如何重新排列wordcount hadoop输出结果并按值排序
How to re-arrange wordcount hadoop output result and sort them by value
我使用下面的代码得到输出结果,如(键,值)
Apple 12
Bee 345
Cat 123
我想要的是按值 ( 345 ) 降序排序并将它们放在键 ( Value , Key ) 之前
345 Bee
123 Cat
12 Apple
我发现有一种叫做 "secondary sorted" 的东西不会说谎,但我迷路了 - 我试图改变.. context.write(key, result);
但惨遭失败。我是 Hadoop 的新手,不确定如何开始解决这个问题。任何建议将不胜感激。我需要更改哪个功能?或者我需要修改哪个 class ?
这是我的 classes :
package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.StringTokenizer;
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 WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
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: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
您已经能够正确统计字数了。
您将需要第二个 map only 作业来执行降序排序和键值交换的第二个要求
- 使用 DecreasingComparator 作为排序比较器
- 使用 InverseMapper 交换键和值
- 使用 Identity Reducer,即 Reducer.class - 如果使用 Identity Reducer,则不会发生聚合(因为每个值都是针对键单独输出的)
- 将 reduce 任务数设置为 1 或使用 TotalOderPartitioner
我使用下面的代码得到输出结果,如(键,值)
Apple 12
Bee 345
Cat 123
我想要的是按值 ( 345 ) 降序排序并将它们放在键 ( Value , Key ) 之前
345 Bee
123 Cat
12 Apple
我发现有一种叫做 "secondary sorted" 的东西不会说谎,但我迷路了 - 我试图改变.. context.write(key, result);
但惨遭失败。我是 Hadoop 的新手,不确定如何开始解决这个问题。任何建议将不胜感激。我需要更改哪个功能?或者我需要修改哪个 class ?
这是我的 classes :
package org.apache.hadoop.examples;
import java.io.IOException;
import java.util.StringTokenizer;
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 WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
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: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
您已经能够正确统计字数了。
您将需要第二个 map only 作业来执行降序排序和键值交换的第二个要求
- 使用 DecreasingComparator 作为排序比较器
- 使用 InverseMapper 交换键和值
- 使用 Identity Reducer,即 Reducer.class - 如果使用 Identity Reducer,则不会发生聚合(因为每个值都是针对键单独输出的)
- 将 reduce 任务数设置为 1 或使用 TotalOderPartitioner