It sounds like you want to implement the producer/consumer pattern with eight workers. Python has a Queue class for this purpose, and it is thread-safe. Each worker should call get() on the queue to retrieve a task. This call will block if no tasks are available, causing the worker to go idle until one becomes … Read more
t = threading.Thread(target=f, kwargs={‘x’: 1,’y’: 2}) this will pass a dictionary with the keyword arguments’ names as keys and argument values as values in the dictionary. the other answer above won’t work, because the “x” and “y” are undefined in that scope. another example, this time with multiprocessing, passing both positional and keyword arguments: the … Read more
Is this saying that this program won’t ever finish? Yes, that program won’t finish, just try it out. I ask because I have a situation where in my main thread I’m calling sys.exit(), and the process just hangs and my other threads are running as I can see the log. Does this have anything to … Read more
In Python 3, thread has been renamed to _thread. It is infrastructure code that is used to implement threading, and normal Python code shouldn’t be going anywhere near it. _thread exposes a fairly raw view of the underlying OS level processes. This is almost never what you want, hence the rename in Py3k to indicate … Read more
If you make your worker threads daemon threads, they will die when all your non-daemon threads (e.g. the main thread) have exited. http://docs.python.org/library/threading.html#threading.Thread.daemon
First, in Python, if your code is CPU-bound, multithreading won’t help, because only one thread can hold the Global Interpreter Lock, and therefore run Python code, at a time. So, you need to use processes, not threads. This is not true if your operation “takes forever to return” because it’s IO-bound—that is, waiting on the … Read more
The problem is that you transform the result of ThreadPoolExecutor.map to a list. If you don’t do this and instead iterate over the resulting generator directly, the results are still yielded in the original order but the loop continues before all results are ready. You can test this with this example: import time import concurrent.futures … Read more
Your indentation is goofed, and you’ve mixed tabs and spaces. Run the script with python -tt to verify.
The best way is to start the timer thread once. Inside your timer thread you’d code the following class MyThread(Thread): def __init__(self, event): Thread.__init__(self) self.stopped = event def run(self): while not self.stopped.wait(0.5): print(“my thread”) # call a function In the code that started the timer, you can then set the stopped event to stop the … Read more
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool. The code below comes from an article/blog post that you should definitely check out (no affiliation) – Parallelism in one line: A Better Model for Day to Day Threading Tasks. I’ll … Read more