[Python-ideas] PEP draft: context variables

Koos Zevenhoven k7hoven at gmail.com
Mon Sep 4 17:50:35 EDT 2017

Hi all,

as promised, here is a draft PEP for context variable semantics and
implementation. Apologies for the slight delay; I had a not-so-minor
autosave accident and had to retype the majority of this first draft.

During the past years, there has been growing interest in something like
task-local storage or async-local storage. This PEP proposes an alternative
approach to solving the problems that are typically stated as motivation
for such concepts.

This proposal is based on sketches of solutions since spring 2015, with
some minor influences from the recent discussion related to PEP 550. I can
also see some potential implementation synergy between this PEP and PEP
550, even if the proposed semantics are quite different.

So, here it is. This is the first draft and some things are still missing,
but the essential things should be there.

-- Koos


PEP: 999
Title: Context-local variables (contextvars)
Version: $Revision$
Last-Modified: $Date$
Author: Koos Zevenhoven
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: DD-Mmm-YYYY
Post-History: DD-Mmm-YYYY


Sometimes, in special cases, it is desired that code can pass information
down the function call chain to the callees without having to explicitly
pass the information as arguments to each function in the call chain. This
proposal describes a construct which allows code to explicitly switch in
and out of a context where a certain context variable has a given value
assigned to it. This is a modern alternative to some uses of things like
global variables in traditional single-threaded (or thread-unsafe) code and
of thread-local storage in traditional *concurrency-unsafe* code (single-
or multi-threaded). In particular, the proposed mechanism can also be used
with more modern concurrent execution mechanisms such as asynchronously
executed coroutines, without the concurrently executed call chains
interfering with each other's contexts.

The "call chain" can consist of normal functions, awaited coroutines, or
generators. The semantics of context variable scope are equivalent in all
cases, allowing code to be refactored freely into *subroutines* (which here
refers to functions, sub-generators or sub-coroutines) without affecting
the semantics of context variables. Regarding implementation, this proposal
aims at simplicity and minimum changes to the CPython interpreter and to
other Python interpreters.


Consider a modern Python *call chain* (or call tree), which in this
proposal refers to any chained (nested) execution of *subroutines*, using
any possible combinations of normal function calls, or expressions using
``await`` or ``yield from``. In some cases, passing necessary *information*
down the call chain as arguments can substantially complicate the required
function signatures, or it can even be impossible to achieve in practice.
In these cases, one may search for another place to store this information.
Let us look at some historical examples.

The most naive option is to assign the value to a global variable or
similar, where the code down the call chain can access it. However, this
immediately makes the code thread-unsafe, because with multiple threads,
all threads assign to the same global variable, and another thread can
interfere at any point in the call chain.

A somewhat less naive option is to store the information as per-thread
information in thread-local storage, where each thread has its own "copy"
of the variable which other threads cannot interfere with. Although
non-ideal, this has been the best solution in many cases. However, thanks
to generators and coroutines, the execution of the call chain can be
suspended and resumed, allowing code in other contexts to run concurrently.
Therefore, using thread-local storage is *concurrency-unsafe*, because
other call chains in other contexts may interfere with the thread-local

Note that in the above two historical approaches, the stored information
has the *widest* available scope without causing problems. For a third
solution along the same path, one would first define an equivalent of a
"thread" for asynchronous execution and concurrency. This could be seen as
the largest amount of code and nested calls that is guaranteed to be
executed sequentially without ambiguity in execution order. This might be
referred to as concurrency-local or task-local storage. In this meaning of
"task", there is no ambiguity in the order of execution of the code within
one task. (This concept of a task is close to equivalent to a ``Task`` in
``asyncio``, but not exactly.) In such concurrency-locals, it is possible
to pass information down the call chain to callees without another code
path interfering with the value in the background.

Common to the above approaches is that they indeed use variables with a
wide but just-narrow-enough scope. Thread-locals could also be called
thread-wide globals---in single-threaded code, they are indeed truly
global. And task-locals could be called task-wide globals, because tasks
can be very big.

