How to use Python regular expressions for multi-process programming

Python is a widely used programming language whose regular expression module can be used to match, search, and manipulate strings. Multi-process programming is a programming technology that processes tasks in parallel, which can improve the running efficiency of the program. This article will introduce how to use Python regular expressions for multi-process programming to process large amounts of data faster.

1. Import the necessary libraries

When performing multi-process programming, you need to use the multiprocessing and re libraries. multiprocessing is used to create and manage processes, and re is used to apply regular expressions.

import multiprocessing
import re

2. Prepare data

Before using regular expressions, you need some data to match. This article will use a list of strings that need to be matched as sample data.

data = [
    'xyz_123_mn1_na1234_qwe_rty',
    'pqr_234_mn2_na2345_asd_fgh',
    'hjk_345_mn3_na3456_zxc_vbn',
    'lmn_456_mn4_na4567_qaz_wsx',
    'hgo_567_mn5_na5678_edc_rfv'
]

3. Define regular expression

Before performing regular expression matching, you need to define a regular expression.

The regular expression in this example is used to match numbers in a string, where d means matching one or more groups of numbers.

pattern = re.compile(r'd+')

4. Define processing function

When doing multi-process programming, you need to first define a function and pass the data to the function for processing. The function in this example takes the parameter data as input, extracts the matching number in each string, and returns it as the return value.

def get_numbers(data):
    result = list()
    for string in data:
        numbers = pattern.findall(string)
        result.append(numbers)
    return result

5. Create a process pool

After preparing the data and functions, you can create a process pool to process the data in parallel.

if __name__ == '__main__':
    # 创建进程池
    pool = multiprocessing.Pool()

6. Submit the task to the process pool

Using the map() method of the process pool, the task can be submitted to the process pool for parallel processing.

    # 将任务提交到进程池
    result = pool.map(get_numbers, [data])

7. Close the process pool and output the results

After submitting the task, you need to close the process pool to release resources. At the same time, the processing results also need to be output.

    # 关闭进程池
    pool.close()
    pool.join()

    # 输出结果
    print(result)

Complete code example:

import multiprocessing
import re

data = [
    'xyz_123_mn1_na1234_qwe_rty',
    'pqr_234_mn2_na2345_asd_fgh',
    'hjk_345_mn3_na3456_zxc_vbn',
    'lmn_456_mn4_na4567_qaz_wsx',
    'hgo_567_mn5_na5678_edc_rfv'
]

pattern = re.compile(r'd+')


def get_numbers(data):
    result = list()
    for string in data:
        numbers = pattern.findall(string)
        result.append(numbers)
    return result


if __name__ == '__main__':
    # 创建进程池
    pool = multiprocessing.Pool()

    # 将任务提交到进程池
    result = pool.map(get_numbers, [data])

    # 关闭进程池
    pool.close()
    pool.join()

    # 输出结果
    print(result)

Conclusion

Using Python regular expressions and multi-process programming can greatly improve the efficiency of data processing. This article explains how to use Python regular expressions for multi-process programming so that you can complete tasks faster when working with large amounts of data in the future.

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