Map Reduce筛选记录

Question

我有一组记录，我只需要处理男性记录，在map reduce程序中，如果条件仅过滤男性记录，但使用下面的程序，则会给出零记录作为输出。

输入文件：

1，布兰登巴克纳，小时量，女性，525
2，吠陀霍普金斯，小时量，男性，633
3，齐亚Underwood的，扑热息痛，男性，980
4，奥斯汀迈耶，对乙酰氨基酚，女性，338
5，马拉希金斯，小时量，女性，153
6，西比尔克罗斯比，小时量，男性，193
7，泰勒罗萨莱斯，扑热息痛，男性，778
8，伊万黑尔， avil，女，454
9，阿利卡吉尔摩，对乙酰氨基酚，女，833
10，LEN伯吉斯，metacin，男，325

的MapReduce程序：

package org.samples.mapreduce.training; 

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.input.TextInputFormat; 
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; 
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; 


public class patientrxMR_filter { 

    public static class MapDemohadoop extends 
      Mapper<LongWritable, Text, Text, IntWritable> { 

     // setup , map, run, cleanup 

     public void map(LongWritable key, Text value, Context context) 
       throws IOException, InterruptedException { 
      String line = value.toString(); 
      String[] elements = line.split(","); 



String gender =elements[3]; 


if (gender == "male") { 

    Text tx = new Text(elements[2]); 
       int i = Integer.parseInt(elements[4]); 
       IntWritable it = new IntWritable(i); 
       context.write(tx, it); 
} 
     } 
    } 

    public static class Reduce extends 
      Reducer<Text, IntWritable, Text, IntWritable> { 

     // setup, reduce, run, cleanup 
     // innput - para [150,100] 
     public void reduce(Text key, Iterable<IntWritable> values, 
       Context context) throws IOException, InterruptedException { 
      int sum = 0; 
      for (IntWritable val : values) { 
       sum += val.get(); 
      } 
      context.write(key, new IntWritable(sum)); 
     } 
    } 

    public static void main(String[] args) throws Exception { 

     if (args.length != 2) { 
      System.err.println("Insufficient args"); 
      System.exit(-1); 
     } 
     Configuration conf = new Configuration(); 

     //conf.set("fs.default.name","hdfs://localhost:50000"); 
     conf.set("mapred.job.tracker", "hdfs://localhost:50001"); 

//  conf.set("DrugName", args[3]); 
     Job job = new Job(conf, "Drug Amount Spent"); 

     job.setJarByClass(patientrxMR_filter.class); // class conmtains mapper and 
               // reducer class 

     job.setMapOutputKeyClass(Text.class); // map output key class 
     job.setMapOutputValueClass(IntWritable.class);// map output value class 
     job.setOutputKeyClass(Text.class); // output key type in reducer 
     job.setOutputValueClass(IntWritable.class);// output value type in 
                // reducer 

     job.setMapperClass(MapDemohadoop.class); 
     job.setReducerClass(Reduce.class); 
     job.setNumReduceTasks(1); 
     job.setInputFormatClass(TextInputFormat.class); // default -- inputkey 
                 // type -- longwritable 
                 // : valuetype is text 
     job.setOutputFormatClass(TextOutputFormat.class); 



     FileInputFormat.addInputPath(job, new Path(args[0])); 
     FileOutputFormat.setOutputPath(job, new Path(args[1])); 

     job.waitForCompletion(true); 

    } 

} 

Answer 1

public void map(LongWritable key, Text value, Context context) 
throws IOException, InterruptedException { 
String line = value.toString(); 
String[] elements = line.split(","); 

Hadoop是使用分布式文件系统，在“字符串line = value.toString（）;“ 行是具有偏移（键）的块中的文件内容。在这种情况下，该行会加载整个测试文件，该文件显然可以放入一个块中，而不是像预期的那样放入文件中的每一行。

Answer 2

if (gender == "male") 

此行不为平等的检查工作，在Java中使用PLS的Object.Equals（）

i.e 
if (gender.equals("male"))