[Python-Dev] PEP 554 v4 (new interpreters module)

Eric Snow ericsnowcurrently at gmail.com
Tue Dec 5 21:51:17 EST 2017

Hi all,

I've finally updated PEP 554.  Feedback would be most welcome.  The
PEP is in a pretty good place now and I hope to we're close to a
decision to accept it. :)

In addition to resolving the open questions, I've also made the
following changes to the PEP:

* put an API summary at the top and moved the full API description down
* add the "is_shareable()" function to indicate if an object can be shared
* added None as a shareable object

Regarding the open questions:

 * "Leaking exceptions across interpreters"

I chose to go with an approach that effectively creates a
traceback.TracebackException proxy of the original exception, wraps
that in a RuntimeError, and raises that in the calling interpreter.
Raising an exception that safely preserves the original exception and
traceback seems like the most intuitive behavior (to me, as a user).
The only alternative that made sense is to fully duplicate the
exception and traceback (minus stack frames) in the calling
interpreter, which is probably overkill and likely to be confusing.

 * "Initial support for buffers in channels"

I chose to add a "SendChannel.send_buffer(obj)" method for this.
Supporting buffer objects from the beginning makes sense, opening good
experimentation opportunities for a valuable set of users.  Supporting
buffer objects separately and explicitly helps set clear expectations
for users.  I decided not to go with a separate class (e.g.
MemChannel) as it didn't seem like there's enough difference to
warrant keeping them strictly separate.

FWIW, I'm still strongly in favor of support for passing (copies of)
bytes objects via channels.  Passing objects to SendChannel.send() is
obvious.  Limiting it, for now, to bytes (and None) helps us avoid
tying ourselves strongly to any particular implementation (it seems
like all the reservations were relative to the implementation).  So I
do not see a reason to wait.

 * "Pass channels explicitly to run()?"

I've applied the suggested solution (make "channels" an explicit
keyword argument).


I've include the latest full text
(https://www.python.org/dev/peps/pep-0554/) below:


PEP: 554
Title: Multiple Interpreters in the Stdlib
Author: Eric Snow <ericsnowcurrently at gmail.com>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 2017-09-05
Python-Version: 3.7
Post-History: 07-Sep-2017, 08-Sep-2017, 13-Sep-2017, 05-Dec-2017


CPython has supported multiple interpreters in the same process (AKA
"subinterpreters") since version 1.5.  The feature has been available
via the C-API. [c-api]_ Subinterpreters operate in
`relative isolation from one another <Interpreter Isolation_>`_, which
provides the basis for an
`alternative concurrency model <Concurrency_>`_.

This proposal introduces the stdlib ``interpreters`` module.  The module
will be `provisional <Provisional Status_>`_.  It exposes the basic
functionality of subinterpreters already provided by the C-API, along
with new functionality for sharing data between interpreters.


The ``interpreters`` module will be added to the stdlib.  It will
provide a high-level interface to subinterpreters and wrap a new
low-level ``_interpreters`` (in the same was as the ``threading``
module).  See the `Examples`_ section for concrete usage and use cases.

Along with exposing the existing (in CPython) subinterpreter support,
the module will also provide a mechanism for sharing data between
interpreters.  This mechanism centers around "channels", which are
similar to queues and pipes.

Note that *objects* are not shared between interpreters since they are
tied to the interpreter in which they were created.  Instead, the
objects' *data* is passed between interpreters.  See the `Shared data`_
section for more details about sharing between interpreters.

At first only the following types will be supported for sharing:

* None
* bytes
* PEP 3118 buffer objects (via ``send_buffer()``)

Support for other basic types (e.g. int, Ellipsis) will be added later.

API summary for interpreters module

Here is a summary of the API for the ``interpreters`` module.  For a
more in-depth explanation of the proposed classes and functions, see
the `"interpreters" Module API`_ section below.

For creating and using interpreters:

| signature                    | description                                  |
| list_all() -> [Intepreter]   | Get all existing interpreters.               |
| get_current() -> Interpreter | Get the currently running interpreter.       |
| create() -> Interpreter      | Initialize a new (idle) Python interpreter.  |


| signature             | description                                         |
| class Interpreter(id) | A single interpreter.                               |
| .id                   | The interpreter's ID (read-only).                   |
| .is_running() -> Bool | Is the interpreter currently executing code?        |
| .destroy()            | Finalize and destroy the interpreter.               |
| .run(src_str, /, \*,  | | Run the given source code in the interpreter.     |
|      channels=None)   | | (This blocks the current thread until done.)      |

