熟悉常用的HBase操作,编写MapReduce作业

1. 以下关系型数据库中的表和数据,要求将其转换为适合于HBase存储的表并插入数据:

学生表(Student)(不包括最后一列)

学号(S_No)

姓名(S_Name)

性别(S_Sex)

年龄(S_Age)

课程(course)

2015001

Zhangsan

male

23

 

2015003

Mary

female

22

 

2015003

Lisi

male

24

数学(Math)85

 

create 'Student', ' S_No  ','S_Name', ’S_Sex’,'S_Age'
put 'Student','1','S_No','2015001'
put 'Student','1','S_Name','Wangwu'
put 'Student','1','S_Sex','male'
put 'Student','1','S_Age','23'
put 'Student','2','S_No','2015003'
put 'Student','2','S_Name','Mary'
put 'Student','2','S_Sex','female'
put 'Student','2','S_Age','22'
put 'Student','3','S_No','2015003'
put 'Student','3','S_Name','Lisi'
put 'Student','3','S_Sex','male'
put 'Student','3','S_Age','24'

 

2. 用Hadoop提供的HBase Shell命令完成相同任务:

  • 列出HBase所有的表的相关信息;list
  • 在终端打印出学生表的所有记录数据;
  • 向学生表添加课程列族;
  • 向课程列族添加数学列并登记成绩为85;
  • 删除课程列;
  • 统计表的行数;count 's1'
  • 清空指定的表的所有记录数据;truncate 's1'

 

scan 'Student'
alter 'Student','NAME'=>'course'
put 'Student','3','course:Math','85'
dorp 'Student','course'
count 's1'
truncate 's1'

 

3. 用Python编写WordCount程序任务

程序

WordCount

输入

一个包含大量单词的文本文件

输出

文件中每个单词及其出现次数(频数),并按照单词字母顺序排序,每个单词和其频数占一行,单词和频数之间有间隔

  1. 编写map函数,reduce函数
    #创造mapper.py文件
    cd /home/hadoop/wc
    sudo gedit mapper.py
     
     
    #map函数
    #!/usr/bin/env python
    import sys
    for i in stdin:
        i = i.strip()
        words = i.split()
        for word in words:
        print '%s\t%s' % (word,1)
     
     
    #reduce函数
    #!/usr/bin/env python
    from operator import itemgetter
    import sys
     
    current_word = None
    current_count = 0
    word = None
     
    for i in stdin:
        i = i.strip()
        word, count = i.split('\t',1)
        try:
        count = int(count)
        except ValueError:
        continue
     
        if current_word == word:
        current_count += count 
        else:
        if current_word:
            print '%s\t%s' % (current_word, current_count)
        current_count = count
        current_word = word
     
    if current_word == word:
        print '%s\t%s' % (current_word, current_count)

     

  2. 将其权限作出相应修改
    #!/usr/bin/env python
    cd /home/hadoop/wc
    sudo gedit reducer.py
    #赋予权限
    chmod a+x /home/hadoop/mapper.py

     

  3. 本机上测试运行代码
    echo "foo foo quux labs foo bar quux" | /home/hadoop/wc/mapper.py
    echo "foo foo quux labs foo bar quux" | /home/hadoop/wc/mapper.py | sort -k1,1 | /home/hadoop/wc/reducer.p

     

  4. 放到HDFS上运行
  5. 下载并上传文件到hdfs上
    #上传文件
    cd  /home/hadoop/wc
    wget http://www.gutenberg.org/files/5000/5000-8.txt
    wget http://www.gutenberg.org/cache/epub/20417/pg20417.txt
     
    #下载文件
    cd /usr/hadoop/wc
    hdfs dfs -put /home/hadoop/hadoop/gutenberg/*.txt /user/hadoop/input

     

  6. 用Hadoop Streaming命令提交任务

 

posted @ 2018-05-08 18:42  165邝启彬  阅读(130)  评论(0编辑  收藏  举报