how to write thread-safe module ? and pytz

Does someone know if the module pytz
(http://sourceforge.net/projects/pytz/) is thread-safe ?
On that, I don't know.
That's because in the file pytz/tzinfo.py, I see global variables
_timedelta_cache, _datetime_cache, _ttinfo_cache, which are
dictionnaries and are used as cache.
I always thought that you must protect shared data with locks when
multithreading, but I don't see any lock anywhere in pytz.
Definitely stick with what you always thought.
However, pytz seems to work well with multiple threads creating various
timezone objects at the same time.
Buggy threading can seem to work well for along time. It may be
a billion-to-one shot that a thread switch happens at just the
wrong time.
I don't understand where the trick is, that allows multiple threads to
access this module without any locking and that all this seems to work
without any problem...

Does this mean that there is a means to write a module that is
thread-safe, without importing the threading module and creating locks
?

Or have I not understood something ...?

Can someone give me a hint ?

Does someone know if the module pytz
(http://sourceforge.net/projects/pytz/) is thread-safe ?
I have not seen it explicitely stated, and just wanted to be sure, as I
want to use it.

That's because in the file pytz/tzinfo.py, I see global variables
_timedelta_cache, _datetime_cache, _ttinfo_cache, which are
dictionnaries and are used as cache.
I always thought that you must protect shared data with locks when
multithreading, but I don't see any lock anywhere in pytz.
Dictionaries (and probably most other Python types that are implemented
in C) are inherently thread safe.

This applies only to the individual methods of dictionaries. The
following code would still require a lock:
if mydict.has_key (keyval):
variable = mydict [keyval]
because a second thread could delete the entry between the calls to
has_key and __getvalue__.

mydict [keyval] = mydict.get (keyval, 0) + 1
is also an candidate for problems.
However, pytz seems to work well with multiple threads creating various
timezone objects at the same time.

Dictionaries (and probably most other Python types that are implemented
in C) are inherently thread safe.

Does someone know if the module pytz
(http://sourceforge.net/projects/pytz/) is thread-safe ?
I have not seen it explicitely stated, and just wanted to be sure, as I
want to use it.
pytz is thread safe.
That's because in the file pytz/tzinfo.py, I see global variables
_timedelta_cache, _datetime_cache, _ttinfo_cache, which are
dictionnaries and are used as cache. I always thought that you must protect shared data with locks when
multithreading, but I don't see any lock anywhere in pytz.
However, pytz seems to work well with multiple threads creating various
timezone objects at the same time.
I don't understand where the trick is, that allows multiple threads to
access this module without any locking and that all this seems to work
without any problem...

from threading import Thread
import time
stack = []
stack2 = []
def doit(i): .... stack.append(i)
.... time.sleep(0.1)
.... stack2.append(stack.pop())
.... threads = [Thread(target=doit, args=(i,)) for i in range(0,100)]
for t in threads: t.start() .... for t in threads: t.join() .... len(stack2) 100 stack2 [99, 95, 98, 94, 93, 97, 92, 91, 96, 88, 87, 86, 85, 84, 83, 90, 79, 78, 77,
76, 74, 73, 72, 71, 70, 75, 82, 81, 80, 89, 69, 67, 66, 65, 64, 68, 60, 59,
58, 57, 56, 55, 63, 62, 61, 49, 54, 53, 52, 51, 46, 45, 44, 50, 48, 47, 29,
28, 35, 34, 33, 43, 42, 41, 40, 39, 38, 32, 37, 31, 30, 36, 27, 26, 25, 24,
23, 22, 21, 20, 19, 18, 17, 12, 16, 15, 14, 13, 11, 10, 9, 8, 7, 6, 4, 3, 2,
1, 0, 5]

Note that the value being appended to 'stack2' might not be the value that
was appended to 'stack' in any particular thread - in this case, we don't
care (but is the sort of thing you might need to watch out for).

In the code you mention in pytz, there *is* a race condition. However, if
this condition occurs the side effects are so trivial as to not worry about
locking. ie. if a number of threads call memorized_timedelta(seconds=60)
simultaneously, there is a slight chance that each thread will get a
different timedelta instance. This is extremely unlikely, and the rest of
the code doesn't care at all. If pytz compared the timedeltas using 'is'
instead of '==' at any point, it would be a bug (but it doesn't, so it isn't).

So you can write thread safe Python code without locks provided you are
using the builtin types, and keep a close eye out for race conditions. This
might sound error prone, but it is quite doable provided the critical areas
that are accessing shared objects are kept isolated, short and simple.

Here is an thread unsafe example. Here the mistake is made that the length
of stack will not change after checking it. Also because we don't use the
atomic stack.pop(), two threads might add the same value to stack2:
from threading import Thread
import time
stack = range(0, 50)
stack2 = []
def doit(): .... if len(stack) > 0:
.... stack2.append(stack[-1])
.... time.sleep(0.1)
.... del stack[-1]
.... threads = [Thread(target=doit) for i in range(0, 100)]
for t in threads: t.start()