[Python-Dev] PEP 567 v3
Yury Selivanov
yselivanov.ml at gmail.com
Tue Jan 16 17:44:14 EST 2018
Hi,
This is a third version of PEP 567.
Changes from v2:
1. PyThreadState now references Context objects directly (instead of
referencing _ContextData). This fixes out of sync Context.get() and
ContextVar.get().
2. Added a new Context.copy() method.
3. Renamed Token.old_val property to Token.old_value
4. ContextVar.reset(token) now raises a ValueError if the token was
created in a different Context.
5. All areas of the PEP were updated to be more precise. Context is
*no longer* defined as a read-only or an immutable mapping;
ContextVar.get() behaviour is fully defined; the immutability is only
mentioned in the Implementation section to avoid confusion; etc.
6. Added a new Examples section.
The reference implementation has been updated to include all these changes.
The only open question I personally have is whether ContextVar.reset()
should be idempotent or not. Maybe we should be strict and raise an
error if a user tries to reset a variable more than once with the same
token object?
Other than that, I'm pretty happy with this version. Big thanks to
everybody helping with the PEP!
PEP: 567
Title: Context Variables
Version: $Revision$
Last-Modified: $Date$
Author: Yury Selivanov <yury at magic.io>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 12-Dec-2017
Python-Version: 3.7
Post-History: 12-Dec-2017, 28-Dec-2017, 16-Jan-2018
Abstract
========
This PEP proposes a new ``contextvars`` module and a set of new
CPython C APIs to support context variables. This concept is
similar to thread-local storage (TLS), but, unlike TLS, it also allows
correctly keeping track of values per asynchronous task, e.g.
``asyncio.Task``.
This proposal is a simplified version of :pep:`550`. The key
difference is that this PEP is concerned only with solving the case
for asynchronous tasks, not for generators. There are no proposed
modifications to any built-in types or to the interpreter.
This proposal is not strictly related to Python Context Managers.
Although it does provide a mechanism that can be used by Context
Managers to store their state.
Rationale
=========
Thread-local variables are insufficient for asynchronous tasks that
execute concurrently in the same OS thread. Any context manager that
saves and restores a context value using ``threading.local()`` will
have its context values bleed to other code unexpectedly when used
in async/await code.
A few examples where having a working context local storage for
asynchronous code is desirable:
* Context managers like ``decimal`` contexts and ``numpy.errstate``.
* Request-related data, such as security tokens and request
data in web applications, language context for ``gettext``, etc.
* Profiling, tracing, and logging in large code bases.
Introduction
============
The PEP proposes a new mechanism for managing context variables.
The key classes involved in this mechanism are ``contextvars.Context``
and ``contextvars.ContextVar``. The PEP also proposes some policies
for using the mechanism around asynchronous tasks.
The proposed mechanism for accessing context variables uses the
``ContextVar`` class. A module (such as ``decimal``) that wishes to
use the new mechanism should:
* declare a module-global variable holding a ``ContextVar`` to
serve as a key;
* access the current value via the ``get()`` method on the
key variable;
* modify the current value via the ``set()`` method on the
key variable.
The notion of "current value" deserves special consideration:
different asynchronous tasks that exist and execute concurrently
may have different values for the same key. This idea is well-known
from thread-local storage but in this case the locality of the value is
not necessarily bound to a thread. Instead, there is the notion of the
"current ``Context``" which is stored in thread-local storage.
Manipulation of the current context is the responsibility of the
task framework, e.g. asyncio.
A ``Context`` is a mapping of ``ContextVar`` objects to their values.
The ``Context`` itself exposes the ``abc.Mapping`` interface
(not ``abc.MutableMapping``!), so it cannot be modified directly.
To set a new value for a context variable in a ``Context`` object,
the user needs to:
* make the ``Context`` object "current" using the ``Context.run()``
method;
* use ``ContextVar.set()`` to set a new value for the context
variable.
The ``ContextVar.get()`` method looks for the variable in the current
``Context`` object using ``self`` as a key.
It is not possible to get a direct reference to the current ``Context``
object, but it is possible to obtain a shallow copy of it using the
``contextvars.copy_context()`` function. This ensures that the
caller of ``Context.run()`` is the sole owner of its ``Context``
object.
Specification
=============
A new standard library module ``contextvars`` is added with the
following APIs:
1. ``copy_context() -> Context`` function is used to get a copy of
the current ``Context`` object for the current OS thread.
2. ``ContextVar`` class to declare and access context variables.
