MapReduce计算框架

下面是MapReduce的流程架构图：

下面是MapReduce简单的word count的流程

下面是MapReduce简单的word count的代码

1，主类

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
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 WCJob {
	
	public static void main(String[] args) throws Exception{
     Configuration conf = new Configuration();

     Job job = Job.getInstance(conf);
     job.setJarByClass(WCJob.class);
     
     job.setMapperClass(WCMapper.class);
     job.setMapOutputKeyClass(Text.class);
     job.setMapOutputValueClass(IntWritable.class);
     
     job.setReducerClass(WCReducer.class);
     job.setCombinerClass(WCReducer.class);
     FileInputFormat.addInputPath(job, new Path("/user/lyl/wc"));
     Path outpath = new Path("/user/lyl/wcout");
     
     FileSystem fs = FileSystem.get(conf);
     if(fs.exists(outpath)){
    	 fs.delete(outpath,true);
     }
     
     FileOutputFormat.setOutputPath(job, outpath);
     boolean flag = job.waitForCompletion(true);
     
     if(flag){
    	 System.out.println("job success!");
     }
  }
}

2，map类

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;
import org.apache.hadoop.util.StringUtils;

public class WCMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
	
	@Override
	protected void map(LongWritable key, Text value,
			Context context)
			throws IOException, InterruptedException {
		String str = value.toString();
		String[] strs = StringUtils.split(str ,' ');
		for (String s:strs) {
           context.write(new Text(s), new IntWritable(1));			
		}
	}
}

3，reduce类

import java.io.IOException;

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

public class WCReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
	
	@Override
	protected void reduce(Text text, Iterable<IntWritable> iterable,
			Context context)
			throws IOException, InterruptedException {

		int sum = 0;
		for (IntWritable i :iterable) {
			sum +=i.get();
		}
		context.write(text, new IntWritable(sum));
	}
}