一、代码编写

1.1 单词统计

　　回顾我们以前单词统计的例子，如代码1.1所示。

 package counter;

 import java.net.URI;

 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.Counter;

 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 WordCountApp {

     static final String INPUT_PATH = "hdfs://hadoop:9000/hello";

     static final String OUT_PATH = "hdfs://hadoop:9000/out";

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

         Configuration conf = new Configuration();

         final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);

         final Path outPath = new Path(OUT_PATH);

         if(fileSystem.exists(outPath)){

             fileSystem.delete(outPath, true);

         }

         final Job job = new Job(conf , WordCountApp.class.getSimpleName());

         FileInputFormat.setInputPaths(job, INPUT_PATH);//1.1指定读取的文件位于哪里

         job.setInputFormatClass(TextInputFormat.class);//指定如何对输入文件进行格式化，把输入文件每一行解析成键值对

         job.setMapperClass(MyMapper.class);//1.2 指定自定义的map类

         job.setMapOutputKeyClass(Text.class);//map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致，则可以省略

         job.setMapOutputValueClass(LongWritable.class);

         job.setPartitionerClass(HashPartitioner.class);//1.3 分区

         job.setNumReduceTasks(1);//有一个reduce任务运行

         job.setReducerClass(MyReducer.class);//2.2 指定自定义reduce类

         job.setOutputKeyClass(Text.class);//指定reduce的输出类型

         job.setOutputValueClass(LongWritable.class);

         FileOutputFormat.setOutputPath(job, outPath);//2.3 指定写出到哪里

         job.setOutputFormatClass(TextOutputFormat.class);//指定输出文件的格式化类

         job.waitForCompletion(true);//把job提交给JobTracker运行

     }

     /**

      * KEYIN    即k1        表示行的偏移量

      * VALUEIN    即v1        表示行文本内容

      * KEYOUT    即k2        表示行中出现的单词

      * VALUEOUT    即v2        表示行中出现的单词的次数，固定值1

      */

     static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

         protected void map(LongWritable k1, Text v1, Context context) throws java.io.IOException ,InterruptedException {

         //    final Counter helloCounter = context.getCounter("Sensitive Words", "hello");

             final String line = v1.toString();

         /*    if(line.contains("hello")){

                 //记录敏感词出现在一行中

                 helloCounter.increment(1L);

             }*/

             final String[] splited = line.split(" ");

             for (String word : splited) {

                 context.write(new Text(word), new LongWritable(1));

             }

         };

     }

     /**

      * KEYIN    即k2        表示行中出现的单词

      * VALUEIN    即v2        表示行中出现的单词的次数

      * KEYOUT    即k3        表示文本中出现的不同单词

      * VALUEOUT    即v3        表示文本中出现的不同单词的总次数

      *

      */

     static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

         protected void reduce(Text k2, java.lang.Iterable<LongWritable> v2s, Context ctx) throws java.io.IOException ,InterruptedException {

             long times = 0L;

             for (LongWritable count : v2s) {

                 times += count.get();

             }

             ctx.write(k2, new LongWritable(times));

         };

     }

 }

代码 1.1

　　分析上面代码,我们会发现该单词统计方法的输入输出路径都已经写死了,比如输入路径：INPUT_PATH = "hdfs://hadoop:9000/hello"输出路径：OUT_PATH = "hdfs://hadoop:9000/out"。这样一来，这个算法的输入出路径也就固定死了，想要使用这个算法，相应的数据就必须满足这个固定的路径要求，从而算法的灵活性和可操作性也就大大降低了，也就是说我们的算法，目前还不算是一个通用的算法。所以为了提高算法灵活性和可操作性，应该通过指令运行时参数来指定输入输出路径。

1.2 在命令行运行的单词统计

　　在命令行运行的单词统计程序，如代码1.2所示。

 package cmd;

 import java.net.URI;

 import org.apache.hadoop.conf.Configuration;

 import org.apache.hadoop.conf.Configured;

 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;

 import org.apache.hadoop.util.Tool;

 import org.apache.hadoop.util.ToolRunner;

 public class WordCountApp extends Configured implements Tool{

     static String INPUT_PATH = "";

     static String OUT_PATH = "";

     @Override

     public int run(String[] arg0) throws Exception {

         INPUT_PATH = arg0[0];

         OUT_PATH = arg0[1];

         Configuration conf = new Configuration();

         final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);

         final Path outPath = new Path(OUT_PATH);

         if(fileSystem.exists(outPath)){

             fileSystem.delete(outPath, true);

