14.1 Threads in Python

Python offers multithreading on platforms that support threads, such as Win32, Linux, and most variants of Unix. The Python interpreter does not freely switch threads. Python uses a global interpreter lock (GIL) to ensure that switching between threads happens only between bytecode instructions or when C code deliberately releases the GIL (Python's C code releases the GIL around blocking I/O and sleep operations). An action is said to be atomic if it's guaranteed that no thread switching within Python's process occurs between the start and the end of the action. In practice, an operation that looks atomic actually is atomic when executed on an object of a built-in type (augmented assignment on an immutable object, however, is not atomic). However, in general it is not a good idea to rely on atomicity. For example, you never know when you might be dealing with a derived class rather than an object of a built-in type, meaning there might be callbacks to Python code.

Python offers multithreading in two different flavors. An older and lower-level module, thread, offers a bare minimum of functionality, and is not recommended for direct use by your code. The higher-level module threading, built on top of thread, was loosely inspired by Java's threads, and is the recommended tool. The key design issue in multithreading systems is most often how best to coordinate multiple threads. threading therefore supplies several synchronization objects. Module Queue is very useful for thread synchronization as it supplies a synchronized FIFO queue type, which is extremely handy for communication and coordination between threads.