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PEP: 567
Title: Context Variables
Version: $Revision$
Last-Modified: $Date$
Author: Yury Selivanov <yury@edgedb.com>
Status: Final
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.
API Design and Implementation Revisions
=======================================
In **Python 3.7.1** the signatures of all context variables
C APIs were **changed** to use ``PyObject *`` pointers instead
of ``PyContext *``, ``PyContextVar *``, and ``PyContextToken *``,
e.g.::
// in 3.7.0:
PyContext *PyContext_New(void);
// in 3.7.1+:
PyObject *PyContext_New(void);
See [6]_ for more details. The `C API`_ section of this PEP was
updated to reflect the change.
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. The ``copy_context() -> Context`` function is used to get a copy of
the current ``Context`` object for the current OS thread.
2. The ``ContextVar`` class to declare and access context variables.
3. The ``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)::
# Assume: var.get(None) is None
# Set 'var' to 1:
token = var.set(1)
try:
# var.get() == 1
finally:
var.reset(token)
# After reset: var.get(None) is None,
# i.e. 'var' was removed from the current context.
The ``ContextVar.reset()`` method raises:
* a ``ValueError`` if it is called with a token object created
by another variable;
* a ``ValueError`` if the current ``Context`` object does not match
the one where the token object was created;
* a ``RuntimeError`` if the token object has already been used once
to reset the variable.
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 main():
# 'var' was set to 'spam' before
# calling 'copy_context()' and 'ctx.run(main)', so:
# var.get() == ctx[var] == 'spam'
var.set('ham')
# Now, after setting 'var' to 'ham':
# var.get() == ctx[var] == 'ham'
ctx = copy_context()
# Any changes that the 'main' function makes to 'var'
# will be contained in 'ctx'.
ctx.run(main)
# The 'main()' function was run in the 'ctx' context,
# so changes to 'var' are contained in it:
# ctx[var] == 'ham'
# However, outside of 'ctx', 'var' is still set to 'spam':
# var.get() == 'spam'
``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 the built-in dict type::
class _ContextData:
def __init__(self):
self._mapping = dict()
def __getitem__(self, key):
return self._mapping[key]
def __contains__(self, key):
return key in self._mapping
def __len__(self):
return len(self._mapping)
def __iter__(self):
return iter(self._mapping)
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()
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
# Implement abstract Mapping.__getitem__
def __getitem__(self, var):
return self._data[var]
# Implement abstract Mapping.__contains__
def __contains__(self, var):
return var in self._data
# Implement abstract Mapping.__len__
def __len__(self):
return len(self._data)
# Implement abstract Mapping.__iter__
def __iter__(self):
return iter(self._data)
# The rest of the Mapping methods are implemented
# by collections.abc.Mapping.
``contextvars.copy_context()`` is implemented as follows::
def copy_context():
ts: PyThreadState = PyThreadState_Get()
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()
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()
data: _ContextData = ts.context._data
try:
old_value = data[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._used:
raise RuntimeError("Token has already been used once")
if token._var is not self:
raise ValueError(
"Token was created by a different ContextVar")
ts: PyThreadState = PyThreadState_Get()
if token._context is not ts.context:
raise ValueError(
"Token was created in a different Context")
if token._old_value is Token.MISSING:
ts.context._data = ts.context._data.delete(token._var)
else:
ts.context._data = ts.context._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, context, var, old_value):
self._context = context
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. ``PyObject * 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(PyObject *, 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. ``PyObject * PyContextVar_Set(PyObject *, PyObject *)``:
set the value of the variable in the current context.
4. ``PyContextVar_Reset(PyObject *, PyObject *)``:
reset the value of the context variable.
5. ``PyObject * PyContext_New()``: create a new empty context.
6. ``PyObject * PyContext_Copy(PyObject *)``: return a shallow
copy of the passed context object.
7. ``PyObject * PyContext_CopyCurrent()``: get a copy of the current
context.
8. ``int PyContext_Enter(PyObject *)`` and
``int PyContext_Exit(PyObject *)`` allow to set and restore
the context for the current OS thread. It is required to always
restore the previous context::
PyObject *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;
Rejected Ideas
==============
Replicating threading.local() interface
---------------------------------------
Please refer to :pep:`550` where this topic is covered in detail: [2]_.
Replacing Token with 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
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.set(new_value)
try:
# cv.get() is new_value
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)
Having Token.reset() instead of ContextVar.reset()
--------------------------------------------------
Nathaniel Smith suggested to implement the ``ContextVar.reset()``
method directly on the ``Token`` class, so instead of::
token = var.set(value)
# ...
var.reset(token)
we would write::
token = var.set(value)
# ...
token.reset()
Having ``Token.reset()`` would make it impossible for a user to
attempt to reset a variable with a token object created by another
variable.
This proposal was rejected for the reason of ``ContextVar.reset()``
being clearer to the human reader of the code which variable is
being reset.
Making Context objects picklable
--------------------------------
Proposed by Antoine Pitrou, this could enable transparent
cross-process use of ``Context`` objects, so the
`Offloading execution to other threads`_ example would work with
a ``ProcessPoolExecutor`` too.
