我在主要...
job.setMapperClass(AverageIntMapper.class);
job.setCombinerClass(AverageIntCombiner.class);
job.setReducerClass(AverageIntReducer.class);
Combiner 有不同的代码,但 Combiner 被完全忽略,因为 Reducer 使用的输出是 Mapper 的输出。
我知道可能不会使用 Combiner,但我认为当 Combiner 与 Reducer 相同时就是这种情况。我真的不明白能够创建自定义 Combiner 的意义,但系统仍然可以跳过它的使用。
如果这不应该发生,那么没有使用 Combiner 的原因可能是什么?
代码...
import java.io.IOException;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
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 AverageInt {
public static class AverageIntMapper extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String n_string = value.toString();
context.write(new Text("Value"), new Text(n_string));
}
}
public static class AverageIntCombiner extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
int count = 0;
for(IntWritable value : values) {
int temp = Integer.parseInt(value.toString());
sum += value.get();
count += 1;
}
String sum_count = Integer.toString(sum) + "," + Integer.toString(count);
context.write(key, new Text(sum_count));
}
}
public static class AverageIntReducer extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
int total = 0;
int count = 0;
for(Text value : values) {
String temp = value.toString();
String[] split = temp.split(",");
total += Integer.parseInt(split[0]);
count += Integer.parseInt(split[1]);
}
Double average = (double)total/count;
context.write(key, new Text(average.toString()));
}
}
public static void main(String[] args) throws Exception {
if(args.length != 2) {
System.err.println("Usage: AverageInt <input path> <output path>");
System.exit(-1);
}
Job job = new Job();
job.setJarByClass(AverageInt.class);
job.setJobName("Average");
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(AverageIntMapper.class);
job.setCombinerClass(AverageIntCombiner.class);
job.setReducerClass(AverageIntReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
最佳答案
如果您查看映射器发出的内容:
public void map(LongWritable key, Text value, Context context)
它发送了两个 Text
对象,但是当您正确声明组合器类本身时,reduce 方法具有:
public void reduce(Text key, Iterable<IntWritable> values, Context context)
应该是:
public void reduce(Text key, Iterable<Text> values, Context context)
关于hadoop - 使用自定义组合器......它可能会被忽略?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/46386364/