我必须加入6组数据，这些数据与不同频道上某些电视节目的观看量有关。6组数据中的3组包含一个节目列表和每个节目的观看量，例如：
显示\u名称201
另一个节目105
等等。。。
其他3组数据包含每个节目的节目和频道，例如：
显示名称
另一个节目是cnn
等等。。。
我用python编写了以下Map器，以便在频道上查找：

# !/usr/bin/env python

import sys

all_shows_views = []
shows_on_ABC = []

for line in sys.stdin:
    line       = line.strip()   #strip out carriage return (i.e. removes line breaks).
    key_value  = line.split(",")   #split line into key and value, returns a list.
    key_in     = key_value[0]     #.split(" ") - Dont need the split(" ") b/c there is no date. 
    value_in   = key_value[1]     #value is 2nd item. 

    if value_in.isdigit():
        show = key_in
    all_shows_views.append(show + "\t" + value_in)
    if value_in == "ABC":            #check if the TV Show is ABC.       
    show = key_in
           shows_on_ABC.append(show)

for i in range(len(all_shows_views)):
    show_view = all_shows_views[i].split("\t")
    for c in range(len(shows_on_ABC)):
        if show_view[0] == shows_on_ABC[c]:
            print (show_view[0] + "\t" + show_view[1])

# Note that Hadoop expects a tab to separate key value

# but this program assumes the input file has a ',' separating key value.

Map器只传递上的节目名称和视图数量，例如：
在 120上显示\u name\u
在python中，reducer如下所示：

prev_show          = "  "    #initialize previous word  to blank string
line_cnt           = 0      #count input lines.
count            = 0        #keep running total.

for line in sys.stdin:
    line       = line.strip()           #strip out carriage return
    key_value  = line.split('\t')       #split line, into key and value, returns a list
    line_cnt   = line_cnt+1   
    curr_show  = key_value[0]             #key is first item in list, indexed by 0
    value_in   = key_value[1]             #value is 2nd item

    if curr_show != prev_show and line_cnt>1:
    #print "\n"
    #print "---------------------Total---------------------"
    #print "\n"
    print (prev_show + "\t" + str(count))
    #print "\n"
    #print "------------------End of Item------------------"
    #print "\n"
    count = 0
    else:
    count = count + int(key_value[1])
        #print key_value[0] + "\t" + key_value[1]

    prev_show = curr_show  #set up previous show for the next set of input lines.

print (curr_show + "\t" + str(count))

reducer获取上的显示列表和视图数量，并保持每个显示的计数平均值，并打印出每个显示的总数（hadoop会根据键自动按字母顺序排列数据，在本例中，键是显示的名称）。
在终端中使用piping命令运行此命令时，如下所示：

cat Data*.text | /home/cloudera/mapper.py |sort| /home/coudera/reducer.py

我得到一个整洁的输出，正确的总数如下：
差不多了吧49237
几乎所有的新闻45589
差不多了49186
小游戏50603
当我在终端中使用hadoop命令运行此问题时，使用以下命令：

> hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming.jar \
   -input /user/cloudera/input \
   -output /user/cloudera/output_join \   
   -mapper /home/cloudera/mapper.py \   
   -reducer /home/cloudera/reducer.py

我得到了一个失败的错误，减速机是罪魁祸首。完全错误如下：

15/11/15 09:16:54 INFO mapreduce.Job: Job job_1447598349691_0003 failed with state FAILED due to: Task failed task_1447598349691_0003_r_000000
Job failed as tasks failed. failedMaps:0 failedReduces:1

15/11/15 09:16:54 INFO mapreduce.Job: Counters: 37
    File System Counters
        FILE: Number of bytes read=0
        FILE: Number of bytes written=674742
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=113784
        HDFS: Number of bytes written=0
        HDFS: Number of read operations=18
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=0
    Job Counters 
        Failed reduce tasks=4
        Launched map tasks=6
        Launched reduce tasks=4
        Data-local map tasks=6
        Total time spent by all maps in occupied slots (ms)=53496
        Total time spent by all reduces in occupied slots (ms)=18565
        Total time spent by all map tasks (ms)=53496
        Total time spent by all reduce tasks (ms)=18565
        Total vcore-seconds taken by all map tasks=53496
        Total vcore-seconds taken by all reduce tasks=18565
        Total megabyte-seconds taken by all map tasks=54779904
        Total megabyte-seconds taken by all reduce tasks=19010560
    Map-Reduce Framework
        Map input records=6600
        Map output records=0
        Map output bytes=0
        Map output materialized bytes=36
        Input split bytes=729
        Combine input records=0
        Spilled Records=0
        Failed Shuffles=0
        Merged Map outputs=0
        GC time elapsed (ms)=452
        CPU time spent (ms)=4470
        Physical memory (bytes) snapshot=1628909568
        Virtual memory (bytes) snapshot=9392836608
        Total committed heap usage (bytes)=1279262720
    File Input Format Counters 
        Bytes Read=113055
15/11/15 09:16:54 ERROR streaming.StreamJob: Job not successful!
Streaming Command Failed!

为什么管道命令可以工作而hadoop执行不能？