发布时间:2019-08-25 09:34:29编辑:auto阅读(1249)
用Python来写分布式的程序。这样速度快。便于调试,更有实际意义。MapReduce适合于对文本文件的处理及数据挖掘用:
在每台机器上:
su - hadoop
wget http://www.python.org/ftp/python/3.0.1/Python-3.0.1.tar.bz2
tar jxvf Python-3.0.1.tar.bz2
cd Python-3.0.1
./configure --prefix=/home/hadoop/python;make;make install
vi /home/hadoop/mapper.py
#!/home/hadoop/python/bin/python3.0 import sys for line in sys.stdin: line = line.strip() words = line.split() for word in words: print ("%st%s" % (word, 1))
vi /home/hadoop/reduce.py
#!/home/hadoop/python/bin/python3.0 from operator import itemgetter import sys word2count = {} for line in sys.stdin: line = line.strip() word, count = line.split('t', 1) try: count = int(count) word2count[word] = word2count.get(word, 0) + count except ValueError: pass sorted_word2count = sorted(word2count.items(), key=itemgetter(0)) for word, count in sorted_word2count: print ("%st%s" % (word, count))
测测好不好用:
echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py
foo 1
foo 1
quux 1
labs 1
foo 1
bar 1
quux 1
echo "foo foo quux labs foo bar quux" | /home/hadoop/mapper.py | sort | /home/hadoop/reduce.py
bar 1
foo 3
labs 1
quux 2
在各个节点上都要准备好这两个文件啊!!!
在master主节点上执行:
# 拷贝conf目录到hdfs文件系统中
$ cd /home/hadoop/hadoop-0.19.1
$ bin/hadoop dfs -copyFromLocal conf 111
# 查看一下是否已经拷过去了
$ bin/hadoop dfs -ls
Found 1 items
drwxr-xr-x - hadoop supergroup 0 2009-05-18 15:27 /user/hadoop/111
# 分布计算
$ bin/hadoop jar contrib/streaming/hadoop-0.19.1-streaming.jar -mapper /home/hadoop/mapper.py -reducer /home/hadoop/reduce.py -input 111/* -output 111-output
additionalConfSpec_:null
null=@@@userJobConfProps_.get(stream.shipped.hadoopstreaming
packageJobJar: [/tmp/hadoop-hadoop/hadoop-unjar29198/] [] /tmp/streamjob29199.jar tmpDir=null
[...] INFO mapred.FileInputFormat: Total input paths to process : 12
[...] INFO streaming.StreamJob: getLocalDirs(): [/tmp/hadoop-hadoop/mapred/local]
[...] INFO streaming.StreamJob: Running job: job_200905191453_0001
[...] INFO streaming.StreamJob: To kill this job, run:
...
[...]
[...] INFO streaming.StreamJob: map 0% reduce 0%
[...] INFO streaming.StreamJob: map 43% reduce 0%
[...] INFO streaming.StreamJob: map 86% reduce 0%
[...] INFO streaming.StreamJob: map 100% reduce 0%
[...] INFO streaming.StreamJob: map 100% reduce 33%
[...] INFO streaming.StreamJob: map 100% reduce 70%
[...] INFO streaming.StreamJob: map 100% reduce 77%
[...] INFO streaming.StreamJob: map 100% reduce 100%
[...] INFO streaming.StreamJob: Job complete: job_200905191453_0001
[...] INFO streaming.StreamJob: Output: 111-output [hadoop@wangyin4 hadoop-0.19.1]$
$ bin/hadoop dfs -ls 111-output
Found 2 items
drwxr-xr-x - hadoop supergroup 0 2009-05-19 14:54 /user/hadoop/111-output/_logs
-rw-r--r-- 2 hadoop supergroup 30504 2009-05-19 16:26 /user/hadoop/111-output/part-00000
$ bin/hadoop dfs -cat 111-output/part-00000
you 3
you've 1
your 1
zero 3
zero, 1
Over,搞定。大家可以拓展这个例子,写出自己的应用来。
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