Hadoop 自定义计数器

public static class mapper extends Mapper<Text, BytesWritable, Text , Text>{

		private Counter c ;
		@Override
		protected void setup(Context context) throws IOException,
				InterruptedException {
			c = context.getCounter("FILE", "COUNT");
		}

		@Override
		protected void map(Text key, BytesWritable value, Context context)
				throws IOException, InterruptedException {
			c.increment(1);
			context.write(key, new Text(value.getBytes()));
		}

		@Override
		protected void cleanup(Context context)
				throws IOException, InterruptedException {
			
		}
		
	}
	
	public static class reducer extends Reducer<Text, Text, Text, Text>{
		
		@Override
		protected void reduce(Text arg0, Iterable<Text> arg1,
				Context context)
				throws IOException, InterruptedException {
			Iterator<Text> itr = arg1.iterator();
			while(itr.hasNext()){
				itr.next();
			}
			
			context.write(arg0, new Text("heihei"));
		}
		
	}
	
	public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException{
		
		Configuration conf = new Configuration();
		Job job = new Job(conf);
		job.setJarByClass(testCounter.class);
		
		FileInputFormat.addInputPath(job, new Path(args[0]));
		FileOutputFormat.setOutputPath(job, new Path(args[1]));
		
		job.setMapperClass(mapper.class);
		job.setReducerClass(reducer.class);
		
		job.setInputFormatClass(WholeFileInputFormat.class);
		job.setOutputFormatClass(TextOutputFormat.class);
		
		job.setMapOutputKeyClass(Text.class);
		job.setMapOutputValueClass(Text.class);
		
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(Text.class);
		
		job.waitForCompletion(true);
		
		String num = job.getCounters().findCounter("Map-Reduce Framework", "Map input records").getName();
		System.out.println(num);
	}

 获取计数器只能在job完成之后，也就是job.waitForCompletion(true);之后，放在之前的话回报一个非法安全的错误，但是在reducer函数里面，则不错报错，也不能获取计数器值，可能设计人员没有扑捉reducer里面的异常吧...

计数器有自定义计数器和内置计数器

静态计数器和动态计数器，前者用枚举，更安全一些，后者直接用字符串，有时为了显示方便，会创建一个properties属性文件。