mapreduce main代码
时间: 2024-06-13 19:07:32 浏览: 25
根据提供的引用内容,我们可以了解到MapReduce是一种用于大规模数据处理的编程模型和算法。下面是一个MapReduce的主要代码示例:
```python
from mrjob.job import MRJob
class MRWordFrequencyCount(MRJob):
def mapper(self, _, line):
words = line.split()
for word in words:
yield word.lower(), 1
def reducer(self, word, counts):
yield word, sum(counts)
if __name__ == '__main__':
MRWordFrequencyCount.run()
```
这段代码实现了一个简单的单词计数功能。其中,mapper函数将输入的每一行文本拆分成单词,并将每个单词映射为一个键值对,其中键为单词的小写形式,值为1。reducer函数将相同键的值进行累加,最终输出每个单词出现的次数。
需要注意的是,这段代码是使用Python的mrjob库实现的,该库可以方便地在Hadoop集群上运行MapReduce作业。
相关问题
MapReduce排序代码
以下是一个基本的MapReduce排序代码示例:
Mapper函数:
```
public static class SortMapper extends Mapper<Object, Text, IntWritable, IntWritable>{
private IntWritable num = new IntWritable();
public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
num.set(Integer.parseInt(line));
context.write(num, new IntWritable(1));
}
}
```
Reducer函数:
```
public static class SortReducer extends Reducer<IntWritable,IntWritable,IntWritable,IntWritable> {
private IntWritable rank = new IntWritable(1);
public void reduce(IntWritable key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
for (IntWritable value : values) {
context.write(rank, key);
rank = new IntWritable(rank.get() + 1);
}
}
}
```
驱动函数:
```
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "sort");
job.setJarByClass(Sort.class);
job.setMapperClass(SortMapper.class);
job.setReducerClass(SortReducer.class);
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(IntWritable.class);
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
```
在这个示例中,Mapper函数将输入文件中的每一行转换为一个整数,并将其作为键输出。值设置为1,因为我们只关心键的数量。在Reducer函数中,我们循环遍历每个键值对,并将它们的排名作为键,原始键作为值输出。驱动函数设置了输入和输出路径,并启动了MapReduce作业。
mapreduce wordcount代码
以下是MapReduce中WordCount程序的示例代码:
```
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;
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();
Job job = Job.getInstance(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);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
```