学习hadoop2.7 API:通过eclipse查看hadoop2.7 包及 wordcount例子源码

问题导读

1.获取查看hadoop2.7 jar包对应的源码？
2.如何获取hadoop2.7 wordcount例子?

参考：
从零教你如何获取hadoop2.4源码并使用eclipse关联hadoop2.4源码

下面我们导入hadoop2.7 包及hadoop2.7自带例子

添加外部包.png

在添加外部包的时候，在路径中

[mw_shl_code=bash,true]/usr/hadoop-2.7.0/share/hadoop/mapreduce[/mw_shl_code]
找到

hadoop-mapreduce-examples-2.7.0.jar

添加进来。
这时候我们双击 .class文件，我们即可看到源码

然后添加zip包
hadoop2.7源码
链接：http://pan.baidu.com/s/1mgsxuNA 密码：8kqm

同样这也是正宗的hadoop2.7 api
[mw_shl_code=bash,true]/**
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements.  See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership.  The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License.  You may obtain a copy of the License at
*
*    http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.hadoop.examples;

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {

  public static class TokenizerMapper
   extends Mapper<Object, Text, Text, IntWritable>{

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

public void map(Object key, Text value, Context context
                  ) throws IOException, InterruptedException {
   StringTokenizer itr = new StringTokenizer(value.toString());
   while (itr.hasMoreTokens()) {
      word.set(itr.nextToken());
      context.write(word, one);
   }
}
  }

  public static class IntSumReducer
   extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values,
                     Context context
                     ) throws IOException, InterruptedException {
   int sum = 0;
   for (IntWritable val : values) {
      sum += val.get();
   }
   result.set(sum);
   context.write(key, result);
}
  }

  public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
   System.err.println("Usage: wordcount <in> [<in>...] <out>");
   System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
   FileInputFormat.addInputPath(job, new Path(otherArgs));
}
FileOutputFormat.setOutputPath(job,
   new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}
[/mw_shl_code]

这样我们就可以在原先的基础上，改造hadoop2.7 wordcount了