hadoop的三种运行模式

转载:http://www.2cto.com/kf/201607/522848.html

Hadoop的运行模式分为3种:本地运行模式,伪分布运行模式,集群运行模式,相应概念如下:

1、独立模式即本地运行模式(standalone或local mode)
无需运行任何守护进程(daemon),所有程序都在单个JVM上执行。由于在本机模式下测试和调试MapReduce程序较为方便,因此,这种模式适宜用在开发阶段。
2、伪分布运行模式
伪分布:如果Hadoop对应的Java进程都运行在一个物理机器上,称为伪分布运行模式,如下图所示:

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<code class = "hljs ruby" >[root @hadoop20 dir2]# jps
8993 Jps
7409 SecondaryNameNode
7142 NameNode
7260 DataNode
8685 NodeManager
8590 ResourceManager</code>

3、集群模式
如果Hadoop对应的Java进程运行在多台物理机器上,称为集群模式.[集群就是有主有从] ,如下图所示:

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<code class = "hljs perl" >[root @hadoop11 local]# jps
18046 NameNode
30927 Jps
18225 SecondaryNameNode</code>
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<code class = "hljs ruby" >[root @hadoop22 ~]# jps
9741 ResourceManager
16569 Jps</code>
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<code class = "hljs ruby" >[root @hadoop33 ~]# jps
12775 DataNode
20189 Jps
12653 NodeManager</code>
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<code class = "hljs ruby" >[root @hadoop44 ~]# jps
10111 DataNode
17519 Jps
9988 NodeManager</code>
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<code class = "hljs ruby" >[root @hadoop55 ~]# jps
11563 NodeManager
11686 DataNode
19078 Jps</code>
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<code class = "hljs ruby" >[root @hadoop66 ~]# jps
10682 DataNode
10560 NodeManager
18085 Jps</code>

注意:伪分布模式就是在一台服务器上面模拟集群环境,但仅仅是机器数量少,其通信机制与运行过程与真正的集群模式是一样的,hadoop的伪分布运行模式可以看做是集群运行模式的特殊情况。
为了方便文章的后续说明,先介绍一下hadoop的体系结构:
这里写图片描述从Hadoop的体系结构可以看出,HDFS与MapReduce分别是Hadoop的标配文件系统与标配计算框架,但是呢?–我们完全可以选择别的文件系统(如Windows的NTFS,Linux的ext4)与别的计算框架(如spark、storm等)为Hadoop所服务,这恰恰说明了hadoop的松耦合性。在hadoop的配置文件中,我们是通过core-site.xml这个配置文件指定所用的文件系统的。

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<code class = "hljs xml" ><property>
     <name>fs.defaultFS</name>
     <value>hdfs: //hadoop11:9000</value>
</property></code>

下面将基于Linux与Windows两种开发环境详细说明hadoop的本地运行模式,其中核心知识点如下:
Hadoop的本地执行模式:
1、在windows的eclipse里面直接运行main方法,就会将job提交给本地执行器localjobrunner执行
—-输入输出数据可以放在本地路径下(c:/wc/srcdata/)
—-输入输出数据也可以放在hdfs中(hdfs://hadoop20:9000/dir)

2、在linux的eclipse里面直接运行main方法,但是不要添加yarn相关的配置,也会提交给localjobrunner执行
—-输入输出数据可以放在本地路径下(/usr/local/)
—-输入输出数据也可以放在hdfs中(hdfs://hadoop20:9000/dir)
首先先基于Linux的开发环境进行介绍:
这里写图片描述



以WordCount程序为例,输入输出文件都放在本地路径下,代码如下:

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<code class = "hljs avrasm" > package MapReduce;
 
import java.io.IOException;
 
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
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;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 
 
 
 
public class WordCount
{
      public static String path1 = "file:///usr/local/word.txt" ; //file:///代表本地文件系统中路径的意思
      public static String path2 = "file:///usr/local/dir1" ;
      public static void main(String[] args) throws Exception
      {
          Configuration conf = new Configuration();
          FileSystem fileSystem = FileSystem.get(conf);
 
          if (fileSystem.exists( new Path(path2)))
          {
              fileSystem.delete( new Path(path2), true );
          }
          Job job = Job.getInstance(conf);
          job.setJarByClass(WordCount. class );
 
