问题描述
我正在使用带有 threading 线程的 Python 2 subprocess 来获取标准输入，并使用二进制文件 A、B 和 C 并将修改后的数据写入标准输出.
I am using Python 2 subprocess with threading threads to take standard input, process it with binaries A, B, and C and write modified data to standard output.
这个脚本(我们称之为:A_to_C.py)非常慢，我想学习如何修复它.
This script (let's call it: A_to_C.py) is very slow and I'd like to learn how to fix it.
大致流程如下:
A_process = subprocess.Popen(['A', '-'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
produce_A_thread = threading.Thread(target=produceA, args=(sys.stdin, A_process.stdin))
B_process = subprocess.Popen(['B', '-'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
convert_A_to_B_thread = threading.Thread(target=produceB, args=(A_process.stdout, B_process.stdin))
C_process = subprocess.Popen(['C', '-'], stdin=subprocess.PIPE)
convert_B_to_C_thread = threading.Thread(target=produceC, args=(B_process.stdout, C_process.stdin))
produce_A_thread.start()
convert_A_to_B_thread.start()
convert_B_to_C_thread.start()
produce_A_thread.join()
convert_A_to_B_thread.join()
convert_B_to_C_thread.join()
A_process.wait()
B_process.wait()
C_process.wait()
这个想法是标准输入进入A_to_C.py:
The idea is that standard input goes into A_to_C.py:
A二进制文件处理一大块标准输入，并使用函数produceA创建A输出.B二进制文件处理A的标准输出块并通过produceB函数创建B输出代码>.C二进制文件通过produceC函数处理B的标准输出块并写入C- 输出到标准输出.
- The
Abinary processes a chunk of standard input and createsA-output with the functionproduceA. - The
Bbinary processes a chunk ofA's standard output and createsB-output via the functionproduceB. - The
Cbinary processes a chunk ofB's standard output via the functionproduceCand writesC-output to standard output.
我使用 cProfile 进行了分析，并且该脚本中的几乎所有时间似乎都花在了获取线程锁上.
I did profiling with cProfile and nearly all of the time in this script appears to be spent in acquiring thread locks.
例如，在测试 417s 作业中，416s(>99% 的总运行时间)用于获取线程锁:
For instance, in a test 417s job, 416s (>99% of the total runtime) is spent on acquiring thread locks:
$ python
Python 2.6.6 (r266:84292, Nov 21 2013, 10:50:32)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-4)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import pstats
>>> p = pstats.Stats('1.profile')
>>> p.sort_stats('cumulative').print_stats(10)
Thu Jun 12 22:19:07 2014 1.profile
1755 function calls (1752 primitive calls) in 417.203 CPU seconds
Ordered by: cumulative time
List reduced from 162 to 10 due to restriction <10>
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.020 0.020 417.203 417.203 A_to_C.py:90(<module>)
1 0.000 0.000 417.123 417.123 A_to_C.py:809(main)
6 0.000 0.000 416.424 69.404 /foo/python/2.7.3/lib/python2.7/threading.py:234(wait)
32 416.424 13.013 416.424 13.013 {method 'acquire' of 'thread.lock' objects}
3 0.000 0.000 416.422 138.807 /foo/python/2.7.3/lib/python2.7/threading.py:648(join)
3 0.000 0.000 0.498 0.166 A_to_C.py:473(which)
37 0.000 0.000 0.498 0.013 A_to_C.py:475(is_exe)
3 0.496 0.165 0.496 0.165 {posix.access}
6 0.000 0.000 0.194 0.032 /foo/python/2.7.3/lib/python2.7/subprocess.py:475(_eintr_retry_call)
3 0.000 0.000 0.191 0.064 /foo/python/2.7.3/lib/python2.7/subprocess.py:1286(wait)
我的 threading.Thread 和/或 subprocess.Popen 安排有什么问题导致此问题?
What am I doing wrong with my threading.Thread and/or subprocess.Popen arrangement which is causing this issue?
推荐答案
您对 subprocess.Popen() 的调用隐式指定了 bufsize 的默认值 0，这会强制无缓冲 I/O.尝试添加一个合理的缓冲区大小(4K、16K，甚至 1M)，看看是否有什么不同.
Your calls to subprocess.Popen() implicitly specify the default value of bufsize, 0, which forces unbuffered I/O. Try adding a reasonable buffer size (4K, 16K, even 1M) and see if it makes any difference.
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