[Python-Dev] PEP 567 v2

Yury Selivanov yselivanov.ml at gmail.com
Thu Dec 28 01:08:13 EST 2017

This is a second version of PEP 567.

A few things have changed:

1. I now have a reference implementation:

2. The C API was updated to match the implementation.

3. The get_context() function was renamed to copy_context() to better
reflect what it is really doing.

4. Few clarifications/edits here and there to address earlier feedback.


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


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.

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.


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.


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
store a context variable 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, and
is accessed via ``contextvars.copy_context()`` function.
Manipulation of the current ``Context`` is the responsibility of the
task framework, e.g. asyncio.

A ``Context`` is conceptually a read-only mapping, implemented using
an immutable dictionary.  The ``ContextVar.get()`` method does a
lookup in the current ``Context`` with ``self`` as a key, raising a
``LookupError``  or returning a default value specified in
the constructor.

The ``ContextVar.set(value)`` method clones the current ``Context``,
assigns the ``value`` to it with ``self`` as a key, and sets the
new ``Context`` as the new current ``Context``.


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 manually.
   Instead, the ``Context.run(callable, *args, **kwargs)`` method is
   used to run Python code in another context.


The ``ContextVar`` class has the following constructor signature:
``ContextVar(name, *, default=_NO_DEFAULT)``.  The ``name`` parameter
is used only 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()`` returns a value for context variable from the
current ``Context``::

    # Get the value of `var`.

``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.

``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``::

    assert var.get(None) is None

    token = var.set(1)

    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.


``contextvars.Token`` is an opaque object that should be used to
restore the ``ContextVar`` to its previous value, or 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.

Having the ``ContextVar.set()`` method returning a ``Token`` object
and the ``ContextVar.reset(token)`` method, allows context variables
to be removed from the context if they were not in it before the
``set()`` call.


``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()``


Any changes to any context variables that ``function`` causes will
be contained in the ``ctx`` context::

    var = ContextVar('var')

    def function():
        assert var.get() == 'spam'

        assert var.get() == 'ham'

    ctx = copy_context()

    # Any changes that 'function' makes to 'var' will stay
    # isolated in the 'ctx'.

    assert var.get() == 'spam'

Any changes to the context will be contained in the ``Context``
object on which ``run()`` is called on.

``Context.run()`` is used to control in which context asyncio
callbacks and Tasks are executed.  It can also be used to run some
code in a different thread in the context of the current thread::

    executor = ThreadPoolExecutor()
    current_context = contextvars.copy_context()

        lambda: current_context.run(some_function))

``Context`` objects implement the ``collections.abc.Mapping`` ABC.
This can be used to introspect context objects::

    ctx = contextvars.copy_context()

    # Print all context variables and their values in 'ctx':

    # Print the value of 'some_variable' in context 'ctx':


``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)


1. ``PyContextVar * PyContextVar_New(char *name, PyObject *default)``:
   create a ``ContextVar`` object.

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 borrowed

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;


This section explains high-level implementation details in
pseudo-code.  Some optimizations are omitted to keep this section
short and clear.

For the purposes of this section, we implement an immutable dictionary
using ``dict.copy()``::

    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 ``_ContextData``.
``PyThreadState`` is updated with a new ``context_data`` field that
points to a ``_ContextData`` object::

    class PyThreadState:
        context_data: _ContextData

``contextvars.copy_context()`` is implemented as follows::

    def copy_context():
        ts : PyThreadState = PyThreadState_Get()

        if ts.context_data is None:
            ts.context_data = _ContextData()

        ctx = Context()
        ctx._data = ts.context_data
        return ctx

``contextvars.Context`` is a wrapper around ``_ContextData``::

    class Context(collections.abc.Mapping):

        def __init__(self):
            self._data = _ContextData()

        def run(self, callable, *args, **kwargs):
            ts : PyThreadState = PyThreadState_Get()
            saved_data : _ContextData = ts.context_data

                ts.context_data = self._data
                return callable(*args, **kwargs)
                self._data = ts.context_data
                ts.context_data = saved_data

        # Mapping API methods are implemented by delegating
        # `get()` and other Mapping calls to `self._data`.

``contextvars.ContextVar`` interacts with
``PyThreadState.context_data`` directly::

    class ContextVar:

        def __init__(self, name, *, default=_NO_DEFAULT):
            self._name = name
            self._default = default

        def name(self):
            return self._name

        def get(self, default=_NO_DEFAULT):
            ts : PyThreadState = PyThreadState_Get()
            data : _ContextData = ts.context_data

                return data.get(self)
            except KeyError:

            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

                old_value = data.get(self)
            except KeyError:
                old_value = Token.MISSING

            ts.context_data = data.set(self, value)
            return Token(self, old_value)

        def reset(self, token):
            if token._used:

            if token._old_value is Token.MISSING:
                ts.context_data = data.delete(token._var)
                ts.context_data = data.set(token._var,

            token._used = True

    class Token:

        MISSING = object()

        def __init__(self, var, old_value):
            self._var = var
            self._old_value = old_value
            self._used = False

        def var(self):
            return self._var

        def old_value(self):
            return self._old_value

Implementation Notes

* The internal immutable dictionary for ``Context`` is implemented
  using Hash Array Mapped Tries (HAMT).  They allow for O(log N)
  ``set`` operation, and for O(1) ``copy_context()`` function, where
  *N* is the number of items in the dictionary.  For a detailed
  analysis of HAMT performance please refer to :pep:`550` [1]_.

* ``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 a great detail.

Summary of the New APIs

* A new ``contextvars`` module with ``ContextVar``, ``Context``,
  and ``Token`` classes, and a ``copy_context()`` function.

* ``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.

* ``asyncio.Task`` is modified internally to maintain its own

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()
        # code


    _deleted = object()
    old = cv.get(default=_deleted)
        # code
        if old is _deleted:

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.

Reference Implementation

The reference implementation can be found here: [3]_.


.. [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


This document has been placed in the public domain.

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