Kay Ousterhout created SPARK-4085:
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Summary: Job will fail if a shuffle file that's read locally gets
deleted
Key: SPARK-4085
URL: https://issues.apache.org/jira/browse/SPARK-4085
Project: Spark
Issue Type: Bug
Affects Versions: 1.2.0
Reporter: Kay Ousterhout
Assignee: Reynold Xin
This commit:
https://github.com/apache/spark/commit/665e71d14debb8a7fc1547c614867a8c3b1f806a
changed the behavior of fetching local shuffle blocks such that if a shuffle
block is not found locally, the shuffle block is no longer marked as failed,
and a fetch failed exception is not thrown (this is because the "catch" block
here won't ever be invoked:
https://github.com/apache/spark/commit/665e71d14debb8a7fc1547c614867a8c3b1f806a#diff-e6e1631fa01e17bf851f49d30d028823R202
because the exception called from getLocalFromDisk() doesn't get thrown until
next() gets called on the iterator).
[~rxin] [~matei] it looks like you guys changed the test for this to catch the
new exception that gets thrown
(https://github.com/apache/spark/commit/665e71d14debb8a7fc1547c614867a8c3b1f806a#diff-9c2e1918319de967045d04caf813a7d1R93).
Was that intentional? Because the new exception is a SparkException and not
a FetchFailedException, jobs with missing local shuffle data will now fail,
rather than having the map stage get retried.
This problem is reproducible with this test case:
test("hash shuffle manager recovers when local shuffle files get deleted") {
val conf = new SparkConf(false)
conf.set("spark.shuffle.manager", "hash")
sc = new SparkContext("local", "test", conf)
val rdd = sc.parallelize(1 to 10, 2).map((_, 1)).reduceByKey(_+_)
rdd.count()
// Delete one of the local shuffle blocks.
sc.env.blockManager.diskBlockManager.getFile(new ShuffleBlockId(0, 0,
0)).delete()
rdd.count()
}
which will fail on the second rdd.count().
This is a regression from 1.1.
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