hadoop的java代码实现

Hadoop的Java代码实现通常包括以下几个步骤：
1. 配置Hadoop环境：加载配置文件，实例化Job对象，并设置作业的名称、输入路径和输出路径等。
2. 设置作业参数：设置Reduce任务的数量、Map输出键值对的类型、分区器类型、Map输出键的比较器、分组比较器、Mapper类和Reducer类等。
3. 执行作业：调用job.waitForCompletion(true)方法执行作业。

相关问题

hadoop mapreduce代码实现

Hadoop MapReduce是一种分布式计算框架，可以用于处理大规模数据集。下面是一个简单的Hadoop MapReduce代码示例：

1. Mapper类

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

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

2. Reducer类

public class MyReducer 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);
    }
}

3. Driver类

public class MyDriver {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(MyDriver.class);
        job.setMapperClass(MyMapper.class);
        job.setCombinerClass(MyReducer.class);
        job.setReducerClass(MyReducer.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);
    }
}

在这个示例中，Mapper类将输入数据按照单词进行分割，然后输出<单词,1>的键值对。Reducer类将相同的单词进行合并，并输出<单词,总数>的键值对。Driver类设置了MapReduce作业的输入和输出路径，并指定了Mapper和Reducer类。最后，启动作业并等待它的完成。

用hadoop代码实现

Hadoop是一个开源的分布式计算框架，主要用于处理大规模数据集。在Hadoop中，主要使用Java编写MapReduce编程模型来执行并行处理任务。以下是使用Hadoop的基本步骤和关键代码片段：

1. **设置环境**：
首先，确保已经安装了Hadoop和配置了环境变量。在命令行中运行`hadoop version`检查是否安装正确。

2. **创建MapReduce程序**：
使用Java或Python（Hadoop Streaming）编写Mapper和Reducer类。例如，一个简单的MapReduce示例：

   // Mapper.java
   import java.io.IOException;
   import org.apache.hadoop.io.IntWritable;
   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();

       public 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);
           }
       }
   }
   
   // Reducer.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();

       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);
       }
   }

3. **编译并运行**：
在Hadoop的`bin`目录下，使用`javac`编译Mapper和Reducer，然后使用`jar`命令打包成JAR文件。运行`hadoop jar`命令，指定JAR文件和输入、输出路径。

hadoop jar your-wordcount-jar.jar WordCountMapper input_path output_path