数据去重,只是让出现的数据仅一次,所以在reduce阶段key作为输入,而对于values-in没有要求,即输入的key直接作为输出的key,并将value置空。具体步骤类似于wordcount:
Tip:输入输出路径配置。
import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; 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; import org.apache.hadoop.util.GenericOptionsParser; public class Dedup { /** * @param XD */ public static class Map extends Mapper<Object,Text,Text,Text>{ private static Text line = new Text(); //map function public void map(Object key,Text value,Context context) throws IOException, InterruptedException{ line = value; context.write(line, new Text("")); } } public static class Reduce extends Reducer<Text,Text,Text,Text>{ public void reduce(Text key,Iterable<Text>values,Context context) throws IOException, InterruptedException{ context.write(key, new Text("")); } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { // TODO Auto-generated method stub //初始化配置 Configuration conf = new Configuration(); /*类比与之前默认的args,只是在程序中实现配置,这样不必去eclipse的arguments属性添加参数, **但是认为作用一样根据个人喜好设置,如下图所示: */ //设置输入输出路径 String[] ioArgs = new String[]{"hdfs://localhost:9000/home/xd/hadoop_tmp/DedupIn", "hdfs://localhost:9000/home/xd/hadoop_tmp/DedupOut"}; String[] otherArgs = new GenericOptionsParser(conf,ioArgs).getRemainingArgs(); if(otherArgs.length!=2){ System.err.println("Usage:Data Deduplication <in> <out>"); System.exit(2); } //设置作业 Job job = new Job(conf,"Dedup Job"); job.setJarByClass(Dedup.class); //设置处理map,combine,reduce的类 job.setMapperClass(Map.class); job.setCombinerClass(Reduce.class); job.setReducerClass(Reduce.class); //设置输入输出格式的处理 job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); //设定路径 FileInputFormat.addInputPath(job,new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job,new Path(otherArgs[1])); /* * 对应于自动的寻找路径 * FileInputFormat.addInputPath(job,new Path(args[0])); * FileOutputFormat.setOutputPath(job,new Path(args[1])); * */ job.waitForCompletion(true); //打印相关信息 System.out.println("任务名称: "+job.getJobName()); System.out.println("任务成功: "+(job.isSuccessful()?"Yes":"No")); } }
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.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; import org.apache.hadoop.util.GenericOptionsParser; public class DataSort { /** * @param XD */ public static class Map extends Mapper<Object,Text,IntWritable,IntWritable>{ private static IntWritable data = new IntWritable(); public void map(Object key,Text value,Context context) throws IOException, InterruptedException{ String line = value.toString(); data.set(Integer.parseInt(line)); context.write(data, new IntWritable(1)); } } public static class Reduce extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable>{ private static IntWritable linenum = new IntWritable(1); public void reduce(IntWritable key,Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{ for(IntWritable val:values){ context.write(linenum,key); linenum = new IntWritable(linenum.get()+1); } } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { // TODO Auto-generated method stub //初始化配置 Configuration conf = new Configuration(); /*类比与之前默认的args,只是在程序中实现配置,这样不必去eclipse的arguments属性添加参数, **但是认为作用一样根据个人喜好设置,如下图所示: */ //设置输入输出路径 String[] ioArgs = new String[]{"hdfs://localhost:9000/home/xd/hadoop_tmp/Sort_in", "hdfs://localhost:9000/home/xd/hadoop_tmp/Sort_out"}; String[] otherArgs = new GenericOptionsParser(conf,ioArgs).getRemainingArgs(); if(otherArgs.length!=2){ System.err.println("Usage:Data Deduplication <in> <out>"); System.exit(2); } //设置作业 Job job = new Job(conf,"Datasort Job"); job.setJarByClass(DataSort.class); //设置处理map,reduce的类 job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); //设置输入输出格式的处理 job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(IntWritable.class); //设定路径 FileInputFormat.addInputPath(job,new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job,new Path(otherArgs[1])); /* * 对应于自动的寻找路径 * FileInputFormat.addInputPath(job,new Path(args[0])); * FileOutputFormat.setOutputPath(job,new Path(args[1])); * */ job.waitForCompletion(true); //打印相关信息 System.out.println("任务名称: "+job.getJobName()); System.out.println("任务成功: "+(job.isSuccessful()?"Yes":"No")); } }
Hadoop mapreduce 数据去重 数据排序小例子,布布扣,bubuko.com
原文地址:http://blog.csdn.net/xd_122/article/details/38684605