3. ``Context`` class encapsulates context state. Every OS thread
stores a reference to its current ``Context`` instance.
It is not possible to control that reference directly.
Instead, the ``Context.run(callable, *args, **kwargs)`` method is
used to run Python code in another context.
contextvars.ContextVar
----------------------
The ``ContextVar`` class has the following constructor signature:
``ContextVar(name, *, default=_NO_DEFAULT)``. The ``name`` parameter
is used for introspection and debug purposes, and is exposed
as a read-only ``ContextVar.name`` attribute. The ``default``
parameter is optional. Example::
# Declare a context variable 'var' with the default value 42.
var = ContextVar('var', default=42)
(The ``_NO_DEFAULT`` is an internal sentinel object used to
detect if the default value was provided.)
``ContextVar.get(default=_NO_DEFAULT)`` returns a value for
the context variable for the current ``Context``::
# Get the value of `var`.
var.get()
If there is no value for the variable in the current context,
``ContextVar.get()`` will:
* return the value of the *default* argument of the ``get()`` method,
if provided; or
* return the default value for the context variable, if provided; or
* raise a ``LookupError``.
``ContextVar.set(value) -> Token`` is used to set a new value for
the context variable in the current ``Context``::
# Set the variable 'var' to 1 in the current context.
var.set(1)
``ContextVar.reset(token)`` is used to reset the variable in the
current context to the value it had before the ``set()`` operation
that created the ``token`` (or to remove the variable if it was
not set)::
assert var.get(None) is None
token = var.set(1)
try:
...
finally:
var.reset(token)
assert var.get(None) is None
``ContextVar.reset()`` method is idempotent and can be called
multiple times on the same Token object: second and later calls
will be no-ops. The method raises a ``ValueError`` if:
* called with a token object created by another variable; or
* the current ``Context`` object does not match the one where
the token object was created.
contextvars.Token
-----------------
``contextvars.Token`` is an opaque object that should be used to
restore the ``ContextVar`` to its previous value, or to remove it from
the context if the variable was not set before. It can be created
only by calling ``ContextVar.set()``.
For debug and introspection purposes it has:
* a read-only attribute ``Token.var`` pointing to the variable
that created the token;
* a read-only attribute ``Token.old_value`` set to the value the
variable had before the ``set()`` call, or to ``Token.MISSING``
if the variable wasn't set before.
contextvars.Context
-------------------
``Context`` object is a mapping of context variables to values.
``Context()`` creates an empty context. To get a copy of the current
``Context`` for the current OS thread, use the
``contextvars.copy_context()`` method::
ctx = contextvars.copy_context()
To run Python code in some ``Context``, use ``Context.run()``
method::
ctx.run(function)
Any changes to any context variables that ``function`` causes will
be contained in the ``ctx`` context::
var = ContextVar('var')
var.set('spam')
def function():
assert var.get() == 'spam'
assert ctx[var] == 'spam'
var.set('ham')
assert var.get() == 'ham'
assert ctx[var] == 'ham'
ctx = copy_context()
# Any changes that 'function' makes to 'var' will stay
# isolated in the 'ctx'.
ctx.run(function)
assert var.get() == 'spam'
assert ctx[var] == 'ham'
``Context.run()`` raises a ``RuntimeError`` when called on the same
context object from more than one OS thread, or when called
recursively.
``Context.copy()`` returns a shallow copy of the context object.
``Context`` objects implement the ``collections.abc.Mapping`` ABC.
This can be used to introspect contexts::
ctx = contextvars.copy_context()
# Print all context variables and their values in 'ctx':
print(ctx.items())
# Print the value of 'some_variable' in context 'ctx':
print(ctx[some_variable])
Note that all Mapping methods, including ``Context.__getitem__`` and
``Context.get``, ignore default values for context variables
(i.e. ``ContextVar.default``). This means that for a variable *var*
that was created with a default value and was not set in the
*context*:
* ``context[var]`` raises a ``KeyError``,
* ``var in context`` returns ``False``,
* the variable isn't included in ``context.items()``, etc.
asyncio
-------
``asyncio`` uses ``Loop.call_soon()``, ``Loop.call_later()``,
and ``Loop.call_at()`` to schedule the asynchronous execution of a
function. ``asyncio.Task`` uses ``call_soon()`` to run the
wrapped coroutine.