For sharing data between interpreters:

| signature                      | description                                |
| is_shareable(obj) -> Bool      | | Can the object's data be shared          |
|                                | | between interpreters?                    |
| create_channel() ->            | | Create a new channel for passing         |
|   (RecvChannel, SendChannel)   | | data between interpreters.               |
| list_all_channels() ->         | Get all open channels.                     |
|   [(RecvChannel, SendChannel)] |                                            |


| signature                     | description
| class RecvChannel(id)         | The receiving end of a channel.
| .id                           | The channel's unique ID.
| .interpreters                 | The list of associated interpreters.
| .recv() -> object             | | Get the next object from the
channel,       |
|                               | | and wait if none have been sent.
|                               | | Associate the interpreter with the
channel. |
| .recv_nowait(default=None) -> | | Like recv(), but return the
default         |
|   object                      | | instead of waiting.
| .close()                      | | No longer associate the current
interpreter |
|                               | | with the channel (on the receiving
end).    |


| signature                 | description                                     |
| class SendChannel(id)     | The sending end of a channel.                   |
| .id                       | The channel's unique ID.                        |
| .interpreters             | The list of associated interpreters.            |
| .send(obj)                | | Send the object (i.e. its data) to the        |
|                           | | receiving end of the channel and wait.        |
|                           | | Associate the interpreter with the channel.   |
| .send_nowait(obj)         | | Like send(), but Fail if not received.        |
| .send_buffer(obj)         | | Send the object's (PEP 3118) buffer to the    |
|                           | | receiving end of the channel and wait.        |
|                           | | Associate the interpreter with the channel.   |
| .send_buffer_nowait(obj)  | | Like send_buffer(), but fail if not received. |
| .close()                  | | No longer associate the current interpreter   |
|                           | | with the channel (on the sending end).        |


Run isolated code


   interp = interpreters.create()

Run in a thread


   interp = interpreters.create()
   def run():
   t = threading.Thread(target=run)

Pre-populate an interpreter


   interp = interpreters.create()
       import some_lib
       import an_expensive_module

Handling an exception


   interp = interpreters.create()
           raise KeyError
   except KeyError:
       print("got the error from the subinterpreter")

Synchronize using a channel


   interp = interpreters.create()
   r, s = interpreters.create_channel()
   def run():
   t = threading.Thread(target=run)

Sharing a file descriptor


   interp = interpreters.create()
   r1, s1 = interpreters.create_channel()
   r2, s2 = interpreters.create_channel()
   def run():
           fd = int.from_bytes(
                   reader.recv(), 'big')
           for line in os.fdopen(fd):
           reader=r1, writer=s2)
   t = threading.Thread(target=run)
   with open('spamspamspam') as infile:
       fd = infile.fileno().to_bytes(1, 'big')

Passing objects via marshal


   interp = interpreters.create()
   r, s = interpreters.create_fifo()
       import marshal
   def run():
           data = reader.recv()
           while data:
               obj = marshal.loads(data)
               data = reader.recv()
   t = threading.Thread(target=run)
   for obj in input:
       data = marshal.dumps(obj)

Passing objects via pickle


   interp = interpreters.create()
   r, s = interpreters.create_channel()
       import pickle
   def run():
           data = reader.recv()
           while data:
               obj = pickle.loads(data)
               data = reader.recv()
   t = threading.Thread(target=run)
   for obj in input:
       data = pickle.dumps(obj)

Running a module


   interp = interpreters.create()
   main_module = mod_name
   interp.run(f'import runpy; runpy.run_module({main_module!r})')

Running as script (including zip archives & directories)


   interp = interpreters.create()
   main_script = path_name
   interp.run(f"import runpy; runpy.run_path({main_script!r})")

Running in a thread pool executor


   interps = [interpreters.create() for i in range(5)]
   with concurrent.futures.ThreadPoolExecutor(max_workers=len(interps)) as pool:
       for interp in interps:
           pool.submit(interp.run, 'print("starting"); print("stopping")'


Running code in multiple interpreters provides a useful level of
isolation within the same process.  This can be leveraged in a number
of ways.  Furthermore, subinterpreters provide a well-defined framework
in which such isolation may extended.

Nick Coghlan explained some of the benefits through a comparison with
multi-processing [benefits]_::

   [I] expect that communicating between subinterpreters is going
   to end up looking an awful lot like communicating between
   subprocesses via shared memory.

   The trade-off between the two models will then be that one still
   just looks like a single process from the point of view of the
   outside world, and hence doesn't place any extra demands on the
   underlying OS beyond those required to run CPython with a single
   interpreter, while the other gives much stricter isolation
   (including isolating C globals in extension modules), but also
   demands much more from the OS when it comes to its IPC

   The security risk profiles of the two approaches will also be quite
   different, since using subinterpreters won't require deliberately
   poking holes in the process isolation that operating systems give
   you by default.

