Cool. To confirm, you said you can access the class and construct new objects -- did you do this in the shell itself (i.e., on the driver), or on the executors?
Specifically, one of the following two should fail in the shell: > new mypackage.MyClass() > sc.parallelize(0 until 10, 2).foreach(_ => new mypackage.MyClass()) (or just import it) You could also try running MASTER=local-cluster[2,1,512] which launches 2 executors, 1 core each, with 512MB in a setup that mimics a real cluster more closely, in case it's a bug only related to using local mode. On Sat, Jan 4, 2014 at 7:07 PM, Aureliano Buendia <buendia...@gmail.com>wrote: > > > > On Sun, Jan 5, 2014 at 2:28 AM, Aaron Davidson <ilike...@gmail.com> wrote: > >> Additionally, which version of Spark are you running? >> > > 0.8.1. > > Unfortunately, this doesn't work either: > > MASTER=local[2] ADD_JARS=/path/to/my/jar > SPARK_CLASSPATH=/path/to/my/jar./spark-shell > > >> >> >> On Sat, Jan 4, 2014 at 6:27 PM, Aaron Davidson <ilike...@gmail.com>wrote: >> >>> I am not an expert on these classpath issues, but if you're using local >>> mode, you might also try to set SPARK_CLASSPATH to include the path to the >>> jar file as well. This should not really help, since "adding jars" is the >>> right way to get the jars to your executors (which is where the exception >>> appears to be happening), but it would sure be interesting if it did. >>> >>> >>> On Sat, Jan 4, 2014 at 4:50 PM, Aureliano Buendia >>> <buendia...@gmail.com>wrote: >>> >>>> I should add that I can see in the log that the jar being shipped to >>>> the workers: >>>> >>>> 14/01/04 15:34:52 INFO Executor: Fetching >>>> http://192.168.1.111:51031/jars/my.jar.jar with timestamp 1388881979092 >>>> 14/01/04 15:34:52 INFO Utils: Fetching >>>> http://192.168.1.111:51031/jars/my.jar.jar to >>>> /var/folders/3g/jyx81ctj3698wbvphxhm4dw40000gn/T/fetchFileTemp8322008964976744710.tmp >>>> 14/01/04 15:34:53 INFO Executor: Adding >>>> file:/var/folders/3g/jyx81ctj3698wbvphxhm4dw40000gn/T/spark-d8ac8f66-fad6-4b3f-8059-73f13b86b070/my.jar.jar >>>> to class loader >>>> >>>> >>>> On Sun, Jan 5, 2014 at 12:46 AM, Aureliano Buendia < >>>> buendia...@gmail.com> wrote: >>>> >>>>> Hi, >>>>> >>>>> I'm trying to access my stand alone spark app from spark-shell. I >>>>> tried starting the shell by: >>>>> >>>>> MASTER=local[2] ADD_JARS=/path/to/my/jar ./spark-shell >>>>> >>>>> The log shows that the jar file was loaded. Also, I can access and >>>>> create a new instance of mypackage.MyClass. >>>>> >>>>> The problem is that myRDD.collect() returns RDD[MyClass], and that >>>>> throws this exception: >>>>> >>>>> java.lang.ClassNotFoundException: mypackage.MyClass >>>>> at java.net.URLClassLoader$1.run(URLClassLoader.java:366) >>>>> at java.net.URLClassLoader$1.run(URLClassLoader.java:355) >>>>> at java.security.AccessController.doPrivileged(Native Method) >>>>> at java.net.URLClassLoader.findClass(URLClassLoader.java:354) >>>>> at java.lang.ClassLoader.loadClass(ClassLoader.java:423) >>>>> at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308) >>>>> at java.lang.ClassLoader.loadClass(ClassLoader.java:356) >>>>> at java.lang.Class.forName0(Native Method) >>>>> at java.lang.Class.forName(Class.java:264) >>>>> at java.io.ObjectInputStream.resolveClass(ObjectInputStream.java:622) >>>>> at >>>>> java.io.ObjectInputStream.readNonProxyDesc(ObjectInputStream.java:1593) >>>>> at >>>>> java.io.ObjectInputStream.readClassDesc(ObjectInputStream.java:1514) >>>>> at java.io.ObjectInputStream.readArray(ObjectInputStream.java:1642) >>>>> at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1341) >>>>> at java.io.ObjectInputStream.readObject(ObjectInputStream.java:369) >>>>> at org.apache.spark.util.Utils$.deserialize(Utils.scala:59) >>>>> at >>>>> org.apache.spark.SparkContext$$anonfun$objectFile$1.apply(SparkContext.scala:573) >>>>> at >>>>> org.apache.spark.SparkContext$$anonfun$objectFile$1.apply(SparkContext.scala:573) >>>>> at scala.collection.Iterator$$anon$21.hasNext(Iterator.scala:440) >>>>> at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:702) >>>>> at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:698) >>>>> at >>>>> org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:872) >>>>> at >>>>> org.apache.spark.SparkContext$$anonfun$runJob$4.apply(SparkContext.scala:872) >>>>> at >>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:107) >>>>> at org.apache.spark.scheduler.Task.run(Task.scala:53) >>>>> at >>>>> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:215) >>>>> at >>>>> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:50) >>>>> at >>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:182) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1110) >>>>> at >>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:603) >>>>> at java.lang.Thread.run(Thread.java:722) >>>>> >>>>> Does this mean that my jar was not shipped to the workers? Is this a >>>>> known issue, or am I doing something wrong here? >>>>> >>>> >>>> >>> >> >
