A process is an executing instance of an application, for example, double-clicking on the Internet browser icon on the desktop will start a process than runs the browser. A thread is an active flow of control that can be activated in parallel with other threads within the same process. The term "flow control" means a sequential execution of machine instructions. Also, a process can contain multiple threads, so starting the browser, the operating system creates a process and begins executing the primary threads of that process. Each thread can execute a set of instructions (typically, a function) independently and in parallel with other processes or threads. However, being the different active threads within the same process, they share space addressing and then the data structures. A thread is sometimes called a lightweight process because it shares many characteristics of a process, in particular, the characteristics of being a sequential flow of control that is executed in parallel with other control flows that are sequential. The term "light" is intended to indicate that the implementation of a thread is less onerous than that of a real process. However, unlike the processes, multiple threads may share many resources, in particular, space addressing and then the data structures.
Let's recap:
A process can consist of multiple parallel threads.
Normally, the creation and management of a thread by the operating system is less expensive in terms of CPU's resources than the creation and management of a process. Threads are used for small tasks, whereas processes are used for more heavyweight tasks—basically, the execution of applications.
The threads of the same process share the address space and other resources, while processes are independent of each other.
Before examining in detail the features and functionality of Python modules for the management of parallelism via threads and processes, let's first look at how the Python programming language works with these two entities.