使用mapreduce清洗简单日志文件并导入hive数据库

Result文件数据说明：

Ip：106.39.41.166,（城市）

Date：10/Nov/2016:00:01:02 +0800,（日期）

Day：10,（天数）

Traffic: 54 ,（流量）

Type: video,（类型：视频video或文章article）

Id: 8701（视频或者文章的id）

文件部分如下：

1.192.25.84 2016-11-10-00:01:14 10 54 video 5551 
1.194.144.222 2016-11-10-00:01:20 10 54 video 3589 
1.194.187.2 2016-11-10-00:01:05 10 54 video 2212 
1.203.177.243 2016-11-10-00:01:18 10 6050 video 7361 
1.203.177.243 2016-11-10-00:01:19 10 72 video 7361 
1.203.177.243 2016-11-10-00:01:22 10 6050 video 7361 
1.30.162.63 2016-11-10-00:01:46 10 54 video 3639 
1.84.205.195 2016-11-10-00:01:12 10 54 video 1412 
1.85.61.18 2016-11-10-00:01:31 10 54 video 6578 
1.85.61.37 2016-11-10-00:01:36 10 54 video 7212 
101.200.101.13 2016-11-10-00:01:06 10 524288 video 11938 
101.200.101.201 2016-11-10-00:01:03 10 4468 article 4779 
101.200.101.204 2016-11-10-00:01:10 10 4468 article 11325 
101.200.101.207 2016-11-10-00:01:08 10 4468 article 11325 

流程：

数据清洗：按照进行数据清洗，并将清洗后的数据导入hive数据库中。

两阶段数据清洗：

（1）第一阶段：把需要的信息从原始日志中提取出来

ip:    199.30.25.88

time:  10/Nov/2016:00:01:03 +0800

traffic:  62

文章： article/11325

视频： video/3235

（2）第二阶段：根据提取出来的信息做精细化操作

ip--->城市 city（IP）

date--> time:2016-11-10 00:01:03

day: 10

traffic:62

type:article/video

id:11325

（3）hive数据库表结构:（将清洗出来的文件导入hive表中）

create table if not exists data(
mip string,
mtime string,
mday string,
mtraffic bigint,
mtype string,
mid string)
row format delimited fields terminated by ' ' lines terminated by ' ';//导入数据以' '分隔，' '换行

源代码：

 

import java.io.IOException;
import java.lang.String;
import java.util.*;
import java.text.SimpleDateFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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;

public class Dataclean{
       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-HH:mm:ss");//现时间格式

       private  static 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 static  String[] parse(String line) {

            String ip = parseIP(line);       //ip

            String time = parseTime(line);   //时间
            String day=parseDay(line);//天数

            String type = parseType(line);     //视频video或文章article

            String id = parseId(line); //视频或者文章的id

            String traffic = parseTraffic(line);//流量
            return new String[] { ip, time,day,traffic,type,id};

        } 
        
        private  static  String parseIP(String line) {     //ip

            String ip = line.split(",")[0].trim();//str.trim(); 去掉首尾空格
            return ip;

        }
        
        private  static  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  static  String parseDay(String line) {    //天数

         String day = line.split(",")[2].trim();
            return day;

        }
        private static  String parseTraffic(String line) {    //流量,转为int型

         String traffic= line.split(",")[3].trim();
            return traffic;

        }
        private  static String parseType(String line) {  

         String day = line.split(",")[4].replace(" ", "");
            return day;

        }
        private static String parseId(String line) {    

         String day = line.split(",")[5].replace(" ", "");//去掉所有空格
            return day;

        }
        public static class Map extends Mapper<Object, Text, Text, NullWritable> {
         public static Text word = new Text();
         public void map(Object key, Text value, Context context)throws IOException, InterruptedException {

          // 将输入的纯文本文件的数据转化成String
          String line = value.toString();
          String arr[] = parse(line);
             word.set(arr[0]+"	"+arr[1]+"	"+arr[2]+"	"+arr[3]+"	"+arr[4]+"	"+arr[5]+"	");//一定用'	'，空格容易乱会有意想不到的问题
            context.write(word,NullWritable.get());
         }
        }
        public static class Reduce extends Reducer<Text, NullWritable, Text, NullWritable> {

         // 实现reduce函数

         public void reduce(Text key, Iterable<NullWritable> values,Context context) throws IOException, InterruptedException {

          context.write(key, NullWritable.get());

         }
        }
        public static void main(String[] args) throws Exception {
         Configuration conf=new Configuration();   
   System.out.println("start");
   Job job=Job.getInstance(conf); 
   job.setJarByClass(Dataclean.class);
   job.setMapperClass(Map.class);  
   job.setReducerClass(Reduce.class);
      job.setOutputKeyClass(Text.class);  
      job.setOutputValueClass(NullWritable.class);//设置map的输出格式
      job.setInputFormatClass(TextInputFormat.class);
      job.setOutputFormatClass(TextOutputFormat.class);
      Path in = new Path("hdfs://localhost:9000/mapReduce/mymapreduce1/result.txt");
      Path out = new Path("hdfs://localhost:9000/mapReduce/mymapreduce1/out"); 
      FileInputFormat.addInputPath(job,in );  
      FileOutputFormat.setOutputPath(job,out);  
      boolean flag = job.waitForCompletion(true);
      System.out.println(flag);
      System.exit(flag? 0 : 1);
        }
}

 

 清洗所得部分结果如下：

 

1.192.25.84  2016-11-10-00:01:14  10  54  video    5551
1.194.144.222  2016-11-10-00:01:20  10  54  video    3589
1.194.187.2  2016-11-10-00:01:05  10  54  video    2212
1.203.177.243  2016-11-10-00:01:18  10  6050  video    7361
1.203.177.243  2016-11-10-00:01:19  10  72  video    7361
1.203.177.243  2016-11-10-00:01:22  10  6050  video    7361
1.30.162.63  2016-11-10-00:01:46  10  54  video    3639
1.84.205.195  2016-11-10-00:01:12  10  54  video    1412
1.85.61.18  2016-11-10-00:01:31  10  54  video    6578
1.85.61.37  2016-11-10-00:01:36  10  54  video    7212

 将清洗文件导入hive数据库表：

hive> create table if not exists data(
    > mip string,
    > mtime string,
    > mday string,
    > mtraffic bigint,
    > mtype string,
    > mid string)
    > row format delimited fields terminated by '	' lines terminated by '
';
OK
Time taken: 0.135 seconds
hive> load data local inpath "/home/hadoop/out" into table data;  //注：table后边的data是表名，前一个data不用动
Loading data to table default.data
Table default.data stats: [numFiles=1, totalSize=63923]
OK
Time taken: 0.315 seconds
hive> select * from data limit 3;
OK
1.192.25.84 2016-11-10-00:01:14 10 54 video 5551
1.194.144.222 2016-11-10-00:01:20 10 54 video 3589
1.194.187.2 2016-11-10-00:01:05 10 54 video 2212
Time taken: 0.124 seconds, Fetched: 3 row(s)
hive>

 查看数据库表数据：