iSharkFly-Open
/
python-peps
mirror of https://github.com/python/peps.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity
python-peps/pep-0554.rst

1454 lines
56 KiB
ReStructuredText
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

PEP: 554
Title: Multiple Interpreters in the Stdlib
Author: Eric Snow <ericsnowcurrently@gmail.com>
BDFL-Delegate: Antoine Pitrou <antoine@python.org>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 05-Sep-2017
Python-Version: 3.12
Post-History: 07-Sep-2017, 08-Sep-2017, 13-Sep-2017, 05-Dec-2017,
09-May-2018, 20-Apr-2020, 04-May-2020
Abstract
========
CPython has supported multiple interpreters in the same process (AKA
"subinterpreters") since version 1.5 (1997). The feature has been
available via the C-API. [c-api]_ Multiple interpreters operate in
`relative isolation from one another <Interpreter Isolation_>`_, which
facilitates novel alternative approaches to
`concurrency <Concurrency_>`_.
This proposal introduces the stdlib ``interpreters`` module. It exposes
the basic functionality of multiple interpreters already provided by the
C-API, along with a *very* basic way to communicate
(i.e. pass data between interpreters).
A Disclaimer about the GIL
==========================
To avoid any confusion up front: This PEP is meant to be independent
of any efforts to stop sharing the GIL between interpreters (:pep:`684`).
At most this proposal will allow users to take advantage of any
GIL-related work.
The author's position here is that exposing multiple interpreters
to Python code is worth doing, even if they still share the GIL.
Conversations with past steering councils indicates they do not
necessarily agree.
Proposal
========
The "interpreters" Module
-------------------------
The ``interpreters`` module will provide a high-level interface
to the multiple interpreter functionality, and wrap a new low-level
``_interpreters`` (in the same way as the ``threading`` module).
See the `Examples`_ section for concrete usage and use cases.
Along with exposing the existing (in CPython) multiple interpreter
support, the module will also support a very basic mechanism for
passing data between interpreters. That involves setting simple objects
in the ``__main__`` module of a target subinterpreter. If one end of
an ``os.pipe()`` is passed this way then that pipe can be used to send
bytes between the two interpreters.
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`_
and `API For Sharing Data`_ sections for more details about
sharing/communicating between interpreters.
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() -> [Interpreter]`` | Get all existing interpreters. |
+----------------------------------+----------------------------------------------+
| ``get_current() -> Interpreter`` | Get the currently running interpreter. |
+----------------------------------+----------------------------------------------+
| ``get_main() -> Interpreter`` | Get the main interpreter. |
+----------------------------------+----------------------------------------------+
| ``create() -> Interpreter`` | Initialize a new (idle) Python interpreter. |
+----------------------------------+----------------------------------------------+
|
+---------------------------------------------------+---------------------------------------------------+
| signature | description |
+===================================================+===================================================+
| ``class Interpreter`` | A single interpreter. |
+---------------------------------------------------+---------------------------------------------------+
| ``.id`` | The interpreter's ID (read-only). |
+---------------------------------------------------+---------------------------------------------------+
| ``.is_running() -> bool`` | Is the interpreter currently executing code? |
+---------------------------------------------------+---------------------------------------------------+
| ``.close()`` | Finalize and destroy the interpreter. |
+---------------------------------------------------+---------------------------------------------------+
| ``.run(src_str, /, *, shared=None) -> Status`` | | Run the given source code in the interpreter |
| | | (in its own thread). |
+---------------------------------------------------+---------------------------------------------------+
.. XXX Support blocking interp.run() until the interpreter
finishes its current work.
|
+--------------------+------------------+------------------------------------------------------+
| exception | base | description |
+====================+==================+======================================================+
| ``RunFailedError`` | ``RuntimeError`` | Interpreter.run() resulted in an uncaught exception. |
+--------------------+------------------+------------------------------------------------------+
.. XXX Add "InterpreterAlreadyRunningError"?
