StackOverflow-error when applying pyspark ALS'

Question

Looking for expertise to guide me on issue below.

Background:

• I'm trying to get going with a basic PySpark script inspired on this example
• As deploy infrastructure I use a Google Cloud Dataproc Cluster.
• Cornerstone in my code is the function "recommendProductsForUsers" documented here which gives me back the top X products for all users in the model

Issue I incur

• The ALS.Train script runs smoothly and scales well on GCP (Easily >1mn customers).

• However, applying the predictions: i.e. using funcitons 'PredictAll' or 'recommendProductsForUsers', does not scale at all. My script runs smooth for a small dataset (<100 Customer with <100 products). However, when bringing it to a business-relevant size, I don't manage to scale it (e.g., >50k Customers and >10k products)

• Error I then get is below:

   16/08/16 14:38:56 WARN org.apache.spark.scheduler.TaskSetManager:
     Lost task 22.0 in stage 411.0 (TID 15139,
     productrecommendation-high-w-2.c.main-nova-558.internal):
     java.lang.StackOverflowError
          at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
          at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
          at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
          at java.io.ObjectInputStream.readObject(ObjectInputStream.java:373)
          at scala.collection.immutable.$colon$colon.readObject(List.scala:362)
          at sun.reflect.GeneratedMethodAccessor11.invoke(Unknown Source)
          at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
          at java.lang.reflect.Method.invoke(Method.java:498)
          at java.io.ObjectStreamClass.invokeReadObject(ObjectStreamClass.java:1058)
          at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1909)
          at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
          at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
          at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2018)
          at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
          at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
          at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
          at java.io.ObjectInputStream.defaultReadFields(ObjectInputStream.java:2018)
          at java.io.ObjectInputStream.readSerialData(ObjectInputStream.java:1942)
          at java.io.ObjectInputStream.readOrdinaryObject(ObjectInputStream.java:1808)
          at java.io.ObjectInputStream.readObject0(ObjectInputStream.java:1353)
          at java.io.ObjectInputStream.readObject(ObjectInputStream.java:373)
          at scala.collection.immutable.$colon$colon.readObject(List.scala:362)
  

• I even went as far as getting a 300 GB Cluster (1 main node of 108GB + 2 nodes of 108 GB RAM) to try it to run it; it works for 50k customers but not for anything more

• Ambition is to have a setup where I can run for >800k customers

Details

Code line where it fails

predictions = model.recommendProductsForUsers(10).flatMap(lambda p: p[1]).map(lambda p: (str(p[0]), str(p[1]), float(p[2])))
pprint.pprint(predictions.take(10))
schema = StructType([StructField("customer", StringType(), True), StructField("sku", StringType(), True), StructField("prediction", FloatType(), True)])
dfToSave = sqlContext.createDataFrame(predictions, schema).dropDuplicates()

How do you suggest to proceed? I feel that the 'merging' part at the end of my script (i.e. when I write it to dfToSave) causes the error; is there a way to bypass this & save part-by-part?

Answer 1

From the stack trace this appears to be the same issue as Spark gives a StackOverflowError when training using ALS

Basically, Spark expresses RDD lineage recursively so that you end up with deeply nested objects when things haven't been lazy evaluated over the course of an iterative workload. Calling sc.setCheckpointDir and adjusting the checkpoint interval will mitigate the length of this RDD lineage.