在实例测试前先确保hadoop 启动正确

　　Master.Hadoop：

word 1
[hadoop@Master input]$ jps
6736 Jps
6036 NameNode
4697 SecondaryNameNode
4849 ResourceManager
[hadoop@Master input]$

　　Slave1.Hadoop

[hadoop@Slave1 sources]$ jps
8086 SecondaryNameNode
8961 Jps
8320 NodeManager
7935 DataNode

在测试过程中遇到的错与与解决方案：

问题一：
[hadoop@Master input]$ hadoop fs -ls /
ls: Call From Master.Hadoop/192.168.160.131 to Master.Hadoop:9000 failed on connection exception: java.net.ConnectException: Connection refused; For more details see:  http://wiki.apache.org/hadoop/ConnectionRefused
解决方案：
1、格式化HDFS文件系统：hadoop namenode -format
 
问题二：
/home/hadoop/WordCount/input
[hadoop@Master input]$ hadoop fs -put ./ /input
15/06/30 17:10:45 WARN hdfs.DFSClient: DataStreamer Exception
org.apache.hadoop.ipc.RemoteException(java.io.IOException): File /input/input/test1.txt._COPYING_ could only be replicated to 0 nodes instead of minReplication (=1).  There are 0 datanode(s) running and no node(s) are excluded in this operation.
        at org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.chooseTarget(BlockManager.java:1441)
解决方案：

1. 先执行stop-all.sh暂停所有服务

2. 将所有Salve节点上的tmp , logs 文件夹删除 ， 然后重新建立tmp , logs 文件夹

3. 格式化HDFS文件系统：hadoop namenode -format

转载地址：

http://linux.it.net.cn/e/cluster/hadoop/2014/1215/10427.html

转载内容：

装好的hadoop测试一1个示例程序WordCount，首先需要在操作系统上新建两个任意文件，然后上传到hadoop，再运行该程序统计文件中单词的个数，最后查看结果。

在操作系统上新建任意文件：

例如：
[hadoop@hadoop01 input]$ ls
test1.txt  test2.txt

查看hadoop的文件系统目录

[hadoop@hadoop01 input]$ hadoop fs -ls /
Found 1 items
drwxr-xr-x   - hadoop supergroup          0 2013-10-30 00:00 /input

上传至hadoop的/input下

[hadoop@hadoop01 input]$ hadoop fs -put ./ /input
[hadoop@hadoop01 input]$ hadoop fs -ls /input
Found 2 items
-rw-r--r--   3 hadoop supergroup         12 2013-10-30 00:00 /input/test1.txt
-rw-r--r--   3 hadoop supergroup         13 2013-10-30 00:00 /input/test2.txt

在hadoop文件系统命令查看这两个文件的内容：

[hadoop@hadoop01 test]$ hadoop fs -cat /input/test1.txt 
hello world
[hadoop@hadoop01 test]$ hadoop fs -cat /input/test2.txt
hello hadoop

