"concurrent.interpreters" — Multiple interpreters in the same process
*********************************************************************

Added in version 3.14.

**Source code:** Lib/concurrent/interpreters

======================================================================

The "concurrent.interpreters" module constructs higher-level
interfaces on top of the lower level "_interpreters" module.

The module is primarily meant to provide a basic API for managing
interpreters (AKA “subinterpreters”) and running things in them.
Running mostly involves switching to an interpreter (in the current
thread) and calling a function in that execution context.

For concurrency, interpreters themselves (and this module) don’t
provide much more than isolation, which on its own isn’t useful.
Actual concurrency is available separately through "threads"  See
below

See also:

  "InterpreterPoolExecutor"
     Combines threads with interpreters in a familiar interface.

  Isolating Extension Modules
     How to update an extension module to support multiple
     interpreters.

  **PEP 554**

  **PEP 734**

  **PEP 684**

Availability: not WASI.

This module does not work or is not available on WebAssembly. See
WebAssembly platforms for more information.


Key details
===========

Before we dive in further, there are a small number of details to keep
in mind about using multiple interpreters:

* isolated, by default

* no implicit threads

* not all PyPI packages support use in multiple interpreters yet


Introduction
============

An “interpreter” is effectively the execution context of the Python
runtime.  It contains all of the state the runtime needs to execute a
program.  This includes things like the import state and builtins.
(Each thread, even if there’s only the main thread, has some extra
runtime state, in addition to the current interpreter, related to the
current exception and the bytecode eval loop.)

The concept and functionality of the interpreter have been a part of
Python since version 2.2, but the feature was only available through
the C-API and not well known, and the isolation was relatively
incomplete until version 3.12.


Multiple Interpreters and Isolation
-----------------------------------

A Python implementation may support using multiple interpreters in the
same process.  CPython has this support.  Each interpreter is
effectively isolated from the others (with a limited number of
carefully managed process-global exceptions to the rule).

That isolation is primarily useful as a strong separation between
distinct logical components of a program, where you want to have
careful control of how those components interact.

Note:

  Interpreters in the same process can technically never be strictly
  isolated from one another since there are few restrictions on memory
  access within the same process.  The Python runtime makes a best
  effort at isolation but extension modules may easily violate that.
  Therefore, do not use multiple interpreters in security-sensitive
  situations, where they shouldn’t have access to each other’s data.


Running in an Interpreter
-------------------------

Running in a different interpreter involves switching to it in the
current thread and then calling some function.  The runtime will
execute the function using the current interpreter’s state.  The
"concurrent.interpreters" module provides a basic API for creating and
managing interpreters, as well as the switch-and-call operation.

No other threads are automatically started for the operation. There is
a helper for that though. There is another dedicated helper for
calling the builtin "exec()" in an interpreter.

When "exec()" (or "eval()") are called in an interpreter, they run
using the interpreter’s "__main__" module as the “globals” namespace.
The same is true for functions that aren’t associated with any module.
This is the same as how scripts invoked from the command-line run in
the "__main__" module.


Concurrency and Parallelism
---------------------------

As noted earlier, interpreters do not provide any concurrency on their
own.  They strictly represent the isolated execution context the
runtime will use *in the current thread*.  That isolation makes them
similar to processes, but they still enjoy in-process efficiency, like
threads.

All that said, interpreters do naturally support certain flavors of
concurrency. There’s a powerful side effect of that isolation.  It
enables a different approach to concurrency than you can take with
async or threads.  It’s a similar concurrency model to CSP or the
actor model, a model which is relatively easy to reason about.

You can take advantage of that concurrency model in a single thread,
switching back and forth between interpreters, Stackless-style.
However, this model is more useful when you combine interpreters with
multiple threads.  This mostly involves starting a new thread, where
you switch to another interpreter and run what you want there.

Each actual thread in Python, even if you’re only running in the main
thread, has its own *current* execution context.  Multiple threads can
use the same interpreter or different ones.

At a high level, you can think of the combination of threads and
interpreters as threads with opt-in sharing.

As a significant bonus, interpreters are sufficiently isolated that
they do not share the *GIL*, which means combining threads with
multiple interpreters enables full multi-core parallelism. (This has
been the case since Python 3.12.)


Communication Between Interpreters
----------------------------------

In practice, multiple interpreters are useful only if we have a way to
communicate between them.  This usually involves some form of message
passing, but can even mean sharing data in some carefully managed way.

With this in mind, the "concurrent.interpreters" module provides a
"queue.Queue" implementation, available through "create_queue()".