Asynchronous results:
+--------------------------------------------------+---------------------------------------------------+
| signature | description |
+==================================================+===================================================+
| ``class Status`` | Tracks if a request is complete. |
+--------------------------------------------------+---------------------------------------------------+
| ``.wait(timeout=None)`` | Block until the requested work is done. |
+--------------------------------------------------+---------------------------------------------------+
| ``.done() -> bool`` | Has the requested work completed (or failed)? |
+--------------------------------------------------+---------------------------------------------------+
| ``.exception() -> Exception | None`` | Return any exception from the requested work. |
+--------------------------------------------------+---------------------------------------------------+
+--------------------------+------------------------+------------------------------------------------+
| exception | base | description |
+==========================+========================+================================================+
| ``NotFinishedError`` | ``Exception`` | The request has not completed yet. |
+--------------------------+------------------------+------------------------------------------------+
For sharing data between interpreters:
+---------------------------------------------------------+--------------------------------------------+
| signature | description |
+=========================================================+============================================+
| ``is_shareable(obj) -> Bool`` | | Can the object's data be shared |
| | | between interpreters? |
+---------------------------------------------------------+--------------------------------------------+
Help for Extension Module Maintainers
-------------------------------------
In practice, an extension that implements multi-phase init (:pep:`489`)
is considered isolated and thus compatible with multiple interpreters.
Otherwise it is "incompatible".
Many extension modules are still incompatible. The maintainers and
users of such extension modules will both benefit when they are updated
to support multiple interpreters. In the meantime, users may become
confused by failures when using multiple interpreters, which could
negatively impact extension maintainers. See `Concerns`_ below.
To mitigate that impact and accelerate compatibility, we will do the
following:
* be clear that extension modules are *not* required to support use in
multiple interpreters
* raise ``ImportError`` when an incompatible module is imported
in a subinterpreter
* provide resources (e.g. docs) to help maintainers reach compatibility
* reach out to the maintainers of Cython and of the most used extension
modules (on PyPI) to get feedback and possibly provide assistance
Examples
========
Run isolated code
-----------------
::
interp = interpreters.create()
print('before')
interp.run('print("during")').wait()
print('after')
Pre-populate an interpreter
---------------------------
::
interp = interpreters.create()
st = interp.run(tw.dedent("""
import some_lib
import an_expensive_module
some_lib.set_up()
"""))
wait_for_request()
st.wait()
interp.run(tw.dedent("""
some_lib.handle_request()
"""))
Handling an exception
---------------------
::
interp = interpreters.create()
try:
interp.run(tw.dedent("""
raise KeyError
""")).wait()
except interpreters.RunFailedError as exc:
print(f"got the error from the subinterpreter: {exc}")
Re-raising an exception
-----------------------
::
interp = interpreters.create()
try:
try:
interp.run(tw.dedent("""
raise KeyError
""")).wait()
except interpreters.RunFailedError as exc:
raise exc.__cause__
except KeyError:
print("got a KeyError from the subinterpreter")
Note that this pattern is a candidate for later improvement.
Synchronize using an OS pipe
----------------------------
::
interp = interpreters.create()
r, s = os.pipe()
print('before')
interp.run(tw.dedent("""
import os
os.read(reader, 1)
print("during")
"""),
shared=dict(
reader=r,
),
)
print('after')
os.write(s, '')
Sharing a file descriptor
-------------------------
::
interp = interpreters.create()
r1, s1 = os.pipe()
r2, s2 = os.pipe()
interp.run(tw.dedent("""
import os
fd = int.from_bytes(
os.read(reader, 10), 'big')
for line in os.fdopen(fd):
print(line)
os.write(writer, b'')
"""),
shared=dict(
reader=r1,
writer=s2,
),
)
with open('spamspamspam') as infile:
fd = infile.fileno().to_bytes(1, 'big')
os.write(s1, fd)
os.read(r2, 1)
Passing objects via pickle
--------------------------
::
interp = interpreters.create()
r, s = os.pipe()
interp.run(tw.dedent("""
import os
import pickle
"""),
shared=dict(
reader=r,
),
).wait()
interp.run(tw.dedent("""
data = b''
c = os.read(reader, 1)
while c != b'\x00':
while c != b'\x00':
data += c
c = os.read(reader, 1)
obj = pickle.loads(data)
do_something(obj)
c = os.read(reader, 1)
"""))
for obj in input:
data = pickle.dumps(obj)
os.write(s, data)
os.write(s, b'\x00')
os.write(s, b'\x00')
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})")
Rationale
=========
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. (See :pep:`684`.)
