进程可能有多个线程。这些线程可以共享内存，并且是进程中的执行单元。

进程运行在CPU上，因此线程驻留在每个进程之下。进程是独立运行的独立实体。如果您想在每个进程之间共享数据或状态，您可以使用内存存储工具，如缓存(redis, memcache)，文件或数据库。

线程模块使用线程，多处理模块使用进程。不同之处在于线程在相同的内存空间中运行，而进程有单独的内存。这使得在多进程之间共享对象变得有点困难。由于线程使用相同的内存，必须采取预防措施，否则两个线程将同时写入同一内存。这就是全局解释器锁的作用。

生成进程比生成线程要慢一些。

另一件没有提到的事情是，它取决于你使用的是什么操作系统。在Windows中，进程是昂贵的，所以线程在Windows中会更好，但在unix中，进程比它们的Windows变体更快，所以在unix中使用进程要安全得多，而且生成速度快。

Threading's job is to enable applications to be responsive. Suppose you have a database connection and you need to respond to user input. Without threading, if the database connection is busy the application will not be able to respond to the user. By splitting off the database connection into a separate thread you can make the application more responsive. Also because both threads are in the same process, they can access the same data structures - good performance, plus a flexible software design.

注意，由于GIL，应用程序实际上并没有同时做两件事，但我们所做的是将数据库上的资源锁放在一个单独的线程中，这样CPU时间就可以在它和用户交互之间切换。CPU时间在线程之间分配。

Multiprocessing is for times when you really do want more than one thing to be done at any given time. Suppose your application needs to connect to 6 databases and perform a complex matrix transformation on each dataset. Putting each job in a separate thread might help a little because when one connection is idle another one could get some CPU time, but the processing would not be done in parallel because the GIL means that you're only ever using the resources of one CPU. By putting each job in a Multiprocessing process, each can run on it's own CPU and run at full efficiency.

Other answers have focused more on the multithreading vs multiprocessing aspect, but in python Global Interpreter Lock (GIL) has to be taken into account. When more number (say k) of threads are created, generally they will not increase the performance by k times, as it will still be running as a single threaded application. GIL is a global lock which locks everything out and allows only single thread execution utilizing only a single core. The performance does increase in places where C extensions like numpy, Network, I/O are being used, where a lot of background work is done and GIL is released. So when threading is used, there is only a single operating system level thread while python creates pseudo-threads which are completely managed by threading itself but are essentially running as a single process. Preemption takes place between these pseudo threads. If the CPU runs at maximum capacity, you may want to switch to multiprocessing. Now in case of self-contained instances of execution, you can instead opt for pool. But in case of overlapping data, where you may want processes communicating you should use multiprocessing.Process.

关键的优势是隔离。进程崩溃不会导致其他进程崩溃，而线程崩溃可能会对其他线程造成严重破坏。