[ https://issues.apache.org/jira/browse/SPARK-26089?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Imran Rashid reassigned SPARK-26089: ------------------------------------ Assignee: Ankur Gupta > Handle large corrupt shuffle blocks > ----------------------------------- > > Key: SPARK-26089 > URL: https://issues.apache.org/jira/browse/SPARK-26089 > Project: Spark > Issue Type: Improvement > Components: Scheduler, Shuffle, Spark Core > Affects Versions: 2.4.0 > Reporter: Imran Rashid > Assignee: Ankur Gupta > Priority: Major > Fix For: 3.0.0 > > > We've seen a bad disk lead to corruption in a shuffle block, which lead to > tasks repeatedly failing after fetching the data with an IOException. The > tasks get retried, but the same corrupt data gets fetched again, and the > tasks keep failing. As there isn't a fetch-failure, the jobs eventually > fail, spark never tries to regenerate the shuffle data. > This is the same as SPARK-4105, but that fix only covered small blocks. > There was some discussion during that change about this limitation > (https://github.com/apache/spark/pull/15923#discussion_r88756017) and > followups to cover larger blocks (which would involve spilling to disk to > avoid OOM), but it looks like that never happened. > I can think of a few approaches to this: > 1) wrap the shuffle block input stream with another input stream, that > converts all exceptions into FetchFailures. This is similar to the fix of > SPARK-4105, but that reads the entire input stream up-front, and instead I'm > proposing to do it within the InputStream itself so its streaming and does > not have a large memory overhead. > 2) Add checksums to shuffle blocks. This was proposed > [here|https://github.com/apache/spark/pull/15894] and abandoned as being too > complex. > 3) Try to tackle this with blacklisting instead: when there is any failure in > a task that is reading shuffle data, assign some "blame" to the source of the > shuffle data, and eventually blacklist the source. It seems really tricky to > get sensible heuristics for this, though. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org