CPython has supported subinterpreters, with increasing levels of
support, since version 1.5.  While the feature has the potential
to be a powerful tool, subinterpreters have suffered from neglect
because they are not available directly from Python.  Exposing the
existing functionality in the stdlib will help reverse the situation.

This proposal is focused on enabling the fundamental capability of
multiple isolated interpreters in the same Python process.  This is a
new area for Python so there is relative uncertainly about the best
tools to provide as companions to subinterpreters.  Thus we minimize
the functionality we add in the proposal as much as possible.


* "subinterpreters are not worth the trouble"

Some have argued that subinterpreters do not add sufficient benefit
to justify making them an official part of Python.  Adding features
to the language (or stdlib) has a cost in increasing the size of
the language.  So an addition must pay for itself.  In this case,
subinterpreters provide a novel concurrency model focused on isolated
threads of execution.  Furthermore, they provide an opportunity for
changes in CPython that will allow simulateous use of multiple CPU
cores (currently prevented by the GIL).

Alternatives to subinterpreters include threading, async, and
multiprocessing.  Threading is limited by the GIL and async isn't
the right solution for every problem (nor for every person).
Multiprocessing is likewise valuable in some but not all situations.
Direct IPC (rather than via the multiprocessing module) provides
similar benefits but with the same caveat.

Notably, subinterpreters are not intended as a replacement for any of
the above.  Certainly they overlap in some areas, but the benefits of
subinterpreters include isolation and (potentially) performance.  In
particular, subinterpreters provide a direct route to an alternate
concurrency model (e.g. CSP) which has found success elsewhere and
will appeal to some Python users.  That is the core value that the
``interpreters`` module will provide.

* "stdlib support for subinterpreters adds extra burden
  on C extension authors"

In the `Interpreter Isolation`_ section below we identify ways in
which isolation in CPython's subinterpreters is incomplete.  Most
notable is extension modules that use C globals to store internal
state.  PEP 3121 and PEP 489 provide a solution for most of the
problem, but one still remains. [petr-c-ext]_  Until that is resolved,
C extension authors will face extra difficulty to support

Consequently, projects that publish extension modules may face an
increased maintenance burden as their users start using subinterpreters,
where their modules may break.  This situation is limited to modules
that use C globals (or use libraries that use C globals) to store
internal state.  For numpy, the reported-bug rate is one every 6
months. [bug-rate]_

Ultimately this comes down to a question of how often it will be a
problem in practice: how many projects would be affected, how often
their users will be affected, what the additional maintenance burden
will be for projects, and what the overall benefit of subinterpreters
is to offset those costs.  The position of this PEP is that the actual
extra maintenance burden will be small and well below the threshold at
which subinterpreters are worth it.

About Subinterpreters


Concurrency is a challenging area of software development.  Decades of
research and practice have led to a wide variety of concurrency models,
each with different goals.  Most center on correctness and usability.

One class of concurrency models focuses on isolated threads of
execution that interoperate through some message passing scheme.  A
notable example is `Communicating Sequential Processes`_ (CSP), upon
which Go's concurrency is based.  The isolation inherent to
subinterpreters makes them well-suited to this approach.

Shared data

Subinterpreters are inherently isolated (with caveats explained below),
in contrast to threads.  So the same communicate-via-shared-memory
approach doesn't work.  Without an alternative, effective use of
concurrency via subinterpreters is significantly limited.

The key challenge here is that sharing objects between interpreters
faces complexity due to various constraints on object ownership,
visibility, and mutability.  At a conceptual level it's easier to
reason about concurrency when objects only exist in one interpreter
at a time.  At a technical level, CPython's current memory model
limits how Python *objects* may be shared safely between interpreters;
effectively objects are bound to the interpreter in which they were
created.  Furthermore the complexity of *object* sharing increases as
subinterpreters become more isolated, e.g. after GIL removal.

Consequently,the mechanism for sharing needs to be carefully considered.
There are a number of valid solutions, several of which may be
appropriate to support in Python.  This proposal provides a single basic
solution: "channels".  Ultimately, any other solution will look similar
to the proposed one, which will set the precedent.  Note that the
implementation of ``Interpreter.run()`` can be done in a way that allows
for multiple solutions to coexist, but doing so is not technically
a part of the proposal here.

Regarding the proposed solution, "channels", it is a basic, opt-in data
sharing mechanism that draws inspiration from pipes, queues, and CSP's
channels. [fifo]_

As simply described earlier by the API summary,
channels have two operations: send and receive.  A key characteristic
of those operations is that channels transmit data derived from Python
objects rather than the objects themselves.  When objects are sent,
their data is extracted.  When the "object" is received in the other
interpreter, the data is converted back into an object.

