Hi David and Saisai, Are the exceptions you two are observing similar to the first one at https://issues.apache.org/jira/browse/SPARK-3633 ? Copied below:
14/09/19 12:10:38 WARN TaskSetManager: Lost task 51.0 in stage 2.1 (TID 552, c1705.halxg.cloudera.com): FetchFailed(BlockManagerId(1, c1706.halxg.cloudera.com, 49612, 0), shuffleId=3, mapId=75, reduceId=120) I'm seeing the same using Spark SQL on 1.1.0 -- I think there may have been a regression in 1.1 because the same SQL query works on the same cluster when back on 1.0.2 Thanks! Andrew On Sun, Sep 21, 2014 at 5:15 AM, David Rowe <davidr...@gmail.com> wrote: > Hi, > > I've seen this problem before, and I'm not convinced it's GC. > > When spark shuffles it writes a lot of small files to store the data to be > sent to other executors (AFAICT). According to what I've read around the > place the intention is that these files be stored in disk buffers, and > since sync() is never called, they exist only in memory. The problem is > when you have a lot of shuffle data, and the executors are configured to > use, say 90% of your memory, one of those is going to be written to disk - > either the JVM will be swapped out, or the files will be written out of > cache. > > So, when these nodes are timing out, it's not a GC problem, it's that the > machine is actually thrashing. > > I've had some success with this problem by reducing the amount of memory > that the executors are configured to use from say 90% to 60%. I don't know > the internals of the code, but I'm sure this number is related to the > fraction of your data that's going to be shuffled to other nodes. In any > case, it's not something that I can estimate in my own jobs very well - I > usually have to find the right number by trial and error. > > Perhaps somebody who knows the internals a bit better can shed some light. > > Cheers > > Dave > > On Sun, Sep 21, 2014 at 9:54 PM, Shao, Saisai <saisai.s...@intel.com> > wrote: > >> Hi, >> >> >> >> I’ve also met this problem before, I think you can try to set >> “spark.core.connection.ack.wait.timeout” to a large value to avoid ack >> timeout, default is 60 seconds. >> >> >> >> Sometimes because of GC pause or some other reasons, acknowledged message >> will be timeout, which will lead to this exception, you can try setting a >> large value of this configuration. >> >> >> >> Thanks >> >> Jerry >> >> >> >> *From:* Julien Carme [mailto:julien.ca...@gmail.com] >> *Sent:* Sunday, September 21, 2014 7:43 PM >> *To:* user@spark.apache.org >> *Subject:* Issues with partitionBy: FetchFailed >> >> >> >> Hello, >> >> I am facing an issue with partitionBy, it is not clear whether it is a >> problem with my code or with my spark setup. I am using Spark 1.1, >> standalone, and my other spark projects work fine. >> >> So I have to repartition a relatively large file (about 70 million >> lines). Here is a minimal version of what is not working fine: >> >> myRDD = sc.textFile("...").map { line => (extractKey(line),line) } >> >> myRepartitionedRDD = myRDD.partitionBy(new HashPartitioner(100)) >> >> myRepartitionedRDD.saveAsTextFile(...) >> >> It runs quite some time, until I get some errors and it retries. Errors >> are: >> >> FetchFailed(BlockManagerId(3,myWorker2, 52082,0), >> shuffleId=1,mapId=1,reduceId=5) >> >> Logs are not much more infomrative. I get: >> >> Java.io.IOException : sendMessageReliability failed because ack was not >> received within 60 sec >> >> >> >> I get similar errors with all my workers. >> >> Do you have some kind of explanation for this behaviour? What could be >> wrong? >> >> Thanks, >> >> >> >> >> > >
