Mapreuduce实现网络数据包的清洗工作

处理后的数据可直接放到hive或者mapreduce程序来统计网络数据流的信息，比如当前实现的是比较简单的http的Get请求的统计

第一个mapreduce：将时间、十六进制包头信息提取出来，并放在一行（这里涉及到mapreduce的键值对的对多行的特殊处理，是个值得注意的地方）

主要遇到两个问题：

　　一个数据包包含时间，包头的简单信息，包头的详细信息，初衷是想要把一个数据包的时间、包十六进制详细信息（存在于很多行里）按照顺序放置到一行，在java里面按行读取，很好实现。

针对mapreduce的键值对处理的特性，原来想到有两种方式解决：

（1）以时间的key值为准，一个包的信息key值与其相同

但MR的map每次只处理一行信息，而reduce只对键相同的行做处理，而且从map阶段到reduce的过程中有一个shuffle、sort阶段（估计是这个原因，也可能是因为离reduce近的机器处理完直接发给reduce，先到先处理），相同的key的value是乱序的。

(2)所有的key值递增

这样就没有相同的key值，无法放置到一行

最后的解决办法：

（3）以时间的key值为准，同一个包的信息的key值与其相同，但在十六进制行里加一个递增的id，放置到一行，虽然是乱序的，但自带ID，就重新排一下就好啦，妙！

第二个mapreduce: 对十六进制信息进行排序，是第一个mapreduce的补充，至此，清洗工作完毕，可以统计任意位置的十六进制来分析数据

第三个mapreduce：统计http发送的GET请求个数

static int id=1;
	static int hexId=1;
  public static class TokenizerMapper 
       extends Mapper<Object, Text, IntWritable, Text>
 {
    private final static IntWritable one = new IntWritable(2);
    private Text word = new Text();
      
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException
    {
    	//匹配时间
	 	String regexTime = "([0-2][0-4]):([0-5][0-9]):([0-5][0-9]).[0-9]{6}";// 11:08:56.149361
		Pattern patternTime = Pattern.compile(regexTime);
		Matcher matchTime = patternTime.matcher(value.toString());
		while (matchTime.find()) {
			String time ="time: " + matchTime.group()+" ";
			id=id+1;
			word.set(time);
			one.set(id);
			context.write(one, word);
		}
		//匹配十六进制
//		String regexHex = "0x[0-9]{4}:  ([A-Za-z0-9]{4} )+";
		String regexHex = " ([A-Za-z0-9]{4} )+";
		Pattern patternHex = Pattern.compile(regexHex);
		Matcher matchHex = patternHex.matcher(value.toString());
		while (matchHex.find()) {
			String hex = " "+ matchHex.group();
			hexId=hexId+1; 
			hex="id:"+String.valueOf(hexId)+" "+hex;
			word.set(hex);
			one.set(id);
			context.write(one, word);
		}
    }
  }
  
  public static class IntSumReducer 
       extends Reducer<IntWritable,Text,IntWritable,Text> 
{
    private Text result = new Text();

    public void reduce(IntWritable key, Iterable<Text> values, 
                       Context context
                       ) throws IOException, InterruptedException
  {
      String sum = "";
      for (Text val : values) 
        {
          sum += val.toString();
         }
      result.set(sum);
      context.write(key, result);
    }
  }

　　

public static class TokenizerMapper 
       extends Mapper<Object, Text, Text, Text>
 {
    private final static Text one = new Text();
    private Text word = new Text();
      
    public void map(Object key, Text value, Context context
                    ) throws IOException, InterruptedException
    {
    	//匹配时间
	 	String regexTime = "time: ([0-2][0-4]):([0-5][0-9]):([0-5][0-9]).[0-9]{6}";// 11:08:56.149361
		Pattern patternTime = Pattern.compile(regexTime);
		Matcher matchTime = patternTime.matcher(value.toString());
		while (matchTime.find()) {
//			String time ="time: " + matchTime.group()+" ";
			String temptime =matchTime.group();
			String time =temptime.substring(6, temptime.length()-1);
			one.set(time);
		}
		
		//排序十六进制
//		String regexHex = "0x[0-9]{4}:  ([A-Za-z0-9]{4} )+";
		List<Bar> list = new ArrayList<Bar>();
		String regexHex = "id:([0-9])+   ([A-Za-z0-9]{4} )+";
		Pattern patternHex = Pattern.compile(regexHex);
		Matcher matchHex = patternHex.matcher(value.toString());
		while (matchHex.find()) {
			Bar bar = new Bar();
			String hexline = matchHex.group();
			String regexHex2 ="id:([0-9])+"; //一行十六进制的序号
			Pattern patternHex2 = Pattern.compile(regexHex2);
			Matcher matchHex2 = patternHex2.matcher(hexline);
			while (matchHex2.find()) {
				String lineId=matchHex2.group().toString().substring(3);
				bar.setId(lineId);
			}
			String regexHex3 ="([A-Za-z0-9]{4} )+"; //一行十六进制
			Pattern patternHex3 = Pattern.compile(regexHex3);
			Matcher matchHex3 = patternHex3.matcher(hexline);
			while (matchHex3.find()) {
				String lineHex= matchHex3.group().toString();
				bar.setHexValue(lineHex);
			}
			list.add(bar);
		}
		
		StringBuffer buffer = new StringBuffer("");
		 Collections.sort(list);
		for(int i=0;i<list.size();i++){
			Bar bar=list.get(i);
			String lineHex=bar.getHexValue();
			buffer.append(lineHex);
		}
		String hexOne= buffer.toString();
		
		word.set(hexOne);
		context.write(one, word);
    }
  }
  
  public static class IntSumReducer 
       extends Reducer<Text,Text,Text,Text> 
{
    private Text result = new Text();

    public void reduce(Text key, Iterable<Text> values, 
                       Context context
                       ) throws IOException, InterruptedException
  {
      String sum = "";
      for (Text val : values) 
        {
    	  context.write(key, val);
         }
    }
  }

　　

	public static class TokenizerMapper extends
			Mapper<Object, Text, Text, IntWritable> {
		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text("sumGet");

		public void map(Object key, Text value, Context context)
				throws IOException, InterruptedException {
			int timelen=15;
			int getlen=20*5+timelen;
			String strline=value.toString();
			if (strline.length() > getlen) {// ||hexValue[20].equals("4854")
				String getPos=strline.substring(timelen+20*5,timelen+21*5-1);
				 if(getPos.equals("4745")){
					 context.write(word, one);
				 }
			}
		}
	}

	public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		public void reduce(Text key, Iterable<IntWritable> values, Context context)
				throws IOException, InterruptedException {
			int sum =0;
			for (IntWritable val : values) {
				sum+=val.get();
			}
			result.set(sum);
			context.write(key, result);
		}
	}