网站首页代码,深圳小型网站建设,网络营销推广方法视频,怎么做公司网站需要什么数据排序是许多实际任务执行时要完成的第一项工作#xff0c;比如学生成绩评比、数据建立索引等。这个实例和数据去重类似#xff0c;都是先对原始数据进行初步处理#xff0c;为进一步的数据操作打好基础
1 实例描述
对输入文件中数据进行排序。输入文件中的…数据排序是许多实际任务执行时要完成的第一项工作比如学生成绩评比、数据建立索引等。这个实例和数据去重类似都是先对原始数据进行初步处理为进一步的数据操作打好基础
1 实例描述
对输入文件中数据进行排序。输入文件中的每行内容均为一个数字即一个数据。要求在输出中每行有两个间隔的数字其中第一个代表原始数据在原始数据集中的位次第二个代表原始数据。 样例输入如下所示 1file1
2
32
654
32
15
756
65223
2file2
5956
22
650
923file3
26
54
6期望输出
1 2
2 6
3 15
4 22
5 26
6 32
7 32
8 54
9 92
10 650
11 654
12 756
13 5956
14 652232 问题分析
这个实例仅仅要求对输入数据进行排序
分析 MapReduce过程中就有排序,它的默认排序规则按照key值进行排序的如果key为封装int的IntWritable类型那么MapReduce按照数字大小对key排序如果key为封装为String的Text类型那么MapReduce按照字典顺序对字符串排序。 使用封装int的IntWritable型数据结构了。也就是在map中将读入的数据转化成IntWritable型然后作为key值输出value任意。reduce拿到keyvalue-list之后将输入的key作为value输出并根据value-list中元素的个数决定输出的次数。输出的key即代码中的linenum是一个全局变量它统计当前key的位次。需要注意的是这个程序中没有配置Combiner也就是在MapReduce过程中不使用Combiner。这主要是因为使用map和reduce就已经能够完成任务了。 3.实现步骤
在map中将读入的数据转化成IntWritable型然后作为key值输出value任意。 reduce拿到keyvalue-list之后将输入的key作为value输出并根据value-list中元素的个数决定输出的次数输出的key是一个全局变量它统计当前key的位次 4.关键代码
正序
package com.mk.mapreduce;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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 java.io.IOException;
import java.net.URI;public class Sort {public static class SortMapper extends MapperLongWritable, Text, IntWritable, IntWritable {Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {IntWritable v new IntWritable(Integer.parseInt(value.toString().trim()));context.write(v, new IntWritable(1));}}public static class SortReducer extends ReducerIntWritable, IntWritable, IntWritable, IntWritable {int count 1;Overrideprotected void reduce(IntWritable key, IterableIntWritable values, Context context) throws IOException, InterruptedException {for (IntWritable v: values) {context.write(new IntWritable(count ), key);}}}public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {String uri hdfs://192.168.150.128:9000;String input /sort/input;String output /sort/output;Configuration conf new Configuration();if(System.getProperty(os.name).toLowerCase().contains(win))conf.set(mapreduce.app-submission.cross-platform,true);FileSystem fileSystem FileSystem.get(URI.create(uri), conf);Path path new Path(output);fileSystem.delete(path,true);Job job new Job(conf,Sort);job.setJar(./out/artifacts/hadoop_test_jar/hadoop-test.jar);job.setJarByClass(Sort.class);job.setMapperClass(SortMapper.class);job.setReducerClass(SortReducer.class);job.setMapOutputKeyClass(IntWritable.class);job.setMapOutputValueClass(IntWritable.class);job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPaths(job, uri input);FileOutputFormat.setOutputPath(job, new Path(uri output));boolean ret job.waitForCompletion(true);System.out.println(job.getJobName() ----- ret);}
}逆序
package com.mk.mapreduce;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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 java.io.IOException;
import java.net.URI;public class Sort {public static class SortMapper extends MapperLongWritable, Text, IntWritable, IntWritable {Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {IntWritable v new IntWritable(Integer.parseInt(value.toString().trim()));context.write(v, new IntWritable(1));}}public static class SortReducer extends ReducerIntWritable, IntWritable, IntWritable, IntWritable {int count 1;Overrideprotected void reduce(IntWritable key, IterableIntWritable values, Context context) throws IOException, InterruptedException {for (IntWritable v: values) {context.write(new IntWritable(count ), key);}}}public static class SortComparator implements RawComparatorIntWritable {Overridepublic int compare(byte[] b1, int s1, int l1, byte[] b2, int s2, int l2) {return IntWritable.Comparator.compareBytes(b2, s2, l2, b1, s1, l1);}Overridepublic int compare(IntWritable o1, IntWritable o2) {return o2.get() - o1.get();}}public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {String uri hdfs://192.168.150.128:9000;String input /sort/input;String output /sort/output;Configuration conf new Configuration();if(System.getProperty(os.name).toLowerCase().contains(win))conf.set(mapreduce.app-submission.cross-platform,true);FileSystem fileSystem FileSystem.get(URI.create(uri), conf);Path path new Path(output);fileSystem.delete(path,true);Job job new Job(conf,Sort);job.setJar(./out/artifacts/hadoop_test_jar/hadoop-test.jar);job.setJarByClass(Sort.class);job.setMapperClass(SortMapper.class);job.setReducerClass(SortReducer.class);job.setMapOutputKeyClass(IntWritable.class);job.setMapOutputValueClass(IntWritable.class);job.setOutputKeyClass(IntWritable.class);job.setOutputValueClass(IntWritable.class);FileInputFormat.addInputPaths(job, uri input);FileOutputFormat.setOutputPath(job, new Path(uri output));job.setSortComparatorClass(SortComparator.class);boolean ret job.waitForCompletion(true);System.out.println(job.getJobName() ----- ret);}
}
