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From Debasish Das <debasish.da...@gmail.com>
Subject Re: Using mllib-1.1.0-SNAPSHOT on Spark 1.0.1
Date Wed, 06 Aug 2014 19:01:51 GMT
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 <dbtsai@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.das83@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.das83@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 <mengxr@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.das83@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 <mengxr@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.das83@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 <mengxr@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.das83@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
>> >> >> >
>> >> >> >
>> >> >
>> >> >
>> >>
>> >
>> >
>>
>

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