Hadoop的streamingAPI与MapReduce[Python]

文章目录

1.创建模拟文本
[2. 使用mapperduce统计标签分布和抽取指定标签](#2. 使用mapperduce统计标签分布和抽取指定标签)
[3. 运行Map函数并排序结果以模拟Reduce任务：](#3. 运行Map函数并排序结果以模拟Reduce任务：)
4.运行在无网络开发机上

1.创建模拟文本

1.1 机器模拟生成

python 复制代码

from collections import namedtuple
from faker import Faker

# 初始化Faker
fake = Faker()

# 定义一个namedtuple类型，包含id, subject, text字段
GaokaoQuestion = namedtuple('GaokaoQuestion', 'id subject text')

# 定义生成模拟数据的函数
def generate_faker_data(num_samples):
    data = []
    for _ in range(num_samples):
        # 使用faker生成数据
        subject = fake.word()
        text = fake.sentence()
        
        # 使用md5生成id
        id_value = f"{text} {subject}"
        id_hash = hashlib.md5(id_value.encode('utf-8')).hexdigest()
        
        # 创建namedtuple实例
        question = GaokaoQuestion(id=id_hash, subject=subject, text=text)
        data.append(question)
    return data

# 生成3条模拟数据
samples = generate_faker_data(3)

# 打印生成的数据
for sample in samples:
    print(sample)

2.手动生成

shell 复制代码

cat > test_data.jsonl << EOF
{"id":"1", "subject":"Math", "text":"Math question"}
{"id":"2", "subject":"Science", "text":"Science question"}
{"id":"3", "subject":"Math", "text":"Another Math question"}
EOF

2. 使用mapperduce统计标签分布和抽取指定标签

python 复制代码

#!/usr/bin/env python3
import sys
import json
from collections import defaultdict

# 指定需要抽取的subject标签列表
TARGET_SUBJECTS = ["数学", "物理"]

def mapper():
    for line in sys.stdin:
        data = json.loads(line)
        if data['subject'] in TARGET_SUBJECTS:
            print(json.dumps(data))


def reducer():
    counts = defaultdict(int)
    for line in sys.stdin:
        subject, count = line.strip().split('\t')
        counts[subject] += int(count)
    for subject, count in counts.items():
        print(f"{subject}\t{count}")

if __name__ == "__main__":
    if len(sys.argv) > 1 and sys.argv[1] == 'reduce':
        reducer()
    else:
        mapper()

3. 运行Map函数并排序结果以模拟Reduce任务：

python 复制代码

cat test_data.jsonl | python3 mapper_reducer.py | sort -k1,1 | python3 mapper_reducer.py reduce

4.运行在无网络开发机上

Shell 复制代码

# 假设input_data.jsonl是HDFS上的输入文件路径
# 假设output是HDFS上输出结果的路径

# 运行Map任务
hadoop fs -get /path/to/input_data.jsonl input_data.jsonl
python \Auser\tmp\mapper_reducer_script\mapper_reducer.py | sort -k1,1 > mapped_output.txt

# 运行Reduce任务
python \Auser\tmp\mapper_reducer_script\mapper_reducer.py reduce < mapped_output.txt > reduced_output.txt

# 将结果上传到HDFS
hadoop fs -put reduced_output.txt /path/to/output/