转自：http://my.oschina.net/leejun2005/blog/276891?utm_source=tuicool&utm_medium=referralhtml
在许多状况下，一个用户须要了解待分析的数据，尽管这并不是所要执行的分析任务 的核心内容。以统计数据集中无效记录数目的任务为例，若是发现无效记录的比例 至关高，那么就须要认真思考为什么存在如此多无效记录。是所采用的检测程序存在 缺陷，仍是数据集质量确实很低，包含大量无效记录？若是肯定是数据集的质量问 题，则可能须要扩大数据集的规模，以增大有效记录的比例，从而进行有意义的 分析。
计数器是一种收集做业统计信息的有效手段，用于质量控制或应用级统计。计数器 还可辅助诊断系统故障。若是须要将日志信息传输到map或reduce任务，更好的 方法一般是尝试传输计数器值以监测某一特定事件是否发生。对于大型分布式做业 而言，使用计数器更为方便。首先，获取计数器值比输出日志更方便，其次，根据 计数器值统计特定事件的发生次数要比分析一堆日志文件容易得多。 java
Hadoop为每一个做业维护若干内置计数器, 以描述该做业的各项指标。例如，某些计数器记录已处理的字节数和记录数，使用户可监控已处理的输入数据量和已产生的输出数据量，并以此对 job 作适当的优化。apache
14/06/08 15:13:35 INFO mapreduce.Job: Counters: 46 File System Counters FILE: Number of bytes read=159 FILE: Number of bytes written=159447 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=198 HDFS: Number of bytes written=35 HDFS: Number of read operations=6 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Rack-local map tasks=1 Total time spent by all maps in occupied slots (ms)=3896 Total time spent by all reduces in occupied slots (ms)=9006 Map-Reduce Framework Map input records=3 Map output records=12 Map output bytes=129 Map output materialized bytes=159 Input split bytes=117 Combine input records=0 Combine output records=0 Reduce input groups=4 Reduce shuffle bytes=159 Reduce input records=12 Reduce output records=4 Spilled Records=24 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=13 CPU time spent (ms)=3830 Physical memory (bytes) snapshot=537718784 Virtual memory (bytes) snapshot=7365263360 Total committed heap usage (bytes)=2022309888 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=81 File Output Format Counters Bytes Written=35
计数器由其关联任务维护，并按期传到tasktracker，再由tasktracker传给 jobtracker.所以，计数器可以被全局地汇集。详见第 hadoop 权威指南第170页的“进度和状态的更新”小节。与其余计数器（包括用户定义的计数器）不一样，内置的做业计数器实际上 由jobtracker维护，没必要在整个网络中发送。
一个任务的计数器值每次都是完整传输的，而非自上次传输以后再继续数未完成的传输，以免因为消息丢失而引起的错误。另外，若是一个任务在做业执行期间失 败，则相关计数器值会减少。仅当一个做业执行成功以后，计数器的值才是完整可 靠的。 网络
MapReduce容许用户编写程序来定义计数器，计数器的值可在mapper或reducer 中增长。多个计数器由一个Java枚举(enum)类型来定义，以便对计数器分组。一 个做业能够定义的枚举类型数量不限，各个枚举类型所包含的字段数量也不限。枚 举类型的名称即为组的名称，枚举类型的字段就是计数器名称。计数器是全局的。 换言之，MapReduce框架将跨全部map和reduce汇集这些计数器，并在做业结束 时产生一个最终结果。app
Note1： 须要说明的是，不一样的 hadoop 版本定义的方式会有些许差别。框架
（1）在0.20.x版本中使用counter很简单,直接定义便可，如无此counter，hadoop会自动添加此counter.
Counter ct = context.getCounter("INPUT_WORDS", "count");
ct.increment(1);
（2）在0.19.x版本中,须要定义enum
enum MyCounter {INPUT_WORDS };
reporter.incrCounter(MyCounter.INPUT_WORDS, 1);
RunningJob job = JobClient.runJob(conf);
Counters c = job.getCounters();
long cnt = c.getCounter(MyCounter.INPUT_WORDS);分布式
Notice2： 使用计数器须要清楚的是它们都存储在jobTracker的内存里。Mapper/Reducer 任务序列化它们，连同更新状态被发送。为了运行正常且jobTracker不会出问题，计数器的数量应该在10-100个，计数器不只仅只用来聚合MapReduce job的统计值。新版本的hadoop限制了计数器的数量，以防给jobTracker带来损害。你最不想看到的事情就是因为定义上百个计数器而使jobTracker宕机。
下面我们来看一个计数器的实例（如下代码请运行在 0.20.1 版本以上）： ide
3.1 测试数据：函数
hello world 2013 mapreduce hello world 2013 mapreduce hello world 2013 mapreduce
3.2 代码：oop
/**
* Project Name:CDHJobs
* File Name:MapredCounter.java
* Package Name:tmp
* Date:2014-6-8下午2:12:48
* Copyright (c) 2014, decli#qq.com All Rights Reserved.
*
*/
package tmp; import java.io.IOException; import java.util.StringTokenizer; import org.apache.commons.lang3.StringUtils; 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.Counter; import org.apache.hadoop.mapreduce.CounterGroup; import org.apache.hadoop.mapreduce.Counters; 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 WordCountWithCounter { static enum WordsNature { STARTS_WITH_DIGIT, STARTS_WITH_LETTER, ALL } /** * The map class of WordCount. */ public static class TokenCounterMapper extends Mapper<Object, Text, Text, IntWritable> { 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()); context.write(word, one); } } } /** * The reducer class of WordCount */ public static class TokenCounterReducer extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; String token = key.toString(); if (StringUtils.isNumeric(token)) { context.getCounter(WordsNature.STARTS_WITH_DIGIT).increment(1); } else if (StringUtils.isAlpha(token)) { context.getCounter(WordsNature.STARTS_WITH_LETTER).increment(1); } context.getCounter(WordsNature.ALL).increment(1); for (IntWritable value : values) { sum += value.get(); } context.write(key, new IntWritable(sum)); } } /** * The main entry point. */ public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "WordCountWithCounter"); job.setJarByClass(WordCountWithCounter.class); job.setMapperClass(TokenCounterMapper.class); job.setReducerClass(TokenCounterReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path("/tmp/dsap/rawdata/june/a.txt")); FileOutputFormat.setOutputPath(job, new Path("/tmp/dsap/rawdata/june/a_result")); int exitCode = job.waitForCompletion(true) ? 0 : 1; Counters counters = job.getCounters(); Counter c1 = counters.findCounter(WordsNature.STARTS_WITH_DIGIT); System.out.println("-------------->>>>: " + c1.getDisplayName() + ": " + c1.getValue()); // The below example shows how to get built-in counter groups that Hadoop provides basically. for (CounterGroup group : counters) { System.out.println("=========================================================="); System.out.println("* Counter Group: " + group.getDisplayName() + " (" + group.getName() + ")"); System.out.println(" number of counters in this group: " + group.size()); for (Counter counter : group) { System.out.println(" ++++ " + counter.getDisplayName() + ": " + counter.getName() + ": " + counter.getValue()); } }