Using Hadoop in python to process a large csv file

Question

I have a huge CSV file I would like to process using Hadoop MapReduce on Amazon EMR (python).

The file has 7 fields, however, I am only looking at the date and quantity field.

 "date" "receiptId" "productId" "quantity"  "price" "posId" "cashierId"

Firstly, my mapper.py

import sys

def main(argv):
    line = sys.stdin.readline()
    try:
        while line:
            list = line.split('\t')

            #If date meets criteria, add quantity to express key
                if int(list[0][11:13])>=17 and int(list[0][11:13])<=19:
                    print '%s\t%s' % ("Express", int(list[3]))
            #Else, add quantity to non-express key
                else:
                    print '%s\t%s' % ("Non-express", int(list[3]))

            line =  sys.stdin.readline()
except "end of file":
        return None
if __name__ == "__main__":
        main(sys.argv)

For the reducer, I will be using the streaming command: aggregate.

Question:

1. Is my code right? I ran it in Amazon EMR but i got an empty output.

2. So my end result should be: express, XXX and non-express, YYY. Can I have it do a divide operation before returning the result? Just the result of XXX/YYY. Where should i put this code? A reducer??

3. Also, this is a huge CSV file, so will mapping break it up into a few partitions? Or do I need to explicitly call a FileSplit? If so, how do I do that?

Answer 1

Answering my own question here!

1. The code is wrong. If you're using aggregate library to reduce, your output does not follow the usual key value pair. It requires a "prefix".

   if int(list[0][11:13])>=17 and int(list[0][11:13])<=19:
       #This is the correct way of printing for aggregate library
       #Print all as a string.
       print  "LongValueSum:" + "Express" + "\t" + list[3]
   

   The other "prefixes" available are: DoubleValueSum, LongValueMax, LongValueMin, StringValueMax, StringValueMin, UniqValueCount, ValueHistogram. For more info, look here http://hadoop.apache.org/common/docs/r0.15.2/api/org/apache/hadoop/mapred/lib/aggregate/package-summary.html.

2. Yes, if you want to do more than just the basic sum, min, max or count, you need to write your own reducer.

3. I do not yet have the answer.