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