          FileInputFormat.setInputPaths(job, new Path(path1));
          job.setInputFormatClass(TextInputFormat. class );
          job.setMapperClass(MyMapper. class );
          job.setMapOutputKeyClass(Text. class );
          job.setMapOutputValueClass(LongWritable. class );
 
          job.setNumReduceTasks( 1 );
          job.setPartitionerClass(HashPartitioner. class );
 
 
          job.setReducerClass(MyReducer. class );
          job.setOutputKeyClass(Text. class );
          job.setOutputValueClass(LongWritable. class );
          job.setOutputFormatClass(TextOutputFormat. class );
          FileOutputFormat.setOutputPath(job, new Path(path2));
          job.waitForCompletion( true );
      }   
      public  static  class MyMapper extends Mapper<longwritable, longwritable= "" >
      {
              protected void map(LongWritable k1, Text v1,Context context) throws IOException, InterruptedException
             {
                  String[] splited = v1.toString().split( "\t" );
                  for (String string : splited)
                 {
                        context.write( new Text(string), new LongWritable(1L));
                 }
             }    
      }
      public  static class MyReducer extends Reducer<text, longwritable= "" >
      {
         protected void reduce(Text k2, Iterable<longwritable> v2s,Context context) throws IOException, InterruptedException
         {
                  long sum = 0L;
                  for (LongWritable v2 : v2s)
                 {
                     sum += v2.get();
                 }
                 context.write(k2, new LongWritable(sum));
         }
      }
}
</longwritable></text,></longwritable,></code>

在程序的运行过程中,相应的java进程如下:

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<code class = "hljs cs" >[root @hadoop20 local]# jps
7621                //对应的是启动的eclipse
9833 Jps
9790 WordCount      //对应的是WordCount程序</code>

下面我们在本地查看运行结果:

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<code class = "hljs perl" >[root @hadoop20 dir]# pwd
/usr/local/dir1
[root @hadoop20 dir1]# more part-r- 00000
hello   2
me      1
you     1 </code>

接下来我们将输入路径选择HDFS文件系统中的路径,输出路径还是本地linux文件系统,首先我们在linux上面启动HDFS分布式文件系统。

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<code class = "hljs applescript" >[root @hadoop20 dir]# start-dfs.sh
Starting namenodes on [hadoop20]
hadoop20: starting namenode, logging to /usr/local/hadoop/logs/hadoop-root-namenode-hadoop20.out
hadoop20: starting datanode, logging to /usr/local/hadoop/logs/hadoop-root-datanode-hadoop20.out
Starting secondary namenodes [ 0.0 . 0.0 ]
0.0 . 0.0 : starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-root-secondarynamenode-hadoop20.out
[root @hadoop20 dir]# jps
10260 SecondaryNameNode
7621
10360 Jps
9995 NameNode
10110 DataNode</code>

还是以WordCount程序为例,代码如下:

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<code class = "hljs avrasm" > package MapReduce;
 
import java.io.IOException;
 
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
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;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 
 
 
 
public class WordCount
{
      public static String path1 = "hdfs://hadoop90:2000/word.txt" ;//读取HDFS中的测试集
      public static String path2 = "file:///usr/local/dir2" ;  //输出数据输出到本地文件系统中
      public static void main(String[] args) throws Exception
      {
          Configuration conf = new Configuration();
          FileSystem fileSystem = FileSystem.get(conf); //默认获取的是本地文件系统的FileSystem实例(在这里就是linux文件系统的实例)
 
          if (fileSystem.exists( new Path(path2)))
          {
              fileSystem.delete( new Path(path2), true );
          }
          Job job = Job.getInstance(conf);
          job.setJarByClass(WordCount. class );
 