“Sharing” Objects
-----------------

Any data actually shared between interpreters loses the thread-safety
provided by the *GIL*.  There are various options for dealing with
this in extension modules.  However, from Python code the lack of
thread-safety means objects can’t actually be shared, with a few
exceptions.  Instead, a copy must be created, which means mutable
objects won’t stay in sync.

By default, most objects are copied with "pickle" when they are passed
to another interpreter.  Nearly all of the immutable builtin objects
are either directly shared or copied efficiently.  For example:

* "None"

* "bool" ("True" and "False")

* "bytes"

* "str"

* "int"

* "float"

* "tuple" (of similarly supported objects)

There is a small number of Python types that actually share mutable
data between interpreters:

* "memoryview"

* "Queue"


Reference
=========

This module defines the following functions:

concurrent.interpreters.list_all()

   Return a "list" of "Interpreter" objects, one for each existing
   interpreter.

concurrent.interpreters.get_current()

   Return an "Interpreter" object for the currently running
   interpreter.

concurrent.interpreters.get_main()

   Return an "Interpreter" object for the main interpreter. This is
   the interpreter the runtime created to run the *REPL* or the script
   given at the command-line.  It is usually the only one.

concurrent.interpreters.create()

   Initialize a new (idle) Python interpreter and return a
   "Interpreter" object for it.

concurrent.interpreters.create_queue()

   Initialize a new cross-interpreter queue and return a "Queue"
   object for it.


Interpreter objects
-------------------

class concurrent.interpreters.Interpreter(id)

   A single interpreter in the current process.

   Generally, "Interpreter" shouldn’t be called directly. Instead, use
   "create()" or one of the other module functions.

   id

      (read-only)

      The underlying interpreter’s ID.

   whence

      (read-only)

      A string describing where the interpreter came from.

   is_running()

      Return "True" if the interpreter is currently executing code in
      its "__main__" module and "False" otherwise.

   close()

      Finalize and destroy the interpreter.

   prepare_main(ns=None, **kwargs)

      Bind objects in the interpreter’s "__main__" module.

      Some objects are actually shared and some are copied
      efficiently, but most are copied via "pickle".  See “Sharing”
      Objects.

   exec(code, /, dedent=True)

      Run the given source code in the interpreter (in the current
      thread).

   call(callable, /, *args, **kwargs)

      Return the result of calling running the given function in the
      interpreter (in the current thread).

   call_in_thread(callable, /, *args, **kwargs)

      Run the given function in the interpreter (in a new thread).


Exceptions
----------

exception concurrent.interpreters.InterpreterError

   This exception, a subclass of "Exception", is raised when an
   interpreter-related error happens.

exception concurrent.interpreters.InterpreterNotFoundError

   This exception, a subclass of "InterpreterError", is raised when
   the targeted interpreter no longer exists.

exception concurrent.interpreters.ExecutionFailed

   This exception, a subclass of "InterpreterError", is raised when
   the running code raised an uncaught exception.

   excinfo

      A basic snapshot of the exception raised in the other
      interpreter.

exception concurrent.interpreters.NotShareableError

   This exception, a subclass of "TypeError", is raised when an object
   cannot be sent to another interpreter.


Communicating Between Interpreters
----------------------------------

class concurrent.interpreters.Queue(id)

   A wrapper around a low-level, cross-interpreter queue, which
   implements the "queue.Queue" interface.  The underlying queue can
   only be created through "create_queue()".

   Some objects are actually shared and some are copied efficiently,
   but most are copied via "pickle".  See “Sharing” Objects.

   id

      (read-only)

      The queue’s ID.

exception concurrent.interpreters.QueueEmptyError

   This exception, a subclass of "queue.Empty", is raised from
   "Queue.get()" and "Queue.get_nowait()" when the queue is empty.

exception concurrent.interpreters.QueueFullError

   This exception, a subclass of "queue.Full", is raised from
   "Queue.put()" and "Queue.put_nowait()" when the queue is full.


Basic usage
===========

Creating an interpreter and running code in it:

   from concurrent import interpreters

   interp = interpreters.create()

   # Run in the current OS thread.

   interp.exec('print("spam!")')

   interp.exec("""if True:
       print('spam!')
       """)

   from textwrap import dedent
   interp.exec(dedent("""
       print('spam!')
       """))

   def run(arg):
       return arg

   res = interp.call(run, 'spam!')
   print(res)

   def run():
       print('spam!')

   interp.call(run)

   # Run in new OS thread.

   t = interp.call_in_thread(run)
   t.join()