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
capabilities.
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 multiple interpreters, with increasing levels
of support, since version 1.5. While the feature has the potential
to be a powerful tool, it has suffered from neglect
because the multiple interpreter capabilities are not readily 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 interpreters, isolated from each other,
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 interpreters. Thus we minimize
the functionality we add in the proposal as much as possible.
Concerns
--------
* "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, multiple interpreter support provide a novel concurrency
model focused on isolated threads of execution. Furthermore, they
provide an opportunity for changes in CPython that will allow
simultaneous use of multiple CPU cores (currently prevented
by the GIL--see :pep:`684`).
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 multiple interpreters 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
(see :pep:`573`), C extension authors will face extra difficulty
to support subinterpreters.
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.
* "creating a new concurrency API deserves much more thought and
experimentation, so the new module shouldn't go into the stdlib
right away, if ever"
Introducing an API for a new concurrency model, like happened with
asyncio, is an extremely large project that requires a lot of careful
consideration. It is not something that can be done a simply as this
PEP proposes and likely deserves significant time on PyPI to mature.
(See `Nathaniel's post <nathaniel-asyncio_>`_ on python-dev.)
However, this PEP does not propose any new concurrency API.
At most it exposes minimal tools (e.g. subinterpreters, simple "sharing")
which may be used to write code that follows patterns associated with
(relatively) new-to-Python `concurrency models <Concurrency_>`_.
Those tools could also be used as the basis for APIs for such
concurrency models. Again, this PEP does not propose any such API.
* "there is no point to exposing subinterpreters if they still share
the GIL"
* "the effort to make the GIL per-interpreter is disruptive and risky"
A common misconception is that this PEP also includes a promise that
interpreters will no longer share the GIL. When that is clarified,
the next question is "what is the point?". This is already answered
at length in this PEP. Just to be clear, the value lies in::
* increase exposure of the existing feature, which helps improve
the code health of the entire CPython runtime
* expose the (mostly) isolated execution of interpreters
* preparation for per-interpreter GIL
* encourage experimentation
* "data sharing can have a negative impact on cache performance
in multi-core scenarios"
(See [cache-line-ping-pong]_.)
This shouldn't be a problem for now as we have no immediate plans
to actually share data between interpreters, instead focusing
on copying.
About Subinterpreters
=====================
Concurrency
-----------
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 roughly based). The inteded isolation
inherent to CPython's interpreters makes them well-suited
to this approach.
Shared Data
-----------
CPython's interpreters 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 multiple interpreters
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
interpreters become more isolated, e.g. after GIL removal (though this
is mitigated somewhat for some "immortal" objects (see :pep:`683`).
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. Earlier versions of this proposal
included a basic capability ("channels"), though most of the options
were quite similar.
Note that the implementation of ``Interpreter.run()`` will be done
in a way that allows for may of these solutions to be implemented
independently and to coexist, but doing so is not technically
a part of the proposal here.
The fundamental enabling feature for communication is that most objects
can be converted to some encoding of underlying raw data, which is safe
to be passed between interpreters. For example, an ``int`` object can
be turned into a C ``long`` value, send to another interpreter, and
turned back into an ``int`` object there.
Regardless, the effort to determine the best way forward here is outside
the scope of this PEP. In the meantime, this proposal provides a basic
interim solution, described in `API For Sharing Data`_ below.
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
area:
* 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]_
* interpreters share memory management (e.g. allocators, gc)
* interpreters share the GIL
Existing Usage
--------------
Multiple interpreter support is not a widely used feature. In fact,
the only documented cases of widespread usage are
`mod_wsgi <https://github.com/GrahamDumpleton/mod_wsgi>`_,
`OpenStack Ceph <https://github.com/ceph/ceph/pull/14971>`_, and
`JEP <https://github.com/ninia/jep>`_. On the one hand, these cases
provide confidence that existing multiple interpreter 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.
