I was having this same problem early this week and had to include my changes in the assembly.
On Sat, Aug 9, 2014 at 9:59 AM, Debasish Das <debasish.da...@gmail.com> wrote: > I validated that I can reproduce this problem with master as well (without > adding any of my mllib changes)... > > I separated mllib jar from assembly, deploy the assembly and then I supply > the mllib jar as --jars option to spark-submit... > > I get this error: > > 14/08/09 12:49:32 INFO DAGScheduler: Failed to run count at ALS.scala:299 > > Exception in thread "main" org.apache.spark.SparkException: Job aborted due > to stage failure: Task 238 in stage 40.0 failed 4 times, most recent > failure: Lost task 238.3 in stage 40.0 (TID 10002, > tblpmidn05adv-hdp.tdc.vzwcorp.com): java.lang.ClassCastException: > scala.Tuple1 cannot be cast to scala.Product2 > > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5$$anonfun$apply$4.apply(CoGroupedRDD.scala:159) > > scala.collection.Iterator$$anon$11.next(Iterator.scala:328) > > > > org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:138) > > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:159) > > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$5.apply(CoGroupedRDD.scala:158) > > > > scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) > > > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > > > scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771) > > org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:158) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:129) > > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:126) > > > > scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) > > > > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > > scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) > > > > scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771) > > org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:126) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > > > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > > org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33) > > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > > org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:61) > > org.apache.spark.rdd.RDD.iterator(RDD.scala:227) > > org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:62) > > org.apache.spark.scheduler.Task.run(Task.scala:54) > > > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:199) > > > > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > > > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > > java.lang.Thread.run(Thread.java:744) > > Driver stacktrace: > > at org.apache.spark.scheduler.DAGScheduler.org > > $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1153) > > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1142) > > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1141) > > at > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1141) > > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:682) > > at > > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:682) > > at scala.Option.foreach(Option.scala:236) > > at > > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:682) > > at > > org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1359) > > at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) > > at akka.actor.ActorCell.invoke(ActorCell.scala:456) > > at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) > > at akka.dispatch.Mailbox.run(Mailbox.scala:219) > > at > > akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) > > at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) > > at > > scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) > > at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) > > at > > scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) > > I will try now with mllib inside the assembly....If that works then > something is weird here ! > > > On Sat, Aug 9, 2014 at 12:46 AM, Debasish Das <debasish.da...