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in Big Data Hadoop & Spark by (11.4k points)

I am reading in a CSV as a Spark DataFrame and performing machine learning operations upon it. I keep getting a Python serialization EOFError - any idea why? I thought it might be a memory issue - i.e. file exceeding available RAM - but drastically reducing the size of the DataFrame didn't prevent the EOF error.

Toy code and error below.

#set spark context
conf = SparkConf().setMaster("local").setAppName("MyApp")
sc = SparkContext(conf = conf)
sqlContext = SQLContext(sc)

#read in 500mb csv as DataFrame
df ='com.databricks.spark.csv').options(header='true',

#get dataframe into machine learning format
r_formula = RFormula(formula = "outcome ~ .")
mldf =

#fit random forest model
rf = RandomForestClassifier(numTrees = 3, maxDepth = 2)
model =
result = model.transform(mldf).head()

Running the above code with spark-submit on a single node repeatedly throws the following error, even if the size of the DataFrame is reduced prior to fitting the model (e.g. tinydf = df.sample(False, 0.00001):

Traceback (most recent call last):
  File "/home/hduser/spark1.6/python/lib/", line 157,
     in manager
  File "/home/hduser/spark1.6/python/lib/", line 61,
     in worker
  File "/home/hduser/spark1.6/python/lib/", line 136,
     in main if read_int(infile) == SpecialLengths.END_OF_STREAM:
  File "/home/hduser/spark1.6/python/lib/", line 545,
     in read_int
    raise EOFError

1 Answer

0 votes
by (32.3k points)

I don’t know if you have checked where in your code the EOF error is arising. As you have not mentioned it in, my guess would be that it's coming as you attempt to define df with, since that's the only place in your code where the file is getting read.

df ='somethingspark.csv').options(header='true',


At every point after this line, your code is working with the variable df, not the file itself, therefore it is very likely that the error is getting generated in this line. A simple way to test if this is the case or not would be to comment out the rest of your code, and/or place a line like this right after the line above.


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