         }

         final Job job = new Job(conf , WordCountApp.class.getSimpleName());        

         job.setJarByClass(WordCountApp.class);//打包运行必须执行的秘密方法

         FileInputFormat.setInputPaths(job, INPUT_PATH);//1.1指定读取的文件位于哪里

         job.setInputFormatClass(TextInputFormat.class);//指定如何对输入文件进行格式化，把输入文件每一行解析成键值对

         job.setMapperClass(MyMapper.class);//1.2 指定自定义的map类

         job.setMapOutputKeyClass(Text.class);//map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致，则可以省略

         job.setMapOutputValueClass(LongWritable.class);

         job.setPartitionerClass(HashPartitioner.class);//1.3 分区

         job.setNumReduceTasks(1);//有一个reduce任务运行

         job.setReducerClass(MyReducer.class);//2.2 指定自定义reduce类

         job.setOutputKeyClass(Text.class);//指定reduce的输出类型

         job.setOutputValueClass(LongWritable.class);

         FileOutputFormat.setOutputPath(job, outPath);//2.3 指定写出到哪里

         job.setOutputFormatClass(TextOutputFormat.class);//指定输出文件的格式化类

         job.waitForCompletion(true);//把job提交给JobTracker运行

         return 0;

     }

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

         ToolRunner.run(new WordCountApp(), args);

     }

     /**

      * KEYIN    即k1        表示行的偏移量

      * VALUEIN    即v1        表示行文本内容

      * KEYOUT    即k2        表示行中出现的单词

      * VALUEOUT    即v2        表示行中出现的单词的次数，固定值1

      */

     static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable>{

         protected void map(LongWritable k1, Text v1, Context context) throws java.io.IOException ,InterruptedException {

             final String[] splited = v1.toString().split("\t");

             for (String word : splited) {

                 context.write(new Text(word), new LongWritable(1));

             }

         };

     }

     /**

      * KEYIN    即k2        表示行中出现的单词

      * VALUEIN    即v2        表示行中出现的单词的次数

      * KEYOUT    即k3        表示文本中出现的不同单词

      * VALUEOUT    即v3        表示文本中出现的不同单词的总次数

      *

      */

     static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{

         protected void reduce(Text k2, java.lang.Iterable<LongWritable> v2s, Context ctx) throws java.io.IOException ,InterruptedException {

             long times = 0L;

             for (LongWritable count : v2s) {

                 times += count.get();

             }

             ctx.write(k2, new LongWritable(times));

         };

     }

 }

代码 1.2

　　在编写能够在命令行运行的单词统计程序时，我们的类要继承Configured类实现Tool接口，实现Tool接口就要添加一个run()方法。在run()方法中执行我们原来在Main()方法中运行的配置代码。然而run方法如何运行呢？那就要在Main方法中调用run方法，调用方式如代码1.3所示。

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

         ToolRunner.run(new WordCountApp(), args);

     }

代码 1.3

　　我们看一下run方法的参数，ToolRunner.run(Tool tool, String[] args)，第一参数为Tool接口，我们知道该程序的类就是Tool的实现类所以我们可以，用该程序类的对象来作为参数。他的第二个参数，是一个字符数组args。在这里我们先讲一下，main函数的args参数。这个参数是运行程序前给它的参数。如果你在你程序要用这个参数的话，就需要在运行前指定。比如一个打印helloworld的程序如下：

public class HelloWorld{

    public static void main(String[] args) {

        System.out.println(args[0]);

    }

}

　　执行命令java HelloWorld ceshi ceshi1 ceshi2，那么在HelloWorld的main方法里面 args就是{"ceshi", "ceshi1", "ceshi2"}，打印的结果就是creshi。

　　经过对main方法的分析，我应该就知道了，run方法的第二个参数就应该是main函数的参数，这样就能够接受命令行所指定的参数了。那么既然输入输出路径由运行时的命令行的参数指定，那么就不需要在代码中指定路径了，所以将INPUT_PATH和OUT_PATH初始化为空。然后在run方法中通过由命令行传过来的参数来进行赋值，如下所示。

INPUT_PATH = arg0[0];//表示输入路径

OUT_PATH = arg0[1];//表示输出路径

　　而为了我们的程序能够在命令行运行，必须添加“job.setJarByClass(WordCountApp.class);”代码，表示我们的程序以打包的方式运行。

二、运行方式

2.1 将程序以.jar类型导出到桌面

<1> 选择WordCountApp右击选择Export，如图2.1所示。

图 2.1

<2>  选择JAR file，选择Next，如图2.2所示。

图 2.2

<3> 选择Next后，弹出如下界面，如图2.3，再次选择Next。

图 2.3

<4> 选择Next之后，弹出如图2.4的界面，选择Browse。

图 2.4

<5> 选择Browse后，在弹出的界面选择ok，如图2.5所示。

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" alt="" />

图 2.5

<6> 选择Ok后，直接选择finish即可，如图2.6所示。

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" alt="" />

图 2.6

2.2 将jar包传到Linux

　　使用WinScp将程序的jar包传到Linux，如图2.7所示。

图 2.7

2.3 在Linux命令行执行jar包

2.3.1 创建输入输出路径

　　执行命令：

hadoop fs -mkdir /input

hadoop fs -mkdir /output

2.3.2 编写、上传file文件

　　执行命令：vi file1

　　输入内容：

　　　　　　　　hello    word
　　　　　　　　hello    me

　　执行命令：hadoop fs -put file1 /

2.3.3 执行程序

执行命令：hadoop jar jar.jar hdfs://hadoop:9000/input  hdfs: //hadoop:9000/output

运行过程：

14/09/28 20:08:08 WARN mapred.JobClient: Use GenericOptionsParser for parsi                                                                                             ng the arguments. Applications should implement Tool for the same.