Alternate Python Implementations
================================
I've solicited feedback from various Python implementors about support
for subinterpreters. Each has indicated that they would be able to
support multiple interpreters in the same process (if they choose to)
without a lot of trouble. Here are the projects I contacted:
* jython ([jython]_)
* ironpython (personal correspondence)
* pypy (personal correspondence)
* micropython (personal correspondence)
.. _interpreters-list-all:
.. _interpreters-get-current:
.. _interpreters-create:
.. _interpreters-Interpreter:
"interpreters" Module API
=========================
The module provides the following functions::
list_all() -> [Interpreter]
Return a list of all existing interpreters.
get_current() => Interpreter
Return the currently running interpreter.
get_main() => Interpreter
Return the main interpreter. If the Python implementation
has no concept of a main interpreter then return None.
create() -> Interpreter
Initialize a new Python interpreter and return it.
It will remain idle until something is run in it and always
run in its own thread.
The module also provides the following classes::
class Interpreter(id):
id -> int:
The interpreter's ID. (read-only)
is_running() -> bool:
Return whether or not the interpreter is currently executing
code. Calling this on the current interpreter will always
return True.
close():
Finalize and destroy the interpreter.
This may not be called on an already running interpreter.
Doing so results in a RuntimeError.
run(source_str, /, *, shared=None) -> Status:
Run the provided Python source code in the interpreter and
return a Status object that tracks when it finishes.
If the "shared" keyword argument is provided (and is a mapping
of attribute name keys) then each key-value pair is added to
the interpreter's execution namespace (the interpreter's
"__main__" module). If any of the values are not a shareable
object (see below) 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 Thread.start() call. That code
starts running in a background thread and "run()" returns. At
that point, the code that called "run()" continues executing
(in the original interpreter). If any "return" value is
needed, pass it out via a pipe (os.pipe()). If there is any
uncaught exception then the returned Status object will expose it.
The big difference from functions or threading.Thread is that
"run()" executes the code in an entirely different interpreter,
with entirely separate state. The state of the current
interpreter in the original OS thread does not affect that of
the target interpreter (the one that will execute the code).
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.
Supported code: source text.
class Status:
# This is similar to concurrent.futures.Future.
wait(timeout=None):
Block until the requested work has finished.
done() -> bool:
Has the requested work completed (or failed)?
exception() -> Exception | None:
Return the exception raised by the requested work, if any.
If the work has not completed yet then ``NotFinishedError``
is raised.
Uncaught Exceptions
-------------------
Regarding uncaught exceptions in ``Interpreter.run()``, we noted that
they are exposed via the returned ``Status`` object. To prevent leaking
exceptions (and tracebacks) between interpreters, we create a surrogate
of the exception and its traceback
(see ``traceback.TracebackException``). This is returned by
``Status.exception()``. ``Status.wait()`` set it to ``__cause__``
on a new ``RunFailedError``, and raise that.
Raising (a proxy of) the exception directly is problematic since it's
harder to distinguish between an error in the ``wait()`` call and an
uncaught exception from the subinterpreter.
API For Sharing Data
--------------------
As discussed in `Shared Data`_ above, multiple interpreter support
is less useful without a mechanism for sharing data (communicating)
between them. Sharing actual Python objects between interpreters,
however, has enough potential problems that we are avoiding support
for that in this proposal. Nor, as mentioned earlier, are we adding
anything more than the most minimal mechanism for communication.
That very basic mechanism, using pipes (see ``os.pipe()``), will allow
users to send data (bytes) from one interpreter to another. We'll
take a closer look in a moment. Fundamentally, it's a simple
application of the underlying sharing capability proposed here.
The various aspects of the approach, including keeping the API minimal,
helps us avoid further exposing any underlying complexity
to Python users.
.. _interpreters-is-shareable:
Shareable Objects
'''''''''''''''''
A "shareable" object is one that the runtime knows how to safely "share"
between interpreters. For now this actually means that a copy of the
object is provided to the second interpreter. Legitimate sharing is
feasible but beyond the scope of this proposal.
In fact, this proposal only covers very minimal "sharing" of a handful
of simple, immutable object types. We will initially limit the types
that are shareable to the following:
* ``None``
* ``bytes``
* ``str``
* ``int``
Support for other basic types (e.g. ``bool``, ``float``, ``Ellipsis``)
will be added later, separately.
Limiting the initial shareable types is a practical matter, reducing
the potential complexity of the initial implementation. There are a
number of solutions we may pursue in the future to expand supported
objects and object sharing strategies.
