hive 的日志处理与python的数据清洗

hive的部分

• 一：hive 清理日志处理 统计PV、UV 访问量
• 二： hive 数据python 的数据清洗

一： 日志处理

统计每个时段网站的访问量:

1.1 在hive 上面创建表结构：

在创建表时不能直接导入问题
create table db_bflog.bf_log_src (
remote_addr string,
remote_user string,
time_local string,
request string,
status string,
body_bytes_sent string,
request_body string,
http_referer string,
http_user_agent string,
http_x_forwarded_for string,
host string
)
ROW FORMAT SERDE 'org.apache.hadoop.hive.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
  "input.regex" = "(\"[^ ]*\") (\"-|[^ ]*\") (\"[^\]]*\") (\"[^\"]*\") (\"[0-9]*\") (\"[0-9]*\") (-|[^ ]*) (\"[^ ]*\") (\"[^\"]*\") (-|[^ ]*) (\"[^ ]*\")"
)
STORED AS TEXTFILE;

1.2 加载数据到 hive 表当中：

load data local inpath '/home/hadoop/moodle.ibeifeng.access.log' into table db_bflog.bf_log_src ;

1.3 自定义UDF函数

1.3.1：udf函数去除相关引号

package org.apache.hadoop.udf;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.Text;
/**
 * * New UDF classes need to inherit from this UDF class.
 * 
 * @author zhangyy
 *
 */
public class RemoveQuotesUDF extends UDF {
    /*
    1. Implement one or more methods named "evaluate" which will be called by Hive.
    2."evaluate" should never be a void method. However it can return "null" if needed.
    */
    public Text evaluate(Text str){
        if(null == str){
            return null;
        }
        // validate 
        if(StringUtils.isBlank(str.toString())){
            return null ;
        }
        // lower
        return new Text(str.toString().replaceAll("\"", ""));
    }
    public static void main(String[] args) {
        System.out.println(new RemoveQuotesUDF().evaluate(new Text("\"GET /course/view.php?id=27 HTTP/1.1\"")));
    }
}

1.3.2：udf函数时间格式进行转换

package org.apache.hadoop.udf;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Locale;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.io.Text;
/**
 * * New UDF classes need to inherit from this UDF class.
 * 
 * @author zhangyy
 *
 */
public class DateTransformUDF extends UDF {
    private final SimpleDateFormat inputFormat = new SimpleDateFormat("dd/MMM/yy:HH:mm:ss", Locale.ENGLISH) ;
    private final SimpleDateFormat outputFormat = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss") ;
    /*
    1. Implement one or more methods named "evaluate" which will be called by Hive.
    2."evaluate" should never be a void method. However it can return "null" if needed.
    */
    /**
     * input:
     *      31/Aug/2015:00:04:37 +0800
     * output:
     *      2015-08-31 00:04:37
     */
    public Text evaluate(Text str){
        Text output = new Text() ;
        if(null == str){
            return null;
        }
        // validate 
        if(StringUtils.isBlank(str.toString())){
            return null ;
        }
        try{
            // 1) parse 
            Date parseDate = inputFormat.parse(str.toString().trim());
            // 2) transform
            String outputDate = outputFormat.format(parseDate) ;
            // 3) set
            output.set(outputDate);
        }catch(Exception e){
            e.printStackTrace();
        }
        // lower
        return output;
    }
    public static void main(String[] args) {
        System.out.println(new DateTransformUDF().evaluate(new Text("31/Aug/2015:00:04:37 +0800")));
    }
}

将RemoveQuotesUDF 与 DateTransformUDF 到出成jar 包 放到/home/hadoop/jars 目录下面：

1.4 去hive 上面 生成 udf 函数

  RemoveQuotesUDF 加载成udf函数 ：
  add jar /home/hadoop/jars/RemoveQuotesUDF.jar ;
  create temporary function My_RemoveQuotes as "org.apache.hadoop.udf.RemoveQuotesUDF" ;
  DateTransformUDF 加载成udf 函数：
  add jar /home/hadoop/jars/DateTransformUDF.jar ;
  create temporary function My_DateTransform as "org.apache.hadoop.udf.DateTransformUDF" ;

1.5 创建生成所要要求表：

create table db_bflog.bf_log_comm(
remote_addr string,
time_local string,
request string,
http_referer string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ','
STORED AS ORC tblproperties ("orc.compress"="SNAPPY");

从原有表中提取 相关的数据处理：

insert into table db_bflog.bf_log_comm select remote_addr, time_local, request, http_referer from db_bflog.bf_log_src ;

执行sql 统计每小时的pv 访问量：

select t.hour,count(*) cnt
from
(select substring(my_datetransform(my_removequotes(time_local)),12,2) hour from bf_log_comm) t
group by t.hour order by cnt desc ;

二： hive 数据python 的数据清洗

  统计国外一家影院的每周看电影的人数
  测试数据下载地址：
 wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
 unzip ml-100k.zip

2.1 创建hive 的数据表

 CREATE TABLE u_data (
  userid INT,
  movieid INT,
  rating INT,
  unixtime STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE;

2.2 加载数据：

LOAD DATA LOCAL INPATH '/home/hadoop/ml-100k/u.data'
OVERWRITE INTO TABLE u_data;

2.3 创建weekday_mapper.py 脚本

import sys
import datetime
for line in sys.stdin:
  line = line.strip()
  userid, movieid, rating, unixtime = line.split('\t')
  weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()
  print '\t'.join([userid, movieid, rating, str(weekday)])

2.4 创建临时hive 表 用于提取数据：

 CREATE TABLE u_data_new (
  userid INT,
  movieid INT,
  rating INT,
  weekday INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t';
增加python 脚本到hive
add FILE /home/hadoop/weekday_mapper.py;

2.5 从旧表中数据提取

INSERT OVERWRITE TABLE u_data_new
SELECT
  TRANSFORM (userid, movieid, rating, unixtime)
  USING 'python weekday_mapper.py'
  AS (userid, movieid, rating, weekday)
FROM u_data;

2.6 查找所需要的数据：

SELECT weekday, COUNT(*)
FROM u_data_new
GROUP BY weekday;