11月13日的实验进度

还没有完成，主要是hive没有配置好。。。。。。。。程序的清洗已经做得差不多了，之前一直有出现数组溢出的情况，主要原因是我还没有理解mapreduce的工作模式。代码如下：

import java.lang.String;
import java.io.IOException;
import java.util.*;
import java.text.SimpleDateFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.io.NullWritable;
 
public class Namecount {
 
         public static final SimpleDateFormat FORMAT = new SimpleDateFormat("d/MMM/yyyy:HH:mm:ss", Locale.ENGLISH); //原时间格式
         public static final SimpleDateFormat dateformat1 = new SimpleDateFormat("yyyy-MM-dd");//现时间格式
       private Date parseDateFormat(String string) {         //转换时间格式
            Date parse = null;
            try {
                parse = FORMAT.parse(string);
            } catch (Exception e) {
                e.printStackTrace();
            }
            return parse;
        }
        
        public String[] parse(String line) {
            public static ArrayList<String> ip = new ArrayList<String>();
    public static ArrayList<String> date = new ArrayList<String>();
    public static ArrayList<String> day = new ArrayList<String>();
    public static ArrayList<Long> traffic = new ArrayList<Long>();
    public static ArrayList<String> type = new ArrayList<String>();
    public static ArrayList<String> id = new ArrayList<String>();
 
            return new String[] { ip, time, url, status, traffic };
        } 
        private String parseTraffic(String line) {    //流量
            final String trim = line.substring(line.lastIndexOf(""") + 1)
                    .trim();
            String traffic = trim.split(" ")[1];
            return traffic;
        }
       private String parseStatus(String line) {     //状态
            final String trim = line.substring(line.lastIndexOf(""") + 1)
                    .trim();
            String status = trim.split(" ")[0];
            return status;
        }
 
        private String parseURL(String line) {       //url
            final int first = line.indexOf(""");
            final int last = line.lastIndexOf(""");
            String url = line.substring(first + 1, last);
            return url;
        }
        private String parseTime(String line) {    //时间
            final int first = line.indexOf("[");
            final int last = line.indexOf("+0800]");
            String time = line.substring(first + 1, last).trim();
            Date date = parseDateFormat(time);
            return dateformat1.format(date);
        }
        private String parseIP(String line) {     //ip
            String ip = line.split("- -")[0].trim();
            return ip;
        }
    public static class Map extends
            Mapper<LongWritable, Text, Text, IntWritable> {
                
        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            // 将输入的纯文本文件的数据转化成String
            Text outputValue = new Text();
            String line = value.toString();
             Namecount aa=new Namecount();
            StringTokenizer tokenizerArticle = new StringTokenizer(line, "
");
 
            // 分别对每一行进行处理
            while (tokenizerArticle.hasMoreElements()) {
                // 每行按空格划分
              String stra=tokenizerArticle.nextToken().toString();
              String [] Newstr=aa.parse(stra);
 
           if (Newstr[2].startsWith("GET /")) { //过滤开头字符串
                Newstr[2] = Newstr[2].substring("GET /".length());
            } 
          else if (Newstr[2].startsWith("POST /")) {
                Newstr[2] = Newstr[2].substring("POST /".length());
            }
           if (Newstr[2].endsWith(" HTTP/1.1")) { //过滤结尾字符串
                Newstr[2] = Newstr[2].substring(0, Newstr[2].length()
                        - " HTTP/1.1".length());
            }
              String[] words = Newstr[2].split("/");
              if(words.length==4){
                  outputValue.set(Newstr[0] + "	" + Newstr[1] + "	" + words[0]+"	"+words[1]+"	"+words[2]+"	"+words[3]+"	"+"0");
                   context.write(outputValue,new IntWritable(1));                 
}    
    }
  }
}
 
    public static class Reduce extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        // 实现reduce函数
        public void reduce(Text key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
          int sum = 0;
            Iterator<IntWritable> iterator = values.iterator();
            while (iterator.hasNext()) {
                sum += iterator.next().get();
            }
            context.write(key, new IntWritable(sum));
        }
    }
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        
    conf.set("mapred.jar","Namecount.jar");
 
        String[] ioArgs = new String[] { "name", "name_out" };
        String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();
        if (otherArgs.length != 2) {
            System.err.println("Usage: Score Average <in> <out>");
            System.exit(2);
        }
 
        Job job = new Job(conf, "name_goods_count");
        job.setJarByClass(Namecount.class);
 
        // 设置Map、Combine和Reduce处理类
        job.setMapperClass(Map.class);
        job.setCombinerClass(Reduce.class);
        job.setReducerClass(Reduce.class);
 
        // 设置输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
 
        // 将输入的数据集分割成小数据块splites，提供一个RecordReder的实现
        job.setInputFormatClass(TextInputFormat.class);
        // 提供一个RecordWriter的实现，负责数据输出
        job.setOutputFormatClass(TextOutputFormat.class);
 
        // 设置输入和输出目录
        Path in=new Path("hdfs://localhost:9000/mymapreduce3/123/12345.txt");  
        Path out=new Path("hdfs://localhost:9000/mymapreduce3/out");  
        FileInputFormat.addInputPath(job,in);  
        FileOutputFormat.setOutputPath(job,out);  
    }

如此，还有一点小错误，明天应该可以完成生于部分以及导入hive了