理解MapReduce

标签：ftp   处理   oop   table   保存   put   mis   nat   col   

cd /home/hadoop/wc
sudo gedit mapper.py

#!/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)

chmod a+x /home/hadoop/mapper.py

cd /home/hadoop/wc
sudo gedit reducer.py

#!/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)

chmod a+x /home/hadoop/reduce.py

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

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

2. 用mapreduce 处理气象数据集

编写程序求每日最高最低气温，区间最高最低气温

1. 气象数据集下载地址为：ftp://ftp.ncdc.noaa.gov/pub/data/noaa
2. 按学号后三位下载不同年份月份的数据（例如201506110136号同学，就下载2013年以6开头的数据，看具体数据情况稍有变通）
3. 解压数据集，并保存在文本文件中
4. 对气象数据格式进行解析
5. 编写map函数，reduce函数
6. 将其权限作出相应修改
7. 本机上测试运行代码
8. 放到HDFS上运行
   1. 将之前爬取的文本文件上传到hdfs上
   2. 用Hadoop Streaming命令提交任务
9. 查看运行结果

   cd /usr/hadoop
   sodu mkdir qx
   cd /usr/hadoop/qx
   wget -D --accept-regex=REGEX -P data -r -c ftp://ftp.ncdc.noaa.gov/pub/data/noaa/2008/6*
   cd /usr/hadoop/qx/data/ftp.ncdc.noaa.gov/pub/data/noaa/2008
   sudo zcat 1*.gz >qxdata.txt
   cd /usr/hadoop/qx
   from operator import itemggetter
   import sys
    
   current_word = None
   current_count = 0
   word = None
    
   for i in sys.stdin:
        i = i.strip()
        word,count = i.split(‘\t‘, 1)
        try:
             count = int(count)
        except ValueError:
             continue
    
        if current_word == word:
            if current_count > count:
                 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)

   chmod a+x /usr/hadoop/qx/mapper.py
   chmod a+x /usr/hadoop/qx/reducer.py

   　　

理解MapReduce

标签：ftp   处理   oop   table   保存   put   mis   nat   col   

原文地址：https://www.cnblogs.com/AMINOAC/p/9021803.html