14/09/28 20:08:09 INFO input.FileInputFormat: Total input paths to process                                                                                              : 1

14/09/28 20:08:09 INFO util.NativeCodeLoader: Loaded the native-hadoop libr                                                                                             ary

14/09/28 20:08:09 WARN snappy.LoadSnappy: Snappy native library not loaded

14/09/28 20:08:11 INFO mapred.JobClient: Running job: job_201409281916_0001

14/09/28 20:08:12 INFO mapred.JobClient:  map 0% reduce 0%

14/09/28 20:09:03 INFO mapred.JobClient:  map 100% reduce 0%

14/09/28 20:09:14 INFO mapred.JobClient:  map 100% reduce 100%

14/09/28 20:09:14 INFO mapred.JobClient: Job complete: job_201409281916_0001

14/09/28 20:09:14 INFO mapred.JobClient: Counters: 29

14/09/28 20:09:14 INFO mapred.JobClient:   Job Counters

14/09/28 20:09:14 INFO mapred.JobClient:     Launched reduce tasks=1

14/09/28 20:09:14 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=47675

14/09/28 20:09:14 INFO mapred.JobClient:     Total time spent by all reduces waiting after rese                                                                         rving slots (ms)=0

14/09/28 20:09:14 INFO mapred.JobClient:     Total time spent by all maps waiting after reservi                                                                         ng slots (ms)=0

14/09/28 20:09:14 INFO mapred.JobClient:     Launched map tasks=1

14/09/28 20:09:14 INFO mapred.JobClient:     Data-local map tasks=1

14/09/28 20:09:14 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=10902

14/09/28 20:09:14 INFO mapred.JobClient:   File Output Format Counters

14/09/28 20:09:14 INFO mapred.JobClient:     Bytes Written=21

14/09/28 20:09:14 INFO mapred.JobClient:   FileSystemCounters

14/09/28 20:09:14 INFO mapred.JobClient:     FILE_BYTES_READ=67

14/09/28 20:09:14 INFO mapred.JobClient:     HDFS_BYTES_READ=116

14/09/28 20:09:14 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=105834

14/09/28 20:09:14 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=21

14/09/28 20:09:14 INFO mapred.JobClient:   File Input Format Counters

14/09/28 20:09:14 INFO mapred.JobClient:     Bytes Read=21

14/09/28 20:09:14 INFO mapred.JobClient:   Map-Reduce Framework

14/09/28 20:09:14 INFO mapred.JobClient:     Map output materialized bytes=67

14/09/28 20:09:14 INFO mapred.JobClient:     Map input records=2

14/09/28 20:09:14 INFO mapred.JobClient:     Reduce shuffle bytes=67

14/09/28 20:09:14 INFO mapred.JobClient:     Spilled Records=8

14/09/28 20:09:14 INFO mapred.JobClient:     Map output bytes=53

14/09/28 20:09:14 INFO mapred.JobClient:     CPU time spent (ms)=35140

14/09/28 20:09:14 INFO mapred.JobClient:     Total committed heap usage (bytes)=131665920

14/09/28 20:09:14 INFO mapred.JobClient:     Combine input records=0

14/09/28 20:09:14 INFO mapred.JobClient:     SPLIT_RAW_BYTES=95

14/09/28 20:09:14 INFO mapred.JobClient:     Reduce input records=4

14/09/28 20:09:14 INFO mapred.JobClient:     Reduce input groups=3

14/09/28 20:09:14 INFO mapred.JobClient:     Combine output records=0

14/09/28 20:09:14 INFO mapred.JobClient:     Physical memory (bytes) snapshot=181952512

14/09/28 20:09:14 INFO mapred.JobClient:     Reduce output records=3

14/09/28 20:09:14 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=752697344

14/09/28 20:09:14 INFO mapred.JobClient:     Map output records=4

执行结果：

[root@hadoop Downloads]# hadoop fs -ls /output

Found 3 items

-rw-r--r--   1 root supergroup          0 2014-09-28 20:09 /output/_SUCCESS

drwxr-xr-x   - root supergroup          0 2014-09-28 20:08 /output/_logs

-rw-r--r--   1 root supergroup         21 2014-09-28 20:09 /output/part-r-00000

[root@hadoop Downloads]# hadoop fs -cat /output/part-r-00000

hello   2

me      1

world   1

[root@hadoop Downloads]#