咱们一般写mapreduce是通过java和streaming来写的，身为pythoner的我，
java不会，没办法就用streaming来写mapreduce日志分析。 这里要介绍一个
模块，是基于streaming搞的东西。
mrjob 可以让用 Python 来编写 MapReduce 运算，并在多个不同平台上运行，你可以：
使用纯 Python 编写多步的 MapReduce 作业
在本机上进行测试
在 Hadoop 集群上运行
pip 的安装方法：
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pip install mrjob
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我测试的脚本
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#coding:utf-
8
from mrjob.job
import
MRJob
import
re
#xiaorui.cc
#WORD_RE = re.compile(r
"[\w']+"
)
WORD_RE = re.compile(r
"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}"
)
class
MRWordFreqCount(MRJob):
def mapper(self, word, line):
for
word
in
WORD_RE.findall(line):
yield word.lower(),
1
def combiner(self, word, counts):
yield word, sum(counts)
def reducer(self, word, counts):
yield word, sum(counts)
if
__name__ ==
'__main__'
:
MRWordFreqCount.run()
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用法算简单：
python i.py -r inline input1 input2 input3 > out 命令可以将处理多个文件的结果输出到out文件里面。
本地模拟hadoop运行：python 1.py -r local <input> output
这个会把结果输出到output里面，这个output必须写。
hadoop集群上运行：python 1.py -r hadoop <input> output
执行脚本 ～
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[root@kspc ~]# python mo.py -r local <
10.7
.
17.7
-dnsquery.log.
1
> output
no configs found; falling back on auto-configuration
no configs found; falling back on auto-configuration
creating tmp directory /tmp/mo.root.
20131224.040935
.
241241
reading from STDIN
writing to /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00000
> /usr/bin/python mo.py --step-num=
0
--mapper /tmp/mo.root.
20131224.040935
.
241241
/input_part-
00000
| sort | /usr/bin/python mo.py --step-num=
0
--combiner > /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00000
writing to /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00001
> /usr/bin/python mo.py --step-num=
0
--mapper /tmp/mo.root.
20131224.040935
.
241241
/input_part-
00001
| sort | /usr/bin/python mo.py --step-num=
0
--combiner > /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00001
Counters from step
1
:
(no counters found)
writing to /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper-sorted
> sort /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00000
/tmp/mo.root.
20131224.040935
.
241241
/step-
0
-mapper_part-
00001
writing to /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00000
> /usr/bin/python mo.py --step-num=
0
--reducer /tmp/mo.root.
20131224.040935
.
241241
/input_part-
00000
> /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00000
writing to /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00001
> /usr/bin/python mo.py --step-num=
0
--reducer /tmp/mo.root.
20131224.040935
.
241241
/input_part-
00001
> /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00001
Counters from step
1
:
(no counters found)
Moving /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00000
-> /tmp/mo.root.
20131224.040935
.
241241
/output/part-
00000
Moving /tmp/mo.root.
20131224.040935
.
241241
/step-
0
-reducer_part-
00001
-> /tmp/mo.root.
20131224.040935
.
241241
/output/part-
00001
Streaming
final
output from /tmp/mo.root.
20131224.040935
.
241241
/output
removing tmp directory /tmp/mo.root.
20131224.040935
.
241241
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执行的时候，资源的占用情况。
发现一个很奇妙的东西，mrjob居然调用shell下的sort来排序。。。。
为了更好的理解mrjob的用法，再来个例子。
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from mrjob.job
import
MRJob
#from xiaorui.cc
class
MRWordFrequencyCount(MRJob):
#把东西拼凑起来
def mapper(self, _, line):
yield
"chars"
, len(line)
yield
"words"
, len(line.split())
yield
"lines"
,
1
#总结kv
def reducer(self, key, values):
yield key, sum(values)
if
__name__ ==
'__main__'
:
MRWordFrequencyCount.run()
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看下结果：
下面是官网给的一些个用法：
我们可以看到他是支持hdfs和s3存储的 ！
Running your job different ways
The most basic way to run your job is on the command line:
$ python my_job.py input.txt
By default, output will be written to stdout.
You can pass input via stdin, but be aware that mrjob will just dump it to a file first:
$ python my_job.py < input.txt
You can pass multiple input files, mixed with stdin (using the - character):
$ python my_job.py input1.txt input2.txt - < input3.txt
By default, mrjob will run your job in a single Python process. This provides the friendliest debugging experience, but it’s not exactly distributed computing!
You change the way the job is run with the -r/--runner option. You can use -rinline (the default), -rlocal, -rhadoop, or -remr.
To run your job in multiple subprocesses with a few Hadoop features simulated, use -rlocal.
To run it on your Hadoop cluster, use -rhadoop.
If you have Elastic MapReduce configured (see Elastic MapReduce Quickstart), you can run it there with -remr.
Your input files can come from HDFS if you’re using Hadoop, or S3 if you’re using EMR:
$ python my_job.py -r emr s3://my-inputs/input.txt $ python my_job.py -r hadoop hdfs://my_home/input.txt
