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