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>
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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：
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：
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
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
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
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：
处理措施：
|
1
2
|
<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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2
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4
|
<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)。
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：
|
1
|
<code
class
=
"hljs lua"
>java.io.IOException: Could not locate executable D:\hadoop-
2.4
.
1
\bin\winutils.exe in the Hadoop binaries.</code>
|
|
1
|
<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
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：
其中28568为windows中启动的eclipse进程。
接下来我们查看运行结果：
part-r-00000中的内容如下：
|
1
2
3
|
<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
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：
处理措施同上：
|
1
2
3
|
<code
class
=
"hljs cs"
>Configuration conf =
new
Configuration();
FileSystem fileSystem = FileSystem.get(conf);
//获取HDFS中的FileSystem实例</code>
|
查看运行结果：
|
1
2
3
4
|
<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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