On the partitioning / id keys. If we would look at hash partitioning, how feasible will it be to just allow the user and item ids to be strings? A lot of the time these ids are strings anyway (UUIDs and so on), and it's really painful to translate between String <-> Int the whole time.
Are there any obvious blockers to this? I am a bit rusty on the ALS code but from a quick scan I think this may work. Performance may be an issue with large String keys... Any majore issues/objections to this thinking? I may be able to find time to take a stab at this if there is demand. On Mon, Apr 7, 2014 at 6:08 AM, Xiangrui Meng <men...@gmail.com> wrote: > Hi Deb, > > Are you using the master branch or a particular commit? Do you have > negative or out-of-integer-range user or product ids? There is an > issue with ALS' partitioning > (https://spark-project.atlassian.net/browse/SPARK-1281), but I'm not > sure whether that is the reason. Could you try to see whether you can > reproduce the error on a public data set, e.g., movielens? Thanks! > > Best, > Xiangrui > > On Sat, Apr 5, 2014 at 10:53 PM, Debasish Das <debasish.da...@gmail.com> > wrote: > > Hi, > > > > I deployed apache/spark master today and recently there were many ALS > > related checkins and enhancements.. > > > > I am running ALS with explicit feedback and I remember most enhancements > > were related to implicit feedback... > > > > With 25 factors my runs were successful but with 50 factors I am getting > > array index out of bound... > > > > Note that I was hitting gc errors before with an older version of spark > but > > it seems like the sparse matrix partitioning scheme has changed > now...data > > caching looks much balanced now...earlier one node was becoming > > bottleneck...Although I ran with 64g memory per node... > > > > There are around 3M products, 25M users... > > > > Anyone noticed this bug or something similar ? > > > > 14/04/05 23:03:15 WARN TaskSetManager: Loss was due to > > java.lang.ArrayIndexOutOfBoundsException > > java.lang.ArrayIndexOutOfBoundsException: 81029 > > at > > > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateBlock$1$$anonfun$apply$mcVI$sp$1.apply$mcVI$sp(ALS.scala:450) > > at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141) > > at > > > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateBlock$1.apply$mcVI$sp(ALS.scala:446) > > at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141) > > at org.apache.spark.mllib.recommendation.ALS.org > > $apache$spark$mllib$recommendation$ALS$$updateBlock(ALS.scala:445) > > at > > > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:416) > > at > > > org.apache.spark.mllib.recommendation.ALS$$anonfun$org$apache$spark$mllib$recommendation$ALS$$updateFeatures$2.apply(ALS.scala:415) > > at > > > org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31) > > at > > > org.apache.spark.rdd.MappedValuesRDD$$anonfun$compute$1.apply(MappedValuesRDD.scala:31) > > at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) > > at > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:149) > > at > > > org.apache.spark.rdd.CoGroupedRDD$$anonfun$compute$4.apply(CoGroupedRDD.scala:147) > > at > > > scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) > > at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) > > at org.apache.spark.rdd.CoGroupedRDD.compute(CoGroupedRDD.scala:147) > > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:229) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:220) > > at > > org.apache.spark.rdd.MappedValuesRDD.compute(MappedValuesRDD.scala:31) > > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:229) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:220) > > at > > > org.apache.spark.rdd.FlatMappedValuesRDD.compute(FlatMappedValuesRDD.scala:31) > > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:229) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:220) > > at org.apache.spark.rdd.FlatMappedRDD.compute(FlatMappedRDD.scala:33) > > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:229) > > at org.apache.spark.rdd.RDD.iterator(RDD.scala:220) > > at > > > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:161) > > at > > > org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:102) > > at org.apache.spark.scheduler.Task.run(Task.scala:52) > > at > > > org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:211) > > at > > > org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:43) > > at > > > org.apache.spark.deploy.SparkHadoopUtil$$anon$1.run(SparkHadoopUtil.scala:42) > > at java.security.AccessController.doPrivileged(Native Method) > > at javax.security.auth.Subject.doAs(Subject.java:396) > > at > > > org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1408) > > at > > > org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:42) > > at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:176) > > at > > > java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) > > at > > > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) > > at java.lang.Thread.run(Thread.java:662) > > > > Thanks. > > Deb >
