Hadoop日记Day15---MapReduce新旧api的比较

　　我使用hadoop的是hadoop1.1.2，而很多公司也在使用hadoop0.2x版本，因此市面上的hadoop资料版本不一，为了扩充自己的知识面，MapReduce的新旧api进行了比较研究。

　　hadoop版本1.x的包一般是mapreduce
　　hadoop版本0.x的包一般是mapred

我们还是以单词统计为例进行研究，代码如下，如代码1.1所示：

package old;

import java.io.IOException;
import java.net.URI;
import java.util.Iterator;

import mapreduce.WordCountApp;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
/**
 * hadoop版本1.x的包一般是mapreduce
 * hadoop版本0.x的包一般是mapred
 *
 */
public class OldAPP {
    static final String INPUT_PATH = "hdfs://hadoop:9000/hello";
    static final String OUT_PATH = "hdfs://hadoop:9000/out";
    /**
     * 改动：
     * 1.不再使用Job，而是使用JobConf
     * 2.类的包名不再使用mapreduce，而是使用mapred
     * 3.不再使用job.waitForCompletion(true)提交作业，而是使用JobClient.runJob(job);
     * 
     */
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
        final Path outPath = new Path(OUT_PATH);
        if(fileSystem.exists(outPath)){
            fileSystem.delete(outPath, true);
        }
        
        final JobConf job = new JobConf(conf , WordCountApp.class);
        //1.1指定读取的文件位于哪里
        FileInputFormat.setInputPaths(job, INPUT_PATH);
        //指定如何对输入文件进行格式化，把输入文件每一行解析成键值对
        //job.setInputFormatClass(TextInputFormat.class);
        
        //1.2 指定自定义的map类
        job.setMapperClass(MyMapper.class);
        //map输出的<k,v>类型。如果<k3,v3>的类型与<k2,v2>类型一致，则可以省略
        //job.setMapOutputKeyClass(Text.class);
        //job.setMapOutputValueClass(LongWritable.class);
        
        //1.3 分区
        //job.setPartitionerClass(HashPartitioner.class);
        //有一个reduce任务运行
        //job.setNumReduceTasks(1);
        
        //1.4 TODO 排序、分组
        
        //1.5 TODO 规约
        
        //2.2 指定自定义reduce类
        job.setReducerClass(MyReducer.class);
        //指定reduce的输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        
        //2.3 指定写出到哪里
        FileOutputFormat.setOutputPath(job, outPath);
        //指定输出文件的格式化类
        //job.setOutputFormatClass(TextOutputFormat.class);
        
        //把job提交给JobTracker运行
        JobClient.runJob(job);
    }

    
    
    /**
     * 新api:extends Mapper
     * 老api:extends MapRedcueBase implements Mapper
     */
    static class MyMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, LongWritable>{
        @Override
        public void map(LongWritable k1, Text v1,
                OutputCollector<Text, LongWritable> collector, Reporter reporter)
                throws IOException {
            final String[] splited = v1.toString().split("\t");
            for (String word : splited) {
                collector.collect(new Text(word), new LongWritable(1));
            }
        }
    }
    
    static class MyReducer extends MapReduceBase implements Reducer<Text, LongWritable, Text, LongWritable>{
        @Override
        public void reduce(Text k2, Iterator<LongWritable> v2s,
                OutputCollector<Text, LongWritable> collector, Reporter reporter)
                throws IOException {
            long times = 0L;
            while (v2s.hasNext()) {
                final long temp = v2s.next().get();
                times += temp;
            }
            collector.collect(k2, new LongWritable(times));
        }
    }
}

View Code

代码 1.1