That resolves the issue. What is strange to me is that with just the 'ADD_JARS' set, before the map is called I am able to import my main package object and use functions defined in that jar. The failure is when the mapper function starts to run.
On Mon, Sep 9, 2013 at 1:13 PM, Gary Malouf <[email protected]> wrote: > Will test and report back. Is this the same issue as: > https://groups.google.com/forum/#!topic/spark-users/fEcgIrL-gII? > > > On Mon, Sep 9, 2013 at 1:03 PM, Matei Zaharia <[email protected]>wrote: > >> No, I think this might be an actual bug. The problem seems to be with the >> classpath on the driver program actually, not on the executors. You might >> be able to fix it as follows: export SPARK_CLASSPATH=<your JAR> before >> running spark-shell, in addition to doing ADD_JARS. But even if that fixes >> it you should report the issue. >> >> Matei >> >> On Sep 9, 2013, at 5:18 AM, Gary Malouf <[email protected]> wrote: >> >> Any other checks I should do before filing this as an issue? I know for >> my team it's a significant blocker right now. >> >> >> On Sun, Sep 8, 2013 at 7:59 PM, Gary Malouf <[email protected]>wrote: >> >>> Hi Matei, >>> >>> We are using Spark 0.7.3 on a Mesos cluster. >>> >>> The logs when I start Spark shell include: >>> >>> 13/09/08 23:44:17 INFO spark.SparkContext: Added JAR >>> /opt/spark/mx-lib/verrazano_2.9.3-0.1-SNAPSHOT-assembly.jar at >>> http://10.236.136.202:31658/jars/verrazano_2.9.3-0.1-SNAPSHOT-assembly.jarwith >>> timestamp 1378683857701 >>> >>> I can also confirm that the 'verrazano' jar (my custom one) is in a >>> mesos slave temp directory on all of the slave nodes. >>> >>> >>> >>> >>> On Sun, Sep 8, 2013 at 7:01 PM, Matei Zaharia >>> <[email protected]>wrote: >>> >>>> Which version of Spark is this with? Did the logs print something about >>>> sending the JAR you added with ADD_JARS to the cluster? >>>> >>>> Matei >>>> >>>> On Sep 8, 2013, at 8:56 AM, Gary Malouf <[email protected]> wrote: >>>> >>>> > I built a custom jar with among other things, nscalatime and joda >>>> time packed inside of it. Using the ADD_JARS variable, I have added this >>>> super jar to my classpath on the scheduler when running spark-shell. I >>>> wrote a function that grabs protobuf data, filters and then maps each >>>> message to a (LocalDate, Option[String]) format. Unfortunately, this does >>>> not run and I get the following: >>>> > >>>> > 13/09/08 15:50:43 INFO cluster.TaskSetManager: Finished TID 6 in 348 >>>> ms (progress: 7/576) >>>> > Exception in thread "Thread-159" java.lang.ClassNotFoundException: >>>> org.joda.time.LocalDate >>>> > at java.net.URLClassLoader$1.run(URLClassLoader.java:366) >>>> > at java.net.URLClassLoader$1.run(URLClassLoader.java:355) >>>> > at java.security.AccessController.doPrivileged(Native Method) >>>> > at java.net.URLClassLoader.findClass(URLClassLoader.java:354) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.scala$tools$nsc$util$ScalaClassLoader$$super$findClass(ScalaClassLoader.scala:88) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$class.findClass(ScalaClassLoader.scala:44) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.findClass(ScalaClassLoader.scala:88) >>>> > at java.lang.ClassLoader.loadClass(ClassLoader.java:423) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.scala$tools$nsc$util$ScalaClassLoader$$super$loadClass(ScalaClassLoader.scala:88) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$class.loadClass(ScalaClassLoader.scala:50) >>>> > at >>>> scala.tools.nsc.util.ScalaClassLoader$URLClassLoader.loadClass(ScalaClassLoader.scala:88) >>>> > at java.lang.ClassLoader.loadClass(ClassLoader.java:356) >>>> > at java.lang.Class.forName0(Native Method) >>>> > at java.lang.Class.forName(Class.java:266) >>>> > at >>>> spark.JavaDeserializationStream$$anon$1.resolveClass(JavaSerializer.scala:20) >>>> > at >>>> java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1610) >>>> > at >>>> java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1515) >>>> > at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1769) >>>> > at >>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) >>>> > at >>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >>>> > at >>>> it.unimi.dsi.fastutil.objects.Object2LongOpenHashMap.readObject(Object2LongOpenHashMap.java:757) >>>> > at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>>> > at >>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) >>>> > at >>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>>> > at java.lang.reflect.Method.invoke(Method.java:601) >>>> > at >>>> java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1004) >>>> > at >>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1891) >>>> > at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) >>>> > at >>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) >>>> > at >>>> java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:1989) >>>> > at >>>> java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1913) >>>> > at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1796) >>>> > at >>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) >>>> > at >>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >>>> > at spark.scheduler.TaskResult.readExternal(TaskResult.scala:26) >>>> > at >>>> java.io.ObjectInputStream.readExternalData(ObjectInputStream.java:1835) >>>> > at >>>> java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1794) >>>> > at >>>> java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1348) >>>> > at >>>> java.io.ObjectInputStream.readObject(ObjectInputStream.java:370) >>>> > at >>>> spark.JavaDeserializationStream.readObject(JavaSerializer.scala:23) >>>> > at >>>> spark.JavaSerializerInstance.deserialize(JavaSerializer.scala:45) >>>> > at >>>> spark.scheduler.cluster.TaskSetManager.taskFinished(TaskSetManager.scala:261) >>>> > at >>>> spark.scheduler.cluster.TaskSetManager.statusUpdate(TaskSetManager.scala:236) >>>> > at >>>> spark.scheduler.cluster.ClusterScheduler.statusUpdate(ClusterScheduler.scala:219) >>>> > at >>>> spark.scheduler.mesos.MesosSchedulerBackend.statusUpdate(MesosSchedulerBackend.scala:264) >>>> > 13/09/08 15:50:43 INFO mesos.MesosSchedulerBackend: driver.run() >>>> returned with code DRIVER_ABORTED >>>> > >>>> > >>>> > The code definitely compiles in the interpreter and the executors >>>> seem to find the protobuf messages which are in the same jar - any idea >>>> what could be causing the problem? >>>> >>>> >>> >> >> >