To make this work, the mutable shared state will be managed by the
Python runtime, not by any of the interpreters.  Initially we will
support only one type of objects for shared state: the channels provided
by ``create_channel()``.  Channels, in turn, will carefully manage
passing objects between interpreters.

This approach, including keeping the API minimal, helps us avoid further
exposing any underlying complexity to Python users.  Along those same
lines, we will initially restrict the types that may be passed through
channels to the following:

* None
* bytes
* PEP 3118 buffer objects (via ``send_buffer()``)

Limiting the initial shareable types is a practical matter, reducing
the potential complexity of the initial implementation.  There are a
number of strategies we may pursue in the future to expand supported
objects and object sharing strategies.

Interpreter Isolation

CPython's interpreters are intended to be strictly isolated from each
other.  Each interpreter has its own copy of all modules, classes,
functions, and variables.  The same applies to state in C, including in
extension modules.  The CPython C-API docs explain more. [caveats]_

However, there are ways in which interpreters share some state.  First
of all, some process-global state remains shared:

* file descriptors
* builtin types (e.g. dict, bytes)
* singletons (e.g. None)
* underlying static module data (e.g. functions) for
  builtin/extension/frozen modules

There are no plans to change this.

Second, some isolation is faulty due to bugs or implementations that did
not take subinterpreters into account.  This includes things like
extension modules that rely on C globals. [cryptography]_  In these
cases bugs should be opened (some are already):

* readline module hook functions (http://bugs.python.org/issue4202)
* memory leaks on re-init (http://bugs.python.org/issue21387)

Finally, some potential isolation is missing due to the current design
of CPython.  Improvements are currently going on to address gaps in this

* interpreters share the GIL
* interpreters share memory management (e.g. allocators, gc)
* GC is not run per-interpreter [global-gc]_
* at-exit handlers are not run per-interpreter [global-atexit]_
* extensions using the ``PyGILState_*`` API are incompatible [gilstate]_

Existing Usage

Subinterpreters are not a widely used feature.  In fact, the only
documented cases of wide-spread usage are
`mod_wsgi <https://github.com/GrahamDumpleton/mod_wsgi>`_and
`JEP <https://github.com/ninia/jep>`_.  On the one hand, this case
provides confidence that existing subinterpreter support is relatively
stable.  On the other hand, there isn't much of a sample size from which
to judge the utility of the feature.

Provisional Status

The new ``interpreters`` module will be added with "provisional" status
(see PEP 411).  This allows Python users to experiment with the feature
and provide feedback while still allowing us to adjust to that feedback.
The module will be provisional in Python 3.7 and we will make a decision
before the 3.8 release whether to keep it provisional, graduate it, or
remove it.

Alternate Python Implementations

I'll be soliciting feedback from the different Python implementors about
subinterpreter support.

Multiple-interpter support in the major Python implementations:


* jython: yes [jython]_
* ironpython: yes?
* pypy: maybe not? [pypy]_
* micropython: ???

"interpreters" Module API

The module provides the following functions:


   Return a list of all existing interpreters.


   Return the currently running interpreter.


   Initialize a new Python interpreter and return it.  The
   interpreter will be created in the current thread and will remain
   idle until something is run in it.  The interpreter may be used
   in any thread and will run in whichever thread calls

The module also provides the following class:



      The interpreter's ID (read-only).


      Return whether or not the interpreter is currently executing code.
      Calling this on the current interpreter will always return True.


      Finalize and destroy the interpreter.

      This may not be called on an already running interpreter.  Doing
      so results in a RuntimeError.

   run(source_str, /, *, channels=None):

      Run the provided Python source code in the interpreter.  If the
      "channels" keyword argument is provided (and is a mapping of
      attribute names to channels) then it is added to the interpreter's
      execution namespace (the interpreter's "__main__" module).  If any
      of the values are not are not RecvChannel or SendChannel instances
      then ValueError gets raised.

      This may not be called on an already running interpreter.  Doing
      so results in a RuntimeError.

      A "run()" call is similar to a function call.  Once it completes,
      the code that called "run()" continues executing (in the original
      interpreter).  Likewise, if there is any uncaught exception then
      it effectively (see below) propagates into the code where
      ``run()`` was called.  However, unlike function calls (but like
      threads), there is no return value.  If any value is needed, pass
      it out via a channel.