The issue here is that neither global variables, thread-locals nor
task-locals are really meant to be used for this purpose of passing
information of the execution context down the call chain. Instead of the
widest possible variable scope, the scope of the variables should be
controlled by the programmer, typically of a library, to have the desired
scope---not wider. In other words, task-local variables (and globals and
thread-locals) have nothing to do with the kind of context-bound
information passing that this proposal intends to enable, even if
task-locals can be used to emulate the desired semantics. Therefore, in the
following, this proposal describes the semantics and the outlines of an
implementation for *context-local variables* (or context variables,
contextvars). In fact, as a side effect of this PEP, an async framework can
use the proposed feature to implement task-local variables.


Because the proposed semantics are not a direct extension to anything
already available in Python, this proposal is first described in terms of
semantics and API at a fairly high level. In particular, Python ``with``
statements are heavily used in the description, as they are a good match
with the proposed semantics. However, the underlying ``__enter__`` and
``__exit__`` methods correspond to functions in the lower-level
speed-optimized (C) API. For clarity of this document, the lower-level
functions are not explicitly named in the definition of the semantics.
After describing the semantics and high-level API, the implementation is
described, going to a lower level.

Semantics and higher-level API

Core concept

A context-local variable is represented by a single instance of
``contextvars.Var``, say ``cvar``. Any code that has access to the ``cvar``
object can ask for its value with respect to the current context. In the
high-level API, this value is given by the ``cvar.value`` property::

    cvar = contextvars.Var(default="the default value",
                           description="example context variable")

    assert cvar.value == "the default value"  # default still applies

    # In code examples, all ``assert`` statements should
    # succeed according to the proposed semantics.

No assignments to ``cvar`` have been applied for this context, so
``cvar.value`` gives the default value. Assigning new values to contextvars
is done in a highly scope-aware manner::

    with cvar.assign(new_value):
        assert cvar.value is new_value
        # Any code here, or down the call chain from here, sees:
        #     cvar.value is new_value
        # unless another value has been assigned in a
        # nested context
        assert cvar.value is new_value
    # the assignment of ``cvar`` to ``new_value`` is no longer visible
    assert cvar.value == "the default value"

Here, ``cvar.assign(value)`` returns another object, namely
``contextvars.Assignment(cvar, new_value)``. The essential part here is
that applying a context variable assignment (``Assignment.__enter__``) is
paired with a de-assignment (``Assignment.__exit__``). These operations set
the bounds for the scope of the assigned value.

Assignments to the same context variable can be nested to override the
outer assignment in a narrower context::

    assert cvar.value == "the default value"
    with cvar.assign("outer"):
        assert cvar.value == "outer"
        with cvar.assign("inner"):
            assert cvar.value == "inner"
        assert cvar.value == "outer"
    assert cvar.value == "the default value"

Also multiple variables can be assigned to in a nested manner without
affecting each other::

    cvar1 = contextvars.Var()
    cvar2 = contextvars.Var()

    assert cvar1.value is None # default is None by default
    assert cvar2.value is None

    with cvar1.assign(value1):
        assert cvar1.value is value1
        assert cvar2.value is None
        with cvar2.assign(value2):
            assert cvar1.value is value1
            assert cvar2.value is value2
        assert cvar1.value is value1
        assert cvar2.value is None
    assert cvar1.value is None
    assert cvar2.value is None

Or with more convenient Python syntax::

    with cvar1.assign(value1), cvar2.assign(value2):
        assert cvar1.value is value1
        assert cvar2.value is value2

In another *context*, in another thread or otherwise concurrently executed
task or code path, the context variables can have a completely different
state. The programmer thus only needs to worry about the context at hand.

Refactoring into subroutines

Code using contextvars can be refactored into subroutines without affecting
the semantics.  For instance::

    assi = cvar.assign(new_value)
    def apply():
    assert cvar.value == "the default value"
    assert cvar.value is new_value
    assert cvar.value == "the default value"

Or similarly in an asynchronous context where ``await`` expressions are
used. The subroutine can now be a coroutine::

    assi = cvar.assign(new_value)
    async def apply():
    assert cvar.value == "the default value"
    await apply()
    assert cvar.value is new_value
    assert cvar.value == "the default value"

Or when the subroutine is a generator::

    def apply():

which is called using ``yield from apply()`` or with calls to ``next`` or
``.send``. This is discussed further in later sections.