          FileInputFormat.setInputPaths(job, new Path(path1));
          job.setInputFormatClass(TextInputFormat. class );
          job.setMapperClass(MyMapper. class );
          job.setMapOutputKeyClass(Text. class );
          job.setMapOutputValueClass(LongWritable. class );
 
          job.setNumReduceTasks( 1 );
          job.setPartitionerClass(HashPartitioner. class );
 
 
          job.setReducerClass(MyReducer. class );
          job.setOutputKeyClass(Text. class );
          job.setOutputValueClass(LongWritable. class );
          job.setOutputFormatClass(TextOutputFormat. class );
          FileOutputFormat.setOutputPath(job, new Path(path2));
          job.waitForCompletion( true );
      }   
      public  static  class MyMapper extends Mapper<longwritable, longwritable= "" >
      {
              protected void map(LongWritable k1, Text v1,Context context) throws IOException, InterruptedException
             {
                  String[] splited = v1.toString().split( "\t" );
                  for (String string : splited)
                 {
                        context.write( new Text(string), new LongWritable(1L));
                 }
             }    
      }
      public  static class MyReducer extends Reducer<text, longwritable= "" >
      {
         protected void reduce(Text k2, Iterable<longwritable> v2s,Context context) throws IOException, InterruptedException
         {
                  long sum = 0L;
                  for (LongWritable v2 : v2s)
                 {
                     sum += v2.get();
                 }
                 context.write(k2, new LongWritable(sum));
         }
      }
}
</longwritable></text,></longwritable,></code>

运行结果如下:

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<code class = "hljs perl" >[root @hadoop20 dir2]# more part-r- 00000
hello   2
me      1
you     1
[root @hadoop20 dir2]# pwd
/usr/local/dir2</code>

接下来我们将输入输出路径都换成HDFS中的路径:
代码如下:

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<code class = "hljs avrasm" > package MapReduce;
 
import java.io.IOException;
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
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;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 
 
public class WordCount
{
      public static String path1 = "hdfs://hadoop20:9000/word.txt" ;//读取HDFS中的测试集
      public static String path2 = "hdfs://hadoop20:9000/dir3" ;
      public static void main(String[] args) throws Exception
      {
          Configuration conf = new Configuration();
          FileSystem fileSystem = FileSystem.get(conf);
 
          if (fileSystem.exists( new Path(path2)))
          {
              fileSystem.delete( new Path(path2), true );
          }
          Job job = Job.getInstance(conf);
          job.setJarByClass(WordCount. class );
 
          FileInputFormat.setInputPaths(job, new Path(path1));
          job.setInputFormatClass(TextInputFormat. class );
          job.setMapperClass(MyMapper. class );
          job.setMapOutputKeyClass(Text. class );
          job.setMapOutputValueClass(LongWritable. class );
 
          job.setNumReduceTasks( 1 );
          job.setPartitionerClass(HashPartitioner. class );
 
 
          job.setReducerClass(MyReducer. class );
          job.setOutputKeyClass(Text. class );
          job.setOutputValueClass(LongWritable. class );
          job.setOutputFormatClass(TextOutputFormat. class );
          FileOutputFormat.setOutputPath(job, new Path(path2));
          job.waitForCompletion( true );
      }   
      public  static  class MyMapper extends Mapper<longwritable, longwritable= "" >
      {
              protected void map(LongWritable k1, Text v1,Context context) throws IOException, InterruptedException
             {
                  String[] splited = v1.toString().split( "\t" );
                  for (String string : splited)
                 {
                        context.write( new Text(string), new LongWritable(1L));
                 }
             }    
      }
      public  static class MyReducer extends Reducer<text, longwritable= "" >
      {
         protected void reduce(Text k2, Iterable<longwritable> v2s,Context context) throws IOException, InterruptedException
         {
                  long sum = 0L;
                  for (LongWritable v2 : v2s)
                 {
                     sum += v2.get();
                 }
                 context.write(k2, new LongWritable(sum));
         }
      }
}</longwritable></text,></longwritable,></code>

程序抛出异常:
这里写图片描述
处理措施:

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<code class = "hljs cs" >Configuration conf = new Configuration();
conf.set( "fs.defaultFS" , "hdfs://hadoop20:9000/" );//加入此行代码,表示获取HDFS中的FileSystem实例,而不在是默认linux文件系统的FileSystem实例</code>

