Hadoop之——自定义分组比较器实现分组功能

Hadoop之——自定义分组比较器实现分组功能

不多说，直接上代码,大家都懂得

1、Mapper类的实现

/** * Mapper类的实现 * @author liuyazhuang * */ static class MyMapper extends Mapper{ protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper.Context context) throws java.io.IOException ,InterruptedException { final String[] splited = value.toString().split("\t"); final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1])); final LongWritable v2 = new LongWritable(Long.parseLong(splited[1])); context.write(k2, v2); }; }

2、Reducer类的实现

/** * Reducer类的实现 * @author liuyazhuang * */ static class MyReducer extends Reducer{ protected void reduce(NewK2 k2, java.lang.Iterable v2s, org.apache.hadoop.mapreduce.Reducer.Context context) throws java.io.IOException ,InterruptedException { long min = Long.MAX_VALUE; for (LongWritable v2 : v2s) { if(v2.get()

3、WritableComparable类的实现

/** * 问：为什么实现该类？ * 答：因为原来的v2不能参与排序，把原来的k2和v2封装到一个类中，作为新的k2 * @author liuyazhuang */ static class NewK2 implements WritableComparable{ Long first; Long second; public NewK2(){} public NewK2(long first, long second){ this.first = first; this.second = second; } @Override public void readFields(DataInput in) throws IOException { this.first = in.readLong(); this.second = in.readLong(); } @Override public void write(DataOutput out) throws IOException { out.writeLong(first); out.writeLong(second); } /** * 当k2进行排序时，会调用该方法. * 当第一列不同时，升序；当第一列相同时，第二列升序 * @author liuyazhuang */ @Override public int compareTo(NewK2 o) { final long minus = this.first - o.first; if(minus !=0){ return (int)minus; } return (int)(this.second - o.second); } @Override public int hashCode() { return this.first.hashCode()+this.second.hashCode(); } @Override public boolean equals(Object obj) { if(!(obj instanceof NewK2)){ return false; } NewK2 oK2 = (NewK2)obj; return (this.first==oK2.first)&&(this.second==oK2.second); } }

4、分组比较器RawComparator的实现

/** * 自定义分组比较器 * 问：为什么自定义该类？ * 答：业务要求分组是按照第一列分组，但是NewK2的比较规则决定了不能按照第一列分。只能自定义分组比较器。 * @author liuyazhuang * */ static class MyGroupingComparator implements RawComparator{ @Override public int compare(NewK2 o1, NewK2 o2) { return (int)(o1.first - o2.first); } /** * @param arg0 表示第一个参与比较的字节数组 * @param arg1 表示第一个参与比较的字节数组的起始位置 * @param arg2 表示第一个参与比较的字节数组的偏移量 * * @param arg3 表示第二个参与比较的字节数组 * @param arg4 表示第二个参与比较的字节数组的起始位置 * @param arg5 表示第二个参与比较的字节数组的偏移量 */ @Override public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3, int arg4, int arg5) { return WritableComparator.compareBytes(arg0, arg1, 8, arg3, arg4, 8); } }

5、程序入口Main

public static void main(String[] args) throws Exception{ final Configuration configuration = new Configuration(); final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration); if(fileSystem.exists(new Path(OUT_PATH))){ fileSystem.delete(new Path(OUT_PATH), true); } final Job job = new Job(configuration, GroupApp.class.getSimpleName()); //1.1 指定输入文件路径 FileInputFormat.setInputPaths(job, INPUT_PATH); //指定哪个类用来格式化输入文件 job.setInputFormatClass(TextInputFormat.class); //1.2指定自定义的Mapper类 job.setMapperClass(MyMapper.class); //指定输出的类型 job.setMapOutputKeyClass(NewK2.class); job.setMapOutputValueClass(LongWritable.class); //1.3 指定分区类 job.setPartitionerClass(HashPartitioner.class); job.setNumReduceTasks(1); //1.4 TODO 排序、分区 job.setGroupingComparatorClass(MyGroupingComparator.class); //1.5 TODO （可选）合并 //2.2 指定自定义的reduce类 job.setReducerClass(MyReducer.class); //指定输出的类型 job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(LongWritable.class); //2.3 指定输出到哪里 FileOutputFormat.setOutputPath(job, new Path(OUT_PATH)); //设定输出文件的格式化类 job.setOutputFormatClass(TextOutputFormat.class); //把代码提交给JobTracker执行 job.waitForCompletion(true); }