However, this PEP does provide one concrete addition related to
shareable objects. The ``interpreters`` module provides a function
that users may call to determine whether an object is shareable or not::
is_shareable(obj) -> bool:
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 means.
How Sharing Works
'''''''''''''''''
In this propsal, shareable objects are used with ``Interpreter.run()``.
The steps look something like this:
1. a "shareable" object is mapped to an identifier in some container
2. that mapping is passed as the "shared" argument in the
``Interpreter.run()`` call
3. the mapped object is converted to an object that the target
interpreter may safely use
4. that object is bound to the mapped name in the target interpreter's
``__main__`` module, where the running code has access to it
The critical part is what happens in step 3. The object must be
converted to some cross-interpreter-safe data (its raw data or even
a pointer). Then that data must be converted back into an object
for the target interpreter to use, likely a new object. For example,
an ``int`` object could be converted to the underlying C ``long`` value
and then back into a Python ``int`` object.
To make this work, the intermediate data (and any associated mutable
shared state) will be managed by the Python runtime, not by any of the
interpreters.
The underlying runtime capability that ``Interpreter.run()`` uses is
what enables data/object "sharing", and is available for use elsewhere
in the runtime. In fact, it was used in the implementation of the
"channels" that were part of an earlier version of this PEP.
Likewise, this runtime functionality facilitates most of the possible
solutions to which `Shared Data`_ alluded. Thus any separate effort
to introduce effective means for communicating and sharing data will
be well served by the underlying functionality proposed here.
.. XXX Add Interpreter.set_on___main__() and drop the "shared" arg?
Communicating Through OS Pipes
''''''''''''''''''''''''''''''
As noted, this proposal enables a very basic mechanism for
communicating between interpreters, which makes use of
``Interpreter.run()`` and shareable objects:
1. interpreter A calls ``os.pipe()`` to get a read/write pair
of file descriptors (both shareable ``int`` objects)
2. interpreter A calls ``run()`` on interpreter B, passing
the read FD via the "shared" argument
3. interpreter A writes some bytes to the write FD
4. interpreter B reads those bytes
Several of the earlier examples demonstrate this, such as
`Synchronize using an OS pipe`_.
Interpreter Restrictions
========================
Every new interpreter created by ``interpreters.create()``
now has specific restrictions on any code it runs. This includes the
following:
* importing an extension module fails if it does not implement
multi-phase init
* daemon threads may not be created
* ``os.fork()`` is not allowed (so no ``multiprocessing``)
* ``os.exec*()`` is not allowed
(but "fork+exec", a la ``subprocess`` is okay)
Note that interpreters created with the existing C-API do not have these
restrictions. The same is true for the "main" interpreter, so
existing use of Python will not change.
.. Mention the similar restrictions in PEP 684?
We may choose to later loosen some of the above restrictions or provide
a way to enable/disable granular restrictions individually. Regardless,
requiring multi-phase init from extension modules will always be a
default restriction.
Documentation
=============
The new stdlib docs page for the ``interpreters`` module will include
the following:
* (at the top) a clear note that support for multiple interpreters
is not required from extension modules
* some explanation about what subinterpreters are
* brief examples of how to use multiple interpreters
(and communicating between them)
* a summary of the limitations of using multiple interpreters
* (for extension maintainers) a link to the resources for ensuring
multiple interpreters compatibility
* much of the API information in this PEP
Docs about resources for extension maintainers already exist on the
`Isolating Extension Modules <isolation-howto_>`_ howto page. Any
extra help will be added there. For example, it may prove helpful
to discuss strategies for dealing with linked libraries that keep
their own subinterpreter-incompatible global state.
.. _isolation-howto:
https://docs.python.org/3/howto/isolating-extensions.html
Note that the documentation will play a large part in mitigating any
negative impact that the new ``interpreters`` module might have on
extension module maintainers.
Also, the ``ImportError`` for incompatible extension modules will have
a message that clearly says it is due to missing multiple interpreters
compatibility and that extensions are not required to provide it. This
will help set user expectations properly.
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.
Interpreter.call()
------------------
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.