Semantics for generators and generator-based coroutines

Generators, coroutines and async generators act as subroutines in much the
same way that normal functions do. However, they have the additional
possibility of being suspended by ``yield`` expressions. Assignment
contexts entered inside a generator are normally preserved across yields::

    def genfunc():
        with cvar.assign(new_value):
            assert cvar.value is new_value
            assert cvar.value is new_value
    g = genfunc()
    assert cvar.value == "the default value"
    with cvar.assign(another_value):

However, the outer context visible to the generator may change state across

    def genfunc():
        assert cvar.value is value2
        assert cvar.value is value1
        with cvar.assign(value3):
            assert cvar.value is value3

    with cvar.assign(value1):
        g = genfunc()
        with cvar.assign(value2):
        assert cvar.value is value1

Similar semantics apply to async generators defined by ``async def ...
yield ...`` ).

By default, values assigned inside a generator do not leak through yields
to the code that drives the generator. However, the assignment contexts
entered and left open inside the generator *do* become visible outside the
generator after the generator has finished with a ``StopIteration`` or
another exception::

    assi = cvar.assign(new_value)
    def genfunc():

    g = genfunc()
    assert cvar.value == "the default value"
    assert cvar.value == "the default value"
    next(g)  # assi.__enter__() is called here
    assert cvar.value == "the default value"
    assert cvar.value is new_value

Special functionality for framework authors

Frameworks, such as ``asyncio`` or third-party libraries, can use
additional functionality in ``contextvars`` to achieve the desired
semantics in cases which are not determined by the Python interpreter. Some
of the semantics described in this section are also afterwards used to
describe the internal implementation.

Leaking yields

Using the ``contextvars.leaking_yields`` decorator, one can choose to leak
the context through ``yield`` expressions into the outer context that
drives the generator::

    def genfunc():
        assert cvar.value == "outer"
        with cvar.assign("inner"):
            assert cvar.value == "inner"
        assert cvar.value == "outer"

    g = genfunc():
    with cvar.assign("outer"):
        assert cvar.value == "outer"
        assert cvar.value == "inner"
        assert cvar.value == "outer"

Capturing contextvar assignments

Using ``contextvars.capture()``, one can capture the assignment contexts
that are entered by a block of code. The changes applied by the block of
code can then be reverted and subsequently reapplied, even in another

    assert cvar1.value is None # default
    assert cvar2.value is None # default
    assi1 = cvar1.assign(value1)
    assi2 = cvar1.assign(value2)
    with contextvars.capture() as delta:
        with cvar2.assign("not captured"):
            assert cvar2.value is "not captured"
    assert cvar1.value is value2
    assert cvar1.value is None
    assert cvar2.value is None
    with cvar1.assign(1), cvar2.assign(2):
        assert cvar1.value is value2
        assert cvar2.value == 2

However, reapplying the "delta" if its net contents include deassignments
may not be possible (see also Implementation and Open Issues).

Getting a snapshot of context state

The function ``contextvars.get_local_state()`` returns an object
representing the applied assignments to all context-local variables in the
context where the function is called. This can be seen as equivalent to
using ``contextvars.capture()`` to capture all context changes from the
beginning of execution. The returned object supports methods ``.revert()``
and ``reapply()`` as above.

Running code in a clean state

Although it is possible to revert all applied context changes using the
above primitives, a more convenient way to run a block of code in a clean
context is provided::

    with context_vars.clean_context():
        # here, all context vars start off with their default values
    # here, the state is back to what it was before the with block.


This section describes to a variable level of detail how the described
semantics can be implemented. At present, an implementation aimed at
simplicity but sufficient features is described. More details will be added

Alternatively, a somewhat more complicated implementation offers minor
additional features while adding some performance overhead and requiring
more code in the implementation.

Data structures and implementation of the core concept

Each thread of the Python interpreter keeps its on stack of
``contextvars.Assignment`` objects, each having a pointer to the previous
(outer) assignment like in a linked list. The local state (also returned by
``contextvars.get_local_state()``) then consists of a reference to the top
of the stack and a pointer/weak reference to the bottom of the stack. This
allows efficient stack manipulations. An object produced by
``contextvars.capture()`` is similar, but refers to only a part of the
stack with the bottom reference pointing to the top of the stack as it was
in the beginning of the capture block.