Enabling this is problematic because of the following reasons:
1. ``ContextVar`` objects do not have ``__module__`` and
``__qualname__`` attributes, making straightforward pickling
of ``Context`` objects impossible. This is solvable by modifying
the API to either auto detect the module where a context variable
is defined, or by adding a new keyword-only "module" parameter
to ``ContextVar`` constructor.
2. Not all context variables refer to picklable objects. Making a
``ContextVar`` picklable must be an opt-in.
Given the time frame of the Python 3.7 release schedule it was decided
to defer this proposal to Python 3.8.
Making Context a MutableMapping
-------------------------------
Making the ``Context`` class implement the ``abc.MutableMapping``
interface would mean that it is possible to set and unset variables
using ``Context[var] = value`` and ``del Context[var]`` operations.
This proposal was deferred to Python 3.8+ because of the following:
1. If in Python 3.8 it is decided that generators should support
context variables (see :pep:`550` and :pep:`568`), then ``Context``
would be transformed into a chain-map of context variables mappings
(as every generator would have its own mapping). That would make
mutation operations like ``Context.__delitem__`` confusing, as
they would operate only on the topmost mapping of the chain.
2. Having a single way of mutating the context
(``ContextVar.set()`` and ``ContextVar.reset()`` methods) makes
the API more straightforward.
For example, it would be non-obvious why the below code fragment
does not work as expected::
var = ContextVar('var')
ctx = copy_context()
ctx[var] = 'value'
print(ctx[var]) # Prints 'value'
print(var.get()) # Raises a LookupError
While the following code would work::
ctx = copy_context()
def func():
ctx[var] = 'value'
# Contrary to the previous example, this would work
# because 'func()' is running within 'ctx'.
print(ctx[var])
print(var.get())
ctx.run(func)
3. If ``Context`` was mutable it would mean that context variables
could be mutated separately (or concurrently) from the code that
runs within the context. That would be similar to obtaining a
reference to a running Python frame object and modifying its
``f_locals`` from another OS thread. Having one single way to
assign values to context variables makes contexts conceptually
simpler and more predictable, while keeping the door open for
future performance optimizations.
Having initial values for ContextVars
-------------------------------------
Nathaniel Smith proposed to have a required ``initial_value``
keyword-only argument for the ``ContextVar`` constructor.
The main argument against this proposal is that for some types
there is simply no sensible "initial value" except ``None``.
E.g. consider a web framework that stores the current HTTP
request object in a context variable. With the current semantics
it is possible to create a context variable without a default value::
# Framework:
current_request: ContextVar[Request] = \
ContextVar('current_request')
# Later, while handling an HTTP request:
request: Request = current_request.get()
# Work with the 'request' object:
return request.method
Note that in the above example there is no need to check if
``request`` is ``None``. It is simply expected that the framework
always sets the ``current_request`` variable, or it is a bug (in
which case ``current_request.get()`` would raise a ``LookupError``).
If, however, we had a required initial value, we would have
to guard against ``None`` values explicitly::
# Framework:
current_request: ContextVar[Optional[Request]] = \
ContextVar('current_request', initial_value=None)
# Later, while handling an HTTP request:
request: Optional[Request] = current_request.get()
# Check if the current request object was set:
if request is None:
raise RuntimeError
# Work with the 'request' object:
return request.method
Moreover, we can loosely compare context variables to regular
Python variables and to ``threading.local()`` objects. Both
of them raise errors on failed lookups (``NameError`` and
``AttributeError`` respectively).
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 fragment that uses ``threading.local()`` usually
looks like the following::
class PrecisionStorage(threading.local):
# Subclass threading.local to specify a default value.
value = 0.0
precision = PrecisionStorage()
# To set a new precision:
precision.value = 0.5
# To read the current precision:
print(precision.value)
Such code can be converted to use the ``contextvars`` module::
precision = contextvars.ContextVar('precision', default=0.0)
# To set a new precision:
precision.set(0.5)
# To read the current precision:
print(precision.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(current_context.run, some_function)
Reference Implementation
========================
The reference implementation can be found here: [3]_.
See also issue 32436 [4]_.
Acceptance
==========
:pep:`567` was accepted by Guido on Monday, January 22, 2018 [5]_.
The reference implementation was merged on the same day.
References
==========
.. [1] :pep:`550#appendix-hamt-performance-analysis`
.. [2] :pep:`550#replication-of-threading-local-interface`
.. [3] https://github.com/python/cpython/pull/5027
.. [4] https://bugs.python.org/issue32436
.. [5] https://mail.python.org/pipermail/python-dev/2018-January/151878.html
.. [6] https://bugs.python.org/issue34762
Acknowledgments
===============
I thank Guido van Rossum, Nathaniel Smith, Victor Stinner,
Elvis Pranskevichus, Nick Coghlan, Antoine Pitrou, INADA Naoki,
Paul Moore, Eric Snow, Greg Ewing, and many others for their feedback,
ideas, edits, criticism, code reviews, and discussions around
this PEP.
Copyright
=========
This document has been placed in the public domain.
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