@gmail.com> > wrote: > > > Hi Xiangrui, > > > > Based on your suggestion I moved core and mllib both to > 1.1.0-SNAPSHOT...I > > am still getting class cast exception: > > > > Exception in thread "main" org.apache.spark.SparkException: Job aborted > > due to stage failure: Task 249 in stage 52.0 failed 4 times, most recent > > failure: Lost task 249.3 in stage 52.0 (TID 10002, > > tblpmidn06adv-hdp.tdc.vzwcorp.com): java.lang.ClassCastException: > > scala.Tuple1 cannot be cast to scala.Product2 > > > > I am running ALS.scala merged with my changes. I will try the mllib jar > > without my changes next... > > > > Can this be due to the fact that my jars are compiled with Java 1.7_55 > but > > the cluster JRE is at 1.7_45. > > > > Thanks. > > > > Deb > > > > > > > > > > On Wed, Aug 6, 2014 at 12:01 PM, Debasish Das <debasish.da...@gmail.com> > > wrote: > > > >> I did not play with Hadoop settings...everything is compiled with > >> 2.3.0CDH5.0.2 for me... > >> > >> I did try to bump the version number of HBase from 0.94 to 0.96 or 0.98 > >> but there was no profile for CDH in the pom...but that's unrelated to > this ! > >> > >> > >> On Wed, Aug 6, 2014 at 9:45 AM, DB Tsai <dbt...@dbtsai.com> wrote: > >> > >>> One related question, is mllib jar independent from hadoop version > >>> (doesnt use hadoop api directly)? Can I use mllib jar compile for one > >>> version of hadoop and use it in another version of hadoop? > >>> > >>> Sent from my Google Nexus 5 > >>> On Aug 6, 2014 8:29 AM, "Debasish Das" <debasish.da...@gmail.com> > wrote: > >>> > >>>> Hi Xiangrui, > >>>> > >>>> Maintaining another file will be a pain later so I deployed spark > 1.0.1 > >>>> without mllib and then my application jar bundles mllib 1.1.0-SNAPSHOT > >>>> along with the code changes for quadratic optimization... > >>>> > >>>> Later the plan is to patch the snapshot mllib with the deployed stable > >>>> mllib... > >>>> > >>>> There are 5 variants that I am experimenting with around 400M ratings > >>>> (daily data, monthly data I will update in few days)... > >>>> > >>>> 1. LS > >>>> 2. NNLS > >>>> 3. Quadratic with bounds > >>>> 4. Quadratic with L1 > >>>> 5. Quadratic with equality and positivity > >>>> > >>>> Now the ALS 1.1.0 snapshot runs fine but after completion on this step > >>>> ALS.scala:311 > >>>> > >>>> // Materialize usersOut and productsOut. > >>>> usersOut.count() > >>>> > >>>> I am getting from one of the executors: java.lang.ClassCastException: > >>>> scala.Tuple1 cannot be cast to scala.Product2 > >>>> > >>>> I am debugging it further but I was wondering if this is due to RDD > >>>> compatibility within 1.0.1 and 1.1.0-SNAPSHOT ? > >>>> > >>>> I have built the jars on my Mac which has Java 1.7.0_55 but the > deployed > >>>> cluster has Java 1.7.0_45. > >>>> > >>>> The flow runs fine on my localhost spark 1.0.1 with 1 worker. Can that > >>>> Java > >>>> version mismatch cause this ? > >>>> > >>>> Stack traces are below > >>>> > >>>> Thanks. > >>>> Deb > >>>> > >>>> > >>>> Executor stacktrace: > >>>> > >>>> > >>>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:156) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:154) > >>>> > >>>> > >>>> > >>>> > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > >>>> > >>>> > >>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > >>>> > >>>> > >>>> org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:154) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:126) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:123) > >>>> > >>>> > >>>> > >>>> > scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:772) > >>>> > >>>> > >>>> > >>>> > scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33) > >>>> > >>>> > >>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108) > >>>> > >>>> > >>>> > >>>> > scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:771) > >>>> > >>>> > >>>> org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:123) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33) > >>>> > >>>> > org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:262) > >>>> > >>>> org.apache.spark.rdd.RDD.iterator(RDD.scala:229) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:158) > >>>> > >>>> > >>>> > >>>> > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) > >>>> > >>>> org.apache.spark.scheduler.Task.run(Task.scala:51) > >>>> > >>>> > >>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:183) > >>>> > >>>> > >>>> > >>>> > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > >>>> > >>>> > >>>> > >>>> > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > >>>> > >>>> java.lang.Thread.run(Thread.java:744) > >>>> > >>>> Driver