      The big difference is that "run()" executes the code in an
      entirely different interpreter, with entirely separate state.
      The state of the current interpreter in the current OS thread
      is swapped out with the state of the target interpreter (the one
      that will execute the code).  When the target finishes executing,
      the original interpreter gets swapped back in and its execution

      So calling "run()" will effectively cause the current Python
      thread to pause.  Sometimes you won't want that pause, in which
      case you should make the "run()" call in another thread.  To do
      so, add a function that calls "run()" and then run that function
      in a normal "threading.Thread".

      Note that the interpreter's state is never reset, neither before
      "run()" executes the code nor after.  Thus the interpreter
      state is preserved between calls to "run()".  This includes
      "sys.modules", the "builtins" module, and the internal state
      of C extension modules.

      Also note that "run()" executes in the namespace of the "__main__"
      module, just like scripts, the REPL, "-m", and "-c".  Just as
      the interpreter's state is not ever reset, the "__main__" module
      is never reset.  You can imagine concatenating the code from each
      "run()" call into one long script.  This is the same as how the
      REPL operates.

      Regarding uncaught exceptions, we noted that they are
      "effectively" propagated into the code where ``run()`` was called.
      To prevent leaking exceptions (and tracebacks) between
      interpreters, we create a surrogate of the exception and its
      traceback (see ``traceback.TracebackException``), wrap it in a
      RuntimeError, and raise that.

      Supported code: source text.

API for sharing data

Subinterpreters are less useful without a mechanism for sharing data
between them.  Sharing actual Python objects between interpreters,
however, has enough potential problems that we are avoiding support
for that here.  Instead, only mimimum set of types will be supported.
Initially this will include ``bytes`` and channels.  Further types may
be supported later.

The ``interpreters`` module provides a way for users to determine
whether an object is shareable or not:


   Return True if the object may be shared between interpreters.  This
   does not necessarily mean that the actual objects will be shared.
   Insead, it means that the objects' underlying data will be shared in
   a cross-interpreter way, whether via a proxy, a copy, or some other

This proposal provides two ways to do share such objects between

First, shareable objects may be passed to ``run()`` as keyword arguments,
where they are effectively injected into the target interpreter's
``__main__`` module.  This is mainly intended for sharing meta-objects
(e.g. channels) between interpreters, as it is less useful to pass other
objects (like ``bytes``) to ``run``.

Second, the main mechanism for sharing objects (i.e. their data) between
interpreters is through channels.  A channel is a simplex FIFO similar
to a pipe.  The main difference is that channels can be associated with
zero or more interpreters on either end.  Unlike queues, which are also
many-to-many, channels have no buffer.


   Create a new channel and return (recv, send), the RecvChannel and
   SendChannel corresponding to the ends of the channel.  The channel
   is not closed and destroyed (i.e. garbage-collected) until the number
   of associated interpreters returns to 0.

   An interpreter gets associated with a channel by calling its "send()"
   or "recv()" method.  That association gets dropped by calling
   "close()" on the channel.

   Both ends of the channel are supported "shared" objects (i.e. may be
   safely shared by different interpreters.  Thus they may be passed as
   keyword arguments to "Interpreter.run()".


   Return a list of all open (RecvChannel, SendChannel) pairs.


   The receiving end of a channel.  An interpreter may use this to
   receive objects from another interpreter.  At first only bytes will
   be supported.


      The channel's unique ID.


      The list of associated interpreters: those that have called
      the "recv()" or "__next__()" methods and haven't called "close()".


      Return the next object (i.e. the data from the sent object) from
      the channel.  If none have been sent then wait until the next
      send.  This associates the current interpreter with the channel.

      If the channel is already closed (see the close() method)
      then raise EOFError.  If the channel isn't closed, but the current
      interpreter already called the "close()" method (which drops its
      association with the channel) then raise ValueError.


      Return the next object from the channel.  If none have been sent
      then return the default.  Otherwise, this is the same as the
      "recv()" method.


      No longer associate the current interpreter with the channel (on
      the receiving end) and block future association (via the "recv()"
      method.  If the interpreter was never associated with the channel
      then still block future association.  Once an interpreter is no
      longer associated with the channel, subsequent (or current) send()
      and recv() calls from that interpreter will raise ValueError
      (or EOFError if the channel is actually marked as closed).

      Once the number of associated interpreters on both ends drops
      to 0, the channel is actually marked as closed.  The Python
      runtime will garbage collect all closed channels, though it may
      not be immediately.  Note that "close()" is automatically called
      in behalf of the current interpreter when the channel is no longer
      used (i.e. has no references) in that interpreter.

      This operation is idempotent.  Return True if "close()" has not
      been called before by the current interpreter.


