在IDEA中编码，实现基于hadoop mapreduce分布式计算框架的单词统计，将代码，并打印统计结果截图上传。待分析数据在“news.txt”文件

抱歉，由于我是文本交互的模型，无法直接编写和运行代码并提供截图。但是，我可以指导你如何在IntelliJ IDEA中创建一个基本的MapReduce程序来统计Hadoop中的单词数。首先，你需要确保已经安装了Hadoop和配置好环境。

1. **设置项目和依赖**:
- 创建一个新的Maven项目，在`pom.xml`中添加Hadoop的依赖：

     <dependencies>
       <dependency>
         <groupId>org.apache.hadoop</groupId>
         <artifactId>hadoop-client</artifactId>
         <version>${hadoop.version}</version>
       </dependency>
     </dependencies>

替换`${hadoop.version}`为你实际使用的Hadoop版本号。

2. **编写Mapper和Reducer类**:
- `WordCountMapper.java`:

     import java.io.IOException;
     import org.apache.hadoop.io.LongWritable;
     import org.apache.hadoop.io.Text;
     import org.apache.hadoop.mapreduce.Mapper;

     public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
         private final static IntWritable one = new IntWritable(1);
         private Text word = new Text();

         @Override
         protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
             String[] words = value.toString().split("\\s+");
             for (String w : words) {
                 word.set(w);
                 context.write(word, one);
             }
         }
     }

- `WordCountReducer.java`:

     import java.io.IOException;
     import org.apache.hadoop.io.IntWritable;
     import org.apache.hadoop.io.Text;
     import org.apache.hadoop.mapreduce.Reducer;

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

         @Override
         protected 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);
         }
     }

3. **主类Driver**:

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

   public class WordCount {

       public static void main(String[] args) throws Exception {
           Configuration conf = new Configuration();
           Job job = Job.getInstance(conf, "word count");
           job.setJarByClass(WordCount.class);
           job.setMapperClass(WordCountMapper.class);
           job.setCombinerClass(WordCountReducer.class);
           job.setReducerClass(WordCountReducer.class);
           job.setOutputKeyClass(Text.class);
           job.setOutputValueClass(IntWritable.class);
           FileInputFormat.addInputPath(job, new Path(args[0]));
           FileOutputFormat.setOutputPath(job, new Path(args[1]));
           System.exit(job.waitForCompletion(true) ? 0 : 1);
       }
   }

4. **运行Job**:
在命令行输入类似这样的命令：

   hadoop jar your-jar-name.jar WordCount news.txt output-dir

将`your-jar-name.jar`替换为你的包名生成的jar文件，`news.txt`是输入文件路径，`output-dir`是期望的输出目录。

5. **查看结果**:
输出目录会有一个文本文件，包含每个单词及其计数值。

对于屏幕截图，这取决于你的IDEA配置和个人操作，通常可以在"Run"或者"Debug"窗口找到日志和输出结果。如果你需要帮助理解代码或执行过程，你可以告诉我具体的疑问点，我会尽力解答。同时，记得检查Hadoop是否配置正确，因为这里并未详细说明Hadoop的具体配置步骤。