stacktrace: > >>>> > >>>> at org.apache.spark.scheduler.DAGScheduler.org > >>>> > >>>> > $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1044) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1028) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1026) > >>>> > >>>> at > >>>> > >>>> > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > >>>> > >>>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1026) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:634) > >>>> > >>>> at scala.Option.foreach(Option.scala:236) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:634) > >>>> > >>>> at > >>>> > >>>> > org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1229) > >>>> > >>>> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) > >>>> > >>>> at akka.actor.ActorCell.invoke(ActorCell.scala:456) > >>>> > >>>> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) > >>>> > >>>> at akka.dispatch.Mailbox.run(Mailbox.scala:219) > >>>> > >>>> at > >>>> > >>>> > akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) > >>>> > >>>> at > scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) > >>>> > >>>> at > >>>> > >>>> > scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) > >>>> > >>>> at > >>>> > scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) > >>>> > >>>> at > >>>> > >>>> > scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) > >>>> > >>>> > >>>> On Tue, Aug 5, 2014 at 5:59 PM, Debasish Das < > debasish.da...@gmail.com> > >>>> wrote: > >>>> > >>>> > Hi Xiangrui, > >>>> > > >>>> > I used your idea and kept a cherry picked version of ALS.scala in my > >>>> > application and call it ALSQp.scala...this is a OK workaround for > now > >>>> till > >>>> > a version adds up to master for example... > >>>> > > >>>> > For the bug with userClassPathFirst, looks like Koert already found > >>>> this > >>>> > issue in the following JIRA: > >>>> > > >>>> > https://issues.apache.org/jira/browse/SPARK-1863 > >>>> > > >>>> > By the way the userClassPathFirst feature is very useful since I am > >>>> sure > >>>> > the deployed version of spark on a production cluster will always be > >>>> the > >>>> > last stable (core at 1.0.1 in my case) and people would like to > deploy > >>>> > SNAPSHOT versions of libraries that build on top of spark core > (mllib, > >>>> > streaming etc)... > >>>> > > >>>> > Another way is to have a build option that deploys only the core and > >>>> not > >>>> > the libraries built upon core... > >>>> > > >>>> > Do we have an option like that in make-distribution script ? > >>>> > > >>>> > Thanks. > >>>> > Deb > >>>> > > >>>> > > >>>> > On Tue, Aug 5, 2014 at 10:37 AM, Xiangrui Meng <men...@gmail.com> > >>>> wrote: > >>>> > > >>>> >> If you cannot change the Spark jar deployed on the cluster, an easy > >>>> >> solution would be renaming ALS in your jar. If userClassPathFirst > >>>> >> doesn't work, could you create a JIRA and attach the log? Thanks! > >>>> >> -Xiangrui > >>>> >> > >>>> >> On Tue, Aug 5, 2014 at 9:10 AM, Debasish Das < > >>>> debasish.da...@gmail.com> > >>>> >> wrote: > >>>> >> > I created the assembly file but still it wants to pick the mllib > >>>> from > >>>> >> the > >>>> >> > cluster: > >>>> >> > > >>>> >> > jar tf ./target/ml-0.0.1-SNAPSHOT-jar-with-dependencies.jar | > grep > >>>> >> > QuadraticMinimizer > >>>> >> > > >>>> >> > > >>>> org/apache/spark/mllib/optimization/QuadraticMinimizer$$anon$1.class > >>>> >> > > >>>> >> > /Users/v606014/dist-1.0.1/bin/spark-submit --master > >>>> >> > spark://TUSCA09LMLVT00C.local:7077 --class ALSDriver > >>>> >> > ./target/ml-0.0.1-SNAPSHOT-jar-with-dependencies.jar inputPath > >>>> >> outputPath > >>>> >> > > >>>> >> > Exception in thread "main" java.lang.NoSuchMethodError: > >>>> >> > > >>>> >> > >>>> > org.apache.spark.mllib.recommendation.ALS.setLambdaL1(D)Lorg/apache/spark/mllib/recommendation/ALS; > >>>> >> > > >>>> >> > Now if I force it to use the jar that I gave using > >>>> >> > spark.files.userClassPathFirst, then it fails on some > serialization > >>>> >> > issues... > >>>> >> > > >>>> >> > A simple solution is to cherry pick the files I need from spark > >>>> branch > >>>> >> to > >>>> >> > the application branch but I am not sure that's the right thing > to > >>>> do... > >>>> >> > > >>>> >> > The way userClassPathFirst is behaving, there might be bugs in > >>>> it... > >>>> >> > > >>>> >> > Any suggestions will be appreciated.... > >>>> >> > > >>>> >> > Thanks. > >>>> >> > Deb > >>>> >> > > >>>> >> > > >>>> >> > On Sat, Aug 2, 2014 at 11:12 AM, Xiangrui Meng <men...@gmail.com > > > >>>> >> wrote: > >>>> >> >> > >>>> >> >> Yes, that should work. spark-mllib-1.1.0 should be compatible > with > >>>> >> >> spark-core-1.0.1. > >>>> >> >> > >>>> >> >> On Sat, Aug 2, 2014 at 10:54 AM, Debasish Das < > >>>> >> debasish.da...@gmail.com> > >>>> >> >> wrote: > >>>> >> >> > Let me try it... > >>>> >> >> > > >>>> >> >> > Will this be fixed if I generate a assembly file with > >>>> mllib-1.1.0 > >>>> >> >> > SNAPSHOT > >>>> >> >> > jar and other dependencies with the rest of the application > >>>> code ? > >>>> >> >> > > >>>> >> >> > > >>>> >> >> > > >>>> >> >> > On Sat, Aug 2, 2014 at 10:46 AM, Xiangrui Meng < > >>>> men...@gmail.com> > >>>> >> wrote: > >>>> >> >> >> > >>>> >> >> >> You can try enabling "spark.files.userClassPathFirst". But > I'm > >>>> not > >>>> >> >> >> sure whether it could solve your problem. -Xiangrui > >>>> >> >> >> > >>>> >> >> >> On Sat, Aug 2, 2014 at 10:13 AM, Debasish Das > >>>> >> >> >> <debasish.da...@gmail.com> > >>>> >> >> >> wrote: > >>>> >> >> >> > Hi, > >>>> >> >> >> > > >>>> >> >> >> > I have deployed spark stable 1.0.1 on the cluster but I > have > >>>> new > >>>> >> code > >>>> >> >> >> > that > >>>> >> >> >> > I added in mllib-1.1.0-SNAPSHOT. > >>>> >> >> >> > > >>>> >> >> >> > I am trying to access the new code using spark-submit as > >>>> follows: > >>>> >> >> >> > > >>>> >> >> >> > spark-job --class > >>>> com.verizon.bda.mllib.recommendation.ALSDriver > >>>> >> >> >> > --executor-memory 16g --total-executor-cores 16 --jars > >>>> >> >> >> > spark-mllib_2.10-1.1.0-SNAPSHOT.jar,scopt_2.10-3.2.0.jar > >>>> >> >> >> > sag-core-0.0.1-SNAPSHOT.jar --rank 25 --numIterations 10 > >>>> --lambda > >>>> >> 1.0 > >>>> >> >> >> > --qpProblem 2 inputPath outputPath > >>>> >> >> >> > > >>>> >> >> >> > I can see the jars are getting added to httpServer as > >>>> expected: > >>>> >> >> >> > > >>>> >> >> >> > 14/08/02 12:50:04 INFO SparkContext: Added JAR > >>>> >> >> >> > > >>>> >> file:/vzhome/v606014/spark-glm/spark-mllib_2.10-1.1.0-SNAPSHOT.jar > at > >>>> >> >> >> > > >>>> >> http://10.145.84.20:37798/jars/spark-mllib_2.10-1.1.0-SNAPSHOT.jar > >>>> >> >> >> > with > >>>> >> >> >> > timestamp 1406998204236 > >>>> >> >> >> > > >>>> >> >> >> > 14/08/02 12:50:04 INFO SparkContext: Added JAR > >>>> >> >> >> > file:/vzhome/v606014/spark-glm/scopt_2.10-3.2.0.jar at > >>>> >> >> >> > http://10.145.84.20:37798/jars/scopt_2.10-3.2.0.jar with > >>>> >> timestamp > >>>> >> >> >> > 1406998204237 > >>>> >> >> >> > > >>>> >> >> >> > 14/08/02 12:50:04 INFO SparkContext: Added JAR > >>>> >> >> >> > file:/vzhome/v606014/spark-glm/sag-core-0.0.1-SNAPSHOT.jar > at > >>>> >> >> >> > http://10.145.84.20:37798/jars/sag-core-0.0.1-SNAPSHOT.jar > >>>> with > >>>> >> >> >> > timestamp > >>>> >> >> >> > 1406998204238 > >>>> >> >> >> > > >>>> >> >> >> > But the job still can't access code form mllib-1.1.0 > >>>> >> SNAPSHOT.jar...I > >>>> >> >> >> > think > >>>> >> >> >> > it's picking up the mllib from cluster which is at 1.0.1... > >>>> >> >> >> > > >>>> >> >> >> > Please help. I will ask for a PR tomorrow but internally we > >>>> want > >>>> >> to > >>>> >> >> >> > generate results from the new code. > >>>> >> >> >> > > >>>> >> >> >> > Thanks. > >>>> >> >> >> > > >>>> >> >> >> > Deb > >>>> >> >> > > >>>> >> >> > > >>>> >> > > >>>> >> > > >>>> >> > >>>> > > >>>> > > >>>> > >>> > >> > > >
