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This question is directed towards persons familiar with py4j - and can help to resolve a pickling error. I am trying to add a method to the pyspark PythonMLLibAPI that accepts an RDD of a namedtuple, does some work, and returns a result in the form of an RDD.

This method is modeled after the PYthonMLLibAPI.trainALSModel() method, whose analogous existing relevant portions are:

  def trainALSModel(
    ratingsJRDD: JavaRDD[Rating],
    .. )

The existing python Rating class used to model the new code is:

class Rating(namedtuple("Rating", ["user", "product", "rating"])):
    def __reduce__(self):
        return Rating, (int(self.user), int(self.product), float(self.rating))

Here is the attempt So here are the relevant classes:

New python class pyspark.mllib.clustering.MatrixEntry:

from collections import namedtuple
class MatrixEntry(namedtuple("MatrixEntry", ["x","y","weight"])):
    def __reduce__(self):
        return MatrixEntry, (long(self.x), long(self.y), float(self.weight))

New method foobarRDD In PythonMLLibAPI: 

  def foobarRdd(
    data: JavaRDD[MatrixEntry]): RDD[FooBarResult] = {
    val rdd = data.rdd.map { d => FooBarResult(d.i, d.j, d.value, d.i * 100 + d.j * 10 + d.value)}
    rdd
  }

Now let us try it out:

from pyspark.mllib.clustering import MatrixEntry

def convert_to_MatrixEntry(tuple):
  return MatrixEntry(*tuple)

from pyspark.mllib.clustering import *
pic = PowerIterationClusteringModel(2)
tups = [(1,2,3),(4,5,6),(12,13,14),(15,7,8),(16,17,16.5)]
trdd = sc.parallelize(map(convert_to_MatrixEntry,tups))

# print out the RDD on python side just for validation
print "%s" %(repr(trdd.collect()))

from pyspark.mllib.common import callMLlibFunc
pic = callMLlibFunc("foobar", trdd)

Relevant portions of results:

[(1,2)=3.0, (4,5)=6.0, (12,13)=14.0, (15,7)=8.0, (16,17)=16.5]

which shows the input rdd is 'whole'. However the pickling was unhappy: 

5/04/27 21:15:44 ERROR Executor: Exception in task 6.0 in stage 1.0 (TID 14)
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict
(for pyspark.mllib.clustering.MatrixEntry)
    at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
    at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:617)
    at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:170)
    at net.razorvine.pickle.Unpickler.load(Unpickler.java:84)
    at net.razorvine.pickle.Unpickler.loads(Unpickler.java:97)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:1167)
    at org.apache.spark.mllib.api.python.SerDe$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(PythonMLLibAPI.scala:1166)
    at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
    at scala.collection.Iterator$class.foreach(Iterator.scala:727)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1157)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
    at scala.collection.AbstractIterator.to(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1157)
    at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
    at org.apache.spark.rdd.RDD$$anonfun$17.apply(RDD.scala:819)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1523)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1523)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61)
    at org.apache.spark.scheduler.Task.run(Task.scala:64)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:212)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:724)

Below is a visual of the python invocation stack trace:

enter image description here

WestCoastProjects
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3 Answers3

12

I had the same error as I was using MLlib, and it turned out that I had returned a wrong datatype in one of my functions. It now works after a simple cast on the returned value. This might not be the answer you're seeking but it is at least a hint for the direction to follow.

architectonic
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10

Had the same issue, several times. numpy types don't have implicit conversions to pyspark.sql.types.

Make a simple explicit conversion to the native type system. In my case it was:

float(vector_a.dot(vector_b))
Elior Malul
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2

I received this error using Spark Version >= 2.0.

Spark is transitioning MLlib fucntionality to the newer ML namespace. As a result there are two types of SparseVector: ml.linalg.SparseVector and mllib.linalg.SparseVector

Some MLlib functions still expect the older mllib kind

from pyspark.ml.linalg import Vectors
# convert ML vector to older MLlib vector
old_vec = Vectors.fromML(new_vec)

HTH

stacksonstacks
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  • This was extremely helpful - thank you! Only thing is that in version 2.1.1 `fromML` no longer seems to exist, so I had to create the object manually by doing `pyspark.mllib.linalg.SparseVector(sv.size, sv.indices, sv.values)`, where `sv` was my `pyspark.ml.linalg.SparseVector` object. – LateCoder Feb 26 '18 at 16:29