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,