We modify ``Loop.call_{at,later,soon}`` and
``Future.add_done_callback()`` to accept the new optional *context*
keyword-only argument, which defaults to the current context::
def call_soon(self, callback, *args, context=None):
if context is None:
context = contextvars.copy_context()
# ... some time later
context.run(callback, *args)
Tasks in asyncio need to maintain their own context that they inherit
from the point they were created at. ``asyncio.Task`` is modified
as follows::
class Task:
def __init__(self, coro):
...
# Get the current context snapshot.
self._context = contextvars.copy_context()
self._loop.call_soon(self._step, context=self._context)
def _step(self, exc=None):
...
# Every advance of the wrapped coroutine is done in
# the task's context.
self._loop.call_soon(self._step, context=self._context)
...
Implementation
==============
This section explains high-level implementation details in
pseudo-code. Some optimizations are omitted to keep this section
short and clear.
The ``Context`` mapping is implemented using an immutable dictionary.
This allows for a O(1) implementation of the ``copy_context()``
function. The reference implementation implements the immutable
dictionary using Hash Array Mapped Tries (HAMT); see :pep:`550`
for analysis of HAMT performance [1]_.
For the purposes of this section, we implement an immutable dictionary
using a copy-on-write approach and built-in dict type::
class _ContextData:
def __init__(self):
self._mapping = dict()
def get(self, key):
return self._mapping[key]
def set(self, key, value):
copy = _ContextData()
copy._mapping = self._mapping.copy()
copy._mapping[key] = value
return copy
def delete(self, key):
copy = _ContextData()
copy._mapping = self._mapping.copy()
del copy._mapping[key]
return copy
Every OS thread has a reference to the current ``Context`` object::
class PyThreadState:
context: Context
``contextvars.Context`` is a wrapper around ``_ContextData``::
class Context(collections.abc.Mapping):
_data: _ContextData
_prev_context: Optional[Context]
def __init__(self):
self._data = _ContextData()
self._prev_context = None
def run(self, callable, *args, **kwargs):
if self._prev_context is not None:
raise RuntimeError(
f'cannot enter context: {self} is already entered')
ts: PyThreadState = PyThreadState_Get()
if ts.context is None:
ts.context = Context()
self._prev_context = ts.context
try:
ts.context = self
return callable(*args, **kwargs)
finally:
ts.context = self._prev_context
self._prev_context = None
def copy(self):
new = Context()
new._data = self._data
return new
# Mapping API methods are implemented by delegating
# `get()` and other Mapping methods to `self._data`.
``contextvars.copy_context()`` is implemented as follows::
def copy_context():
ts: PyThreadState = PyThreadState_Get()
if ts.context is None:
ts.context = Context()
return ts.context.copy()
``contextvars.ContextVar`` interacts with ``PyThreadState.context``
directly::
class ContextVar:
def __init__(self, name, *, default=_NO_DEFAULT):
self._name = name
self._default = default
@property
def name(self):
return self._name
def get(self, default=_NO_DEFAULT):
ts: PyThreadState = PyThreadState_Get()
if ts.context is not None:
try:
return ts.context[self]
except KeyError:
pass
if default is not _NO_DEFAULT:
return default
if self._default is not _NO_DEFAULT:
return self._default
raise LookupError
def set(self, value):
ts: PyThreadState = PyThreadState_Get()
if ts.context is None:
ts.context = Context()
data: _ContextData = ts.context._data
try:
old_value = data.get(self)
except KeyError:
old_value = Token.MISSING
updated_data = data.set(self, value)
ts.context._data = updated_data
return Token(ts.context, self, old_value)
def reset(self, token):
if token._var is not self:
raise ValueError(
"Token was created by a different ContextVar")
ts: PyThreadState = PyThreadState_Get()
if token._ctx is not ts.context:
raise ValueError(
"Token was created in a different Context")
if token._used:
return
if token._old_value is Token.MISSING:
ts.context._data = data.delete(token._var)
else:
ts.context._data = data.set(token._var,
token._old_value)
token._used = True
Note that the in the reference implementation, ``ContextVar.get()``
has an internal cache for the most recent value, which allows to
bypass a hash lookup. This is similar to the optimization the
``decimal`` module implements to retrieve its context from
``PyThreadState_GetDict()``. See :pep:`550` which explains the
implementation of the cache in great detail.
The ``Token`` class is implemented as follows::
class Token:
MISSING = object()
def __init__(self, ctx, var, old_value):
self._ctx = ctx
self._var = var
self._old_value = old_value
self._used = False
@property
def var(self):
return self._var
@property
def old_value(self):
return self._old_value
Summary of the New APIs
=======================
Python API
----------
1. A new ``contextvars`` module with ``ContextVar``, ``Context``,
and ``Token`` classes, and a ``copy_context()`` function.
