Threads are another way to start activities running at the same time. They sometimes are called “lightweight processes,” and they are run in parallel like forked processes, but all run within the same single process. For applications that can benefit from parallel processing, threads offer big advantages for programmers:


Because all threads run within the same process, they don’t generally incur a big startup cost to copy the process itself. The costs of both copying forked processes and running threads can vary per platform, but threads are usually considered less expensive in terms of performance overhead.


Threads can be noticeably simpler to program too, especially when some of the more complex aspects of processes enter the picture (e.g., process exits, communication schemes, and “zombie” processes covered in Chapter 10).

Shared global memory

Also because threads run in a single process, every thread shares the same global memory space of the process. This provides a natural and easy way for threads to communicate -- by fetching and setting data in global memory. To the Python programmer, this means that global (module-level) variables and interpreter components are shared among all threads in a program: if one thread assigns a global variable, its new value will be seen by other threads. Some care must be taken to control access to shared global objects, but they are still generally simpler to use than the sorts of process communication tools ...

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