mapreduce怎么用
时间: 2023-12-01 17:40:05 浏览: 27
MapReduce是一种分布式计算模型,用于处理大规模数据集。它将大规模数据集分成小的数据块,然后在集群中分配给不同的计算节点进行处理。MapReduce由两个主要的阶段组成:Map和Reduce。Map阶段将输入数据映射为键值对,Reduce阶段将相同键的值聚合在一起。在Hadoop中,可以使用Java编写MapReduce程序。下面是一个简单的MapReduce程序的示例:
```java
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word, one);
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}
public static void main(String[] args) throws Exception {
Job job = new Job();
job.setJarByClass(WordCount.class);
job.setJobName("wordcount");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
```
这个程序实现了一个简单的单词计数功能。你可以将这个程序打包成一个jar文件,并在Hadoop集群上运行它。具体的使用方法可以参考Hadoop的官方文档。
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