2. ``asyncio.Loop.call_at()``, ``asyncio.Loop.call_later()``,
``asyncio.Loop.call_soon()``, and
``asyncio.Future.add_done_callback()`` run callback functions in
the context they were called in. A new *context* keyword-only
parameter can be used to specify a custom context.
3. ``asyncio.Task`` is modified internally to maintain its own
context.
C API
-----
1. ``PyContextVar * PyContextVar_New(char *name, PyObject *default)``:
create a ``ContextVar`` object. The *default* argument can be
``NULL``, which means that the variable has no default value.
2. ``int PyContextVar_Get(PyContextVar *, PyObject *default_value,
PyObject **value)``:
return ``-1`` if an error occurs during the lookup, ``0`` otherwise.
If a value for the context variable is found, it will be set to the
``value`` pointer. Otherwise, ``value`` will be set to
``default_value`` when it is not ``NULL``. If ``default_value`` is
``NULL``, ``value`` will be set to the default value of the
variable, which can be ``NULL`` too. ``value`` is always a new
reference.
3. ``PyContextToken * PyContextVar_Set(PyContextVar *, PyObject *)``:
set the value of the variable in the current context.
4. ``PyContextVar_Reset(PyContextVar *, PyContextToken *)``:
reset the value of the context variable.
5. ``PyContext * PyContext_New()``: create a new empty context.
6. ``PyContext * PyContext_Copy()``: get a copy of the current context.
7. ``int PyContext_Enter(PyContext *)`` and
``int PyContext_Exit(PyContext *)`` allow to set and restore
the context for the current OS thread. It is required to always
restore the previous context::
PyContext *old_ctx = PyContext_Copy();
if (old_ctx == NULL) goto error;
if (PyContext_Enter(new_ctx)) goto error;
// run some code
if (PyContext_Exit(old_ctx)) goto error;
Design Considerations
=====================
Why contextvars.Token and not ContextVar.unset()?
-------------------------------------------------
The Token API allows to get around having a ``ContextVar.unset()``
method, which is incompatible with chained contexts design of
:pep:`550`. Future compatibility with :pep:`550` is desired
(at least for Python 3.7) in case there is demand to support
context variables in generators and asynchronous generators.
The Token API also offers better usability: the user does not have
to special-case absence of a value. Compare::
token = cv.get()
try:
cv.set(blah)
# code
finally:
cv.reset(token)
with::
_deleted = object()
old = cv.get(default=_deleted)
try:
cv.set(blah)
# code
finally:
if old is _deleted:
cv.unset()
else:
cv.set(old)
Rejected Ideas
==============
Replication of threading.local() interface
------------------------------------------
Please refer to :pep:`550` where this topic is covered in detail: [2]_.
Backwards Compatibility
=======================
This proposal preserves 100% backwards compatibility.
Libraries that use ``threading.local()`` to store context-related
values, currently work correctly only for synchronous code. Switching
them to use the proposed API will keep their behavior for synchronous
code unmodified, but will automatically enable support for
asynchronous code.
Examples
========
Converting code that uses threading.local()
-------------------------------------------
A typical code that uses ``threading.local()`` usually looks like
the following snippet::
class mylocal(threading.local):
# Subclass threading.local to specify a default value.
value = 'spam'
mylocal = mylocal()
# To set a new value:
mylocal.value = 'new value'
# To read the current value:
mylocal.value
Such code can be converted to use the ``contextvars`` module::
mylocal = contextvars.ContextVar('mylocal', 'spam')
# To set a new value:
mylocal.set('new value')
# To read the current value:
mylocal.get()
Offloading execution to other threads
-------------------------------------
It is possible to run code in a separate OS thread using a copy
of the current thread context::
executor = ThreadPoolExecutor()
current_context = contextvars.copy_context()
executor.submit(
lambda: current_context.run(some_function))
Reference Implementation
========================
The reference implementation can be found here: [3]_.
References
==========
.. [1] https://www.python.org/dev/peps/pep-0550/#appendix-hamt-performance-analysis
.. [2] https://www.python.org/dev/peps/pep-0550/#replication-of-threading-local-interface
.. [3] https://github.com/python/cpython/pull/5027
Copyright
=========
This document has been placed in the public domain.
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