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 >>> >> >> >> >
