Python WordCount (Hadoop Streaming)
# mapper.py - Map 단계
#!/usr/bin/env python3
"""
WordCount Mapper
입력: 텍스트 라인
출력: (단어, 1) 쌍
"""
import sys
import re
def mapper():
for line in sys.stdin:
# 소문자 변환 및 특수문자 제거
line = line.strip().lower()
words = re.findall(r'\b[a-z]+\b', line)
for word in words:
# 탭으로 구분된 키-값 출력
print(f"{word}\t1")
if __name__ == "__main__":
mapper()
# reducer.py - Reduce 단계
#!/usr/bin/env python3
"""
WordCount Reducer
입력: 정렬된 (단어, 1) 쌍
출력: (단어, 총 카운트)
"""
import sys
def reducer():
current_word = None
current_count = 0
for line in sys.stdin:
line = line.strip()
try:
word, count = line.split('\t')
count = int(count)
except ValueError:
continue
# 같은 단어면 카운트 누적
if current_word == word:
current_count += count
else:
# 이전 단어 결과 출력
if current_word:
print(f"{current_word}\t{current_count}")
current_word = word
current_count = count
# 마지막 단어 출력
if current_word:
print(f"{current_word}\t{current_count}")
if __name__ == "__main__":
reducer()
Hadoop Streaming 실행
# Hadoop Streaming으로 MapReduce 작업 실행
# 입력 데이터 HDFS에 업로드
hdfs dfs -mkdir -p /input/wordcount
hdfs dfs -put sample_text.txt /input/wordcount/
# MapReduce 작업 실행
hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-*.jar \
-files mapper.py,reducer.py \
-mapper "python3 mapper.py" \
-reducer "python3 reducer.py" \
-input /input/wordcount/ \
-output /output/wordcount
# 결과 확인
hdfs dfs -cat /output/wordcount/part-00000 | head -20
# 로컬 테스트 (Unix 파이프라인)
cat sample_text.txt | python3 mapper.py | sort | python3 reducer.py
Java MapReduce (전통적 방식)
// Java MapReduce WordCount
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
// Mapper 클래스
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object key, Text value, Context context)
throws IOException, InterruptedException {
// 단어 토큰화
StringTokenizer itr = new StringTokenizer(
value.toString().toLowerCase()
);
while (itr.hasMoreTokens()) {
word.set(itr.nextToken().replaceAll("[^a-z]", ""));
if (word.getLength() > 0) {
context.write(word, one);
}
}
}
}
// Combiner/Reducer 클래스
public static class IntSumReducer
extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class); // 로컬 집계
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
MRJob (Python 래퍼 라이브러리)
# MRJob을 사용한 Python MapReduce
from mrjob.job import MRJob
from mrjob.step import MRStep
import re
class WordCount(MRJob):
"""MRJob으로 구현한 WordCount"""
# 정규식 패턴
WORD_RE = re.compile(r"[a-z]+")
def mapper(self, _, line):
"""Map: 라인을 단어로 분할"""
for word in self.WORD_RE.findall(line.lower()):
yield word, 1
def combiner(self, word, counts):
"""Combiner: 로컬 집계 (선택적)"""
yield word, sum(counts)
def reducer(self, word, counts):
"""Reduce: 최종 집계"""
yield word, sum(counts)
class TopNWords(MRJob):
"""상위 N개 단어 찾기 (다단계 MapReduce)"""
def steps(self):
return [
MRStep(mapper=self.mapper_get_words,
combiner=self.combiner_count_words,
reducer=self.reducer_count_words),
MRStep(reducer=self.reducer_find_top_n)
]
def mapper_get_words(self, _, line):
for word in re.findall(r'[a-z]+', line.lower()):
yield word, 1
def combiner_count_words(self, word, counts):
yield word, sum(counts)
def reducer_count_words(self, word, counts):
# 모든 결과를 하나의 Reducer로
yield None, (sum(counts), word)
def reducer_find_top_n(self, _, word_count_pairs):
# 상위 10개 추출
top_n = sorted(word_count_pairs, reverse=True)[:10]
for count, word in top_n:
yield word, count
if __name__ == '__main__':
WordCount.run()
# 실행 방법
# 로컬: python wordcount.py input.txt
# Hadoop: python wordcount.py -r hadoop hdfs:///input/text.txt