Interpreter.run_in_thread()
---------------------------
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,
interpreters do not share state. Data sharing is restricted to the
runtime's shareable objects capability, which does away with the need
for explicit synchronization. If any sort of opt-in shared state
support is added to CPython's interpreters 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 multiple interpreters
support. However, ``Go`` also provides syntactic support, as well as
several builtin concurrency primitives, to make concurrency a
first-class feature. Conceivably, similar syntactic (and builtin)
support could be added to Python using interpreters. However,
that is *way* outside the scope of this PEP!
Multiprocessing
---------------
The ``multiprocessing`` module could support interpreters 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
multiple interpreter support. 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 multiple interpreters support.
However, that would probably exclude many more modules (unnecessarily)
than the opt-out approach. Also, note that :pep:`489` defined that an
extension's use of the PEP's machinery implies multiple interpreters
support.
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 that
there are relatively few cases we know of through mod_wsgi, we can
leave this for later.
Resetting __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
desirable 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::
interp.run('globals().clear()')
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 resetting ``__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.
Resetting an interpreter's state
--------------------------------
It may be nice to re-use an existing subinterpreter instead of
spinning up a new one. Since an interpreter has substantially more
state than just the ``__main__`` module, it isn't so easy to put an
interpreter back into a pristine/fresh state. In fact, there *may*
be parts of the state that cannot be reset from Python code.
A possible solution is to add an ``Interpreter.reset()`` method. This
would put the interpreter back into the state it was in when newly
created. If called on a running interpreter it would fail (hence the
main interpreter could never be reset). This would likely be more
efficient than creating a new interpreter, though that depends on
what optimizations will be made later to interpreter creation.
While this would potentially provide functionality that is not
otherwise available from Python code, it isn't a fundamental
functionality. So in the spirit of minimalism here, this can wait.
Regardless, I doubt it would be controversial to add it post-PEP.
Shareable file descriptors and sockets
--------------------------------------
Given that file descriptors and sockets are process-global resources,
making them shareable is a reasonable idea. They would be a good
candidate for the first effort at expanding the supported shareable
types. They aren't strictly necessary for the initial API.
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?).
The basic functionality of multiple interpreters support does not depend
on async and can be added later.
channels
--------
We could introduce some relatively efficient, native data types for
passing data between interpreters, to use instead of OS pipes. Earlier
versions of this PEP introduced one such mechanism, called "channels".
This can be pursued later.
Pipes and Queues
----------------
With the proposed object passing mechanism of "os.pipe()", other similar
basic types aren't strictly required to achieve the minimal useful
functionality of multiple interpreters. Such types include pipes
(like unbuffered channels, but one-to-one) and queues (like channels,
but more generic). 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.
Support inheriting settings (and more?)
---------------------------------------
Folks might find it useful, when creating a new interpreter, to be
able to indicate that they would like some things "inherited" by the
new interpreter. The mechanism could be a strict copy or it could be
copy-on-write. The motivating example is with the warnings module
(e.g. copy the filters).
The feature isn't critical, nor would it be widely useful, so it
can wait until there's interest. Notably, both suggested solutions
will require significant work, especially when it comes to complex
objects and most especially for mutable containers of mutable
complex objects.
Make exceptions shareable
-------------------------
Exceptions are propagated out of ``run()`` calls, so it isn't a big
leap to make them shareable. However, as noted elsewhere,
it isn't essential or (particularly common) so we can wait on doing
that.
Make RunFailedError.__cause__ lazy
----------------------------------
An uncaught exception in a subinterpreter (from ``run()``) is copied
to the calling interpreter and set as ``__cause__`` on a
``RunFailedError`` which is then raised. That copying part involves
some sort of deserialization in the calling interpreter, which can be
expensive (e.g. due to imports) yet is not always necessary.
So it may be useful to use an ``ExceptionProxy`` type to wrap the
serialized exception and only deserialize it when needed. That could
be via ``ExceptionProxy__getattribute__()`` or perhaps through
``RunFailedError.resolve()`` (which would raise the deserialized
exception and set ``RunFailedError.__cause__`` to the exception.
It may also make sense to have ``RunFailedError.__cause__`` be a
descriptor that does the lazy deserialization (and set ``__cause__``)
on the ``RunFailedError`` instance.
Make everything shareable through serialization
-----------------------------------------------
We could use pickle (or marshal) to serialize everything and thus
make them shareable. Doing this is potentially inefficient,
but it may be a matter of convenience in the end.
We can add it later, but trying to remove it later
would be significantly more painful.
