目的:

基于上篇的方法介绍，开发很不方便。[0007] windows 下 eclipse 开发 hdfs程序样例

装上插件，方便后续直接在windows下的IDE开发调试。

环境：

Linux Hadoop 2.6.4，参考文章 [0001]
Win 7 64 下的 Eclipse Version: Luna Service Release 1 (4.4.1)

工具：

hadoop-eclipse-plugin-2.6.4.jar 下载地址：http://download.csdn.net/detail/tondayong1981/9437360
Hadoop 2.6.4 安装程序包, Hadoop2.6.4源码包
hadoop 2.6 windows插件包地址后面有

说明:

以下整个步骤过程是在全部弄好后，才来填补的。中间修改多次，为了快速成文有些内容从其他地方复制。因此，如果完全照着步骤，可能需要一些小修改。整个思路是对的。

1. 准备Hadoop安装包

在windows下解压 Hadoop 2.6.4 安装程序包。将Linux上的hadoop 安装目录下 etc/hadoop的所有配置文件

全部替换 windows下解压后的配置文件

2 . 安装HDFS eclipse 插件

eclipse关闭状态下, 将 hadoop-eclipse-plugin-2.6.4.jar 放到该目录下 eclipse安装目录\plugins\
启动eclipse
菜单栏->窗口windows->首选项preferences->Hadoop mapeduce ,指定hadoop路径为前面的解压路径
菜单栏->窗口windows->Open Perspective->Other->选择Map/Reduce ok->Map/Reduce Location选项卡 ->右边蓝色小象打开配置窗口如图,进行如下设置,点击ok

1位置为配置的名称，任意。

2位置为mapred-site.xml文件中的mapreduce.jobhistory.address配置,如果没有则默认是10020。

3位置为core-site.xml文件中的fs.defaultFS：hdfs://ssmaster:9000 。

这是网上找到图片,我的设置

hadoop2.6伪分布式,ssmaster:10020,ssmaster:9000

设置成功后，在eclipse这里可以直接显示Linux Hadoop hdfs的文件目录

可以直接在这里下载、上传、删除HDFS上的文件,很方便

3 配置Mapreduce Windows 插件包

3.1 下载hadoop 2.6 windows插件包包

没找到2.6.4的，用2.6的最后也成功了。

其中参考下载地址： http://download.csdn.net/detail/myamor/8393459，这个似乎是win8的，本人的系统win7，不是从这里下的。忘记哪里了。可以搜索 winutils.exe + win7 。下载后的文件应该有 hadoop.dll hadoop.pdb hadoop.lib hadoop.exp winutils.exe winutils.pdb libwinutils.lib

3.2 配置

a 解压上面的插件包, 将文件全部拷贝到 G:\RSoftware\hadoop-2.6.4\hadoop-2.6.4\bin ，该路径为前面"2 . 安装HDFS eclipse 插件"的hadoop指定路径。

b 设置环境变量

HADOOP_HOME =G:\RSoftware\hadoop-2.6.4\hadoop-2.6.4

Path 中添加 G:\RSoftware\hadoop-2.6.4\hadoop-2.6.4\bin

确保有 HADOOP_USER_NAME = hadoop 上一篇 [0007]中设置

重启Eclipse ,读取新环境变量

4 测试Mapreduce

4.1 新建mapreduce 工程

完成后项目会自动把Hadoop的所有jar包导入

4.2 项目配置log4j

在src目录下，创建log4j.properties文件，内容如下

log4j.rootLogger=debug,stdout,R

log4j.appender.stdout=org.apache.log4j.ConsoleAppender

log4j.appender.stdout.layout=org.apache.log4j.PatternLayout

log4j.appender.stdout.layout.ConversionPattern=%5p - %m%n

log4j.appender.R=org.apache.log4j.RollingFileAppender

log4j.appender.R.File=mapreduce_test.log

log4j.appender.R.MaxFileSize=1MB

log4j.appender.R.MaxBackupIndex=1

log4j.appender.R.layout=org.apache.log4j.PatternLayout

log4j.appender.R.layout.ConversionPattern=%p %t %c - %m%

log4j.logger.com.codefutures=DEBUG

4.3 WordCount类中添加代码

在WordCount项目里右键src新建class，包名com.xxm（请自行命明），类名为WordCount

package mp.filetest;

import java.io.IOException;

import java.util.*;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.conf.*;

import org.apache.hadoop.io.*;

import org.apache.hadoop.mapreduce.*;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

