I actually saw the same issue, where we analyzed some container with few
hundreds of GBs zip files - one was corrupted and Spark exit with
Exception on the entire job.
I like SPARK-6593, since it can cover also additional cases, not just in
case of corrupted zip files.
From: Dale Richardson <dale...@hotmail.com>
To: "dev@spark.apache.org" <dev@spark.apache.org>
Date: 29/03/2015 11:48 PM
Subject: One corrupt gzip in a directory of 100s
Recently had an incident reported to me where somebody was analysing a
directory of gzipped log files, and was struggling to load them into spark
because one of the files was corrupted - calling
sc.textFiles('hdfs:///logs/*.gz') caused an IOException on the particular
executor that was reading that file, which caused the entire job to be
cancelled after the retry count was exceeded, without any way of catching
and recovering from the error. While normally I think it is entirely
appropriate to stop execution if something is wrong with your input,
sometimes it is useful to analyse what you can get (as long as you are
aware that input has been skipped), and treat corrupt files as acceptable
losses.
To cater for this particular case I've added SPARK-6593 (PR at
https://github.com/apache/spark/pull/5250). Which adds an option
(spark.hadoop.ignoreInputErrors) to log exceptions raised by the hadoop
Input format, but to continue on with the next task.
Ideally in this case you would want to report the corrupt file paths back
to the master so they could be dealt with in a particular way (eg moved to
a separate directory), but that would require a public API
change/addition. I was pondering on an addition to Spark's hadoop API that
could report processing status back to the master via an optional
accumulator that collects filepath/Option(exception message) tuples so the
user has some idea of what files are being processed, and what files are
being skipped.
Regards,Dale.