Return a value from ``run()``
-----------------------------
Currently ``run()`` always returns None. One idea is to return the
return value from whatever the subinterpreter ran. However, for now
it doesn't make sense. The only thing folks can run is a string of
code (i.e. a script). This is equivalent to ``PyRun_StringFlags()``,
``exec()``, or a module body. None of those "return" anything. We can
revisit this once ``run()`` supports functions, etc.
Add a "tp_share" type slot
--------------------------
This would replace the current global registry for shareable types.
Add a shareable synchronization primitive
-----------------------------------------
This would be ``_threading.Lock`` (or something like it) where
interpreters would actually share the underlying mutex. The main
concern is that locks and isolated interpreters may not mix well
(as learned in Go).
We can add this later if it proves desirable without much trouble.
Propagate SystemExit and KeyboardInterrupt Differently
------------------------------------------------------
The exception types that inherit from ``BaseException`` (aside from
``Exception``) are usually treated specially. These types are:
``KeyboardInterrupt``, ``SystemExit``, and ``GeneratorExit``. It may
make sense to treat them specially when it comes to propagation from
``run()``. Here are some options::
* propagate like normal via RunFailedError
* do not propagate (handle them somehow in the subinterpreter)
* propagate them directly (avoid RunFailedError)
* propagate them directly (set RunFailedError as __cause__)
We aren't going to worry about handling them differently. Threads
already ignore ``SystemExit``, so for now we will follow that pattern.
Auto-run in a thread
--------------------
The PEP proposes a hard separation between interpreters and threads:
if you want to run in a thread you must create the thread yourself and
call ``run()`` in it. However, it might be convenient if ``run()``
could do that for you, meaning there would be less boilerplate.
Furthermore, we anticipate that users will want to run in a thread much
more often than not. So it would make sense to make this the default
behavior. We would add a kw-only param "threaded" (default ``True``)
to ``run()`` to allow the run-in-the-current-thread operation.
Rejected Ideas
==============
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
------------------------------
Queues and buffered channels are almost the same thing. The main
difference is that channels have a stronger relationship with context
(i.e. the associated interpreter).
The name "Channel" was used instead of "Queue" to avoid confusion with
the stdlib ``queue.Queue``.
"enumerate"
-----------
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
``RunFailedError`` instead, for which ``__cause__`` wraps a safe proxy
for the original exception and traceback.
Rejected possible solutions:
* reproduce the exception and traceback in the original interpreter
and raise that.
* raise a subclass of RunFailedError that proxies the original
exception and traceback.
* raise RuntimeError instead of RunFailedError
* 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]_
(unnecessary 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 ``os.pipe()``);
with threads you have to do something similar
Always associate each new interpreter with its own thread
---------------------------------------------------------
As implemented in the C-API, an interpreter is not inherently tied to
any thread. Furthermore, it will run in any existing thread, whether
created by Python or not. You only have to activate one of its thread
states (``PyThreadState``) in the thread first. This means that the
same thread may run more than one interpreter (though obviously
not at the same time).
The proposed module maintains this behavior. Interpreters are not
tied to threads. Only calls to ``Interpreter.run()`` are. However,
one of the key objectives of this PEP is to provide a more
human-centric concurrency model. With that in mind, from a conceptual
standpoint the module *might* be easier to understand if each
interpreter were associated with its own thread.
That would mean ``interpreters.create()`` would create a new thread
and ``Interpreter.run()`` would only execute in that thread (and
nothing else would). The benefit is that users would not have to
wrap ``Interpreter.run()`` calls in a new ``threading.Thread``. Nor
would they be in a position to accidentally pause the current
interpreter (in the current thread) while their interpreter
executes.
The idea is rejected because the benefit is small and the cost is high.
The difference from the capability in the C-API would be potentially
confusing. The implicit creation of threads is magical. The early
creation of threads is potentially wasteful. The inability to run
arbitrary interpreters in an existing thread would prevent some valid
use cases, frustrating users. Tying interpreters to threads would
require extra runtime modifications. It would also make the module's
implementation overly complicated. Finally, it might not even make
the module easier to understand.