/**

 * 描述：WordCount explains by xxm

 * @author xxm

 */

public class WordCount2 {

 /**

 * Map类：自己定义map方法

 */

 public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {

    /**

    * LongWritable, IntWritable, Text 均是 Hadoop 中实现的用于封装 Java 数据类型的类

    * 都能够被串行化从而便于在分布式环境中进行数据交换，可以将它们分别视为long,int,String 的替代品。

    */

    private final static IntWritable one = new IntWritable(1);

    private Text word = new Text();

    /**

    * Mapper类中的map方法：

    * protected void map(KEYIN key, VALUEIN value, Context context)

    * 映射一个单个的输入k/v对到一个中间的k/v对

    * Context类：收集Mapper输出的<k,v>对。

    */

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String line = value.toString();

        StringTokenizer tokenizer = new StringTokenizer(line);

        while (tokenizer.hasMoreTokens()) {

            word.set(tokenizer.nextToken());

            context.write(word, one);

        }

    }

 } 

 /**

 * Reduce类：自己定义reduce方法

 */

 public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {

    /**

    * Reducer类中的reduce方法：

    * protected void reduce(KEYIN key, Interable<VALUEIN> value, Context context)

    * 映射一个单个的输入k/v对到一个中间的k/v对

    * Context类：收集Reducer输出的<k,v>对。

    */

    public void reduce(Text key, Iterable<IntWritable> values, Context context)

      throws IOException, InterruptedException {

        int sum = 0;

        for (IntWritable val : values) {

            sum += val.get();

        }

        context.write(key, new IntWritable(sum));

    }

 }

 /**

 * main主函数

 */

 public static void main(String[] args) throws Exception {

    Configuration conf = new Configuration();//创建一个配置对象，用来实现所有配置

    Job job = new Job(conf, "wordcount2");//新建一个job，并定义名称

    job.setOutputKeyClass(Text.class);//为job的输出数据设置Key类

    job.setOutputValueClass(IntWritable.class);//为job输出设置value类

    job.setMapperClass(Map.class); //为job设置Mapper类

    job.setReducerClass(Reduce.class);//为job设置Reduce类

    job.setJarByClass(WordCount2.class);

    job.setInputFormatClass(TextInputFormat.class);//为map-reduce任务设置InputFormat实现类

    job.setOutputFormatClass(TextOutputFormat.class);//为map-reduce任务设置OutputFormat实现类

    FileInputFormat.addInputPath(job, new Path(args[0]));//为map-reduce job设置输入路径

    FileOutputFormat.setOutputPath(job, new Path(args[1]));//为map-reduce job设置输出路径

    job.waitForCompletion(true); //运行一个job，并等待其结束

 }

}

可选，如果没有配置，最后可能报这个错误,在文章最后面异常部分，按照异常解决办法配置。

( Y.2 运行过程中异常

1 main"java.lang.UnsatisfiedLinkError:org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)

）

4.4 运行

确保Hadoop已经启动

在WordCount的代码区域，右键，点击Run As—>Run Configurations，配置运行参数，文件夹输入和输出,第2个参数的路径确保HDFS上不存在
hdfs://ssmaster:9000/input
hdfs://ssmaster:9000/output

点击 Run运行,可以直接在eclipse的控制台看到执行进度和结果

INFO - Job job_local1914346901_0001 completed successfully

  INFO - Counters:

    File System Counters

        FILE: Number of bytes read=

        FILE: Number of bytes written=

        FILE: Number of read operations=

        FILE: Number of large read operations=

        FILE: Number of write operations=

        HDFS: Number of bytes read=

        HDFS: Number of bytes written=

        HDFS: Number of read operations=

        HDFS: Number of large read operations=

        HDFS: Number of write operations=

    Map-Reduce Framework

        Map input records=

        Map output records=

        Map output bytes=

        Map output materialized bytes=

        Input split bytes=

        Combine input records=

        Combine output records=

        Reduce input groups=

        Reduce shuffle bytes=

        Reduce input records=

        Reduce output records=

        Spilled Records=

        Shuffled Maps =

        Failed Shuffles=

        Merged Map outputs=

        GC time elapsed (ms)=

        CPU time spent (ms)=

        Physical memory (bytes) snapshot=

        Virtual memory (bytes) snapshot=

        Total committed heap usage (bytes)=

    Shuffle Errors

        BAD_ID=

        CONNECTION=

        IO_ERROR=

        WRONG_LENGTH=

        WRONG_MAP=

        WRONG_REDUCE=

    File Input Format Counters

        Bytes Read=

    File Output Format Counters

        Bytes Written=

执行日志

在“DFS Locations”下，刷新刚创建的“hadoop”看到本次任务的输出目录下是否有输出文件。

4.5 可选命令行下执行,导出成jar包，上传到Linux

右键项目名字->导出->java/jar文件 ->指定jar路径名字->指定main类为完成

先删除刚才的输出目录

 hadoop@ssmaster:~/java_program$ hadoop fs -rm -r /output

hadoop@ssmaster:~/java_program$ hadoop fs -ls /

Found 4 items

drwxr-xr-x   - hadoop supergroup          0 2016-10-24 05:04 /data

drwxr-xr-x   - hadoop supergroup          0 2016-10-23 00:45 /input

drwxr-xr-x   - hadoop supergroup          0 2016-10-24 05:04 /test

drwx------   - hadoop supergroup          0 2016-10-23 00:05 /tmp

执行 hadoop jar hadoop_mapr_wordcount.jar /input /output

hadoop@ssmaster:~/java_program$ hadoop  jar hadoop_mapr_wordcount.jar  /input /output

// :: INFO client.RMProxy: Connecting to ResourceManager at ssmaster/192.168.249.144:

// :: WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.

// :: INFO input.FileInputFormat: Total input paths to process :

// :: INFO mapreduce.JobSubmitter: number of splits:

// :: INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1477315002921_0004

// :: INFO impl.YarnClientImpl: Submitted application application_1477315002921_0004

// :: INFO mapreduce.Job: The url to track the job: http://ssmaster:8088/proxy/application_1477315002921_0004/

// :: INFO mapreduce.Job: Running job: job_1477315002921_0004

// :: INFO mapreduce.Job: Job job_1477315002921_0004 running in uber mode : false

// :: INFO mapreduce.Job:  map % reduce %

// :: INFO mapreduce.Job:  map % reduce %

// :: INFO mapreduce.Job:  map % reduce %

// :: INFO mapreduce.Job: Job job_1477315002921_0004 completed successfully

// :: INFO mapreduce.Job: Counters:

    File System Counters

        FILE: Number of bytes read=

        FILE: Number of bytes written=

        FILE: Number of read operations=

        FILE: Number of large read operations=

        FILE: Number of write operations=

        HDFS: Number of bytes read=

        HDFS: Number of bytes written=

        HDFS: Number of read operations=

        HDFS: Number of large read operations=

        HDFS: Number of write operations=

    Job Counters

        Launched map tasks=

        Launched reduce tasks=

        Data-local map tasks=

        Total time spent by all maps in occupied slots (ms)=

        Total time spent by all reduces in occupied slots (ms)=

        Total time spent by all map tasks (ms)=

        Total time spent by all reduce tasks (ms)=

        Total vcore-milliseconds taken by all map tasks=

        Total vcore-milliseconds taken by all reduce tasks=

        Total megabyte-milliseconds taken by all map tasks=

        Total megabyte-milliseconds taken by all reduce tasks=

    Map-Reduce Framework

        Map input records=

        Map output records=

        Map output bytes=

        Map output materialized bytes=

        Input split bytes=

        Combine input records=

        Combine output records=

        Reduce input groups=

        Reduce shuffle bytes=

        Reduce input records=

        Reduce output records=

        Spilled Records=

        Shuffled Maps =

        Failed Shuffles=

        Merged Map outputs=

        GC time elapsed (ms)=

        CPU time spent (ms)=

        Physical memory (bytes) snapshot=

        Virtual memory (bytes) snapshot=

        Total committed heap usage (bytes)=

    Shuffle Errors

        BAD_ID=

        CONNECTION=

        IO_ERROR=

        WRONG_LENGTH=

        WRONG_MAP=

        WRONG_REDUCE=

    File Input Format Counters

        Bytes Read=

    File Output Format Counters

        Bytes Written=

备注：如何导出包，可以用这种方式执行 hadoop jar xxxx.jar wordcount /input /output [遗留]

Y 异常

Y.1 Permission denied: user=Administrator

在第2步最后, HDFS的某个目录可能提示：

Permission denied: user=Administrator, access=WRITE, inode="hadoop": hadoop:supergroup:rwxr-xr-x

用户Administator在hadoop上执行写操作时被权限系统拒,windows eclipse的默认用用户Administator 去访问hadoop的文件

解决如下：

windows 添加环境变量 HADOOP_USER_NAME ，值为 hadoop (这是Linux上hadoop2.6.4 的用户名)

重启eclipse生效

Y.2 运行过程中异常

1 main"java.lang.UnsatisfiedLinkError:org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z

原因：未知　　

解决：

a 将前面下载的配置包中的 hadoop.dll 文件拷贝到 C:\Windows\System32 ，参考中提示需要重启电脑

b 源码包 hadoop-2.6.4-src.tar.gz解压，hadoop-2.6.4-src\hadoop-common-project\hadoop-common\src\main\java\org\apache\hadoop\io\nativeio下NativeIO.java 复制到对应的Eclipse的project

修改如下地方

2 log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).

log4j:WARN Please initialize the log4j system properly.

log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.