6、完整代码

package com.lyz.hadoop.group;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.RawComparator;import org.apache.hadoop.io.Text;import org.apache.hadoop.io.WritableComparable;import org.apache.hadoop.io.WritableComparator;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;import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;/** * Hadoop实现分组操作 * 当第一列不同时，升序；当第一列相同时，第二列升序 * @author liuyazhuang * */public class GroupApp { //要统计的文件位置 static final String INPUT_PATH = "hdfs://liuyazhuang:9000/input"; //统计结果输出的位置 static final String OUT_PATH = "hdfs://liuyazhuang:9000/out"; public static void main(String[] args) throws Exception{ final Configuration configuration = new Configuration(); final FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), configuration); if(fileSystem.exists(new Path(OUT_PATH))){ fileSystem.delete(new Path(OUT_PATH), true); } final Job job = new Job(configuration, GroupApp.class.getSimpleName()); //1.1 指定输入文件路径 FileInputFormat.setInputPaths(job, INPUT_PATH); //指定哪个类用来格式化输入文件 job.setInputFormatClass(TextInputFormat.class); //1.2指定自定义的Mapper类 job.setMapperClass(MyMapper.class); //指定输出的类型 job.setMapOutputKeyClass(NewK2.class); job.setMapOutputValueClass(LongWritable.class); //1.3 指定分区类 job.setPartitionerClass(HashPartitioner.class); job.setNumReduceTasks(1); //1.4 TODO 排序、分区 job.setGroupingComparatorClass(MyGroupingComparator.class); //1.5 TODO （可选）合并 //2.2 指定自定义的reduce类 job.setReducerClass(MyReducer.class); //指定输出的类型 job.setOutputKeyClass(LongWritable.class); job.setOutputValueClass(LongWritable.class); //2.3 指定输出到哪里 FileOutputFormat.setOutputPath(job, new Path(OUT_PATH)); //设定输出文件的格式化类 job.setOutputFormatClass(TextOutputFormat.class); //把代码提交给JobTracker执行 job.waitForCompletion(true); } /** * Mapper类的实现 * @author liuyazhuang * */ static class MyMapper extends Mapper{ protected void map(LongWritable key, Text value, org.apache.hadoop.mapreduce.Mapper.Context context) throws java.io.IOException ,InterruptedException { final String[] splited = value.toString().split("\t"); final NewK2 k2 = new NewK2(Long.parseLong(splited[0]), Long.parseLong(splited[1])); final LongWritable v2 = new LongWritable(Long.parseLong(splited[1])); context.write(k2, v2); }; } /** * Reducer类的实现 * @author liuyazhuang * */ static class MyReducer extends Reducer{ protected void reduce(NewK2 k2, java.lang.Iterable v2s, org.apache.hadoop.mapreduce.Reducer.Context context) throws java.io.IOException ,InterruptedException { long min = Long.MAX_VALUE; for (LongWritable v2 : v2s) { if(v2.get(){ Long first; Long second; public NewK2(){} public NewK2(long first, long second){ this.first = first; this.second = second; } @Override public void readFields(DataInput in) throws IOException { this.first = in.readLong(); this.second = in.readLong(); } @Override public void write(DataOutput out) throws IOException { out.writeLong(first); out.writeLong(second); } /** * 当k2进行排序时，会调用该方法. * 当第一列不同时，升序；当第一列相同时，第二列升序 * @author liuyazhuang */ @Override public int compareTo(NewK2 o) { final long minus = this.first - o.first; if(minus !=0){ return (int)minus; } return (int)(this.second - o.second); } @Override public int hashCode() { return this.first.hashCode()+this.second.hashCode(); } @Override public boolean equals(Object obj) { if(!(obj instanceof NewK2)){ return false; } NewK2 oK2 = (NewK2)obj; return (this.first==oK2.first)&&(this.second==oK2.second); } } /** * 自定义分组比较器 * 问：为什么自定义该类？ * 答：业务要求分组是按照第一列分组，但是NewK2的比较规则决定了不能按照第一列分。只能自定义分组比较器。 * @author liuyazhuang * */ static class MyGroupingComparator implements RawComparator{ @Override public int compare(NewK2 o1, NewK2 o2) { return (int)(o1.first - o2.first); } /** * @param arg0 表示第一个参与比较的字节数组 * @param arg1 表示第一个参与比较的字节数组的起始位置 * @param arg2 表示第一个参与比较的字节数组的偏移量 * * @param arg3 表示第二个参与比较的字节数组 * @param arg4 表示第二个参与比较的字节数组的起始位置 * @param arg5 表示第二个参与比较的字节数组的偏移量 */ @Override public int compare(byte[] arg0, int arg1, int arg2, byte[] arg3, int arg4, int arg5) { return WritableComparator.compareBytes(arg0, arg1, 8, arg3, arg4, 8); } }}

版权声明：本文内容由网络用户投稿，版权归原作者所有，本站不拥有其著作权，亦不承担相应法律责任。如果您发现本站中有涉嫌抄袭或描述失实的内容，请联系我们jiasou666@gmail.com 处理，核实后本网站将在24小时内删除侵权内容。