name := "Simple Project" version := "1.0"
scalaVersion := "2.9.3" libraryDependencies += "org.apache.spark" %% "spark-core" % "0.8.0-incubating" resolvers += "Akka Repository" at "http://repo.akka.io/releases/"; resolvers += "Cloudera Repository" at " https://repository.cloudera.com/artifactory/cloudera-repos/"; libraryDependencies += "org.apache.hadoop" % "hadoop-client" % "2.0.0-mr1-cdh4.3.0" On Fri, Oct 18, 2013 at 12:34 PM, Matei Zaharia <[email protected]>wrote: > Can you post the build.sbt for your program? It needs to include > hadoop-client for CDH4.3, and that should *not* be listed as provided. > > Matei > > On Oct 18, 2013, at 8:23 AM, Koert Kuipers <[email protected]> wrote: > > ok this has nothing to do with hadoop access. even a simple program that > uses sc.parallelize blows up in this way. > > so spark-shell works well on the same machine i launch this from. > > if i launch a simple program without using kryo for serializer and closure > serialize i get a different error. see below. > at this point it seems to me i have some issue with task serialization??? > > > > 13/10/18 11:20:37 INFO StandaloneExecutorBackend: Got assigned task 0 > 13/10/18 11:20:37 INFO StandaloneExecutorBackend: Got assigned task 1 > 13/10/18 11:20:37 INFO Executor: Running task ID 1 > 13/10/18 11:20:37 INFO Executor: Running task ID 0 > 13/10/18 11:20:37 INFO Executor: Fetching > http://192.168.3.171:41629/jars/simple-project_2.9.3-1.0.jar with > timestamp 1382109635095 > 13/10/18 11:20:37 INFO Utils: Fetching > http://192.168.3.171:41629/jars/simple-project_2.9.3-1.0.jar to > /tmp/fetchFileTemp378181753997570700.tmp > 13/10/18 11:20:37 INFO Executor: Adding > file:/var/lib/spark/app-20131018112035-0014/1/./simple-project_2.9.3-1.0.jar > to class loader > 13/10/18 11:20:37 INFO Executor: caught throwable with stacktrace > java.io.StreamCorruptedException: invalid type code: 00 > at > java.io.ObjectInputStream$BlockDataInputStream.readBlockHeader(ObjectInputStream.java:2467) > at > java.io.ObjectInputStream$BlockDataInputStream.refill(ObjectInputStream.java:2502) > at > java.io.ObjectInputStream$BlockDataInputStream.read(ObjectInputStream.java:2661) > at > java.io.ObjectInputStream$BlockDataInputStream.read(ObjectInputStream.java:2583) > at java.io.DataInputStream.readFully(DataInputStream.java:178) > at java.io.DataInputStream.readLong(DataInputStream.java:399) > at > java.io.ObjectInputStream$BlockDataInputStream.readLong(ObjectInputStream.java:2803) > at java.io.ObjectInputStream.readLong(ObjectInputStream.java:958) > at > org.apache.spark.rdd.ParallelCollectionPartition.readObject(ParallelCollectionRDD.scala:72) > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) > at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) > at java.lang.reflect.Method.invoke(Method.java:597) > at > java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:969) > at > java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1852) > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1756) > at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1326) > at java.io.ObjectInputStream.readObject(ObjectInputStream.java:348) > at > org.apache.spark.scheduler.ResultTask.readExternal(ResultTask.scala:135) > at > java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1795) > at > java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1754) > at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1326) > at java.io.ObjectInputStream.readObject(ObjectInputStream.java:348) > at > org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:39) > at > org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:61) > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:153) > at > java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) > at java.lang.Thread.run(Thread.java:662) > > > > On Fri, Oct 18, 2013 at 10:59 AM, Koert Kuipers <[email protected]> wrote: > >> i created a tiny sbt project as described here: >> apache.org/docs/latest/quick-start.html#a-standalone-app-in-scala<http://spark.incubator.apache.org/docs/latest/quick-start.html#a-standalone-app-in-scala> >> >> it has the correct dependencies: spark-core and the correct hadoop-client >> for my platform. i tried both the generic spark-core dependency and >> spark-core dependency compiled against my platform. it runs fine in local >> mode, but when i switch to the cluster i still always get the following >> exceptions on tasks: >> >> 13/10/18 10:25:53 ERROR Executor: Uncaught exception in thread >> Thread[pool-5-thread-1,5,main] >> >> java.lang.NullPointerException >> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) >> at java.lang.Thread.run(Thread.java:662) >> >> after adding some additional debugging to Executor i see the cause is >> this: >> 13/10/18 10:54:47 INFO Executor: caught throwable with stacktrace >> java.lang.NullPointerException >> at >> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$2.apply(Executor.scala:155) >> at >> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$2.apply(Executor.scala:155) >> at org.apache.spark.Logging$class.logInfo(Logging.scala:48) >> at org.apache.spark.executor.Executor.logInfo(Executor.scala:36) >> at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:155) >> >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) >> at >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) >> at java.lang.Thread.run(Thread.java:662) >> >> so it seems the offending line is: >> logInfo("Its epoch is " + task.epoch) >> >> i am guessing accessing epoch on the task is throwing the NPE. any ideas? >> >> >> >> On Thu, Oct 17, 2013 at 8:12 PM, Koert Kuipers <[email protected]> wrote: >> >>> sorry one more related question: >>> i compile against a spark build for hadoop 1.0.4, but the actual >>> installed version of spark is build against cdh4.3.0-mr1. this also used to >>> work, and i prefer to do this so i compile against a generic spark build. >>> could this be the issue? >>> >>> >>> On Thu, Oct 17, 2013 at 8:06 PM, Koert Kuipers <[email protected]>wrote: >>> >>>> i have my spark and hadoop related dependencies as "provided" for my >>>> spark job. this used to work with previous versions. are these now supposed >>>> to be compile/runtime/default dependencies? >>>> >>>> >>>> On Thu, Oct 17, 2013 at 8:04 PM, Koert Kuipers <[email protected]>wrote: >>>> >>>>> yes i did that and i can see the correct jars sitting in lib_managed >>>>> >>>>> >>>>> On Thu, Oct 17, 2013 at 7:56 PM, Matei Zaharia < >>>>> [email protected]> wrote: >>>>> >>>>>> Koert, did you link your Spark job to the right version of HDFS as >>>>>> well? In Spark 0.8, you have to add a Maven dependency on "hadoop-client" >>>>>> for your version of Hadoop. See >>>>>> http://spark.incubator.apache.org/docs/latest/quick-start.html#a-standalone-app-in-scala >>>>>> for >>>>>> example. >>>>>> >>>>>> Matei >>>>>> >>>>>> On Oct 17, 2013, at 4:38 PM, Koert Kuipers <[email protected]> wrote: >>>>>> >>>>>> i got the job a little further along by also setting this: >>>>>> System.setProperty("spark.closure.serializer", >>>>>> "org.apache.spark.serializer.KryoSerializer") >>>>>> >>>>>> not sure why i need to... but anyhow, now my workers start and then >>>>>> they blow up on this: >>>>>> >>>>>> 13/10/17 19:22:57 ERROR Executor: Uncaught exception in thread >>>>>> Thread[pool-5-thread-1,5,main] >>>>>> java.lang.NullPointerException >>>>>> at >>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:187) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) >>>>>> at >>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) >>>>>> at java.lang.Thread.run(Thread.java:662) >>>>>> >>>>>> >>>>>> which is: >>>>>> val metrics = attemptedTask.flatMap(t => t.metrics) >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On Thu, Oct 17, 2013 at 7:30 PM, dachuan <[email protected]> wrote: >>>>>> >>>>>>> thanks, Mark. >>>>>>> >>>>>>> >>>>>>> On Thu, Oct 17, 2013 at 6:36 PM, Mark Hamstra < >>>>>>> [email protected]> wrote: >>>>>>> >>>>>>>> SNAPSHOTs are not fixed versions, but are floating names associated >>>>>>>> with whatever is the most recent code. So, Spark 0.8.0 is the current >>>>>>>> released version of Spark, which is exactly the same today as it was >>>>>>>> yesterday, and will be the same thing forever. Spark 0.8.1-SNAPSHOT is >>>>>>>> whatever is currently in branch-0.8. It changes every time new code is >>>>>>>> committed to that branch (which should be just bug fixes and the few >>>>>>>> additional features that we wanted to get into 0.8.0, but that didn't >>>>>>>> quite >>>>>>>> make it.) Not too long from now there will be a release of Spark >>>>>>>> 0.8.1, at >>>>>>>> which time the SNAPSHOT will got to 0.8.2 and 0.8.1 will be forever >>>>>>>> frozen. >>>>>>>> Meanwhile, the wild new development is taking place on the master >>>>>>>> branch, >>>>>>>> and whatever is currently in that branch becomes 0.9.0-SNAPSHOT. This >>>>>>>> could be quite different from day to day, and there are no guarantees >>>>>>>> that >>>>>>>> things won't be broken in 0.9.0-SNAPSHOT. Several months from now >>>>>>>> there >>>>>>>> will be a release of Spark 0.9.0 (unless the decision is made to bump >>>>>>>> the >>>>>>>> version to 1.0.0), at which point the SNAPSHOT goes to 0.9.1 and the >>>>>>>> whole >>>>>>>> process advances to the next phase of development. >>>>>>>> >>>>>>>> The short answer is that releases are stable, SNAPSHOTs are not, >>>>>>>> and SNAPSHOTs that aren't on maintenance branches can break things. >>>>>>>> You >>>>>>>> make your choice of which to use and pay the consequences. >>>>>>>> >>>>>>>> >>>>>>>> On Thu, Oct 17, 2013 at 3:18 PM, dachuan <[email protected]> wrote: >>>>>>>> >>>>>>>>> yeah, I mean 0.9.0-SNAPSHOT. I use git clone and that's what I >>>>>>>>> got.. what's the