以下是使用Python multiprocessing模块处理大文件的示例代码，采用分块读取和并行处理的方式提高效率：
import multiprocessing
import os
def process_chunk(chunk_path, process_func):
"""处理单个文件块"""
with open(chunk_path, 'r') as f:
data = f.read()
result = process_func(data)
with open(chunk_path + '.processed', 'w') as f:
f.write(str(result))
os.remove(chunk_path)
def split_file(file_path, chunk_size=1024*1024):
"""将大文件分割成多个临时块"""
chunks = []
with open(file_path, 'rb') as f:
i = 0
while True:
chunk = f.read(chunk_size)
if not chunk:
break
chunk_path = f"{file_path}.part{i}"
with open(chunk_path, 'wb') as chunk_file:
chunk_file.write(chunk)
chunks.append(chunk_path)
i += 1
return chunks
def parallel_process(file_path, process_func, workers=None):
"""并行处理大文件"""
if not workers:
workers = multiprocessing.cpu_count()
chunks = split_file(file_path)
pool = multiprocessing.Pool(processes=workers)
for chunk in chunks:
pool.apply_async(process_chunk, args=(chunk, process_func))
pool.close()
pool.join()
# 合并处理结果（根据实际需求实现）
# ...
这个实现包含三个主要函数：split_file将大文件分割成多个临时块，process_chunk处理单个文件块，parallel_process管理整个并行处理流程。使用时需要自定义process_func处理函数，workers参数默认为CPU核心数。注意处理完成后需要根据业务需求合并结果。
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