Hi, I am trying to build a simple recommendation engine using spark item similarity (eg with org.apache.mahout.math.cf.SimilarityAnalysis.cooccurrencesIDSs)
Things work fine on comparatively small dataset but I am having difficulty scaling it up The input I am using is CSV data containing 19.988.422 view item events produced by 1.384.107 users. Looking at 5.135.845 distinct products The csv data is stored on hdfs and is split up over 15 files, consequently the resultant RDD will have 15 partitions. After tweaking some parameters I did manage to get the job to run without going out of memory but the job takes a very very long time to run After running for 15 hours it still is stuck on org.apache.spark.rdd.RDD.flatMap(RDD.scala:332) org.apache.mahout.sparkbindings.blas.AtA$.at_a_nongraph_mmul(AtA.scala:254) org.apache.mahout.sparkbindings.blas.AtA$.at_a(AtA.scala:61) org.apache.mahout.sparkbindings.SparkEngine$.tr2phys(SparkEngine.scala:325) org.apache.mahout.sparkbindings.SparkEngine$.tr2phys(SparkEngine.scala:339) org.apache.mahout.sparkbindings.SparkEngine$.toPhysical(SparkEngine.scala:123) org.apache.mahout.math.drm.logical.CheckpointAction.checkpoint(CheckpointAction.scala:41) org.apache.mahout.math.drm.package$.drm2Checkpointed(package.scala:95) org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$3.apply(SimilarityAnalysis.scala:145) org.apache.mahout.math.cf.SimilarityAnalysis$$anonfun$3.apply(SimilarityAnalysis.scala:143) scala.collection.Iterator$$anon$11.next(Iterator.scala:328) scala.collection.Iterator$class.foreach(Iterator.scala:727) scala.collection.AbstractIterator.foreach(Iterator.scala:1157) scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:176) scala.collection.mutable.ListBuffer.$plus$plus$eq(ListBuffer.scala:45) scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)scala.collection.AbstractIterator.to(Iterator.scala:1157) scala.collection.TraversableOnce$class.toList(TraversableOnce.scala:257) scala.collection.AbstractIterator.toList(Iterator.scala:1157) I am using spark on yarn and containers cannot use more than 16gb I figured I would be able to speed things up by throwing a larger number of executors at the problem. but so far that is not working out very well I tried assigning 500 executors and repartitioning the input data to 500 partitions and even changing the spark.yarn.driver.memoryOverhead to crazy values (half of the heap) did not resolve this. Could someone offer any guidance on how to best speed up item similarity jobs ?
