我正在尝试使用并发期货来掌握多线程/多处理。
我已经尝试使用以下几组代码。我知道我总是会遇到磁盘IO问题,但我希望尽可能地最大化我的ram和CPU使用率。
对于大规模处理,最常用/最好的方法是什么?
如何使用并发期货来处理大型数据集?
是否有比下面的方法更受欢迎的方法?
方法1:
for folders in os.path.isdir(path):
p = multiprocessing.Process(pool.apply_async(process_largeFiles(folders)))
jobs.append(p)
p.start()
方法二:
with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:
for folders in os.path.isdir(path):
executor.submit(process_largeFiles(folders), 100)
方法3:
with concurrent.futures.ProcessPoolExecutor(max_workers=10) as executor:
for folders in os.path.isdir(path):
executor.submit(process_largeFiles(folders), 10)
我应该尝试同时使用进程池和线程池吗?
方法(思想):
with concurrent.futures.ProcessPoolExecutor(max_workers=10) as process:
with concurrent.futures.ThreadPoolExecutor(max_workers=100) as thread:
for folders in os.path.isdir(path):
process.submit(thread.submit(process_largeFiles(folders), 100),10)
在最广泛的用例中最大化我的内存和cpu的最有效的方法是什么?
我意识到启动进程需要一点时间,但与正在处理的文件的大小相比,它会更重要吗?
发布于 2018-07-08 02:43:56
使用TreadPoolExecutor打开并读取文件,然后使用ProcessPoolExecutor处理数据。
import concurrent.futures
from collections import deque
TPExecutor = concurrent.futures.ThreadPoolExecutor
PPExecutor = concurrent.futures.ProcessPoolExecutor
def get_file(path):
with open(path) as f:
data = f.read()
return data
def process_large_file(s):
return sum(ord(c) for c in s)
files = [filename1, filename2, filename3, filename4, filename5,
filename6, filename7, filename8, filename9, filename0]
results = []
completed_futures = collections.deque()
def callback(future, completed=completed_futures):
completed.append(future)
with TPExecutor(max_workers = 4) as thread_pool_executor:
data_futures = [thread_pool_executor.submit(get_file, path) for path in files]
with PPExecutor() as process_pool_executor:
for data_future in concurrent.futures.as_completed(data_futures):
future = process_pool_executor.submit(process_large_file, data_future.result())
future.add_done_callback(callback)
# collect any that have finished
while completed_futures:
results.append(completed_futures.pop().result())
使用了一个已完成的回调,这样它就不必等待完成的未来。我不知道这是如何影响效率的--它主要用来简化as_completed
循环中的逻辑/代码。
如果由于内存限制而需要限制文件或数据提交,则需要对其进行重构。根据文件读取时间和处理时间,很难说在任何给定时刻内存中会有多少数据。我认为在as_completed
中收集结果应该有助于缓解这种情况。在设置ProcessPoolExecutor时,data_futures
可能会开始完成-该序列可能需要优化。
https://stackoverflow.com/questions/42941584
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