Exception in thread "main" org.apache.hadoop.mapred.FileAlreadyExistsException: Output directory hdfs://ssmaster:9000/output already exists

    at org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.checkOutputSpecs(FileOutputFormat.java:)

    at org.apache.hadoop.mapreduce.JobSubmitter.checkSpecs(JobSubmitter.java:)

    at org.apache.hadoop.mapreduce.JobSubmitter.submitJobInternal(JobSubmitter.java:)

    at org.apache.hadoop.mapreduce.Job$.run(Job.java:)

    at org.apache.hadoop.mapreduce.Job$.run(Job.java:)

    at java.security.AccessController.doPrivileged(Native Method)

    at javax.security.auth.Subject.doAs(Subject.java:)

    at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:)

    at org.apache.hadoop.mapreduce.Job.submit(Job.java:)

    at org.apache.hadoop.mapreduce.Job.waitForCompletion(Job.java:)

    at mp.filetest.WordCount2.main(WordCount2.java:)

执行错误日志

原因： log4j.properties文件没有

解决：照步骤做 4.2

3 Failed to locate the winutils binary in the hadoop binary path
java.io.IOException: Could not locate executable null\bin\winutils.exe in the

-- ::, WARN  [main] util.NativeCodeLoader (NativeCodeLoader.java:<clinit>()) - Unable to load native-hadoop library for your platform... using builtin-java classes where applicable

-- ::, ERROR [main] util.Shell (Shell.java:getWinUtilsPath()) - Failed to locate the winutils binary in the hadoop binary path

java.io.IOException: Could not locate executable null\bin\winutils.exe in the Hadoop binaries.

    at org.apache.hadoop.util.Shell.getQualifiedBinPath(Shell.java:)

    at org.apache.hadoop.util.Shell.getWinUtilsPath(Shell.java:)

    at org.apache.hadoop.util.Shell.<clinit>(Shell.java:)

    at org.apache.hadoop.util.StringUtils.<clinit>(StringUtils.java:)

    at org.apache.hadoop.security.Groups.parseStaticMapping(Groups.java:)

    at org.apache.hadoop.security.Groups.<init>(Groups.java:)

    at org.apache.hadoop.security.Groups.<init>(Groups.java:)

    at org.apache.hadoop.security.Groups.getUserToGroupsMappingService(Groups.java:)

原因：hadoop 2.6 windows插件包没配置好

解决：安装步骤3.2中配置

Z 总结：

加油，干得好。

后续：

照着参考里面的程序，跑一下，测试直接跑程序能否成功 done

有空弄明白 log4j.properties配置中各个参数含义

将Hadoop源码包导入项目中，以便跟踪调试

C 参考:

c.1 安装： Win7+Eclipse+Hadoop2.6.4开发环境搭建

c.2 安装： Hadoop学习笔记（4）－Linux ubuntu 下 Eclipse下搭建Hadoop2.6.4开发环境

c.3 错误处理：关于使用Hadoop MR的Eclipse插件开发时遇到Permission denied问题的解决办法

c.4 错误处理：解决Exception: org.apache.hadoop.io.nativeio.NativeIO$Windows.access0(Ljava/lang/String;I)Z 等一系列问题

巴特西

[b0008] Windows 7 下 hadoop 2.6.4 eclipse 本地开发调试配置

目的:

环境：

说明:

1. 准备Hadoop安装包

2 . 安装HDFS eclipse 插件

3 配置Mapreduce Windows 插件包

3.1 下载hadoop 2.6 windows插件包包

3.2 配置

4 测试Mapreduce

4.5 可选命令行下执行,导出成jar包，上传到Linux

Y 异常

Y.1 Permission denied: user=Administrator

Y.2 运行过程中异常

Z 总结：

C 参考:

最新文章

热门文章

巴特西

[b0008] Windows 7 下 hadoop 2.6.4 eclipse 本地开发调试配置

目的:

环境：

说明:

1. 准备Hadoop安装包

2 . 安装HDFS eclipse 插件

3 配置Mapreduce Windows 插件包

3.1 下载hadoop 2.6 windows插件包包

3.2 配置

4 测试Mapreduce

4.5 可选 命令行下执行,导出成jar包，上传到Linux

Y 异常

Y.1 Permission denied: user=Administrator

Y.2 运行过程中 异常

Z 总结：

C 参考:

最新文章

热门文章

4.5 可选命令行下执行,导出成jar包，上传到Linux

Y.2 运行过程中异常