   The sending end of a channel.  An interpreter may use this to send
   objects to another interpreter.  At first only bytes will be


      The channel's unique ID.


      The list of associated interpreters (those that have called
      the "send()" method).


      Send the object (i.e. its data) to the receiving end of the
      channel.  Wait until the object is received.  If the the
      object is not shareable then ValueError is raised.  Currently
      only bytes are supported.

      If the channel is already closed (see the close() method)
      then raise EOFError.  If the channel isn't closed, but the current
      interpreter already called the "close()" method (which drops its
      association with the channel) then raise ValueError.


      Send the object to the receiving end of the channel.  If the other
      end is not currently receiving then raise RuntimeError.  Otherwise
      this is the same as "send()".


      Send a MemoryView of the object rather than the object.  Otherwise
      this is the same as send().  Note that the object must implement
      the PEP 3118 buffer protocol.


      Send a MemoryView of the object rather than the object.  If the
      other end is not currently receiving then raise RuntimeError.
      Otherwise this is the same as "send_buffer()".


      This is the same as "RecvChannel.close(), but applied to the
      sending end of the channel.

Note that ``send_buffer()`` is similar to how
``multiprocessing.Connection`` works. [mp-conn]_

Open Questions


Open Implementation Questions

Does every interpreter think that their thread is the "main" thread?

(This is more of an implementation detail that an issue for the PEP.)

CPython's interpreter implementation identifies the OS thread in which
it was started as the "main" thread.  The interpreter the has slightly
different behavior depending on if the current thread is the main one
or not.  This presents a problem in cases where "main thread" is meant
to imply "main thread in the main interpreter" [main-thread]_, where
the main interpreter is the initial one.

Disallow subinterpreters in the main thread?

(This is more of an implementation detail that an issue for the PEP.)

This is a specific case of the above issue.  Currently in CPython,
"we need a main \*thread\* in order to sensibly manage the way signal
handling works across different platforms".  [main-thread]_

Since signal handlers are part of the interpreter state, running a
subinterpreter in the main thread means that the main interpreter
can no longer properly handle signals (since it's effectively paused).

Furthermore, running a subinterpreter in the main thread would
conceivably allow setting signal handlers on that interpreter, which
would likewise impact signal handling when that interpreter isn't
running or is running in a different thread.

Ultimately, running subinterpreters in the main OS thread introduces
complications to the signal handling implementation.  So it may make
the most sense to disallow running subinterpreters in the main thread.
Support for it could be considered later.  The downside is that folks
wanting to try out subinterpreters would be required to take the extra
step of using threads.  This could slow adoption and experimentation,
whereas without the restriction there's less of an obstacle.

Deferred Functionality

In the interest of keeping this proposal minimal, the following
functionality has been left out for future consideration.  Note that
this is not a judgement against any of said capability, but rather a
deferment.  That said, each is arguably valid.


It would be convenient to run existing functions in subinterpreters
directly.  ``Interpreter.run()`` could be adjusted to support this or
a ``call()`` method could be added::

   Interpreter.call(f, *args, **kwargs)

This suffers from the same problem as sharing objects between
interpreters via queues.  The minimal solution (running a source string)
is sufficient for us to get the feature out where it can be explored.

timeout arg to recv() and send()

Typically functions that have a ``block`` argument also have a
``timeout`` argument.  It sometimes makes sense to do likewise for
functions that otherwise block, like the channel ``recv()`` and
``send()`` methods.  We can add it later if needed.


CPython has a concept of a "main" interpreter.  This is the initial
interpreter created during CPython's runtime initialization.  It may
be useful to identify the main interpreter.  For instance, the main
interpreter should not be destroyed.  However, for the basic
functionality of a high-level API a ``get_main()`` function is not
necessary.  Furthermore, there is no requirement that a Python
implementation have a concept of a main interpreter.  So until there's
a clear need we'll leave ``get_main()`` out.


This method would make a ``run()`` call for you in a thread.  Doing this
using only ``threading.Thread`` and ``run()`` is relatively trivial so
we've left it out.

Synchronization Primitives

The ``threading`` module provides a number of synchronization primitives
for coordinating concurrent operations.  This is especially necessary
due to the shared-state nature of threading.  In contrast,
subinterpreters do not share state.  Data sharing is restricted to
channels, which do away with the need for explicit synchronization.  If
any sort of opt-in shared state support is added to subinterpreters in
the future, that same effort can introduce synchronization primitives
to meet that need.

CSP Library

A ``csp`` module would not be a large step away from the functionality
provided by this PEP.  However, adding such a module is outside the
minimalist goals of this proposal.

