python 运行 hadoop 2.0 mapreduce 程序

要点：#!/usr/bin/python  因为要发送到各个节点，所以py文件必须是可执行的。 
1）  统计（所有日志）独立ip数目，即不同ip的总数
####################本地测试############################
cat /home/hadoop/Sep-/*/* | python ipmappper.py | sort | python ipreducer.py
本地部分测试结果：
99.67.46.254    13
99.95.174.29    47
sum of single ip    13349
#####################hadoop集群运行############################
bin/hadoop jar ./share/hadoop/tools/lib/hadoop-streaming-2.2.0.jar -mapper /data/hadoop/jobs_python/job_logstat/ipmapper.py -reducer /data/hadoop/jobs_python/job_logstat/ipreducer.py -input /log_original/* -output /log_ipnum -file /data/hadoop/jobs_python/job_logstat/ipmapper.py -file /data/hadoop/jobs_python/job_logstat/ipreducer.py
集群部分测试结果：
99.67.46.254    13
99.95.174.29    47
sum of single ip    13349

ipmapper.py:
##########################mapper代码#######################################
#!/usr/bin/python
# --*-- coding:utf-8 --*--
import re
import sys

pat = re.compile('(?P<ip>\d+.\d+.\d+.\d+).*?"\w+ (?P<subdir>.*?) ')
for line in sys.stdin:
    match = pat.search(line)
    if match:
        print '%s\t%s' % (match.group('ip'), 1)

ipreducer.py
##########################reducer代码#####################################
#!/usr/bin/python
from operator import itemgetter
import sys

dict_ip_count = {}

for line in sys.stdin:
    line = line.strip()
    ip, num = line.split('\t')
    try:
        num = int(num)
        dict_ip_count[ip] = dict_ip_count.get(ip, 0) + num

    except ValueError:
        pass

sorted_dict_ip_count = sorted(dict_ip_count.items(), key=itemgetter(0))
for ip, count in sorted_dict_ip_count:
    print '%s\t%s' % (ip, count)

2）  统计（所有日志）每个子目录访问次数
########################本地测试######################################
cat /home/hadoop/Sep-2013/*/* | python subdirmapper.py | sort | python subdirreducer.py
部分结果：
http://dongxicheng.org/recommend/    2
http://dongxicheng.org/search-engine/scribe-intro/trackback/    1
http://dongxicheng.org/structure/permutation-combination/    1
http://dongxicheng.org/structure/sort/trackback/    1
http://dongxicheng.org/wp-comments-post.php    5
http://dongxicheng.org/wp-login.php/    3535
http://hadoop123.org/administrator/index.php    4

#######################hadoop集群运行########################################
bin/hadoop jar ./share/hadoop/tools/lib/hadoop-streaming-2.2..jar -mapper /data/hadoop/jobs_python/job_logstat/subdirmapper.py -reducer /data/hadoop/jobs_python/job_logstat/subdirreducer.py -input /log_original/* -output /log_subdirnum -file /data/hadoop/jobs_python/job_logstat/subdirmapper.py -file /data/hadoop/jobs_python/job_logstat/subdirreducer.py
部分结果：
http://dongxicheng.org/search-engine/scribe-intro/trackback/    1
http://dongxicheng.org/structure/permutation-combination/    1
http://dongxicheng.org/structure/sort/trackback/    1
http://dongxicheng.org/wp-comments-post.php    5
http://dongxicheng.org/wp-login.php/    3535
http://hadoop123.org/administrator/index.php    4

#######################################mapper代码###########################################
#!/usr/bin/python
# --*-- coding:utf-8 --*--
import re
import sys

pat = re.compile('(?P<ip>\d+.\d+.\d+.\d+).*?"\w+ (?P<subdir>.*?) ')
for line in sys.stdin:
    match = pat.search(line)
    if match:
        print '%s\t%s' % (match.group('subdir'), 1)
#######################################reducer代码###########################################
#!/usr/bin/python
from operator import itemgetter
import sys

dict_subdir_count = {}

for line in sys.stdin:
    line = line.strip()
    subdir, num = line.split('\t')
    try:
        num = int(num)
        dict_subdir_count[subdir] = dict_subdir_count.get(subdir, 0) + num
    except ValueError:
        pass

sorted_dict_ip_count = sorted(dict_subdir_count.items(), key=itemgetter(0))
for subdir, count in sorted_dict_ip_count:
    print '%s\t%s' % (subdir, count)

【还是用java写mr程序吧】
参考网址：
http://asfr.blogbus.com/logs/44208067.html

bin/hadoop jar ./share/hadoop/tools/lib/hadoop-streaming-2.2.0.jar -mapper /data/hadoop/mapper.py -reducer /data/hadoop/reducer.py -input /in/* -output /py_out -file /data/hadoop/mapper.py -file /data/hadoop/reducer.py 

python开发mapreduce的原理：
》与linux管道机制一致
》通过标准输入输出实现进程间通信
》标准输入输出是任何语言都支持的。
举几个例子：
cat 1.txt | grep 'dong' | sort
cat 1.txt | python grep.py | java sort.jar

以标准输入流作为输入：
c++: cin
c: scanf
以标准输出流作为输出：
c++：count
c：printf

局限性：可以实现Mapper Reducer，其他组件需要用java实现。

hadoop-streaming 进行测试很简单的哦。
编译程序，生成可执行文件
g++ -o mapper mapper.cpp
g++ -o reducer reduer.cpp
测试程序：
cat test.txt | ./mappper | sort | ./reducer


