GenericWritable实例

package inputformat;import java.net.URI;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.GenericWritable;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.Writable;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.KeyValueTextInputFormat;import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;public class MyGenericWritableApp {private static final String INPUT_PATH = "hdfs://chaoren1:9000/files";private static final String OUT_PATH = "hdfs://chaoren1:9000/out";public static void main(String[] args) throws Exception{Configuration conf = new Configuration();final FileSystem filesystem = FileSystem.get(new URI(OUT_PATH), conf);filesystem.delete(new Path(OUT_PATH), true);final Job job = new Job(conf , WordCountApp.class.getSimpleName());job.setJarByClass(WordCountApp.class);MultipleInputs.addInputPath(job, new Path("hdfs://chaoren1:9000/files/hello"), KeyValueTextInputFormat.class, MyMapper.class);MultipleInputs.addInputPath(job, new Path("hdfs://chaoren1:9000/files/hello2"), TextInputFormat.class, MyMapper2.class);//job.setMapperClass(MyMapper.class);//不应该有这一行job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(MyGenericWritable.class);job.setReducerClass(MyReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));job.waitForCompletion(true);}public static class MyMapper extends Mapper<Text, Text, Text, MyGenericWritable>{//解析源文件会产生2个键值对，分别是<0,hello you><10,hello me>；所以map函数会被调用2次protected void map(Text key, Text value, org.apache.hadoop.mapreduce.Mapper<Text,Text,Text,MyGenericWritable>.Context context) throws java.io.IOException ,InterruptedException {context.write(key, new MyGenericWritable(new LongWritable(1)));context.write(value, new MyGenericWritable(new LongWritable(1)));};}public static class MyMapper2 extends Mapper<LongWritable, Text, Text, MyGenericWritable>{//解析源文件会产生2个键值对，分别是<0,hello you><10,hello me>；所以map函数会被调用2次protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper<LongWritable,Text,Text,MyGenericWritable>.Context context) throws java.io.IOException ,InterruptedException {//为什么要把hadoop类型转换为java类型？final String line = value.toString();final String[] splited = line.split(",");//产生的<k,v>对少了for (String word : splited) {//在for循环体内，临时变量word的出现次数是常量1final Text text = new Text("1");context.write(new Text(word), new MyGenericWritable(text));}};}//map产生的<k,v>分发到reduce的过程称作shufflepublic static class MyReducer extends Reducer<Text, MyGenericWritable, Text, LongWritable>{//每一组调用一次reduce函数，一共调用了3次//分组的数量与reduce函数的调用次数有什么关系？//reduce函数的调用次数与输出的<k,v>的数量有什么关系？protected void reduce(Text key, java.lang.Iterable<MyGenericWritable> values, org.apache.hadoop.mapreduce.Reducer<Text,MyGenericWritable,Text,LongWritable>.Context context) throws java.io.IOException ,InterruptedException {//count表示单词key在整个文件中的出现次数long count = 0L;for (MyGenericWritable times : values) {final Writable writable = times.get();if(writable instanceof LongWritable) {count += ((LongWritable)writable).get();}if(writable instanceof Text) {count += Long.parseLong(((Text)writable).toString());}}context.write(key, new LongWritable(count));};}public static class MyGenericWritable extends GenericWritable{public MyGenericWritable() {}public MyGenericWritable(Text text) {super.set(text);}public MyGenericWritable(LongWritable longWritable) {super.set(longWritable);}@Overrideprotected Class<? extends Writable>[] getTypes() {return new Class[] {LongWritable.class, Text.class};   //返回的数据类型}}}