Syntactic Support

The ``Go`` language provides a concurrency model based on CSP, so
it's similar to the concurrency model that subinterpreters support.
``Go`` provides syntactic support, as well several builtin concurrency
primitives, to make concurrency a first-class feature.  Conceivably,
similar syntactic (and builtin) support could be added to Python using
subinterpreters.  However, that is *way* outside the scope of this PEP!


The ``multiprocessing`` module could support subinterpreters in the same
way it supports threads and processes.  In fact, the module's
maintainer, Davin Potts, has indicated this is a reasonable feature
request.  However, it is outside the narrow scope of this PEP.

C-extension opt-in/opt-out

By using the ``PyModuleDef_Slot`` introduced by PEP 489, we could easily
add a mechanism by which C-extension modules could opt out of support
for subinterpreters.  Then the import machinery, when operating in
a subinterpreter, would need to check the module for support.  It would
raise an ImportError if unsupported.

Alternately we could support opting in to subinterpreter support.
However, that would probably exclude many more modules (unnecessarily)
than the opt-out approach.

The scope of adding the ModuleDef slot and fixing up the import
machinery is non-trivial, but could be worth it.  It all depends on
how many extension modules break under subinterpreters.  Given the
relatively few cases we know of through mod_wsgi, we can leave this
for later.

Poisoning channels

CSP has the concept of poisoning a channel.  Once a channel has been
poisoned, and ``send()`` or ``recv()`` call on it will raise a special
exception, effectively ending execution in the interpreter that tried
to use the poisoned channel.

This could be accomplished by adding a ``poison()`` method to both ends
of the channel.  The ``close()`` method could work if it had a ``force``
option to force the channel closed.  Regardless, these semantics are
relatively specialized and can wait.

Sending channels over channels

Some advanced usage of subinterpreters could take advantage of the
ability to send channels over channels, in addition to bytes.  Given
that channels will already be multi-interpreter safe, supporting then
in ``RecvChannel.recv()`` wouldn't be a big change.  However, this can
wait until the basic functionality has been ironed out.

Reseting __main__

As proposed, every call to ``Interpreter.run()`` will execute in the
namespace of the interpreter's existing ``__main__`` module.  This means
that data persists there between ``run()`` calls.  Sometimes this isn't
desireable and you want to execute in a fresh ``__main__``.  Also,
you don't necessarily want to leak objects there that you aren't using
any more.

Note that the following won't work right because it will clear too much
(e.g. ``__name__`` and the other "__dunder__" attributes::


Possible solutions include:

* a ``create()`` arg to indicate resetting ``__main__`` after each
  ``run`` call
* an ``Interpreter.reset_main`` flag to support opting in or out
  after the fact
* an ``Interpreter.reset_main()`` method to opt in when desired
* ``importlib.util.reset_globals()`` [reset_globals]_

Also note that reseting ``__main__`` does nothing about state stored
in other modules.  So any solution would have to be clear about the
scope of what is being reset.  Conceivably we could invent a mechanism
by which any (or every) module could be reset, unlike ``reload()``
which does not clear the module before loading into it.  Regardless,
since ``__main__`` is the execution namespace of the interpreter,
resetting it has a much more direct correlation to interpreters and
their dynamic state than does resetting other modules.  So a more
generic module reset mechanism may prove unnecessary.

This isn't a critical feature initially.  It can wait until later
if desirable.

Support passing ints in channels

Passing ints around should be fine and ultimately is probably
desirable.  However, we can get by with serializing them as bytes
for now.  The goal is a minimal API for the sake of basic
functionality at first.

File descriptors and sockets in channels

Given that file descriptors and sockets are process-global resources,
support for passing them through channels is a reasonable idea.  They
would be a good candidate for the first effort at expanding the types
that channels support.  They aren't strictly necessary for the initial

Integration with async

Per Antoine Pitrou [async]_::

   Has any thought been given to how FIFOs could integrate with async
   code driven by an event loop (e.g. asyncio)?  I think the model of
   executing several asyncio (or Tornado) applications each in their
   own subinterpreter may prove quite interesting to reconcile multi-
   core concurrency with ease of programming.  That would require the
   FIFOs to be able to synchronize on something an event loop can wait
   on (probably a file descriptor?).

A possible solution is to provide async implementations of the blocking
channel methods (``__next__()``, ``recv()``, and ``send()``).  However,
the basic functionality of subinterpreters does not depend on async and
can be added later.

Support for iteration

Supporting iteration on ``RecvChannel`` (via ``__iter__()`` or
``_next__()``) may be useful.  A trivial implementation would use the
``recv()`` method, similar to how files do iteration.  Since this isn't
a fundamental capability and has a simple analog, adding iteration
support can wait until later.

