WordCount案例---MapReduce学习小结(-)

距离第一次接触MapReduce已经过去了很久很久.回忆起来最开始的时候,看到一个程序那么多代码.都不知道如何入手…走了很多的弯路.到后来,慢慢的一步步学习中,才发现它并不是一个什么不可跨越的坑.
多练习,多总结.就能发现其中的秘密.

经典的WordCount

思路:
1. Map阶段:读取文件,这里读的时候,是一行一行的读取.然后,便可以分割开.输出的结果是这样的格式:

package day1;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class wordCountDemo {    public static void main(String[] args) throws Exception {        if (args.length!=2) {            System.err.println("input err!");            System.exit(-1);        }        Job job=new Job(new Configuration(), "WordCount");        job.setJarByClass(wordCountDemo.class);        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        job.setMapperClass(wMap.class);        job.setReducerClass(wReduce.class);         job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(IntWritable.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        job.waitForCompletion(true);    }    //map阶段    public static class wMap extends Mapper<LongWritable, Text, Text,IntWritable>{        @Override        protected void map(LongWritable key, Text value,                Mapper<LongWritable, Text, Text, IntWritable>.Context context)                throws IOException, InterruptedException {            String[] lines = value.toString().split(" ");            for (String words : lines) {                context.write(new Text(words), new IntWritable(1));            }        }    }    //map阶段:<key,value>    //shuffle:<key,{value,value....}    //reduce阶段    public static class wReduce extends Reducer<Text, IntWritable, Text, IntWritable>{        @Override        protected void reduce(Text k1, Iterable<IntWritable> v1,                Reducer<Text, IntWritable, Text, IntWritable>.Context context)                throws IOException, InterruptedException {            int sum=0;            for (IntWritable count : v1) {                sum+=count.get();            }            context.write(k1, new IntWritable(sum));        }    }}

wordCount 其实不难.虽然是入门程序.但也相当的有用啊.很多都和这个的做法一样的..额,复习的时候自己写了一段.

package day1;import java.io.IOException;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/** * 统计学生总分. * 数据格式: * 姓名:科目:成绩 * 张三:语文:12 * 李四:数学:23 * 张三:英语:1000 * .... * @author YXY * */public class SumDemo {    public static void main(String[] args) throws Exception {        if (args.length!=2) {            System.err.println("input err!");            System.exit(-1);        }        Job job=new Job(new Configuration(), "Sumdemo");        job.setJarByClass(wordCountDemo.class);        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        job.setMapperClass(SumMap.class);        job.setReducerClass(SumReduce.class);        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(IntWritable.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        job.waitForCompletion(true);    }    public static class SumMap extends Mapper<LongWritable, Text, Text, IntWritable>{        @Override        protected void map(LongWritable key, Text value,                Mapper<LongWritable, Text, Text, IntWritable>.Context context)                throws IOException, InterruptedException {            String[] lines = value.toString().split(":");            String name = lines[0].trim();            int score=Integer.parseInt(lines[2].trim());            context.write(new Text(name), new IntWritable(score));        }    }    public static class SumReduce extends Reducer<Text, IntWritable, Text,IntWritable>{        @Override        protected void reduce(Text key, Iterable<IntWritable> values,                Reducer<Text, IntWritable, Text, IntWritable>.Context context)                throws IOException, InterruptedException {            int sum=0;            for (IntWritable score : values) {                sum+=score.get();            }            context.write(key, new IntWritable(sum));        }    }}

注意点
1. 使用智能提示的时候,注意要导入正确的包…因为很多时候,你会看到一样的名字.
2. 理解这种思路.复习MapReduce的工作原理.
3. alt+ctrl+L..快捷键.
4. 不要懒惰…刚开始不连续,想通过复制来解决.其实是对自己最大的不负责.