Python 多处理：map vs map_async
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multiprocessing: map vs map_async
提问by aman
What's the difference between using mapand map_async? Are they not running the same function after distributing the items from the list to 4 processes?
usingmap和 和有map_async什么不一样？将列表中的项目分发给 4 个进程后，它们是否没有运行相同的功能？
So is it wrong to presume both are running asynchronous and parallel?
那么假设两者都在异步和并行运行是错误的吗？
def f(x):
return 2*x
p=Pool(4)
l=[1,2,3,4]
out1=p.map(f,l)
#vs
out2=p.map_async(f,l)
回答by quikst3r
There are four choices to mapping jobs to processes. You have to consider multi-args, concurrency, blocking, and ordering. mapand map_asynconly differ with respect to blocking. map_asyncis non-blocking where as mapis blocking
将作业映射到进程有四种选择。您必须考虑多参数、并发、阻塞和排序。map并且map_async仅在阻塞方面有所不同。map_async是非阻塞的，而map阻塞的则是
So let's say you had a function
假设你有一个函数
from multiprocessing import Pool
import time
def f(x):
print x*x
if __name__ == '__main__':
pool = Pool(processes=4)
pool.map(f, range(10))
r = pool.map_async(f, range(10))
# DO STUFF
print 'HERE'
print 'MORE'
r.wait()
print 'DONE'
Example output:
示例输出：
0
1
9
4
16
25
36
49
64
81
0
HERE
1
4
MORE
16
25
36
9
49
64
81
DONE
pool.map(f, range(10))will wait for all 10 of those function calls to finish so we see all the prints in a row.
r = pool.map_async(f, range(10))will execute them asynchronously and only block when r.wait()is called so we see HEREand MOREin between but DONEwill always be at the end.
pool.map(f, range(10))将等待所有 10 个这些函数调用完成，以便我们看到所有打印成一行。
r = pool.map_async(f, range(10))将异步执行它们并且只在r.wait()被调用时阻塞，所以我们看到HERE和MORE在两者之间，但DONE总是在最后。
