此书不错，很短，且想打通PYTHON和大数据架构的关系。
先看一次，计划把这个文档作个翻译。
先来一个模拟MAPREDUCE的东东。。。
mapper.py
class Mapper: def map(self, data): returnval = [] counts = {} for line in data: words = line.split() for w in words: counts[w] = counts.get(w, 0) + 1 for w, c in counts.iteritems(): returnval.append((w, c)) print "Mapper result:" print returnval return returnval
reducer.py
class Reducer: def reduce(self, d): returnval = [] for k, v in d.iteritems(): returnval.append("%s\t%s"%(k, sum(v))) print "Reducer result:" print returnval return returnval
main.py
from mapper import Mapper from reducer import Reducer class JobRunner: def run(self, Mapper, Reducer, data): # map mapper = Mapper() tuples = mapper.map(data) # combine combined = {} for k, v in tuples: if k not in combined: combined[k] = [] combined[k].append(v) print "combined result:" print combined # reduce reducer = Reducer() output = reducer.reduce(combined) # do something with output for line in output: print line runner = JobRunner() runner.run(Mapper, Reducer, open("input.txt"))
