InterruptedExceptions
and fatal errors should be propagated, I'd welcome feedback on the new logic
when possible. Thank you!
Github user jinxing64 commented on the issue:
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Thanks for merging !
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Github user gatorsmile commented on the issue:
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Thanks! Merged to master.
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Github user JoshRosen commented on the issue:
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Updated changes LGTM. Thanks for working on this!
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Github user jinxing64 commented on the issue:
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Thanks a gain for comments !
I updated this pr and added small defensive logic in
SparkUncaughtExceptionHandler.scala.
Please take another look ~
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Github user jinxing64 commented on the issue:
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https://issues.scala-lang.org/browse/SI-9554?orig=1 is still "OPEN", not
sure which scala version can fix this bug.
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Github user JoshRosen commented on the issue:
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Thanks for the updates. The net change / scope of changes have been
significantly reduced here, so I feel that this change is a lot less risky now.
I left only one nitpicky comment at
Github user cloud-fan commented on the issue:
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LGTM, which scala version has fixed this bug?
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Github user kiszk commented on the issue:
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retest this please
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Github user jinxing64 commented on the issue:
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@kiszk @cloud-fan
Thanks a lot for comments.
I tested manually and found that the bug exists in all fatal throwable.
In current change, I catch all fatal throwable and wrap in
Github user jinxing64 commented on the issue:
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I will update the pr title if the change is on the right
direction.
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Github user cloud-fan commented on the issue:
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How about we create a special Exception class to wrap OOM and
InterupttedException, and say it's only used to work around the scala bug?
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Github user kiszk commented on the issue:
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Basically, I agree with this two-step direction. However, I think that it
is too risky to simply throw and catch `SparkException`. This is because other
`SparkException` may be caught in step 2. For
Github user jinxing64 commented on the issue:
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Thanks a lot for looking into this.
The issue is that, sometimes user would configure
`spark.sql.broadcastTimeout` as bigger value, because the `relationFuture` in
Github user JoshRosen commented on the issue:
https://github.com/apache/spark/pull/21342
I'm also in favor of delaying for a couple of days for more detailed review
because historically I think these types of changes have been high risk. The
risk calculus might be a bit different if
Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/21342
Could we hold this PR? This change sounds risky. `OutOfMemoryError` is not
issued by Spark. If we eat it, it could cause serious outcomes. cc @zsxwing
@JoshRosen
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Github user sameeragarwal commented on the issue:
https://github.com/apache/spark/pull/21342
LGTM, Thanks! I've seen variations of this bug in the past as well and am
not aware of a better way to fix this issue.
```scala
scala> Await.result(Future(throw new
Github user kiszk commented on the issue:
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The change looks good.
Is it better to add a comment that describes why we throw `SparkException`
instead of `OOM` with a reference to `scala/bug#9554`. In the future, we would
throw OOM if scala
Github user jinxing64 commented on the issue:
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cc @sameeragarwal @hvanhovell @cloud-fan @jiangxb1987
Please take a look at this when you have time.
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