Now, the stack evolves according to the assignment ``__enter__`` and
``__exit__`` methods. For example::

    cvar1 = contextvars.Var()
    cvar2 = contextvars.Var()
    # stack: []
    assert cvar1.value is None
    assert cvar2.value is None

    with cvar1.assign("outer"):
        # stack: [Assignment(cvar1, "outer")]
        assert cvar1.value == "outer"

        with cvar1.assign("inner"):
            # stack: [Assignment(cvar1, "outer"),
            #         Assignment(cvar1, "inner")]
            assert cvar1.value == "inner"

            with cvar2.assign("hello"):
                # stack: [Assignment(cvar1, "outer"),
                #         Assignment(cvar1, "inner"),
                #         Assignment(cvar2, "hello")]
                assert cvar2.value == "hello"

            # stack: [Assignment(cvar1, "outer"),
            #         Assignment(cvar1, "inner")]
            assert cvar1.value == "inner"
            assert cvar2.value is None

        # stack: [Assignment(cvar1, "outer")]
        assert cvar1.value == "outer"

    # stack: []
    assert cvar1.value is None
    assert cvar2.value is None

Getting a value from the context using ``cvar1.value`` can be implemented
as finding the topmost occurrence of a ``cvar1`` assignment on the stack
and returning the value there, or the default value if no assignment is
found on the stack. However, this can be optimized to instead be an O(1)
operation in most cases. Still, even searching through the stack may be
reasonably fast since these stacks are not intended to grow very large.

The above description is already sufficient for implementing the core
concept. Suspendable frames require some additional attention, as explained
in the following.

Implementation of generator and coroutine semantics

Within generators, coroutines and async generators, assignments and
deassignments are handled in exactly the same way as anywhere else.
However, some changes are needed in the builtin generator methods ``send``,
``__next__``, ``throw`` and ``close``. Here is the Python equivalent of the
changes needed in ``send`` for a generator (here ``_old_send`` refers to
the behavior in Python 3.6)::

    def send(self, value):
        # if decorated with contextvars.leaking_yields
        if self.gi_contextvars is LEAK:
            # nothing needs to be done to leak context through yields :)
            return self._old_send(value)
            with contextvars.capture() as delta:
                if self.gi_contextvars:
                    # non-zero captured content from previous iteration
                ret = self._old_send(value)
        except Exception:
            # suspending, revert context changes but
            self.gi_contextvars = delta
        return ret

The corresponding modifications to the other methods is essentially
identical. The same applies to coroutines and async generators.

For code that does not use ``contextvars``, the additions are O(1) and
essentially reduce to a couple of pointer comparisons. For code that does
use ``contextvars``, the additions are still O(1) in most cases.

More on implementation

The rest of the functionality, including ``contextvars.leaking_yields``,
contextvars.capture()``, ``contextvars.get_local_state()`` and
``contextvars.clean_context()`` are in fact quite straightforward to
implement, but their implementation will be discussed further in later
versions of this proposal. Caching of assigned values is somewhat more
complicated, and will be discussed later, but it seems that most cases
should achieve O(1) complexity.

Backwards compatibility

There are no *direct* backwards-compatibility concerns, since a completely
new feature is proposed.

However, various traditional uses of thread-local storage may need a smooth
transition to ``contextvars`` so they can be concurrency-safe. There are
several approaches to this, including emulating task-local storage with a
little bit of help from async frameworks. A fully general implementation
cannot be provided, because the desired semantics may depend on the design
of the framework.

Another way to deal with the transition is for code to first look for a
context created using ``contextvars``. If that fails because a new-style
context has not been set or because the code runs on an older Python
version, a fallback to thread-local storage is used.

Open Issues

Out-of-order de-assignments

In this proposal, all variable deassignments are made in the opposite order
compared to the preceding assignments. This has two useful properties: it
encourages using ``with`` statements to define assignment scope and has a
tendency to catch errors early (forgetting a ``.__exit__()`` call often
results in a meaningful error. To have this as a requirement requirement is
beneficial also in terms of implementation simplicity and performance.
Nevertheless, allowing out-of-order context exits is not completely out of
the question, and reasonable implementation strategies for that do exist.

Rejected Ideas

Dynamic scoping linked to subroutine scopes

The scope of value visibility should not be determined by the way the code
is refactored into subroutines. It is necessary to have per-variable
control of the assignment scope.


To be added.


To be added.

+ Koos Zevenhoven + http://twitter.com/k7hoven +
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