我想解决字数计算的问题,并希望得到的结果在相反的排序顺序,根据出现的频率在文件中。
以下是我为此编写的四个文件(两个Map器和两个还原器,因为一个Map还原作业无法解决此问题):
1) Map1.py
import sys
import re
reload(sys)
sys.setdefaultencoding('utf-8') # required to convert to unicode
for line in sys.stdin:
try:
article_id, text = unicode(line.strip()).split('\t', 1)
except ValueError as e:
continue
words = re.split("\W*\s+\W*", text, flags=re.UNICODE)
for word in words:
print "%s\t%d" % (word.lower(), 1)
2) 异径管1.py
import sys
current_key = None
word_sum = 0
for line in sys.stdin:
try:
key, count = line.strip().split('\t', 1)
count = int(count)
except ValueError as e:
continue
if current_key != key:
if current_key:
print "%s\t%d" % (current_key, word_sum)
word_sum = 0
current_key = key
word_sum += count
if current_key:
print "%s\t%d" % (current_key, word_sum)
3) Map2.py
import sys
import re
reload(sys)
sys.setdefaultencoding('utf-8') # required to convert to unicode
for line in sys.stdin:
try:
word, count = line.strip().split('\t', 1)
count = int(count)
except ValueError as e:
continue
print "%s\t%d" % (word, count)
4) 异径管2.py
import sys
for line in sys.stdin:
try:
word, count = line.strip().split('\t', 1)
count = int(count)
except ValueError as e:
continue
print "%s\t%d" % (word, count)
下面是我在bash环境中运行的两个yarn命令
OUT_DIR="wordcount_result_1"
NUM_REDUCERS=8
hdfs dfs -rm -r -skipTrash ${OUT_DIR} > /dev/null
yarn jar /opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-streaming.jar \
-D mapred.jab.name="Streaming wordCount" \
-D mapreduce.job.reduces=${NUM_REDUCERS} \
-files mapper1.py,reducer1.py \
-mapper "python mapper1.py" \
-combiner "python reducer1.py" \
-reducer "python reducer1.py" \
-input /test/articles-part-short \
-output ${OUT_DIR} > /dev/null
OUT_DIR_2="wordcount_result_2"
NUM_REDUCERS=1
hdfs dfs -rm -r -skipTrash ${OUT_DIR_2} > /dev/null
yarn jar /opt/cloudera/parcels/CDH/lib/hadoop-mapreduce/hadoop-streaming.jar \
-D mapred.jab.name="Streaming wordCount Rating" \
-D mapreduce.job.output.key.comparator.class=org.apache.hadoop.mapreduce.lib.partition.KeyFieldBasedComparator \
-D map.output.key.field.separator=\t \
-D mapreduce.partition.keycomparator.options=-k2,2nr \
-D mapreduce.job.reduces=${NUM_REDUCERS} \
-files mapper2.py,reducer2.py \
-mapper "python mapper2.py" \
-reducer "python reducer2.py" \
-input ${OUT_DIR} \
-output ${OUT_DIR_2} > /dev/null
hdfs dfs -cat ${OUT_DIR_2}/part-00000 | head
这并没有给我正确的答案。有人能解释一下哪里出了问题吗?
另一方面,
在 mapper2.py
如果我按以下方式打印, print "%d\t%s" % (count, word)
而在 reducer2.py
如果我按以下方式阅读, count, word = line.strip().split('\t', 1)
并将第二个Yarn命令选项编辑为 -D mapreduce.partition.keycomparator.options=-k1,1nr
它给了我正确的答案。
为什么它在上述两种情况下的表现不同?
有人能帮我理解hadoopmapreduce的比较器选项吗?
1条答案
按热度按时间b5lpy0ml1#
这会有用的