hadoop程序MapReduce之SingletonTableJoin
需求:单表关联问题。从文件中孩子和父母的关系挖掘出孙子和爷奶关系
样板:child-parent.txt
xiaoming daxiong
daxiong alice
daxiong jack
输出:xiaoming alice
xiaoming jack
分析设计:
mapper部分设计:
1、<k1,k1>k1代表:一行数据的编号位置,v1代表:一行数据。
2、左表:<k2,v2>k2代表:parent名字,v2代表:(1,child名字),此处1:代表左表标志。
3、右表:<k3,v3>k3代表:child名字,v3代表:(2,parent名字),此处2:代表右表标志。
reduce部分设计:
4、<k4,v4>k4代表:相同的key,v4代表:list<String>
5、求笛卡尔积<k5,v5>:k5代表:grandChild名字,v5代表:grandParent名字。
程序部分:
SingletonTableJoinMapper类
package com.cn.singletonTableJoin; import java.io.IOException; import java.util.StringTokenizer; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class SingletonTableJoinMapper extends Mapper<Object, Text, Text, Text> { @Override protected void map(Object key, Text value, Mapper<Object, Text, Text, Text>.Context context) throws IOException, InterruptedException { String childName = new String(); String parentName = new String(); String relationType = new String(); String[] values=new String[2]; int i = 0; StringTokenizer itr = new StringTokenizer(value.toString()); while(itr.hasMoreElements()){ values[i] = itr.nextToken(); i++; } if(values[0].compareTo("child") != 0){ childName = values[0]; parentName = values[1]; relationType = "1"; context.write(new Text(parentName), new Text(relationType+" "+childName)); relationType = "2"; context.write(new Text(childName), new Text(relationType+" "+parentName)); } } }
SingletonTableJoinReduce类:
package com.cn.singletonTableJoin; import java.io.IOException; import java.util.ArrayList; import java.util.Iterator; import java.util.List; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class SingletonTableJoinReduce extends Reducer<Text, Text, Text, Text> { @Override protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context) throws IOException, InterruptedException { List<String> grandChild = new ArrayList<String>(); List<String> grandParent = new ArrayList<String>(); Iterator<Text> itr = values.iterator(); while(itr.hasNext()){ String[] record = itr.next().toString().split(" "); if(0 == record[0].length()){ continue; } if("1".equals(record[0])){ grandChild.add(record[1]); }else if("2".equals(record[0])){ grandParent.add(record[1]); } } if(0 != grandChild.size() && 0 != grandParent.size()){ for(String grandchild : grandChild){ for(String grandparent : grandParent){ context.write(new Text(grandchild), new Text(grandparent)); } } } } }
SingletonTableJoin类
package com.cn.singletonTableJoin; 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.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; /** * 单表关联 * @author root * */ public class SingletonTableJoin { public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: SingletonTableJoin "); System.exit(2); } //创建一个job Job job = new Job(conf, "SingletonTableJoin"); job.setJarByClass(SingletonTableJoin.class); //设置文件的输入输出路径 FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); //设置mapper和reduce处理类 job.setMapperClass(SingletonTableJoinMapper.class); job.setReducerClass(SingletonTableJoinReduce.class); //设置输出key-value数据类型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); //提交作业并等待它完成 System.exit(job.waitForCompletion(true) ? 0 : 1); } }
把总结当成一种习惯。