[Hadoop] Hadoop Streaming使用Python编程

一时失言乱红尘 2022-05-25 11:23 140阅读 0赞

Hadoop Streaming 是Hadoop提供方的一个编程工具，它允许用户使用任何可执行文件或者脚本作为Mapper和Reducer。

Hadoop Streaming 多语言编程

1. 以标准输入流作为输入：

1) C++: cin

2) Php: stdin

3) Python: sys.stdin

2. 以标准的输出流作为输出：

1) C++: cout

2) Php: echo

3) Python: print

3. 可实现Mapper和Reducer，其他组件（InputFormat，Partitioner等需要用Java语言来实现）

Python实现Wordcount：

1. mapper.py

[root@vm wordcount]# vim mapper.py

写入

#!/usr/bin/python
    import sys
    word2count = {}
    for line in sys.stdin:
        line = line.strip()
        words = filter(lambda word:word,line.split())
        for word in words:
            print("%s\t%s" % (word,1))

2. reducer.py

[root@vm wordcount]# vim reducer.py

写入

#!/usr/bin/python
    from operator import itemgetter
    import sys
    
    word2count = {}
    for line in sys.stdin:
        line = line.strip()
        word,count = line.split()
        try:
            count = int(count)
            word2count[word] = word2count.get(word,0) + count
        except ValueError as err:
            print(err)
            pass
    
    sorted_word2count = sorted(word2count.items(),key=itemgetter(0))
    for word,count in sorted_word2count:
        print("%s\t%s" % (word, count))

3. 准备一个测试文件test.txt

[root@vm wordcount]# vim test.txt

写入

this is a test
    this is a test
    this is a test
    this is a test

4. 本地测试

[root@vm wordcount]# cat test.txt |python mapper.py |sort|python reducer.py 
    a	4
    is	4
    test	4
    this	4
    [root@vm wordcount]#

5. 集群运行

集群运行前要将本地的测试文件上传到hdfs

[root@vm wordcount]# hadoop fs -mkdir /user/root/wordcount
    [root@vm wordcount]# hadoop fs -put test.txt /user/root/wordcount/
    [root@vm wordcount]# hadoop fs -ls /user/root/wordcount/
    Found 1 items
    -rw-r--r--   3 root root         60 2018-05-14 09:58 /user/root/wordcount/test.txt
    [root@vm wordcount]#

运行mapreduce

[root@vm wordcount]# hadoop jar /opt/cloudera/parcels/CDH-5.13.1-1.cdh5.13.1.p0.2/jars/hadoop-streaming-2.6.0-cdh5.13.1.jar -D mapred.reduce.tasks=1 -mapper "python mapper.py" -reducer "python reducer.py" -file mapper.py -file reducer.py -input /user/root/wordcount/test.txt -output /user/root/wordcount/out
    18/05/14 10:00:37 WARN streaming.StreamJob: -file option is deprecated, please use generic option -files instead.
    packageJobJar: [mapper.py, reducer.py] [/opt/cloudera/parcels/CDH-5.13.1-1.cdh5.13.1.p0.2/jars/hadoop-streaming-2.6.0-cdh5.13.1.jar] /tmp/streamjob7327942722840197442.jar tmpDir=null
    18/05/14 10:00:39 INFO mapred.FileInputFormat: Total input paths to process : 1
    18/05/14 10:00:39 INFO mapreduce.JobSubmitter: number of splits:2
    18/05/14 10:00:39 INFO Configuration.deprecation: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
    18/05/14 10:00:40 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1526119303078_0004
    18/05/14 10:00:40 INFO impl.YarnClientImpl: Submitted application application_1526119303078_0004
    18/05/14 10:00:40 INFO mapreduce.Job: The url to track the job: http://g13-1.novalocal:8088/proxy/application_1526119303078_0004/
    18/05/14 10:00:40 INFO mapreduce.Job: Running job: job_1526119303078_0004
    18/05/14 10:00:46 INFO mapreduce.Job: Job job_1526119303078_0004 running in uber mode : false
    18/05/14 10:00:46 INFO mapreduce.Job:  map 0% reduce 0%
    18/05/14 10:00:50 INFO mapreduce.Job:  map 100% reduce 0%
    18/05/14 10:00:56 INFO mapreduce.Job:  map 100% reduce 100%
    18/05/14 10:00:57 INFO mapreduce.Job: Job job_1526119303078_0004 completed successfully
    18/05/14 10:00:57 INFO mapreduce.Job: Counters: 49
    	File System Counters
    		FILE: Number of bytes read=61
    		FILE: Number of bytes written=469610
    		FILE: Number of read operations=0
    		FILE: Number of large read operations=0
    		FILE: Number of write operations=0
    		HDFS: Number of bytes read=290
    		HDFS: Number of bytes written=23
    		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
    		Rack-local map tasks=2
    		Total time spent by all maps in occupied slots (ms)=5834
    		Total time spent by all reduces in occupied slots (ms)=3080
    		Total time spent by all map tasks (ms)=5834
    		Total time spent by all reduce tasks (ms)=3080
    		Total vcore-milliseconds taken by all map tasks=5834
    		Total vcore-milliseconds taken by all reduce tasks=3080
    		Total megabyte-milliseconds taken by all map tasks=5974016
    		Total megabyte-milliseconds taken by all reduce tasks=3153920
    	Map-Reduce Framework
    		Map input records=4
    		Map output records=16
    		Map output bytes=92
    		Map output materialized bytes=104
    		Input split bytes=200
    		Combine input records=0
    		Combine output records=0
    		Reduce input groups=4
    		Reduce shuffle bytes=104
    		Reduce input records=16
    		Reduce output records=4
    		Spilled Records=32
    		Shuffled Maps =2
    		Failed Shuffles=0
    		Merged Map outputs=2
    		GC time elapsed (ms)=235
    		CPU time spent (ms)=3290
    		Physical memory (bytes) snapshot=1292472320
    		Virtual memory (bytes) snapshot=8453484544
    		Total committed heap usage (bytes)=1889533952
    	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=90
    	File Output Format Counters 
    		Bytes Written=23
    18/05/14 10:00:57 INFO streaming.StreamJob: Output directory: /user/root/wordcount/out
    [root@vm wordcount]#

此时可以去Web UI上查看结果。

![70][]

命令行查看结果

[root@vm wordcount]# hadoop fs -cat /user/root/wordcount/out/part-00000
    a	4
    is	4
    test	4
    this	4
    [root@vm wordcount]#

官方文档：https://hadoop.apache.org/docs/r1.0.4/cn/streaming.html

[70]: /images/20220525/163b9b4e5c4c4bd58f26939bb6724d8c.png