mingkangli-db opened a new pull request, #48551:
URL: https://github.com/apache/spark/pull/48551
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### What changes were proposed in this pull request?
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### Summary
This PR addresses the misreporting of fatal errors (such as
`OutOfMemoryError`) as uncaught exceptions in `SparkUncaughtExceptionHandler`.
### Context
In `Executor.scala`, an exception is considered fatal if any exception in
the chain (or its causes) is fatal. This behavior is controlled by the
`spark.executor.killOnFatalError.depth` config, which limits the depth to which
the chain is inspected. If a fatal error is found,
`SparkUncaughtExceptionHandler` is called.
Currently, `SparkUncaughtExceptionHandler` only considers the top-level
exception when reporting the exit code, rather than traversing the full
exception chain to identify the true fatal cause. As a result, some fatal
errors, such as `OutOfMemoryError`, are mistakenly reported as uncaught
exceptions.
For instance, if we have an OOM exception with the following structure:
RuntimeException
- Caused by: RuntimeException
- Caused by: java.lang.OutOfMemory
SparkUncaughtExceptionHandler would quit the executor with error code
SparkExitCode.UNCAUGHT_EXCEPTION, when the true cause is an OOM error.
### Fix
This PR modifies `SparkUncaughtExceptionHandler` to:
- Inspect the exception chain (up to the configured depth).
- Ensure that the actual fatal error is correctly identified and reflected
in the exit code.
### Why are the changes needed?
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This is a bug because some nonfatal exceptions are mistakenly reported as an
uncaught exception when the true cause is some other fatal error down the
exception chain.
### Does this PR introduce _any_ user-facing change?
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No.
### How was this patch tested?
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Unit tests are added to `SparkUncaughtExceptionHandlerSuite.scala`.
### Was this patch authored or co-authored using generative AI tooling?
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No.
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