查看运行结果:

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<code class = "hljs ruby" >[root @hadoop20 hadoop]# hadoop fs -cat /dir3/part-r- 00000
hello   2
me      1
you     1 </code>

好了,从上面的3个例子可以看出,在Linux这种开发环境下,Hadoop的本地运行模式是很简单的,不用配置任何文件,但是在Windows开发环境下,我们却需要配置很多文件。
在这里先说明一下,因为我的电脑是64位,所以我在windows上面安装的jdk1.7、eclipse、hadoop2.4.1都是64位的,下载链接如下:
http://blog.youkuaiyun.com/a2011480169/article/details/51814212
在Windows开发环境中实现Hadoop的本地运行模式,详细步骤如下:
1、在本地安装好jdk、hadoop2.4.1,并配置好环境变量:JAVA_HOME、HADOOP_HOME、Path路径(配置好环境变量后最好重启电脑)。
这里写图片描述


这里写图片描述
这里写图片描述
这里写图片描述
2、用hadoop-common-2.2.0-bin-master的bin目录替换本地hadoop2.4.1的bin目录,因为hadoop2.0版本中没有hadoop.dll和winutils.exe这两个文件。
hadoop-common-2.2.0-bin-master的下载链接如下:
http://blog.youkuaiyun.com/a2011480169/article/details/51814212
如果缺少hadoop.dll和winutils.exe话,程序将会抛出下面异常:

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<code class = "hljs lua" >java.io.IOException: Could not locate executable D:\hadoop- 2.4 . 1 \bin\winutils.exe in the Hadoop binaries.</code>
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<code class = "hljs avrasm" >java.lang.Exception: java.lang.NullPointerException</code>

所以用hadoop-common-2.2.0-bin-master的bin目录替换本地hadoop2.4.1的bin目录是必要的一个步骤。
注意:如果只是将hadoop-common-2.2.0-bin-master的bin目录中的hadoop.dll和winutils.exe这两个文件添加到hadoop2.4.1的bin目录中,也是可行的,但最好用用hadoop-common-2.2.0-bin-master的bin目录替换本地hadoop2.4.1的bin目录。
上面这两个步骤完成之后我们就可以跑程序了,从而实现Hadoop的本地运行模式:
首先输入输出路径都选择windows的文件系统:
代码如下:

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<code class = "hljs avrasm" > package MapReduce;
 
import java.io.IOException;
 
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
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;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 
 
public class WordCount
{
      public static String path1 = "file:///C:\\word.txt" ;//读取本地windows文件系统中的数据
      public static String path2 = "file:///D:\\dir" ;
      public static void main(String[] args) throws Exception
      {
          Configuration conf = new Configuration();
          FileSystem fileSystem = FileSystem.get(conf);
 
          if (fileSystem.exists( new Path(path2)))
          {
              fileSystem.delete( new Path(path2), true );
          }
          Job job = Job.getInstance(conf);
          job.setJarByClass(WordCount. class );
 
          FileInputFormat.setInputPaths(job, new Path(path1));
          job.setInputFormatClass(TextInputFormat. class );
          job.setMapperClass(MyMapper. class );
          job.setMapOutputKeyClass(Text. class );
          job.setMapOutputValueClass(LongWritable. class );
 
          job.setNumReduceTasks( 1 );
          job.setPartitionerClass(HashPartitioner. class );
 
 
          job.setReducerClass(MyReducer. class );
          job.setOutputKeyClass(Text. class );
          job.setOutputValueClass(LongWritable. class );
          job.setOutputFormatClass(TextOutputFormat. class );
          FileOutputFormat.setOutputPath(job, new Path(path2));
          job.waitForCompletion( true );
      }   
      public  static  class MyMapper extends Mapper<longwritable, longwritable= "" >
      {
              protected void map(LongWritable k1, Text v1,Context context) throws IOException, InterruptedException
             {
                  String[] splited = v1.toString().split( "\t" );
                  for (String string : splited)
                 {
                        context.write( new Text(string), new LongWritable(1L));
                 }
             }    
      }
      public  static class MyReducer extends Reducer<text, longwritable= "" >
      {
         protected void reduce(Text k2, Iterable<longwritable> v2s,Context context) throws IOException, InterruptedException
         {
                  long sum = 0L;
                  for (LongWritable v2 : v2s)
                 {
                     sum += v2.get();
                 }
                 context.write(k2, new LongWritable(sum));
         }
      }
}
</longwritable></text,></longwritable,></code>

