以下是使用AWS Hadoop MapReduce解决Word Count Average问题的示例代码:
Mapper类:
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordCountMapper extends Mapper
Reducer类:
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable values, Context context)
throws IOException, InterruptedException {
int sum = 0;
int count = 0;
for (IntWritable value : values) {
sum += value.get();
count++;
}
int average = sum / count;
result.set(average);
context.write(key, result);
}
}
Driver类:
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCountAverage {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count average");
job.setJarByClass(WordCountAverage.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.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);
}
}
请注意,上述代码仅为示例,您需要根据您的实际需求进行适当的更改和配置。此示例假定输入文件中的每个单词以空格分隔,并且每个单词都位于单独的行中。