运行示例程序（WordCount)：

[hadoop@hadoop01 test]$ hadoop jar $HADOOP_HOME/share/hadoop/mapreduce/sources/hadoop-mapreduce-examples-2.2.0-sources.jar org.apache.hadoop.examples.WordCount /input /output
13/11/06 21:33:40 INFO Configuration.deprecation: session.id is deprecated. Instead, use dfs.metrics.session-id
13/11/06 21:33:40 INFO jvm.JvmMetrics: Initializing JVM Metrics with processName=JobTracker, sessionId=
13/11/06 21:33:40 INFO input.FileInputFormat: Total input paths to process : 2
13/11/06 21:33:41 INFO mapreduce.JobSubmitter: number of splits:2
13/11/06 21:33:41 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class IT网，http://www.it.net.cn 
13/11/06 21:33:41 INFO Configuration.deprecation: mapreduce.combine.class is deprecated. Instead, use mapreduce.job.combine.class
13/11/06 21:33:41 INFO Configuration.deprecation: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
13/11/06 21:33:41 INFO Configuration.deprecation: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class 
13/11/06 21:33:41 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
13/11/06 21:33:41 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_local382050821_0001
13/11/06 21:33:41 WARN conf.Configuration: file:/hadoop/hdfs/tmp/hadoop-hadoop/mapred/staging/hadoop382050821/.staging/job_local382050821_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring.
13/11/06 21:33:41 WARN conf.Configuration: file:/hadoop/hdfs/tmp/hadoop-hadoop/mapred/staging/hadoop382050821/.staging/job_local382050821_0001/job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
13/11/06 21:33:42 WARN conf.Configuration: file:/hadoop/hdfs/tmp/hadoop-hadoop/mapred/local/localRunner/hadoop/job_local382050821_0001/job_local382050821_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring. Linux学习，http:// linux.it.net.cn 
13/11/06 21:33:42 WARN conf.Configuration: file:/hadoop/hdfs/tmp/hadoop-hadoop/mapred/local/localRunner/hadoop/job_local382050821_0001/job_local382050821_0001.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
13/11/06 21:33:42 INFO mapreduce.Job: The url to track the job: http://localhost:8080/
13/11/06 21:33:42 INFO mapreduce.Job: Running job: job_local382050821_0001
13/11/06 21:33:42 INFO mapred.LocalJobRunner: OutputCommitter set in config null
13/11/06 21:33:42 INFO mapred.LocalJobRunner: OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter
13/11/06 21:33:42 INFO mapred.LocalJobRunner: Waiting for map tasks
13/11/06 21:33:42 INFO mapred.LocalJobRunner: Starting task: attempt_local382050821_0001_m_000000_0
13/11/06 21:33:42 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
13/11/06 21:33:42 INFO mapred.MapTask: Processing split: hdfs://hadoop01:9000/input/test2.txt:0+13 Linux学习，http:// linux.it.net.cn 
13/11/06 21:33:42 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
13/11/06 21:33:42 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
13/11/06 21:33:42 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100
13/11/06 21:33:42 INFO mapred.MapTask: soft limit at 83886080
13/11/06 21:33:42 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
13/11/06 21:33:42 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
13/11/06 21:33:43 INFO mapred.LocalJobRunner: 
13/11/06 21:33:43 INFO mapred.MapTask: Starting flush of map output
13/11/06 21:33:43 INFO mapred.MapTask: Spilling map output
13/11/06 21:33:43 INFO mapred.MapTask: bufstart = 0; bufend = 21; bufvoid = 104857600
13/11/06 21:33:43 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214392(104857568); length = 5/6553600
13/11/06 21:33:43 INFO mapred.MapTask: Finished spill 0
13/11/06 21:33:43 INFO mapred.Task: Task:attempt_local382050821_0001_m_000000_0 is done. And is in the process of committing 
13/11/06 21:33:43 INFO mapreduce.Job: Job job_local382050821_0001 running in uber mode : false
13/11/06 21:33:43 INFO mapreduce.Job:  map 0% reduce 0%
13/11/06 21:33:43 INFO mapred.LocalJobRunner: map
13/11/06 21:33:43 INFO mapred.Task: Task 'attempt_local382050821_0001_m_000000_0' done.
13/11/06 21:33:43 INFO mapred.LocalJobRunner: Finishing task: attempt_local382050821_0001_m_000000_0
