Github user srowen commented on the pull request:
https://github.com/apache/spark/pull/5250#issuecomment-87688106
You can use Spark to do this too, sure. Functions can call the HDFS API to
check and delete files in parallel. Roughly:
```
sc.parallelize(fs.listStatus(...).map(_.getPath.toString)).map { pathStr =>
val path = new Path(pathStr)
val in = new GZIPInputStream(fs.open(path))
try {
in.read()
} catch {
case e: ZipException => fs.delete(path, false)
} finally {
in.close()
}
}
```
I'm sure that's not 100% right but you see the idea.
I am not proposing that this become a Spark API. It seems like an
application-specific piece of logic that can be written using Spark. I don't
claim Scala + Spark + Hadoop is easy, but it is directly doable with these
tools.
I think the point stands that this change does not help solve the problem
directly, as the above does. It ignores the problem, which is sometimes a fine
strategy, but at the cost of significant side effects. The side effects are the
non-starter, to me. But the upside is I think there is a direct solution too.
Well I've said enough so it's time to let others weigh in too.
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