从this guide,我已经成功运行了示例练习。但是在运行mapreduce作业时,出现以下错误ERROR streaming.StreamJob: Job not Successful!
日志文件中的错误
10/12/16 17:13:38 INFO streaming.StreamJob: killJob...
Streaming Job Failed!
java.lang.RuntimeException: PipeMapRed.waitOutputThreads(): subprocess failed with code 2
at org.apache.hadoop.streaming.PipeMapRed.waitOutputThreads(PipeMapRed.java:311)
at org.apache.hadoop.streaming.PipeMapRed.mapRedFinished(PipeMapRed.java:545)
at org.apache.hadoop.streaming.PipeMapper.close(PipeMapper.java:132)
at org.apache.hadoop.mapred.MapRunner.run(MapRunner.java:57)
at org.apache.hadoop.streaming.PipeMapRunner.run(PipeMapRunner.java:36)
at org.apache.hadoop.mapred.MapTask.runOldMapper(MapTask.java:358)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:307)
at org.apache.hadoop.mapred.Child.main(Child.java:170)
映射器
import sys
i=0
for line in sys.stdin:
i+=1
count={}
for word in line.strip().split():
count[word]=count.get(word,0)+1
for word,weight in count.items():
print '%s\t%s:%s' % (word,str(i),str(weight))
Reducer.py
import sys
keymap={}
o_tweet="2323"
id_list=[]
for line in sys.stdin:
tweet,tw=line.strip().split()
#print tweet,o_tweet,tweet_id,id_list
tweet_id,w=tw.split(':')
w=int(w)
if tweet.__eq__(o_tweet):
for i,wt in id_list:
print '%s:%s\t%s' % (tweet_id,i,str(w+wt))
id_list.append((tweet_id,w))
else:
id_list=[(tweet_id,w)]
o_tweet=tweet
[edit]命令运行作业:
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-0.20.0-streaming.jar -file /home/hadoop/mapper.py -mapper /home/hadoop/mapper.py -file /home/hadoop/reducer.py -reducer /home/hadoop/reducer.py -input my-input/* -output my-output
输入是任何随机的句子序列。
谢谢,
最佳答案
您的-mapper和-reducer应该只是脚本名称。
hadoop@ubuntu:/usr/local/hadoop$ bin/hadoop jar contrib/streaming/hadoop-0.20.0-streaming.jar -file /home/hadoop/mapper.py -mapper mapper.py -file /home/hadoop/reducer.py -reducer reducer.py -input my-input/* -output my-output
当脚本在hdfs内另一个文件夹中的作业中时,该作业相对于尝试任务以“。”执行。 (仅供引用,如果您想添加其他文件(例如查找表),则可以在Python中打开它,就像在M / R作业中将其放在与脚本相同的目录中一样)
还请确保您具有chmod a + x mapper.py和chmod a + x reducer.py