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本文將為大家詳細介紹如何使用python實現(xiàn)文件md5碼的批量存儲，希望能為您帶來實用性的參考，助您在學習過程中有所收獲。

python實現(xiàn)文件MD5碼的批量存儲

需求概述

針對一組文件批量計算其MD5碼，并將結果存儲到數(shù)據(jù)庫或其他永久性存儲介質(zhì)中。

實現(xiàn)方法

1. 利用hashlib模塊計算MD5碼

import hashlib  def calculate_md5(path):     with open(path, "rb") as f:         md5_hash = hashlib.md5()         while True:             data = f.read(1024)             if not data:                 break             md5_hash.update(data)     return md5_hash.hexdigest()

2. 通過多線程或多進程提升計算效率

from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor  def calculate_md5_concurrently(paths, max_workers=10):     if max_workers

Python怎么實現(xiàn)文件MD5碼批量存儲

<p><strong>3. 使用數(shù)據(jù)庫或其他存儲機制存儲MD5碼</strong></p> import sqlite3  def store_md5_codes(md5_hashes, database_path):     conn = sqlite3.connect(database_path)     cursor = conn.cursor()     for path, md5_hash in md5_hashes:         cursor.execute("INSERT INTO md5_codes (path, md5_hash) VALUES (?, ?)", (path, md5_hash))     conn.commit()     conn.close() <p><strong>4. 完整示例</strong></p> import hashlib from concurrent.futures import ThreadPoolExecutor import sqlite3  def calculate_and_store_md5_codes(paths, database_path, max_workers=10):     md5_hashes = calculate_md5_concurrently(paths, max_workers)     store_md5_codes(md5_hashes, database_path)  paths = ["path/to/file1.txt", "path/to/file2.txt", ...] database_path = "path/to/md5_codes.db" calculate_and_store_md5_codes(paths, database_path) <p><strong>優(yōu)點</strong></p>