Add a "reraise" method to RunFailedError
----------------------------------------
While having ``__cause__`` set on ``RunFailedError`` helps produce a
more useful traceback, it's less helpful when handling the original
error. To help facilitate this, we could add
``RunFailedError.reraise()``. This method would enable the following
pattern::
try:
try:
interp.run(script)
except RunFailedError as exc:
exc.reraise()
except MyException:
...
This would be made even simpler if there existed a ``__reraise__``
protocol.
All that said, this is completely unnecessary. Using ``__cause__``
is good enough::
try:
try:
interp.run(script)
except RunFailedError as exc:
raise exc.__cause__
except MyException:
...
Note that in extreme cases it may require a little extra boilerplate::
try:
try:
interp.run(script)
except RunFailedError as exc:
if exc.__cause__ is not None:
raise exc.__cause__
raise # re-raise
except MyException:
...
Implementation
==============
The implementation of the PEP has 4 parts:
* the high-level module described in this PEP (mostly a light wrapper
around a low-level C extension
* the low-level C extension module
* additions to the ("private") C=API needed by the low-level module
* secondary fixes/changes in the CPython runtime that facilitate
the low-level module (among other benefits)
These are at various levels of completion, with more done the lower
you go:
* the high-level module has been, at best, roughly implemented.
However, fully implementing it will be almost trivial.
* the low-level module is mostly complete. The bulk of the
implementation was merged into master in December 2018 as the
"_xxsubinterpreters" module (for the sake of testing multiple
interpreters functionality). Only 3 parts of the implementation
remain: "send_wait()", "send_buffer()", and exception propagation.
All three have been mostly finished, but were blocked by work
related to ceval. That blocker is basically resolved now and
finishing the low-level will not require extensive work.
* all necessary C-API work has been finished
* all anticipated work in the runtime has been finished
The implementation effort for :pep:`554` is being tracked as part of
a larger project aimed at improving multi-core support in CPython.
[multi-core-project]_
References
==========
.. [c-api]
https://docs.python.org/3/c-api/init.html#sub-interpreter-support
.. [CSP]
https://en.wikipedia.org/wiki/Communicating_sequential_processes
https://github.com/futurecore/python-csp
.. [caveats]
https://docs.python.org/3/c-api/init.html#bugs-and-caveats
.. [petr-c-ext]
https://mail.python.org/pipermail/import-sig/2016-June/001062.html
https://mail.python.org/pipermail/python-ideas/2016-April/039748.html
.. [cryptography]
https://github.com/pyca/cryptography/issues/2299
.. [global-gc]
http://bugs.python.org/issue24554
.. [gilstate]
https://bugs.python.org/issue10915
http://bugs.python.org/issue15751
.. [global-atexit]
https://bugs.python.org/issue6531
.. [bug-rate]
https://mail.python.org/pipermail/python-ideas/2017-September/047094.html
.. [benefits]
https://mail.python.org/pipermail/python-ideas/2017-September/047122.html
.. [reset_globals]
https://mail.python.org/pipermail/python-dev/2017-September/149545.html
.. [async]
https://mail.python.org/pipermail/python-dev/2017-September/149420.html
https://mail.python.org/pipermail/python-dev/2017-September/149585.html
.. [result-object]
https://mail.python.org/pipermail/python-dev/2017-September/149562.html
.. [jython]
https://mail.python.org/pipermail/python-ideas/2017-May/045771.html
.. [multi-core-project]
https://github.com/ericsnowcurrently/multi-core-python
.. [cache-line-ping-pong]
https://mail.python.org/archives/list/python-dev@python.org/message/3HVRFWHDMWPNR367GXBILZ4JJAUQ2STZ/
.. _nathaniel-asyncio:
https://mail.python.org/archives/list/python-dev@python.org/message/TUEAZNZHVJGGLL4OFD32OW6JJDKM6FAS/
* mp-conn
https://docs.python.org/3/library/multiprocessing.html#connection-objects
* main-thread
https://mail.python.org/pipermail/python-ideas/2017-September/047144.html
https://mail.python.org/pipermail/python-dev/2017-September/149566.html
Copyright
=========
This document has been placed in the public domain.
..
Local Variables:
mode: indented-text
indent-tabs-mode: nil
sentence-end-double-space: t
fill-column: 70
coding: utf-8
End:
Reference in New Issue View Git Blame Copy Permalink