In Python specifically, how do variables get shared between threads?
Although I have used
threading.Thread before I never really understood or saw examples of how variables got shared. Are they shared between the main thread and the children or only among the children? When would I need to use thread local storage to avoid this sharing?
I have seen many warnings about synchronizing access to shared data among threads by using locks but I have yet to see a really good example of the problem.
Thanks in advance!
In Python, everything is shared, except for function-local variables (because each function call gets its own set of locals, and threads are always separate function calls.) And even then, only the variables themselves (the names that refer to objects) are local to the function; objects themselves are always global, and anything can refer to them.
Thread object for a particular thread is not a special object in this regard. If you store the
Thread object somewhere all threads can access (like a global variable) then all threads can access that one
Thread object. If you want to atomically modify anything that another thread has access to, you have to protect it with a lock. And all threads must of course share this very same lock, or it wouldn’t be very effective.
If you want actual thread-local storage, that’s where
threading.local comes in. Attributes of
threading.local are not shared between threads; each thread sees only the attributes it itself placed in there. If you’re curious about its implementation, the source is in _threading_local.py in the standard library.
Consider the following code:
#/usr/bin/env python from time import sleep from random import random from threading import Thread, local data = local() def bar(): print("I'm called from", data.v) def foo(): bar() class T(Thread): def run(self): sleep(random()) data.v = self.getName() # Thread-1 and Thread-2 accordingly sleep(1) foo()
>> T().start(); T().start() I'm called from Thread-2 I'm called from Thread-1
Here threading.local() is used as a quick and dirty way to pass some data from run() to bar() without changing the interface of foo().
Note that using global variables won’t do the trick:
#/usr/bin/env python from time import sleep from random import random from threading import Thread def bar(): global v print("I'm called from", v) def foo(): bar() class T(Thread): def run(self): global v sleep(random()) v = self.getName() # Thread-1 and Thread-2 accordingly sleep(1) foo()
>> T().start(); T().start() I'm called from Thread-2 I'm called from Thread-2
Meanwhile, if you could afford passing this data through as an argument of foo() – it would be a more elegant and well-designed way:
from threading import Thread def bar(v): print("I'm called from", v) def foo(v): bar(v) class T(Thread): def run(self): foo(self.getName())
But this is not always possible when using third-party or poorly designed code.
You can create thread local storage using
4 tls.x 4tls = threading.local() tls.x =
Data stored to the tls will be unique to each thread which will help ensure that unintentional sharing does not occur.
Just like in every other language, every thread in Python has access to the same variables. There’s no distinction between the ‘main thread’ and child threads.
One difference with Python is that the Global Interpreter Lock means that only one thread can be running Python code at a time. This isn’t much help when it comes to synchronising access, however, as all the usual pre-emption issues still apply, and you have to use threading primitives just like in other languages. It does mean you need to reconsider if you were using threads for performance, however.
I may be wrong here. If you know otherwise please expound as this would help explain why one would need to use thread local().
This statement seems off, not wrong: “If you want to atomically modify anything that another thread has access to, you have to protect it with a lock.” I think this statement is ->effectively<- right but not entirely accurate. I thought the term “atomic” meant that the Python interpreter created a byte-code chunk that left no room for an interrupt signal to the CPU.
I thought atomic operations are chunks of Python byte code that does not give access to interrupts. Python statements like “running = True” is atomic. You do not need to lock CPU from interrupts in this case (I believe). The Python byte code breakdown is safe from thread interruption.
Python code like “threads_running = True” is not atomic. There are two chunks of Python byte code here; one to de-reference the list() for an object and another byte code chunk to assign a value to an object, in this case a “place” in a list. An interrupt can be raised –>between<- the two byte-code ->chunks<-. That is were bad stuff happens.
How does thread local() relate to “atomic”? This is why the statement seems misdirecting to me. If not can you explain?