这里以统计单词为例
1 首先建立mapper.py
mkdir /usr/local/hadoop-python
cd /usr/local/hadoop-python
vim mapper.py
mapper.py
#!/usr/bin/env python
import sys
# input comes from STDIN (standard input) 输入来自STDIN(标准输入)
for line in sys.stdin:
# remove leading and trailing whitespace 删除前导和尾随空格
line = line.strip()
# split the line into words 把线分成单词
words = line.split()
# increase counters 增加柜台
for word in words:
# write the results to STDOUT (standard output);
# 将结果写入STDOUT(标准输出);
# what we output here will be the input for the
# Reduce step, i.e. the input for reducer.py
# tab-delimited; the trivial word count is 1
# 我们在此处输出的内容将是Reduce步骤的输入,即reducer.py制表符分隔的输入; # 平凡的字数是1
print '%s\t%s' % (word, 1)
文件保存后,请注意将其权限作出相应修改:
chmod a+x /usr/local/hadoop-python/mapper.py
2 建立reducer.py
vim reducer.py
#!/usr/bin/env python
from operator import itemgetter
import sys
current_word = None
current_count = 0
word = None
# input comes from STDIN 输入来自STDIN
for line in sys.stdin:
# remove leading and trailing whitespace
# 删除前导和尾随空格
line = line.strip()
# parse the input we got from mapper.py
# 解析我们从mapper.py获得的输入
word, count = line.split('\t', 1)
# convert count (currently a string) to int
# 将count(当前为字符串)转换为int
try:
count = int(count)
except ValueError:
# count was not a number, so silently
# ignore/discard this line
# count不是数字,因此请忽略/丢弃此行
continue
# this IF-switch only works because Hadoop sorts map output
# by key (here: word) before it is passed to the reducer
# 该IF开关仅起作用是因为Hadoop在将映射输出传递给reducer之前按键(此处为word)对 # 映射输出进行排序
if current_word == word:
current_count += count
else:
if current_word:
# write result to STDOUT
# 将结果写入STDOUT
print '%s\t%s' % (current_word, current_count)
current_count = count
current_word = word
# do not forget to output the last word if needed!
# 如果需要,不要忘记输出最后一个单词!
if current_word == word:
print '%s\t%s' % (current_word, current_count)
文件保存后,请注意将其权限作出相应修改:
chmod a+x /usr/local/hadoop-python/reducer.py
首先可以在本机上测试以上代码,这样如果有问题可以及时发现:
# echo "foo foo quux labs foo bar quux" | /usr/local/hadoop-python/mapper.py
输出:
foo 1
foo 1
quux 1
labs 1
foo 1
bar 1
quux 1
再运行以下包含reduce.py的代码:
echo "foo foo quux labs foo bar quux" | /usr/local/hadoop-python/mapper.py | sort -k1,1 | /usr/local/hadoop-python/reducer.py
输出:
bar 1
foo 3
labs 1
quux 2
3 在Hadoop上运行Python代码
准备工作:
下载文本文件:
yum install wget -y
mkdir input
cd /usr/local/hadoop-python/input
wget http://www.gutenberg.org/files/5000/5000-8.txt
wget http://www.gutenberg.org/cache/epub/20417/pg20417.txt
然后把这二本书上传到hdfs文件系统上:
# 在hdfs上的该用户目录下创建一个输入文件的文件夹
hdfs dfs -mkdir /input
# 上传文档到hdfs上的输入文件夹中
hdfs dfs -put /usr/local/hadoop-python/input/pg20417.txt /input
寻找你的streaming的jar文件存放地址,注意2.6的版本放到share目录下了,可以进入hadoop安装目录寻找该文件:
cd $HADOOP_HOME
find ./ -name "*streaming*.jar"
然后就会找到我们的share文件夹中的hadoop-straming*.jar文件:
./share/hadoop/tools/lib/hadoop-streaming-2.8.4.jar
./share/hadoop/tools/sources/hadoop-streaming-2.8.4-test-sources.jar
./share/hadoop/tools/sources/hadoop-streaming-2.8.4-sources.jar
/usr/local/hadoop-2.8.4/share/hadoop/tools/lib
由于这个文件的路径比较长,因此我们可以将它写入到环境变量:
vim /etc/profile
export STREAM=/usr/local/hadoop-2.8.4/share/hadoop/tools/lib/hadoop-streaming-2.8.4.jar
由于通过streaming接口运行的脚本太长了,因此直接建立一个shell名称为run.sh来运行:
vim run.sh
hadoop jar /usr/local/hadoop-2.8.4/share/hadoop/tools/lib/hadoop-streaming-2.8.4.jar \
-files /usr/local/hadoop-python/mapper.py,/usr/local/hadoop-python/reducer.py \
-mapper /usr/local/hadoop-python/mapper.py \
-reducer /usr/local/hadoop-python/reducer.py \
-input /input/pg20417.txt \
-output /output1
hadoop jar $STREAM \-files /usr/local/hadoop-python/mapper.py,/usr/local/hadoop-python/reducer.py \-mapper /usr/local/hadoop-python/mapper.py \-reducer /usr/local/hadoop-python/reducer.py \-input /input/pg20417.txt \-output /output1