Yep, this is what I was seeing. I'll experiment tomorrow with a version prior to the changeset in that ticket.
On Mon, Sep 22, 2014 at 8:29 PM, Andrew Ash <and...@andrewash.com> wrote: > 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, >>> >>> >>> >>> >>> >> >> >
