首页 > 代码库 > Hadoop读书笔记（九）MapReduce计数器

Hadoop读书笔记（九）MapReduce计数器

2024-08-04 16:47:04   234人阅读

Hadoop读书笔记系列文章：http://blog.csdn.net/caicongyang/article/category/2166855

1.MapReduce 计数器的作用

统计Map、Reduce以及Combiner执行的次数，可以用户简单判断代码的执行流程

2.MapReduce自带的计数器

14/11/26 22:28:51 INFO mapred.JobClient: Counters: 19
14/11/26 22:28:51 INFO mapred.JobClient:   File Output Format Counters 
14/11/26 22:28:51 INFO mapred.JobClient:     Bytes Written=25
14/11/26 22:28:51 INFO mapred.JobClient:   FileSystemCounters
14/11/26 22:28:51 INFO mapred.JobClient:     FILE_BYTES_READ=343
14/11/26 22:28:51 INFO mapred.JobClient:     HDFS_BYTES_READ=42
14/11/26 22:28:51 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=128056
14/11/26 22:28:51 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=25
14/11/26 22:28:51 INFO mapred.JobClient:   File Input Format Counters 
14/11/26 22:28:51 INFO mapred.JobClient:     Bytes Read=21
14/11/26 22:28:51 INFO mapred.JobClient:   Map-Reduce Framework
14/11/26 22:28:51 INFO mapred.JobClient:     Map output materialized bytes=47
14/11/26 22:28:51 INFO mapred.JobClient:     Map input records=2
14/11/26 22:28:51 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/11/26 22:28:51 INFO mapred.JobClient:     Spilled Records=4
14/11/26 22:28:51 INFO mapred.JobClient:     Map output bytes=37
14/11/26 22:28:51 INFO mapred.JobClient:     Total committed heap usage (bytes)=366034944
14/11/26 22:28:51 INFO mapred.JobClient:     SPLIT_RAW_BYTES=97
14/11/26 22:28:51 INFO mapred.JobClient:     Combine input records=0
14/11/26 22:28:51 INFO mapred.JobClient:     Reduce input records=2
14/11/26 22:28:51 INFO mapred.JobClient:     Reduce input groups=2
14/11/26 22:28:51 INFO mapred.JobClient:     Combine output records=0
14/11/26 22:28:51 INFO mapred.JobClient:     Reduce output records=2
14/11/26 22:28:51 INFO mapred.JobClient:     Map output records=2

3.自定义计数器

package counter;

import java.io.IOException;
import java.net.URI;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
/**
 * 
 * <p> 
 * Title: WordCount.java 
 * Package counter 
 * </p>
 * <p>
 * Description: 自定义计数器
 * <p>
 * @author Tom.Cai
 * @created 2014-11-26 下午10:47:32 
 * @version V1.0 
 *
 */
public class WordCount {
	private static final String INPUT_PATH = "hdfs://192.168.80.100:9000/hello";
	private static final String OUT_PATH = "hdfs://192.168.80.100:9000/out";

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();
		FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH), conf);
		Path outPath = new Path(OUT_PATH);
		if (fileSystem.exists(outPath)) {
			fileSystem.delete(outPath, true);
		}
		Job job = new Job(conf, WordCount.class.getSimpleName());
		FileInputFormat.setInputPaths(job, INPUT_PATH);
		job.setInputFormatClass(TextInputFormat.class);

		job.setMapperClass(MyMapper.class);
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(LongWritable.class);

		job.setPartitionerClass(HashPartitioner.class);
		job.setNumReduceTasks(1);

		job.setReducerClass(MyReducer.class);

		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(LongWritable.class);

		FileOutputFormat.setOutputPath(job, new Path(OUT_PATH));
		job.setOutputFormatClass(TextOutputFormat.class);

		job.waitForCompletion(true);
	}

	static class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
		@Override
		protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
		<span style="color:#ff0000;">	/**
			 * 计数器的使用
			 */
			Counter mycounter = context.getCounter("MyCounter", "hello");
			if (value.toString().contains("hello")) {
				mycounter.increment(1L);
			}</span>
			String[] splited = value.toString().split("\t");
			for (String word : splited) {
				context.write(new Text(word), new LongWritable(1));
			}
		}
	}

	static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
		@Override
		protected void reduce(Text key, Iterable<LongWritable> value, Context context) throws IOException, InterruptedException {
			long count = 0L;
			for (LongWritable times : value) {
				count += times.get();
			}
			context.write(key, new LongWritable(count));
		}

	}

}

3.自定义计数器后输出

4/11/26 22:45:38 INFO mapred.JobClient: Counters: 20
14/11/26 22:45:38 INFO mapred.JobClient:   File Output Format Counters 
14/11/26 22:45:38 INFO mapred.JobClient:     Bytes Written=25
14/11/26 22:45:38 INFO mapred.JobClient:   MyCounter
14/11/26 22:45:38 INFO mapred.JobClient:     hello=2
14/11/26 22:45:38 INFO mapred.JobClient:   FileSystemCounters
14/11/26 22:45:38 INFO mapred.JobClient:     FILE_BYTES_READ=343
14/11/26 22:45:38 INFO mapred.JobClient:     HDFS_BYTES_READ=42
14/11/26 22:45:38 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=128036
14/11/26 22:45:38 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=25
14/11/26 22:45:38 INFO mapred.JobClient:   File Input Format Counters 
14/11/26 22:45:38 INFO mapred.JobClient:     Bytes Read=21
14/11/26 22:45:38 INFO mapred.JobClient:   Map-Reduce Framework
14/11/26 22:45:38 INFO mapred.JobClient:     Map output materialized bytes=47
14/11/26 22:45:38 INFO mapred.JobClient:     Map input records=2
14/11/26 22:45:38 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/11/26 22:45:38 INFO mapred.JobClient:     Spilled Records=4
14/11/26 22:45:38 INFO mapred.JobClient:     Map output bytes=37
14/11/26 22:45:38 INFO mapred.JobClient:     Total committed heap usage (bytes)=366034944
14/11/26 22:45:38 INFO mapred.JobClient:     SPLIT_RAW_BYTES=97
14/11/26 22:45:38 INFO mapred.JobClient:     Combine input records=0
14/11/26 22:45:38 INFO mapred.JobClient:     Reduce input records=2
14/11/26 22:45:38 INFO mapred.JobClient:     Reduce input groups=2
14/11/26 22:45:38 INFO mapred.JobClient:     Combine output records=0
14/11/26 22:45:38 INFO mapred.JobClient:     Reduce output records=2
14/11/26 22:45:38 INFO mapred.JobClient:     Map output records=2



欢迎大家一起讨论学习！

有用的自己收!

记录与分享，让你我共成长！欢迎查看我的其他博客；我的博客地址：http://blog.csdn.net/caicongyang




Hadoop读书笔记（九）MapReduce计数器


联系
我们