How does mapreduce sort and shuffle work?

Question

I am using yelps MRJob library for achieving map-reduce functionality. I know that map reduce has an internal sort and shuffle algorithm which sorts the values on the basis of their keys. So if I have the following results after map phase

(1, 24) (4, 25) (3, 26)

I know the sort and shuffle phase will produce following output

(1, 24) (3, 26) (4, 25)

Which is as expected

But if I have two similar keys and different values why does the sort and shuffle phase sorts the data on the basis of first value that appears?

For example If I have the following list of values from mapper

(2, <25, 26>) (1, <24, 23>) (1, <23, 24>) 

The expected output is

(1, <24, 23>) (1, <23, 24>) (2, <25, 26>)

But the output that I am getting is

(1, <23, 24>) (1, <24, 23>) (2, <25, 26>)

is this MRjob library specific? Is there anyway to stop this sorting on the basis of values??

CODE

from mrjob.job import MRJob
import math

class SortMR(MRJob):



def steps(self):
    return [
        self.mr(mapper=self.rangemr,
                reducer=self.rangesort)]


def rangemr(self, key, line):
    for a in line.split():
        yield 1,a


def rangesort(self,numid,line):
    for a in line:
        yield(1, a)


if __name__ == '__main__':
    SortMR.run()

Answer 1

The local MRjob just uses the operating system 'sort' on the mapper output.

The mapper writes out in the format:

key<-tab->value\n

Thus you end up with the keys sorted primarily by key, but secondarily by value.

As noted, this doesn't happen in the real hadoop version, just the 'local' simulation.