hive 的日志处理统计网站的 PV 、UV案例 与 给合 python的数据清洗数据案例

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;