Почему PySpark терпит неудачу со случайной ошибкой "Socket is closed"?

Я просто прошел курс обучения PySpark, и я компилирую script пример строк кода (что объясняет, почему блок кода ничего не делает). Каждый раз, когда я запускаю этот код, я получаю эту ошибку один или два раза. Линия, которая меняет ее, изменяется между прогонами. Я пробовал настройку spark.executor.memory и spark.executor.heartbeatInterval, но ошибка сохраняется. Я также попытался положить .cache() в конец различных строк без изменений.

Ошибка:

16/09/21 10:29:32 ERROR Utils: Uncaught exception in thread stdout writer for python
java.net.SocketException: Socket is closed
        at java.net.Socket.shutdownOutput(Socket.java:1551)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3$$anonfun$apply$4.apply$mcV$sp(PythonRDD.scala:344)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3$$anonfun$apply$4.apply(PythonRDD.scala:344)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3$$anonfun$apply$4.apply(PythonRDD.scala:344)
        at org.apache.spark.util.Utils$.tryLog(Utils.scala:1870)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:344)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1857)
        at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)

Код:

from pyspark import SparkConf, SparkContext

def parseLine(line):
    fields = line.split(',')
    return (int(fields[0]), float(fields[2]))

def parseGraphs(line):
    fields = line.split()
    return (fields[0]), [int(n) for n in fields[1:]]

# putting the [*] after local makes it run one executor on each core of your local PC
conf = SparkConf().setMaster("local[*]").setAppName("MyProcessName")

sc = SparkContext(conf = conf)

# parse the raw data and map it to an rdd.
# each item in this rdd is a tuple
# two methods to get the exact same data:
########## All of these methods can use lambda or full methods in the same way ##########
# read in a text file
customerOrdersLines = sc.textFile("file:///SparkCourse/customer-orders.csv")
customerOrdersRdd = customerOrdersLines.map(parseLine)
customerOrdersRdd = customerOrdersLines.map(lambda l: (int(l.split(',')[0]), float(l.split(',')[2])))
print customerOrdersRdd.take(1)

# countByValue groups identical values and counts them
salesByCustomer = customerOrdersRdd.map(lambda sale: sale[0]).countByValue()
print salesByCustomer.items()[0]

# use flatMap to cut everything up by whitespace
bookText = sc.textFile("file:///SparkCourse/Book.txt")
bookRdd = bookText.flatMap(lambda l: l.split())
print bookRdd.take(1)

# create key/value pairs that will allow for more complex uses
names = sc.textFile("file:///SparkCourse/marvel-names.txt")
namesRdd = names.map(lambda line: (int(line.split('\"')[0]), line.split('\"')[1].encode("utf8")))
print namesRdd.take(1)

graphs = sc.textFile("file:///SparkCourse/marvel-graph.txt")
graphsRdd = graphs.map(parseGraphs)
print graphsRdd.take(1)

# this will append "extra text" to each name.
# this is faster than a normal map because it doesn't give you access to the keys
extendedNamesRdd = namesRdd.mapValues(lambda heroName: heroName + "extra text")
print extendedNamesRdd.take(1)

# not the best example because the costars is already a list of integers
# but this should return a list, which will update the values
flattenedCostarsRdd = graphsRdd.flatMapValues(lambda costars: costars)
print flattenedCostarsRdd.take(1)

# put the heroes in ascending index order
sortedHeroes = namesRdd.sortByKey()
print sortedHeroes.take(1)

# to sort heroes by alphabetical order, we switch key/value to value/key, then sort
alphabeticalHeroes = namesRdd.map(lambda (key, value): (value, key)).sortByKey()
print alphabeticalHeroes.take(1)

# make sure that "spider" is in the name of the hero
spiderNames = namesRdd.filter(lambda (id, name): "spider" in name.lower())
print spiderNames.take(1)

# reduce by key keeps the key and performs aggregation methods on the values.  in this example, taking the sum
combinedGraphsRdd = flattenedCostarsRdd.reduceByKey(lambda value1, value2: value1 + value2)
print combinedGraphsRdd.take(1)

# broadcast: this is accessible from any executor
sentData = sc.broadcast(["this can be accessed by all executors", "access it using sentData"])

# accumulator:  this is synced across all executors
hitCounter = sc.accumulator(0)