1、分发HDFS压缩文件(-cacheArchive)

需求:wordcount(只统计指定的单词【the,and,had...】),但是该文件存储在HDFS上的压缩文件,压缩文件内可能有多个文件,通过-cacheArchive的方式进行分发;

-cacheArchive hdfs://host:port/path/to/file.tar.gz#linkname.tar.gz #选项在计算节点上缓存文件,streaming程序通过./linkname.tar.gz的方式访问文件。

思路:reducer程序都不需要修改,mapper需要增加用来读取压缩文件的函数(或模块),运行streaming的时候需要使用-cacheArchive 指定hdfs上的文件;

1.1、 streaming命令格式(-cacheArchive)

$HADOOP_HOME/bin/hadoop jar hadoop-streaming.jar \
    -jobconf mapred.job.name="streaming_cacheArchive_demo" \
    -jobconf mapred.job.priority=3 \
    -jobconf mapred.compress.map.output=true \
    -jobconf mapred.map.output.compression_codec=org.apache.hadoop.io.compress.GzipCodec \
    -jobconf mapred.output.compress=true \
    -jobconf mapred.out.compression.codec=org.apache.hadoop.io.compress.GzipCodec \
    -input /input/ \
    -output /output/ \
    -mapper "python mapper.py whc.tar.gz" \
    -reducer "python reducer.py" \
    -cacheArchive "hdfs://master:9000/cache_file/wordwhite.tar.gz#whc.tar.gz"
    -file ./mapper.py \
    -file ./reducer.py 

1.2、mapper程序

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import os
import os.path
import sys

def getCachefile(filename):
    filelist = []
    if os.path.isdir(filename):
        for root, dirs, files, in os.walk(filename):
            for name in files:
                filepath = root + '/' + name
                filelist.append(filepath)
    return filelist

def readWordwhite(filename):
    wordset = set()

    for cachefile in getCachefile(filename):
        with open(cachefile, 'r') as fd:
            for line in fd:
                word = line.strip()
                wordset.add(word)
    return wordset

def mapper(filename):
    wordset = readWordwhite(filename)

    for line in sys.stdin:
        line = line.strip()
        words = line.split()
        for word in words:
            if word != "" and (word in wordset):
                print "%s\t%s" %(word, 1)

if __name__ == "__main__":
    if sys.argv[1]:
        file_fd = sys.argv[1]
        mapper(file_fd)

1.3、 reducer程序

#!/usr/bin/env python
# -*- coding:utf-8 -*-

import sys

def reducer():
    currentword = None
    wordsum = 0

    for line in sys.stdin:
        wordlist = line.strip().split('\t')
        if len(wordlist) < 2:
            continue
        word = wordlist[0].strip()
        wordvalue = wordlist[1].strip()

        if currentword == None:
            currentword = word
        if currentword != word:
            print "%s\t%s" %(currentword, str(wordsum))
            currentword = word
            wordsum = 0
        wordsum += int(wordvalue)

    print "%s\t%s" %(currentword, str(wordsum))

if __name__ == "__main__":
    reducer()

1.4、上传wordwhite.tar.gz

$ ls -R wordwhite
wordwhite:
wordwhite01  wordwhite02  wordwhite03
$ cat wordwhite/wordwhite0*
have
and
had
the
in
this
or
this
to
$ tar zcf wordwhite.tar.gz wordwhite
$ hadoop fs -put wordwhite.tar.gz hdfs://localhost:9000/input/cachefile/

1.5、 run_streaming程序

#!/bin/bash

HADOOP_CMD="/home/hadoop/app/hadoop/hadoop-2.6.0-cdh5.13.0/bin/hadoop"
STREAM_JAR_PATH="/home/hadoop/app/hadoop/hadoop-2.6.0-cdh5.13.0/share/hadoop/tools/lib/hadoop-streaming-2.6.0-cdh5.13.0.jar"

INPUT_FILE_PATH="/input/The_Man_of_Property"
OUTPUT_FILE_PATH="/output/wordcount/WordwhiteCacheArchiveFiletest"

$HADOOP_CMD fs -rmr -skipTrash $OUTPUT_FILE_PATH

$HADOOP_CMD jar $STREAM_JAR_PATH \
                -input $INPUT_FILE_PATH \
                -output $OUTPUT_FILE_PATH \
                -jobconf "mapred.job.name=wordcount_wordwhite_cacheArchivefile_demo" \
                -mapper "python mapper.py WHF.gz" \
                -reducer "python reducer.py" \
                -cacheArchive "hdfs://localhost:9000/input/cachefile/wordwhite.tar.gz#WHF.gz" \
                -file "./mapper.py" \
                -file "./reducer.py"

