[Python-Dev] PEP 567 v3 (original) (raw)
Yury Selivanov yselivanov.ml at gmail.com
Tue Jan 16 17:44:14 EST 2018
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Hi,
This is a third version of PEP 567.
Changes from v2:
PyThreadState now references Context objects directly (instead of referencing _ContextData). This fixes out of sync Context.get() and ContextVar.get().
Added a new Context.copy() method.
Renamed Token.old_val property to Token.old_value
ContextVar.reset(token) now raises a ValueError if the token was created in a different Context.
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.
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: RevisionRevisionRevision Last-Modified: DateDateDate 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 andnumpy.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 theContext.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:
copy_context() -> Context
function is used to get a copy of the currentContext
object for the current OS thread.ContextVar
class to declare and access context variables.Context
class encapsulates context state. Every OS thread stores a reference to its currentContext
instance. It is not possible to control that reference directly. Instead, theContext.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; orreturn 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 theset()
call, or toToken.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 aKeyError
,var in context
returnsFalse
,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
A new
contextvars
module withContextVar
,Context
, andToken
classes, and acopy_context()
function.asyncio.Loop.call_at()
,asyncio.Loop.call_later()
,asyncio.Loop.call_soon()
, andasyncio.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 context.
C API
PyContextVar * PyContextVar_New(char *name, PyObject *default)
: create aContextVar
object. The default argument can beNULL
, which means that the variable has no default value.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 thevalue
pointer. Otherwise,value
will be set todefault_value
when it is notNULL
. Ifdefault_value
isNULL
,value
will be set to the default value of the variable, which can beNULL
too.value
is always a new reference.PyContextToken * PyContextVar_Set(PyContextVar *, PyObject *)
: set the value of the variable in the current context.PyContextVar_Reset(PyContextVar *, PyContextToken *)
: reset the value of the context variable.PyContext * PyContext_New()
: create a new empty context.PyContext * PyContext_Copy()
: get a copy of the current context.int PyContext_Enter(PyContext *)
andint 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.
.. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:
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