1. 如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:python
学生表(Student)(不包括最后一列)shell
学号(S_No)数据库 |
姓名(S_Name)app |
性别(S_Sex)函数 |
年龄(S_Age)oop |
课程(course)测试 |
2015001spa |
Zhangsancode |
maleblog |
23 |
|
2015003 |
Mary |
female |
22 |
|
2015003 |
Lisi |
male |
24 |
数学(Math)85 |
首先启动hadoop,其次启动hbase,最后打开hbase数据库
cd /usr/local/hadoop ./sbin/start-dfs.sh cd /usr/local/hbase ./bin/start-hbase.sh hbase shell
create 'Student',{NAME=>'S_No',VERSIONS=>5},{NAME=>'S_Name',VERSIONS=>5},{NAME=>'S_Sex',VERSIONS=>5},{NAME=>'S_Age',VERSIONS=>5} put 'Student','2015001','sname','Zhangsan' put 'Student','2015001','ssex','male' put 'Student','2015001','sage','23' put 'Student','2015002','sname','Mary' put 'Student','2015002','ssex','female' put 'Student','2015002','sage','22' put 'Student','2015003','sname','Lisi' put 'Student','2015003','ssex','male' put 'Student','2015003','sage','24
2. 用Hadoop提供的HBase Shell命令完成相同任务:
scan 'Student'
alter 'Student','NAME'=>'course'
put 'Student','3','course:Math','85'
truncate 'Student'
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 |
文件中每一个单词及其出现次数(频数),并按照单词字母顺序排序,每一个单词和其频数占一行,单词和频数之间有间隔 |
建立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)
赋予权限
chmod a+x /home/hadoop/mapper.py
建立reducer.py文件
cd /home/hadoop/wc
sudo gedit reducer.py
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)
赋予权限
chmod a+x /home/hadoop/reduce.py
测试代码
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
下载文件上传
cd /home/hadoop/wc wget http://www.gutenberg.org/files/5000/5000-8.txt wget http://www.gutenberg.org/cache/epub/20417/pg20417.txt
cd /usr/hadoop/wc
hdfs dfs -put /home/hadoop/hadoop/gutenberg/*.txt /user/hadoop/input