如何利用Hadoop源码来构建创新的数据解决方案？

Hadoop是一个开源的分布式计算框架，它允许使用简单的编程模型在大量计算机集群上处理大规模数据集，Hadoop的核心组件包括HDFS（Hadoop Distributed File System）和MapReduce，以下是一个简单的Hadoop MapReduce程序示例，用于统计文本文件中单词的出现次数：

1、我们需要创建一个Java项目，并添加Hadoop相关的依赖库，在项目的pom.xml文件中添加以下依赖：

<dependencies>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoopcommon</artifactId>
        <version>3.2.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoopmapreduceclientcore</artifactId>
        <version>3.2.1</version>
    </dependency>
    <dependency>
        <groupId>org.apache.hadoop</groupId>
        <artifactId>hadoophdfs</artifactId>
        <version>3.2.1</version>
    </dependency>
</dependencies>

2、编写一个Mapper类，用于读取输入文件并将每个单词作为键，值初始化为1。

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();
    @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);
        }
    }
}

3、编写一个Reducer类，用于将Mapper输出的键值对进行汇总，得到每个单词的总出现次数。

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> {
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        context.write(key, new IntWritable(sum));
    }
}

4、编写一个驱动程序，用于配置和运行MapReduce作业。

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 {
        if (args.length != 2) {
            System.err.println("Usage: WordCount <input path> <output path>");
            System.exit(1);
        }
        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);
    }
}

5、编译并打包项目，将生成的jar文件上传到Hadoop集群，通过命令行运行以下命令来执行MapReduce作业：

hadoop jar wordcount.jar WordCount input_path output_path

input_path是包含要统计的文本文件的HDFS路径，output_path是结果输出的HDFS路径。