Expand Up @@ -398,6 +398,192 @@ See also the description of the :keyword:`try` statement in section :ref:`try` and :keyword:`raise` statement in section :ref:`raise`. .. _execcomponents: Runtime Components ================== General Computing Model ----------------------- Python's execution model does not operate in a vacuum. It runs on a host machine and through that host's runtime environment, including its operating system (OS), if there is one. When a program runs, the conceptual layers of how it runs on the host look something like this: | **host machine** | **process** (global resources) | **thread** (runs machine code) Each process represents a program running on the host. Think of each process itself as the data part of its program. Think of the process' threads as the execution part of the program. This distinction will be important to understand the conceptual Python runtime. The process, as the data part, is the execution context in which the program runs. It mostly consists of the set of resources assigned to the program by the host, including memory, signals, file handles, sockets, and environment variables. Processes are isolated and independent from one another. (The same is true for hosts.) The host manages the process' access to its assigned resources, in addition to coordinating between processes. Each thread represents the actual execution of the program's machine code, running relative to the resources assigned to the program's process. It's strictly up to the host how and when that execution takes place. From the point of view of Python, a program always starts with exactly one thread. However, the program may grow to run in multiple simultaneous threads. Not all hosts support multiple threads per process, but most do. Unlike processes, threads in a process are not isolated and independent from one another. Specifically, all threads in a process share all of the process' resources. The fundamental point of threads is that each one does *run* independently, at the same time as the others. That may be only conceptually at the same time ("concurrently") or physically ("in parallel"). Either way, the threads effectively run at a non-synchronized rate. .. note:: That non-synchronized rate means none of the process' memory is guaranteed to stay consistent for the code running in any given thread. Thus multi-threaded programs must take care to coordinate access to intentionally shared resources. Likewise, they must take care to be absolutely diligent about not accessing any *other* resources in multiple threads; otherwise two threads running at the same time might accidentally interfere with each other's use of some shared data. All this is true for both Python programs and the Python runtime. The cost of this broad, unstructured requirement is the tradeoff for the kind of raw concurrency that threads provide. The alternative to the required discipline generally means dealing with non-deterministic bugs and data corruption. Python Runtime Model -------------------- The same conceptual layers apply to each Python program, with some extra data layers specific to Python: | **host machine** | **process** (global resources) | Python global runtime (*state*) | Python interpreter (*state*) | **thread** (runs Python bytecode and "C-API") | Python thread *state* At the conceptual level: when a Python program starts, it looks exactly like that diagram, with one of each. The runtime may grow to include multiple interpreters, and each interpreter may grow to include multiple thread states. .. note:: A Python implementation won't necessarily implement the runtime layers distinctly or even concretely. The only exception is places where distinct layers are directly specified or exposed to users, like through the :mod:`threading` module. .. note:: The initial interpreter is typically called the "main" interpreter. Some Python implementations, like CPython, assign special roles to the main interpreter. Likewise, the host thread where the runtime was initialized is known as the "main" thread. It may be different from the process' initial thread, though they are often the same. In some cases "main thread" may be even more specific and refer to the initial thread state. A Python runtime might assign specific responsibilities to the main thread, such as handling signals. As a whole, the Python runtime consists of the global runtime state, interpreters, and thread states. The runtime ensures all that state stays consistent over its lifetime, particularly when used with multiple host threads. The global runtime, at the conceptual level, is just a set of interpreters. While those interpreters are otherwise isolated and independent from one another, they may share some data or other resources. The runtime is responsible for managing these global resources safely. The actual nature and management of these resources is implementation-specific. Ultimately, the external utility of the global runtime is limited to managing interpreters. In contrast, an "interpreter" is conceptually what we would normally think of as the (full-featured) "Python runtime". When machine code executing in a host thread interacts with the Python runtime, it calls into Python in the context of a specific interpreter. .. note:: The term "interpreter" here is not the same as the "bytecode interpreter", which is what regularly runs in threads, executing compiled Python code. In an ideal world, "Python runtime" would refer to what we currently call "interpreter". However, it's been called "interpreter" at least since introduced in 1997 (`CPython:a027efa5b`_). .. _CPython:a027efa5b: https://github.com/python/cpython/commit/a027efa5b Each interpreter completely encapsulates all of the non-process-global, non-thread-specific state needed for the Python runtime to work. Notably, the interpreter's state persists between uses. It includes fundamental data like :data:`sys.modules`. The runtime ensures multiple threads using the same interpreter will safely share it between them. A Python implementation may support using multiple interpreters at the same time in the same process. They are independent and isolated from one another. For example, each interpreter has its own :data:`sys.modules`. For thread-specific runtime state, each interpreter has a set of thread states, which it manages, in the same way the global runtime contains a set of interpreters. It can have thread states for as many host threads as it needs. It may even have multiple thread states for the same host thread, though that isn't as common. Each thread state, conceptually, has all the thread-specific runtime data an interpreter needs to operate in one host thread. The thread state includes the current raised exception and the thread's Python call stack. It may include other thread-specific resources. .. note:: The term "Python thread" can sometimes refer to a thread state, but normally it means a thread created using the :mod:`threading` module. Each thread state, over its lifetime, is always tied to exactly one interpreter and exactly one host thread. It will only ever be used in that thread and with that interpreter. Multiple thread states may be tied to the same host thread, whether for different interpreters or even the same interpreter. However, for any given host thread, only one of the thread states tied to it can be used by the thread at a time. Thread states are isolated and independent from one another and don't share any data, except for possibly sharing an interpreter and objects or other resources belonging to that interpreter. Once a program is running, new Python threads can be created using the :mod:`threading` module (on platforms and Python implementations that support threads). Additional processes can be created using the :mod:`os`, :mod:`subprocess`, and :mod:`multiprocessing` modules. Interpreters can be created and used with the :mod:`~concurrent.interpreters` module. Coroutines (async) can be run using :mod:`asyncio` in each interpreter, typically only in a single thread (often the main thread). .. rubric:: Footnotes .. [#] This limitation occurs because the code that is executed by these operations Expand Down