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更多如何用Hadoop计算平均值
2019-03-28 12:59|来源: 网络
如何用Hadoop计算平均值
数据
data.txt
a 2
a 3
a 4
b 5
b 6
b 7
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.DoubleWritable;
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 Average {
public static class TokenizerMapper extends
Mapper<Object, Text, Text, Text> {
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());
if (itr.hasMoreTokens()) {
context.write(word, new Text(itr.nextToken() + ",1"));
}
}
}
}
static class AverageCombine extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
int sum = 0, cnt = 0;
for (Text val : values) {
String[] s1 = val.toString().split(",");
sum += Integer.parseInt(s1[0]);
cnt += Integer.parseInt(s1[1]);
}
String s;
System.out.println("Combine" + (s = new String(sum + "," + cnt)));
context.write(key, new Text(new String(sum + "," + cnt)));
}
}
static class AverageReducer extends
Reducer<Text, Text, Text, DoubleWritable> {
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
int sum = 0, cnt = 0;
for (Text val : values) {
String[] s = val.toString().split(",");
sum += Integer.parseInt(s[0]);
cnt += Integer.parseInt(s[1]);
}
String s;
System.out.println("reduce"
+ (s = new String(key + "," + (sum * 1.0 / cnt))));
context.write(key, new DoubleWritable(sum * 1.0 / cnt));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = args;
if (otherArgs.length != 2) {
System.err.println("Usage:Data Average <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "Data Average");
job.setJarByClass(Average.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(AverageCombine.class);
job.setReducerClass(AverageReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
执行
bin/hadoop jar Average.jar Average data.txt out
结果
a 3.0
b 6.0
更多Hadoop相关信息见Hadoop 专题页面 http://www.linuxidc.com/topicnews.aspx?tid=13
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