difference? I mean SNAPSHOT and non-SNAPSHOT. >>>>>>>>> >>>>>>>>> >>>>>>>>> On Thu, Oct 17, 2013 at 6:15 PM, Mark Hamstra < >>>>>>>>> [email protected]> wrote: >>>>>>>>> >>>>>>>>>> Of course, you mean 0.9.0-SNAPSHOT. There is no Spark 0.9.0, and >>>>>>>>>> won't be for several months. >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On Thu, Oct 17, 2013 at 3:11 PM, dachuan <[email protected]>wrote: >>>>>>>>>> >>>>>>>>>>> I'm sorry if this doesn't answer your question directly, but I >>>>>>>>>>> have tried spark 0.9.0 and hdfs 1.0.4 just now, it works.. >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Thu, Oct 17, 2013 at 6:05 PM, Koert Kuipers < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> after upgrading from spark 0.7 to spark 0.8 i can no longer >>>>>>>>>>>> access any files on HDFS. >>>>>>>>>>>> i see the error below. any ideas? >>>>>>>>>>>> >>>>>>>>>>>> i am running spark standalone on a cluster that also has >>>>>>>>>>>> CDH4.3.0 and rebuild spark accordingly. the jars in lib_managed >>>>>>>>>>>> look good >>>>>>>>>>>> to me. >>>>>>>>>>>> >>>>>>>>>>>> i noticed similar errors in the mailing list but found no >>>>>>>>>>>> suggested solutions. >>>>>>>>>>>> >>>>>>>>>>>> thanks! koert >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> 13/10/17 17:43:23 ERROR Executor: Exception in task ID 0 >>>>>>>>>>>> java.io.EOFException >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream$BlockDataInputStream.readFully(ObjectInputStream.java:2703) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readFully(ObjectInputStream.java:1008) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.hadoop.io.DataOutputBuffer$Buffer.write(DataOutputBuffer.java:68) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.hadoop.io.DataOutputBuffer.write(DataOutputBuffer.java:106) >>>>>>>>>>>> at org.apache.hadoop.io.UTF8.readChars(UTF8.java:258) >>>>>>>>>>>> at org.apache.hadoop.io.UTF8.readString(UTF8.java:250) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.hadoop.mapred.FileSplit.readFields(FileSplit.java:87) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.hadoop.io.ObjectWritable.readObject(ObjectWritable.java:280) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.hadoop.io.ObjectWritable.readFields(ObjectWritable.java:75) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.spark.SerializableWritable.readObject(SerializableWritable.scala:39) >>>>>>>>>>>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>>>>>>>>>> at >>>>>>>>>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) >>>>>>>>>>>> at >>>>>>>>>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) >>>>>>>>>>>> at java.lang.reflect.Method.invoke(Method.java:597) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:969) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1852) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1756) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1326) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1950) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1874) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1756) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1326) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:348) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.spark.scheduler.ResultTask.readExternal(ResultTask.scala:135) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1795) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1754) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1326) >>>>>>>>>>>> at >>>>>>>>>>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:348) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.spark.serializer.JavaDeserializationStream.readObject(JavaSerializer.scala:39) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.spark.serializer.JavaSerializerInstance.deserialize(JavaSerializer.scala:61) >>>>>>>>>>>> at >>>>>>>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:153) >>>>>>>>>>>> at >>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:895) >>>>>>>>>>>> at >>>>>>>>>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:918) >>>>>>>>>>>> at java.lang.Thread.run(Thread.java:662) >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> -- >>>>>>>>>>> Dachuan Huang >>>>>>>>>>> Cellphone: 614-390-7234 >>>>>>>>>>> 2015 Neil Avenue >>>>>>>>>>> Ohio State University >>>>>>>>>>> Columbus, Ohio >>>>>>>>>>> U.S.A. >>>>>>>>>>> 43210 >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Dachuan Huang >>>>>>>>> Cellphone: 614-390-7234 >>>>>>>>> 2015 Neil Avenue >>>>>>>>> Ohio State University >>>>>>>>> Columbus, Ohio >>>>>>>>> U.S.A. >>>>>>>>> 43210 >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> Dachuan Huang >>>>>>> Cellphone: 614-390-7234 >>>>>>> 2015 Neil Avenue >>>>>>> Ohio State University >>>>>>> Columbus, Ohio >>>>>>> U.S.A. >>>>>>> 43210 >>>>>>> >>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >> > >
