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 >>> >> >> > >>> >> >> > >>> >> > >>> >> > >>> >> >>> > >>> > >>> >> >
