MapReduce基础编程

快速解法

跟着茂神的节奏，直接打印

合并去重

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 Merge {

	/**
	 * @param args
	 * 对A,B两个文件进行合并，并剔除其中重复的内容，得到一个新的输出文件C
	 */
	//在这重载map函数，直接将输入中的value复制到输出数据的key上 注意在map方法中要抛出异常：throws IOException,InterruptedException
	/********** Begin **********/
    public static class Map extends Mapper<Object, Text, Text, Text>{
        private static Text text = new Text();
        public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
            text = value;
            context.write(text, new Text(""));
        }
    }
	/********** End **********/

	
	
	//在这重载reduce函数，直接将输入中的key复制到输出数据的key上  注意在reduce方法上要抛出异常：throws IOException,InterruptedException
	/********** Begin **********/
    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(""));
        }
    }     
	/********** End **********/




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

		// TODO Auto-generated method stub
		Configuration conf = new Configuration();
		conf.set("fs.default.name","hdfs://localhost:9000");
		String[] otherArgs = new String[]{"input","output"}; /* 直接设置输入参数 */
		if (otherArgs.length != 2) {
			System.err.println("Usage: wordcount  ");
			System.exit(2);
			}
		Job job = Job.getInstance(conf,"Merge and duplicate removal");
		job.setJarByClass(Merge.class);
		job.setMapperClass(Map.class);
		job.setCombinerClass(Reduce.class);
		job.setReducerClass(Reduce.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}

}

整合排序

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.Partitioner;
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 MergeSort {

	/**
	 * @param args
	 * 输入多个文件，每个文件中的每行内容均为一个整数
	 * 输出到一个新的文件中，输出的数据格式为每行两个整数，第一个数字为第二个整数的排序位次，第二个整数为原待排列的整数
	 */
	//map函数读取输入中的value，将其转化成IntWritable类型，最后作为输出key
	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{
			/********** Begin **********/
            String text = value.toString();
            data.set(Integer.parseInt(text));
            context.write(data, new IntWritable(1));
			/********** End **********/

		}
	}

	//reduce函数将map输入的key复制到输出的value上，然后根据输入的value-list中元素的个数决定key的输出次数,定义一个全局变量line_num来代表key的位次
	public static class Reduce extends Reducer<IntWritable, IntWritable, IntWritable, IntWritable>{
		private static IntWritable line_num = new IntWritable(1);

		public void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException,InterruptedException{
			/********** Begin **********/
            for(IntWritable val : values){
                context.write(line_num, key);
                line_num = new IntWritable(line_num.get() + 1);
            }
			/********** End **********/
		}
	}

	//自定义Partition函数，此函数根据输入数据的最大值和MapReduce框架中Partition的数量获取将输入数据按照大小分块的边界，然后根据输入数值和边界的关系返回对应的Partiton ID
	public static class Partition extends Partitioner<IntWritable, IntWritable>{
		public int getPartition(IntWritable key, IntWritable value, int num_Partition){
			/********** Begin **********/
			int Maxnumber = 65223;//int型的最大数值
            int bound = Maxnumber/num_Partition+1;
            int keynumber = key.get();
            for (int i = 0; i<num_Partition; i++){
                if(keynumber<bound * (i+1) && keynumber>=bound * i){
                    return i;
                }
            }
            return -1;
			/********** End **********/
		}
	}

	public static void main(String[] args) throws Exception{
		// TODO Auto-generated method stub
		Configuration conf = new Configuration();
		conf.set("fs.default.name","hdfs://localhost:9000");
		String[] otherArgs = new String[]{"input","output"}; /* 直接设置输入参数 */
		if (otherArgs.length != 2) {
			System.err.println("Usage: wordcount  ");
			System.exit(2);
			}
		Job job = Job.getInstance(conf,"Merge and Sort");
		job.setJarByClass(MergeSort.class);
		job.setMapperClass(Map.class);
		job.setReducerClass(Reduce.class);
		job.setPartitionerClass(Partition.class);
		job.setOutputKeyClass(IntWritable.class);
		job.setOutputValueClass(IntWritable.class);
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);

	}

}

信息挖掘

import java.io.IOException;
import java.util.*;

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 simple_data_mining {
	public static int time = 0;
	/**
	 * @param args
	 * 输入一个child-parent的表格
	 * 输出一个体现grandchild-grandparent关系的表格
	 */
	//Map将输入文件按照空格分割成child和parent，然后正序输出一次作为右表，反序输出一次作为左表，需要注意的是在输出的value中必须加上左右表区别标志
	public static class Map extends Mapper<Object, Text, Text, Text>{
		public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
			/********** Begin **********/
            String child_name = new String();
            String parent_name = new String();
            String relation_type = new String();
            String line = value.toString();
            int i = 0;
            while(line.charAt(i) != ' '){
                i++;
            }
            String[] values = {line.substring(0,i),line.substring(i+1)};
            if(values[0].compareTo("child") != 0){
                child_name = values[0];
                parent_name = values[1];
                relation_type = "1";//左右表区分标志
                context.write(new Text(values[1]), new Text(relation_type+"+"+child_name+"+"+parent_name));
                //左表
                relation_type = "2";
                context.write(new Text(values[0]), new Text(relation_type+"+"+child_name+"+"+parent_name));
                //右表
            }
			/********** End **********/
		}
	}

	public static class Reduce extends Reducer<Text, Text, Text, Text>{
		public void reduce(Text key, Iterable<Text> values,Context context) throws IOException,InterruptedException{
				/********** Begin **********/
                if(time == 0){   //输出表头
                context.write(new Text("grand_child"), new Text("grand_parent"));
                time++;
                }
                int grand_child_num = 0;
                String grand_child[] = new String[10];
                int grand_parent_num = 0;
                String grand_parent[]= new String[10];
                Iterator ite = values.iterator();
                while(ite.hasNext()){
                    String record = ite.next().toString();
                    int len = record.length();
                    int i = 2;
                    if(len == 0) continue;
                    char relation_type = record.charAt(0);
                    String child_name = new String();
                    String parent_name = new String();
                    //获取value-list中value的child
                    while(record.charAt(i) != '+'){
                        child_name = child_name + record.charAt(i);
                        i++;
                    }
                    i=i+1;
                    //获取value-list中value的parent
                    while(i<len){
                        parent_name = parent_name+record.charAt(i);
                        i++;
                    }
                    //左表，取出child放入grand_child
                    if(relation_type == '1'){
                        grand_child[grand_child_num] = child_name;
                        grand_child_num++;
                    }
                    else{//右表，取出parent放入grand_parent
                        grand_parent[grand_parent_num] = parent_name;
                        grand_parent_num++;
                    }
                }
                if(grand_parent_num != 0 && grand_child_num != 0 ){
                    for(int m = 0;m<grand_child_num;m++){
                        for(int n=0;n<grand_parent_num;n++){
                            context.write(new Text(grand_child[m]), new Text(grand_parent[n]));
                            //输出结果
                        }
                    }
                }
				/********** End **********/	
		}
	}
	public static void main(String[] args) throws Exception{
		// TODO Auto-generated method stub
		Configuration conf = new Configuration();
		conf.set("fs.default.name","hdfs://localhost:9000");
		String[] otherArgs = new String[]{"input","output"}; /* 直接设置输入参数 */
		if (otherArgs.length != 2) {
			System.err.println("Usage: wordcount  ");
			System.exit(2);
			}
		Job job = Job.getInstance(conf,"Single table join");
		job.setJarByClass(simple_data_mining.class);
		job.setMapperClass(Map.class);
		job.setReducerClass(Reduce.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
		FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
		System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}