1.6、执行程序

$ chmod +x run_streaming.sh
$ ./run_streaming.sh 
rmr: DEPRECATED: Please use 'rm -r' instead.
Deleted /output/wordcount/WordwhiteCacheArchiveFiletest
18/02/01 17:57:00 WARN streaming.StreamJob: -file option is deprecated, please use generic option -files instead.
18/02/01 17:57:00 WARN streaming.StreamJob: -cacheArchive option is deprecated, please use -archives instead.
18/02/01 17:57:00 WARN streaming.StreamJob: -jobconf option is deprecated, please use -D instead.
18/02/01 17:57:00 INFO Configuration.deprecation: mapred.job.name is deprecated. Instead, use mapreduce.job.name
packageJobJar: [./mapper.py, ./reducer.py, /tmp/hadoop-unjar211766205758273068/] [] /tmp/streamjob9043244899616176268.jar tmpDir=null
18/02/01 17:57:01 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/02/01 17:57:01 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
18/02/01 17:57:03 INFO mapred.FileInputFormat: Total input paths to process : 1
18/02/01 17:57:03 INFO mapreduce.JobSubmitter: number of splits:2
18/02/01 17:57:04 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1516345010544_0030
18/02/01 17:57:04 INFO impl.YarnClientImpl: Submitted application application_1516345010544_0030
18/02/01 17:57:04 INFO mapreduce.Job: The url to track the job: http://localhost:8088/proxy/application_1516345010544_0030/
18/02/01 17:57:04 INFO mapreduce.Job: Running job: job_1516345010544_0030
18/02/01 17:57:11 INFO mapreduce.Job: Job job_1516345010544_0030 running in uber mode : false
18/02/01 17:57:11 INFO mapreduce.Job:  map 0% reduce 0%
18/02/01 17:57:20 INFO mapreduce.Job:  map 50% reduce 0%
18/02/01 17:57:21 INFO mapreduce.Job:  map 100% reduce 0%
18/02/01 17:57:27 INFO mapreduce.Job:  map 100% reduce 100%
18/02/01 17:57:28 INFO mapreduce.Job: Job job_1516345010544_0030 completed successfully
18/02/01 17:57:28 INFO mapreduce.Job: Counters: 49
    File System Counters
        FILE: Number of bytes read=113911
        FILE: Number of bytes written=664972
        FILE: Number of read operations=0
        FILE: Number of large read operations=0
        FILE: Number of write operations=0
        HDFS: Number of bytes read=636501
        HDFS: Number of bytes written=68
        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)=12584
        Total time spent by all reduces in occupied slots (ms)=4425
        Total time spent by all map tasks (ms)=12584
        Total time spent by all reduce tasks (ms)=4425
        Total vcore-milliseconds taken by all map tasks=12584
        Total vcore-milliseconds taken by all reduce tasks=4425
        Total megabyte-milliseconds taken by all map tasks=12886016
        Total megabyte-milliseconds taken by all reduce tasks=4531200
    Map-Reduce Framework
        Map input records=2866
        Map output records=14734
        Map output bytes=84437
        Map output materialized bytes=113917
        Input split bytes=198
        Combine input records=0
        Combine output records=0
        Reduce input groups=8
        Reduce shuffle bytes=113917
        Reduce input records=14734
        Reduce output records=8
        Spilled Records=29468
        Shuffled Maps =2
        Failed Shuffles=0
        Merged Map outputs=2
        GC time elapsed (ms)=390
        CPU time spent (ms)=3660
        Physical memory (bytes) snapshot=713809920
        Virtual memory (bytes) snapshot=8331399168
        Total committed heap usage (bytes)=594018304
    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=636303
    File Output Format Counters 
        Bytes Written=68
18/02/01 17:57:28 INFO streaming.StreamJob: Output directory: /output/wordcount/WordwhiteCacheArchiveFiletest

1.7、 查看结果

$ hadoop fs -ls /output/wordcount/WordwhiteCacheArchiveFiletest
Found 2 items
-rw-r--r--   1 hadoop supergroup          0 2018-02-01 17:57 /output/wordcount/WordwhiteCacheArchiveFiletest/_SUCCESS
-rw-r--r--   1 hadoop supergroup         68 2018-02-01 17:57 /output/wordcount/WordwhiteCacheArchiveFiletest/part-00000
$ hadoop fs -text /output/wordcount/WordwhiteCacheArchiveFiletest/part-00000
and 2573
had 1526
have    350
in  1694
or  253
the 5144
this    412
to  2782

以上就完成了分发HDFS上的压缩文件并指定单词的wordcount.

2、hadoop streaming 语法参考