Channel context managers

Context manager support on ``RecvChannel`` and ``SendChannel`` may be
helpful.  The implementation would be simple, wrapping a call to
``close()`` like files do.  As with iteration, this can wait.

Pipes and Queues

With the proposed object passing machanism of "channels", other similar
basic types aren't required to achieve the minimal useful functionality
of subinterpreters.  Such types include pipes (like channels, but
one-to-one) and queues (like channels, but buffered).  See below in
`Rejected Ideas` for more information.

Even though these types aren't part of this proposal, they may still
be useful in the context of concurrency.  Adding them later is entirely
reasonable.  The could be trivially implemented as wrappers around
channels.  Alternatively they could be implemented for efficiency at the
same low level as channels.


As currently proposed, ``Interpreter.run()`` offers you no way to
distinguish an error coming from the subinterpreter from any other
error in the current interpreter.  Your only option would be to
explicitly wrap your ``run()`` call in a
``try: ... except RuntimeError:`` (since we wrap a proxy of the original
exception in a RuntimeError and raise that).

If this is a problem in practice then would could add something like
``interpreters.RunFailedError`` (subclassing RuntimeError) and raise that
in ``run()``.

Return a lock from send()

When sending an object through a channel, you don't have a way of knowing
when the object gets received on the other end.  One way to work around
this is to return a locked ``threading.Lock`` from ``SendChannel.send()``
that unlocks once the object is received.

This matters for buffered channels (i.e. queues).  For unbuffered
channels it is a non-issue.  So this can be dealt with once channels
support buffering.

Rejected Ideas

Explicit channel association

Interpreters are implicitly associated with channels upon ``recv()`` and
``send()`` calls.  They are de-associated with ``close()`` calls.  The
alternative would be explicit methods.  It would be either
``add_channel()`` and ``remove_channel()`` methods on ``Interpreter``
objects or something similar on channel objects.

In practice, this level of management shouldn't be necessary for users.
So adding more explicit support would only add clutter to the API.

Use pipes instead of channels

A pipe would be a simplex FIFO between exactly two interpreters.  For
most use cases this would be sufficient.  It could potentially simplify
the implementation as well.  However, it isn't a big step to supporting
a many-to-many simplex FIFO via channels.  Also, with pipes the API
ends up being slightly more complicated, requiring naming the pipes.

Use queues instead of channels

The main difference between queues and channels is that queues support
buffering.  This would complicate the blocking semantics of ``recv()``
and ``send()``.  Also, queues can be built on top of channels.


The ``list_all()`` function provides the list of all interpreters.
In the threading module, which partly inspired the proposed API, the
function is called ``enumerate()``.  The name is different here to
avoid confusing Python users that are not already familiar with the
threading API.  For them "enumerate" is rather unclear, whereas
"list_all" is clear.

Alternate solutions to prevent leaking exceptions across interpreters

In function calls, uncaught exceptions propagate to the calling frame.
The same approach could be taken with ``run()``.  However, this would
mean that exception objects would leak across the inter-interpreter
boundary.  Likewise, the frames in the traceback would potentially leak.

While that might not be a problem currently, it would be a problem once
interpreters get better isolation relative to memory management (which
is necessary to stop sharing the GIL between interpreters).  We've
resolved the semantics of how the exceptions propagate by raising a
RuntimeError instead, which wraps a safe proxy for the original
exception and traceback.

Rejected possible solutions:

* set the RuntimeError's __cause__ to the proxy of the original
* reproduce the exception and traceback in the original interpreter
  and raise that.
* convert at the boundary (a la ``subprocess.CalledProcessError``)
  (requires a cross-interpreter representation)
* support customization via ``Interpreter.excepthook``
  (requires a cross-interpreter representation)
* wrap in a proxy at the boundary (including with support for
  something like ``err.raise()`` to propagate the traceback).
* return the exception (or its proxy) from ``run()`` instead of
  raising it
* return a result object (like ``subprocess`` does) [result-object]_
  (unecessary complexity?)
* throw the exception away and expect users to deal with unhandled
  exceptions explicitly in the script they pass to ``run()``
  (they can pass error info out via channels); with threads you have
  to do something similar


.. [c-api]

.. _Communicating Sequential Processes:

.. [CSP]

.. [fifo]

.. [caveats]

.. [petr-c-ext]

.. [cryptography]

.. [global-gc]

.. [gilstate]

.. [global-atexit]

.. [mp-conn]

.. [bug-rate]

.. [benefits]

.. [main-thread]

.. [reset_globals]

.. [async]

.. [result-object]

.. [jython]

.. [pypy]


This document has been placed in the public domain.

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