我基本上是通过在hadoop上扩展来实现一个推荐系统。
在第一步中，我尝试计算输入文件中每对项之间的相似性
{项目a，项目b，相似性}
输出文件大小变得非常大（对于60kb的输入，我得到的输出文件大小是6mb）。
因此，我想是否最好将结果存储在python dict中，并在整个map reduce程序结束后只打印一次dict。我没有成功，请帮助我。
我的python代码是：

# !/usr/bin/env python

from mrjob.job import MRJob
from math import sqrt

from itertools import combinations

PRIOR_COUNT = 10

PRIOR_CORRELATION = 0

    prefs={}

    def correlation(size, dot_product, rating_sum, \
        rating2sum, rating_norm_squared, rating2_norm_squared):
'''
  The correlation between two vectors A, B is
      [n * dotProduct(A, B) - sum(A) * sum(B)] /
    sqrt{ [n * norm(A)^2 - sum(A)^2] [n * norm(B)^2 - sum(B)^2] }

'''
numerator = size * dot_product - rating_sum * rating2sum
denominator = sqrt(size * rating_norm_squared - rating_sum * rating_sum) * \
                sqrt(size * rating2_norm_squared - rating2sum * rating2sum)

return (numerator / (float(denominator))) if denominator else 0.0

def regularized_correlation(size, dot_product, rating_sum, \
        rating2sum, rating_norm_squared, rating2_norm_squared,
        virtual_cont, prior_correlation):
    '''
    The Regularized Correlation between two vectors A, B

    RegularizedCorrelation = w * ActualCorrelation + (1 - w) * PriorCorrelation
        where w = # actualPairs / (# actualPairs + # virtualPairs).
    '''
    unregularizedCorrelation = correlation(size, dot_product, rating_sum, \
            rating2sum, rating_norm_squared, rating2_norm_squared)

    w = size / float(size + virtual_cont)

    return w * unregularizedCorrelation + (1.0 - w) * prior_correlation

class SemicolonValueProtocol(object):

  # don't need to implement read() since we aren't using it

  def write(self, key, values):
      return ';'.join(str(v) for v in values)

class BooksSimilarities(MRJob):

# OUTPUT_PROTOCOL = SemicolonValueProtocol

def steps(self):
    return [
        self.mr(mapper=self.group_by_user_rating,
                reducer=self.count_ratings_users_freq),
        self.mr(mapper=self.pairwise_items,
                reducer=self.calculate_similarity),
        self.mr(mapper=self.calculate_ranking,
                reducer=self.top_similar_items)]

def group_by_user_rating(self, key, line):
    '''
    Emit the user_id and group by their ratings (item and rating)

    17  70,3
    35  21,1
    49  19,2
    49  21,1
    49  70,4
    87  19,1
    87  21,2
    98  19,2

    '''
    line=line.replace("\"","");
    user_id, item_id, rating = line.split(',')

    yield  user_id, (item_id, float(rating))

def count_ratings_users_freq(self, user_id, values):
    '''
    For each user, emit a row containing their "postings"
    (item,rating pairs)
    Also emit user rating sum and count for use later steps.

    17    1,3,(70,3)
    35    1,1,(21,1)
    49    3,7,(19,2 21,1 70,4)
    87    2,3,(19,1 21,2)
    98    1,2,(19,2)

    '''
    item_count = 0
    item_sum = 0
    final = []
    for item_id, rating in values:
        item_count += 1
        item_sum += rating
        final.append((item_id, rating))

    yield user_id, (item_count, item_sum, final)

def pairwise_items(self, user_id, values):
    '''
    The output drops the user from the key entirely, instead it emits
    the pair of items as the key:

    19,21  2,1
    19,70  2,4
    21,70  1,4
    19,21  1,2

    '''
    item_count, item_sum, ratings = values
    for item1, item2 in combinations(ratings, 2):
        yield (item1[0], item2[0]), (item1[1], item2[1])

def calculate_similarity(self, pair_key, lines):
    '''
    Sum components of each corating pair across all users who rated both
    item x and item y, then calculate pairwise pearson similarity and
    corating counts.  The similarities are normalized to the [0,1] scale
    because we do a numerical sort.

    19,21   0.4,2
    21,19   0.4,2
    19,70   0.6,1
    70,19   0.6,1
    21,70   0.1,1
    70,21   0.1,1

    '''
    sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
    item_pair, co_ratings = pair_key, lines
    item_xname, item_yname = item_pair
    for item_x, item_y in lines:
        sum_xy += item_x * item_y
        sum_y += item_y
        sum_x += item_x
        sum_xx += item_x * item_x
        sum_yy += item_y * item_y
        n += 1

    reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, \
            sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)

    yield (item_xname, item_yname), (reg_corr_sim, n)

def calculate_ranking(self, item_keys, values):
    '''
    Emit items with similarity in key for ranking:

    19,0.4    70,1
    19,0.6    21,2
    21,0.6    19,2
    21,0.9    70,1
    70,0.4    19,1
    70,0.9    21,1

    '''
    reg_corr_sim, n = values
    item_x, item_y = item_keys
    if int(n) > 0:
        yield (item_x, reg_corr_sim),(item_y, n)

def top_similar_items(self, key_sim, similar_ns):
    '''
    For each item emit K closest items in comma separated file:

    De La Soul;A Tribe Called Quest;0.6;1
    De La Soul;2Pac;0.4;2

    '''
    item_x, reg_corr_sim = key_sim
    for item_y, n in similar_ns:
           #yield None, (item_x, item_y, reg_corr_sim, n)
       prefs.setdefault(item_x,{})
       prefs[item_x][item_y] = float(reg_corr_sim)
       prefs.setdefault(item_y,{})
       prefs[item_y][item_x] = float(reg_corr_sim) 
    print "exiting"

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

所以我执行后想要什么
python thisfile.py<input.csv-r hadoop>output.txt
是一个相对较小的输出文件，没有重复和一个dict。
总之，
目前这个程序打印退出n次，但我想它只打印一次。
除此之外，还有什么更好的方法可以通过在hadoop上以更好的方式扩展来实现协作过滤。
提前多谢了。