在dos下查看运行中的java进程:
这里写图片描述


其中28568为windows中启动的eclipse进程。
接下来我们查看运行结果:
这里写图片描述
part-r-00000中的内容如下:

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<code class = "hljs vbnet" >hello   2
me  1
you 1 </code>

接下来输入路径选择windows本地,输出路径换成HDFS文件系统,代码如下:

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<code class = "hljs avrasm" > package MapReduce;
 
import java.io.IOException;
 
 
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
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;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
 
 
 
public class WordCount
{
      public static String path1 = "file:///C:\\word.txt" ;//读取windows文件系统中的数据
      public static String path2 = "hdfs://hadoop20:9000/dir" ;//输出到hdfs中
      public static void main(String[] args) throws Exception
      {
          Configuration conf = new Configuration();
          FileSystem fileSystem = FileSystem.get(conf);
          if (fileSystem.exists( new Path(path2)))
          {
              fileSystem.delete( new Path(path2), true );
          }
          Job job = Job.getInstance(conf);
          job.setJarByClass(WordCount. class );
 
          FileInputFormat.setInputPaths(job, new Path(path1));
          job.setInputFormatClass(TextInputFormat. class );
          job.setMapperClass(MyMapper. class );
          job.setMapOutputKeyClass(Text. class );
          job.setMapOutputValueClass(LongWritable. class );
 
          job.setNumReduceTasks( 1 );
          job.setPartitionerClass(HashPartitioner. class );
 
 
          job.setReducerClass(MyReducer. class );
          job.setOutputKeyClass(Text. class );
          job.setOutputValueClass(LongWritable. class );
          job.setOutputFormatClass(TextOutputFormat. class );
          FileOutputFormat.setOutputPath(job, new Path(path2));
          job.waitForCompletion( true );
      }   
      public  static  class MyMapper extends Mapper<longwritable, longwritable= "" >
      {
              protected void map(LongWritable k1, Text v1,Context context) throws IOException, InterruptedException
             {
                  String[] splited = v1.toString().split( "\t" );
                  for (String string : splited)
                 {
                        context.write( new Text(string), new LongWritable(1L));
                 }
             }    
      }
      public  static class MyReducer extends Reducer<text, longwritable= "" >
      {
         protected void reduce(Text k2, Iterable<longwritable> v2s,Context context) throws IOException, InterruptedException
         {
                  long sum = 0L;
                  for (LongWritable v2 : v2s)
                 {
                     sum += v2.get();
                 }
                 context.write(k2, new LongWritable(sum));
         }
      }
}</longwritable></text,></longwritable,></code>

程序抛出异常:
这里写图片描述
处理措施同上:

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<code class = "hljs cs" >Configuration conf = new Configuration();
conf.set( "fs.defaultFS" , "hdfs://hadoop20:9000/" );
FileSystem fileSystem = FileSystem.get(conf); //获取HDFS中的FileSystem实例</code>

查看运行结果:

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<code class = "hljs ruby" >[root @hadoop20 dir4]# hadoop fs -cat /dir/part-r- 00000
hello   2
me      1
you     1 </code>

好的,到这里hadoop的本地文件系统就讲述完了,注意一下几点:
1、file:\\ 代表本地文件系统,hdfs:// 代表hdfs分布式文件系统
2、linux下的hadoop本地运行模式很简单,但是windows下的hadoop本地运行模式需要配置相应文件。
3、MapReduce所用的文件放在哪里是没有关系的(可以放在Windows本地文件系统、可以放在Linux本地文件系统、也可以放在HDFS分布式文件系统中),最后是通过FileSystem这个实例来获取文件的。

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