13/11/06 21:33:43 INFO mapred.LocalJobRunner: Starting task: attempt_local382050821_0001_m_000001_0
13/11/06 21:33:43 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
13/11/06 21:33:43 INFO mapred.MapTask: Processing split: hdfs://hadoop01:9000/input/test1.txt:0+12
13/11/06 21:33:43 INFO mapred.MapTask: Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer
13/11/06 21:33:43 INFO mapred.MapTask: (EQUATOR) 0 kvi 26214396(104857584)
13/11/06 21:33:43 INFO mapred.MapTask: mapreduce.task.io.sort.mb: 100 
13/11/06 21:33:43 INFO mapred.MapTask: soft limit at 83886080
13/11/06 21:33:43 INFO mapred.MapTask: bufstart = 0; bufvoid = 104857600
13/11/06 21:33:43 INFO mapred.MapTask: kvstart = 26214396; length = 6553600
13/11/06 21:33:43 INFO mapred.LocalJobRunner: 
13/11/06 21:33:43 INFO mapred.MapTask: Starting flush of map output
13/11/06 21:33:43 INFO mapred.MapTask: Spilling map output
13/11/06 21:33:43 INFO mapred.MapTask: bufstart = 0; bufend = 20; bufvoid = 104857600
13/11/06 21:33:43 INFO mapred.MapTask: kvstart = 26214396(104857584); kvend = 26214392(104857568); length = 5/6553600
13/11/06 21:33:43 INFO mapred.MapTask: Finished spill 0
13/11/06 21:33:43 INFO mapred.Task: Task:attempt_local382050821_0001_m_000001_0 is done. And is in the process of committing
13/11/06 21:33:43 INFO mapred.LocalJobRunner: map
13/11/06 21:33:43 INFO mapred.Task: Task 'attempt_local382050821_0001_m_000001_0' done.
13/11/06 21:33:43 INFO mapred.LocalJobRunner: Finishing task: attempt_local382050821_0001_m_000001_0 
13/11/06 21:33:43 INFO mapred.LocalJobRunner: Map task executor complete.
13/11/06 21:33:43 INFO mapred.Task:  Using ResourceCalculatorProcessTree : [ ]
13/11/06 21:33:43 INFO mapred.Merger: Merging 2 sorted segments
13/11/06 21:33:43 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 36 bytes
13/11/06 21:33:43 INFO mapred.LocalJobRunner: 
13/11/06 21:33:43 INFO Configuration.deprecation: mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords
13/11/06 21:33:44 INFO mapreduce.Job:  map 100% reduce 0%
13/11/06 21:33:44 INFO mapred.Task: Task:attempt_local382050821_0001_r_000000_0 is done. And is in the process of committing
13/11/06 21:33:44 INFO mapred.LocalJobRunner: 
13/11/06 21:33:44 INFO mapred.Task: Task attempt_local382050821_0001_r_000000_0 is allowed to commit now
13/11/06 21:33:44 INFO output.FileOutputCommitter: Saved output of task 'attempt_local382050821_0001_r_000000_0' to hdfs://hadoop01:9000/output/_temporary/0/task_local382050821_0001_r_000000 
13/11/06 21:33:44 INFO mapred.LocalJobRunner: reduce > reduce
13/11/06 21:33:44 INFO mapred.Task: Task 'attempt_local382050821_0001_r_000000_0' done.
13/11/06 21:33:45 INFO mapreduce.Job:  map 100% reduce 100%
13/11/06 21:33:45 INFO mapreduce.Job: Job job_local382050821_0001 completed successfully
13/11/06 21:33:45 INFO mapreduce.Job: Counters: 32
        File System Counters
                FILE: Number of bytes read=812174
                FILE: Number of bytes written=1395157
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=63 
                HDFS: Number of bytes written=25
                HDFS: Number of read operations=25
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=5
        Map-Reduce Framework
                Map input records=2
                Map output records=4
                Map output bytes=41
                Map output materialized bytes=61
                Input split bytes=202
                Combine input records=4
                Combine output records=4 
                Reduce input groups=3
                Reduce shuffle bytes=0
                Reduce input records=4
                Reduce output records=3
                Spilled Records=8
                Shuffled Maps =0
                Failed Shuffles=0
                Merged Map outputs=0
                GC time elapsed (ms)=146
                CPU time spent (ms)=0
                Physical memory (bytes) snapshot=0
                Virtual memory (bytes) snapshot=0 IT网，http://www.it.net.cn 
                Total committed heap usage (bytes)=456732672
        File Input Format Counters 
                Bytes Read=25
        File Output Format Counters 
                Bytes Written=25

