python eventlet 模块学习tpool和greenpool

当我们需要使用到python的c接口，特别是一些对os的系统调用，官方说明如下：

The vast majority of the times you’ll want to use threads are to wrap

some operation that is not “green”, such as a C library that uses its

own OS calls to do socket operations. The tpool module is provided to make these uses simpler.

如：

nova/virt/disk/vfs/guestfs.py：174

def setup(self, mount=True):
LOG.debug("Setting up appliance for %(image)s",
{'image': self.image})
try:
self.handle = tpool.Proxy(
guestfs.GuestFS(python_return_dict=False,
close_on_exit=False))

其中guestfs.GuestFS是一个c接口，作用是对一个image的操作，对image的读写，即对文件系统文件的读写。

In [9]: from eventlet import tpool

In [10]:  import thread

In [11]: def my_func(thread_ident):
...:     print ("now raw thrad id:",  thread_ident)
...:

In [12]: tpool.execute(my_func, thread.get_ident())
('now raw thrad id:', 140736349889472)

In [13]: def my_func(thread_ident):
...:     print ("now raw thrad id:",  thread_ident, thread.get_ident())
...:
...:

In [14]: tpool.execute(my_func, thread.get_ident())
('now raw thrad id:', 140736349889472, 123145477103616)

In [15]: tpool.execute(my_func, thread.get_ident())
('now raw thrad id:', 140736349889472, 123145481310208)

In [16]: tpool.execute(my_func, thread.get_ident())
('now raw thrad id:', 140736349889472, 123145485516800)

明显看出thread进程的多进程都是同一个id，而tpool的则是不同的进程。

同时举个关于greenlet的例子

from eventlet import greenpool
pool = greenpool.GreenPool()
for result in pool.imap(worker ,open('test.txt', 'r')):
print result

worker in thread 140736349889472
.

worker in thread 140736349889472
worker in thread 140736349889472
worker in thread 140736349889472
worker in thread 140736349889472
worker in thread 140736349889472
worker in thread 140736349889472
.

.

.

.

.

.
其中test.txt内容如下：
.
.
.
.
.
.

greenpool得到的是同一个native thread，这样若使用c接口的话，会影响进程的开关以及进程的block。