#!/usr/bin/python
# coding=utf-8

import sys

# input comes from STDIN (standard input)
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()
    # split the line into words
    words = line.split()
    # increase counters
    for word in words:
        # write the results to STDOUT (standard output);
        # 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
        print '%s\t%s' % (word, 1)

#!/usr/bin/python
# coding=utf-8

from operator import itemgetter
import sys

# maps words to their counts
word2count = {}

# input comes from STDIN
for line in sys.stdin:
    # remove leading and trailing whitespace
    line = line.strip()

    # parse the input we got from mapper.py
    word, count = line.split('\t', 1)
    # convert count (currently a string) to int
    try:
        count = int(count)
        word2count[word] = word2count.get(word, 0) + count
    except ValueError:
        # count was not a number, so silently
        # ignore/discard this line
        pass

# sort the words lexigraphically;
#
# this step is NOT required, we just do it so that our
# final output will look more like the official Hadoop
# word count examples
sorted_word2count = sorted(word2count.items(), key=itemgetter(0))

# write the results to STDOUT (standard output)
for word, count in sorted_word2count:
    print '%s\t%s'% (word, count)

packageJobJar: [/data/hadoop/mapper.py, /data/hadoop/reducer.py, /data/hadoop/hadoop_tmp/hadoop-unjar4601454529868960285/] [] /tmp/streamjob2970217681900457939.jar tmpDir=null
14/03/21 16:23:09 INFO client.RMProxy: Connecting to ResourceManager at /192.168.2.200:8032
14/03/21 16:23:09 INFO client.RMProxy: Connecting to ResourceManager at /192.168.2.200:8032
14/03/21 16:23:10 INFO mapred.FileInputFormat: Total input paths to process : 2
14/03/21 16:23:10 INFO mapreduce.JobSubmitter: number of splits:2
14/03/21 16:23:10 INFO Configuration.deprecation: user.name is deprecated. Instead, use mapreduce.job.user.name
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.jar is deprecated. Instead, use mapreduce.job.jar
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.cache.files.filesizes is deprecated. Instead, use mapreduce.job.cache.files.filesizes
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.cache.files is deprecated. Instead, use mapreduce.job.cache.files
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.mapoutput.value.class is deprecated. Instead, use mapreduce.map.output.value.class
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.cache.files.timestamps is deprecated. Instead, use mapreduce.job.cache.files.timestamps
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.mapoutput.key.class is deprecated. Instead, use mapreduce.map.output.key.class
14/03/21 16:23:10 INFO Configuration.deprecation: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
14/03/21 16:23:10 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1394086709210_0008
14/03/21 16:23:10 INFO impl.YarnClientImpl: Submitted application application_1394086709210_0008 to ResourceManager at /192.168.2.200:8032
14/03/21 16:23:10 INFO mapreduce.Job: The url to track the job: http://master:8088/proxy/application_1394086709210_0008/
14/03/21 16:23:10 INFO mapreduce.Job: Running job: job_1394086709210_0008
14/03/21 16:23:14 INFO mapreduce.Job: Job job_1394086709210_0008 running in uber mode : false
14/03/21 16:23:14 INFO mapreduce.Job:  map 0% reduce 0%
14/03/21 16:23:19 INFO mapreduce.Job:  map 100% reduce 0%
14/03/21 16:23:23 INFO mapreduce.Job:  map 100% reduce 100%
14/03/21 16:23:24 INFO mapreduce.Job: Job job_1394086709210_0008 completed successfully
14/03/21 16:23:24 INFO mapreduce.Job: Counters: 43
    File System Counters
        FILE: Number of bytes read=47
        FILE: Number of bytes written=248092
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=197
        HDFS: Number of bytes written=25
        HDFS: Number of read operations=9
        HDFS: Number of large read operations=0
        HDFS: Number of write operations=2
    Job Counters 
        Launched map tasks=2
        Launched reduce tasks=1
        Data-local map tasks=2
        Total time spent by all maps in occupied slots (ms)=5259
        Total time spent by all reduces in occupied slots (ms)=2298
    Map-Reduce Framework
        Map input records=2
        Map output records=4
        Map output bytes=33
        Map output materialized bytes=53
        Input split bytes=172
        Combine input records=0
        Combine output records=0
        Reduce input groups=3
        Reduce shuffle bytes=53
        Reduce input records=4
        Reduce output records=3
        Spilled Records=8
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=71
        CPU time spent (ms)=1300
        Physical memory (bytes) snapshot=678060032
        Virtual memory (bytes) snapshot=2662100992
        Total committed heap usage (bytes)=514326528
    Shuffle Errors
        BAD_ID=0
        CONNECTION=0
        IO_ERROR=0
        WRONG_LENGTH=0
        WRONG_MAP=0
        WRONG_REDUCE=0
    File Input Format Counters 
        Bytes Read=25
    File Output Format Counters 
        Bytes Written=25
14/03/21 16:23:24 INFO streaming.StreamJob: Output directory: /py_out

1，hadoop上在java开发可用：

 

FileSplit fileSplit = (FileSplit)reporter.getInputSplit();

String fileName = fileSplit.getPath().getName();

来获取文件名称。

,2，同样python开发时，可以用：

 

来获取文件名：

 

import os

 

os.environ["map_input_file"]

这里的 map_input_file 相当于map.input.file