查看程序运行结果：

[hadoop@hadoop01 test]$ hadoop fs -cat /output/part-r-00000
hadoop  1
hello   2
world   1

Hadoop2.4.x 实例测试 WordCount程序的更多相关文章

  1. Hadoop集群测试wordcount程序

    一.集群环境搭好了,我们来测试一下吧 1.在java下创建一个wordcount文件夹:mkdir wordcount 2.在此文件夹下创建两个文件,比如file1.txt和file2.txt 在fi ...

  2. hadoop2.7.x运行wordcount程序卡住在INFO mapreduce.Job: Running job:job _1469603958907_0002

    一.抛出问题 Hadoop集群(全分布式)配置好后,运行wordcount程序测试,发现每次运行都会卡住在Running job处,然后程序就呈现出卡死的状态. wordcount运行命令:[hado ...

  3. WordCount程序及测试

    Github地址:https://github.com/CG0317/WordCount PSP表: PSP2.1 PSP阶段 预估耗时 (分钟) 实际耗时 (分钟) Planning 计划  30 ...

  4. spark学习11（Wordcount程序-本地测试）

    wordcount程序 文件wordcount.txt hello wujiadong hello spark hello hadoop hello python 程序示例 package wujia ...

  5. hadoop安装后运行一个单实例（测试MapReduce程序）

    1.安装hadoop 解压hadoop-1.2.1-bin.tar.gz包   tar -zxvf hadoop-1.2.1-bin.tar.gz  /opt/modules/ 解压后在/opt/mo ...

  6. Hadoop环境搭建及wordcount程序

    目的: 前期学习了一些机器学习基本算法,实际企业应用中算法是核心,运行的环境和数据处理的平台是基础. 手段: 搭建简易hadoop集群(由于机器限制在自己的笔记本上通过虚拟机搭建) 一.基础环境介绍 ...

  7. hadoop学习笔记——用python写wordcount程序

    尝试着用3台虚拟机搭建了伪分布式系统,完整的搭建步骤等熟悉了整个分布式框架之后再写,今天写一下用python写wordcount程序(MapReduce任务)的具体步骤. MapReduce任务以来H ...

  8. 50、Spark Streaming实时wordcount程序开发

    一.java版本 package cn.spark.study.streaming; import java.util.Arrays; import org.apache.spark.SparkCon ...

  9. Spark练习之通过Spark Streaming实时计算wordcount程序

    Spark练习之通过Spark Streaming实时计算wordcount程序 Java版本 Scala版本 pom.xml Java版本 import org.apache.spark.Spark ...

随机推荐

  1. java笔记1

    第五天学习笔记 1.面对对象的理解并举例? 面对对象的核心:找合适的对象做合适的事情. 面对对象编程的思想:尽可能的用计算机语言来描述现实生活中的事物. 面对对象:侧重于对象 2.类与对象之间的关系? ...

  2. eval 函数的应用 （去除包装在列表外面的引号）

    a="[u'ANDROID-5a9ac5c22ad94e26b2fa24e296787a35', u'0', 0, 0, 0, 1]" 此时的a是一个字符串,目的是要去掉a上面的引 ...

  3. eclipse 配置scala开发环境

    最近在学习spark相关知识.准备搭建eclipse开发环境.在安装过程中遇到的问题记录下来. 首先在scala网站上下载了scalaIDE:http://scala-ide.org/download ...

  4. Git版本控制教程

    Git 版本控制入门 不了解Git请查看权威Git书籍 ProGit(中文版). 一份很好的 Git 入门教程,点击这里查看. Git客户端下载地址: 官方Git - TortoiseGit - So ...

  5. 联想扬天 电脑 键盘改默认fn功能键

    联想扬天电脑改键工具 windows 8 windows8.1 64位版本:http://support1.lenovo.com.cn/lenovo/wsi/Modules/DriverDetail. ...

  6. C# 生成条形码

    原文地址:http://www.cnblogs.com/xcsn/p/4514759.html 引用BarcodeLib.dll(百度云中有)生成条形 protected void Button2_C ...

  7. Selenium2+python自动化26-js处理内嵌div滚动条

    前言 前面有篇专门用js解决了浏览器滚动条的问题,生活总是多姿多彩,有的滚动条就在页面上,这时候又得仰仗js大哥来解决啦. 一.内嵌滚动条 1.下面这张图就是内嵌div带有滚动条的样子,记住它的长相.

  8. php设计模式学习之观察者模式

    什么都不说,先看代码: interface userOperateImpl { public function operate($username); } class userLoginLog imp ...

  9. 《CSS3秘籍》（第三版）-读书笔记(4)

    第12章  CSS页面布局 网页布局的类型: 固定宽度.不管浏览器窗口的宽度多大,网页内容的宽度始终保持不变. 流式.流式设计采用百分比,它会根据浏览器的宽度(无论有多宽)自动进行调整.网页会随着访问 ...

  10. subprocess添加超时功能

    def TIMEOUT_COMMAND(command, timeout): """call shell-command and either return its ou ...