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我是 hadoop 和 mrjob 的新手，这本书真的帮助了我很多学习。我试图在 hadoop 上运行 mrSVM.py，因为它在本地运行良好。

但是我运行了以下命令：python mrSVM.py -r hadoop kickStart.txt 它给出了以下错误：

no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
creating tmp directory /tmp/mrSVM.manvendra.20140818.075925.908574
writing wrapper script to /tmp/mrSVM.manvendra.20140818.075925.908574/setup-wrapper.sh
Using Hadoop version 2.5.0
Copying local files into hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/
HADOOP: session.id is deprecated. Instead, use dfs.metrics.session-id
HADOOP: Initializing JVM Metrics with processName=JobTracker, sessionId=
HADOOP: Cannot initialize JVM Metrics with processName=JobTracker, sessionId= - already initialized
HADOOP: Cleaning up the staging area file:/tmp/hadoop-manvendra/mapred/staging/manvendra1365509453/.staging/job_local1365509453_0001
HADOOP: Error launching job , bad input path : File does not exist: /tmp/hadoop-manvendra/mapred/staging/manvendra1365509453/.staging/job_local1365509453_0001/archives/mrjob.tar.gz#mrjob.tar.gz
HADOOP: Streaming Command Failed!
Job failed with return code 512: ['/home/manvendra/hadoop-2.5.0/bin/hadoop', 'jar', '/home/manvendra/hadoop-2.5.0/share/hadoop/tools/lib/hadoop-streaming-2.5.0.jar', '-files', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/setup-wrapper.sh#setup-wrapper.sh,hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/mrSVM.py#mrSVM.py', '-archives', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/mrjob.tar.gz#mrjob.tar.gz', '-input', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/kickStart.txt', '-output', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/step-output/1', '-mapper', 'sh -e setup-wrapper.sh python mrSVM.py --step-num=0 --mapper', '-reducer', 'sh -e setup-wrapper.sh python mrSVM.py --step-num=0 --reducer']
Scanning logs for probable cause of failure
Traceback (most recent call last):
File "mrSVM.py", line 81, in <module>
MRsvm.run()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/job.py", line 462, in run
mr_job.execute()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/job.py", line 480, in execute
super(MRJob, self).execute()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/launch.py", line 147, in execute
self.run_job()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/launch.py", line 210, in run_job
runner.run()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/runner.py", line 464, in run
self._run()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/hadoop.py", line 239, in _run
self._run_job_in_hadoop()
File "/usr/local/lib/python2.7/dist-packages/mrjob-0.4.3_dev-py2.7.egg/mrjob/hadoop.py", line 369, in _run_job_in_hadoop
raise CalledProcessError(returncode, step_args)
subprocess.CalledProcessError: Command '['/home/manvendra/hadoop-2.5.0/bin/hadoop', 'jar', '/home/manvendra/hadoop-2.5.0/share/hadoop/tools/lib/hadoop-streaming-2.5.0.jar', '-files', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/setup-wrapper.sh#setup-wrapper.sh,hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/mrSVM.py#mrSVM.py', '-archives', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/mrjob.tar.gz#mrjob.tar.gz', '-input', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/files/kickStart.txt', '-output', 'hdfs:///user/manvendra/tmp/mrjob/mrSVM.manvendra.20140818.075925.908574/step-output/1', '-mapper', 'sh -e setup-wrapper.sh python mrSVM.py --step-num=0 --mapper', '-reducer', 'sh -e setup-wrapper.sh python mrSVM.py --step-num=0 --reducer']' returned non-zero exit status 512

请帮我解决这个问题。

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1 回答 1

4

这是 Hadoop 2.x 和 mrjob 的一个已知问题。请进行以下更改，格式化您的名称节点，重新启动您的 hadoop 实例 + 纱线，一切都会正常。

核心站点.xml

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://localhost:9000</value>
    </property>
 <property>
      <name>hadoop.tmp.dir</name>
      <value>/tmp</value>
      <description>A base for other temporary directories.</description>
    </property>
</configuration>

hdfs-site.xml

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>1</value>
    </property>
 <property>
      <name>hadoop.tmp.dir</name>
      <value>/tmp</value>
      <description>A base for other temporary directories.</description>
    </property>
</configuration>

mapred-site.xml

<configuration>
  <property> 
    <name>mapreduce.framework.name</name> 
    <value>yarn</value> 
  </property>
</configuration>

纱线站点.xml

<configuration>

    <!-- Site specific YARN configuration properties -->
    <property>
        <name>yarn.scheduler.minimum-allocation-mb</name>
        <value>128</value>
        <description>Minimum limit of memory to allocate to each container request at the Resource Manager.</description>
    </property>
    <property>
        <name>yarn.scheduler.maximum-allocation-mb</name>
        <value>2048</value>
        <description>Maximum limit of memory to allocate to each container request at the Resource Manager.</description>
    </property>
    <property>
        <name>yarn.scheduler.minimum-allocation-vcores</name>
        <value>1</value>
        <description>The minimum allocation for every container request at the RM, in terms of virtual CPU cores. Requests lower than this won't take effect, and the specified value will get allocated the minimum.</description>
    </property>
    <property>
        <name>yarn.scheduler.maximum-allocation-vcores</name>
        <value>2</value>
        <description>The maximum allocation for every container request at the RM, in terms of virtual CPU cores. Requests higher than this won't take effect, and will get capped to this value.</description>
    </property>
    <property>
        <name>yarn.nodemanager.resource.memory-mb</name>
        <value>4096</value>
        <description>Physical memory, in MB, to be made available to running containers</description>
    </property>
    <property>
        <name>yarn.nodemanager.resource.cpu-vcores</name>
        <value>4</value>
        <description>Number of CPU cores that can be allocated for containers.</description>
    </property>

    <property>
      <name>yarn.nodemanager.aux-services</name>
      <value>mapreduce_shuffle</value>
      <description>shuffle service that needs to be set for Map Reduce to run </description>
    </property>
</configuration>

然后运行：

hdfs namenode -format
start-dfs.sh
start-yarn.sh

干杯，

图斯詹坦·库本德拉纳坦

于 2014-09-18T02:09:35.000 回答