Extracontext: Context Local Variables for everyone

Description

Provides PEP 567 compliant drop-in replacement for threading.local namespaces.

The main goal of PEP 567, supersedding PEP 550 is to create a way to preserve information in concurrent running contexts, including multithreading and asynchronous (asyncio) tasks, allowing each call stack to have its own versions of variables containing settings, or request parameters.

Quoting from PEP 567 Rationalle:

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.

Rationale for "extracontext"

Contextvars, introduced in Python 3.7, were implemented following a design decision by the which opted-out of the namespace approach used by Python's own threading.local implementation. It then requires an explicit top level declaration of each context-local variable, and the (rather "unpythonic") usage of an explicit call to get and set methods to manipulate those. Also, the only way to run some code in an isolated context copy is to call a function indirectly through means of the context object .run method. This implies that:

1. Knowing when to run something in a different context is responsability of the caller code
2. Breaks the easy-to-use, easy-to-read, aesthetics, and overal complicates one of the most fundamental blocks of programming in inperative languages: calling functions.

This package does away with that, and brings simplicity back, using dotted attributes to a namespace and = for value assigment:

with stdlib native contexvars:

import contextvars

# Variable declaration: top level declaration and WET (write everything twice)
ctx_color = contextvars.ContextVar("ctx_color")
ctx_font = contextvars.ContextVar("ctx_font")

def blah():
    ...
    # use  a set method:
    ctx_color.set("red")
    ctx_font.set("arial")

    ...
    myttext = ...
    # call a markup render function,
    # but take care it wont mix our attributes in temporary context changes
    contextvars.context_copy().run(render_markup, mytext))
    ...

def render_markup(text):
    # markup function: knows it will mess up the context, but can't do
    # a thing about it - the caller has to take care!
    ...

with extracontext:

import extracontext

# the only declaration needed at top level code
ctx = extracontext.ContextLocal()

def blah():
    ctx.color = "red"
    ctx.font = "arial"

    mytext = ...
    # simply calls  the function
    render_markup(mytext)
    ...

@ctx
def render_markup(text):
    # we will mess the context - but the decorator
    # ensures no changes leak back to the caller
    ...

Usage

simply instantiate a ContextLocal namespace, and any attributes set in that namespace will be unique per thread and per asynchronous call chain (i.e. unique for each independent task).

In a sense, these are a drop-in replacement for threading.local, which will also work for asynchronous programming without any change in code.

One should just avoid creating the "ContextLocal" instance itself in a non-setup function or method - as the implementation uses Python contextvars in by default, those are not cleaned-up along with the local scope where they are created - check the docs on the contextvar module for more details.

However, creating the actual variables to use inside this namespace can be made local to functions or methods: the same inner ContextVar instance will be re-used when re-entering the function

Create one or more project-wide instances of "extracontext.ContextLocal" Decorate your functions, co-routines, worker-methods and generators that should hold their own states with that instance itself, using it as a decorator

and use the instance as namespace for private variables that will be local and non-local until entering another callable decorated with the instance itself - that will create a new, separated scope visible inside the decorated callable.

from extracontext import ContextLocal

# global namespace, available in any thread or async task:
ctx = ContextLocal()

def myworker():
    # value set only visible in the current thread or asyncio task:
    ctx.value = "test"

More Features:

extracontext namespaces work for generators

Unlike PEP 567 contextvars, extracontext will sucessfully isolate contexts whe used with generator-functions - meaning, the generator body is actually executed in an isolated context:

Example showing context separation for concurrent generators:

from extracontext import ContextLocal


ctx = ContextLocal()

results = []
@ctx
def contexted_generator(value):
    ctx.value = value
    yield None
    results.append(ctx.value)


    def runner():
    generators = [contexted_generator(i) for i in range(10)]
    any(next(gen) for gen in generators)
    any(next(gen, None) for gen in generators)
    assert results == list(range(10))

This is virtually impossible with contextvars. (Ok, not impossible - the default extracontext backend does that using contextvars after all - but it encapsulates the complications for you)

This feature also works with async generators`

Another example of this feature:

import extracontext
ctx = extracontext.ContextLocal()
@ctx
def isolatedgen(n):
    for i in range(n):
        ctx.myvar = i
        yield i
        print (ctx.myvar)
def test():
    ctx.myvar = "lambs"
    for j in isolatedgen(2):
        print(ctx.myvar)
        ctx.myvar = "wolves"

In [11]: test()
lambs
0
wolves
1

Change context within a context-manager with block:

ContextLocal namespaces can also be isolated by context-manager blocks (with statement):

from extracontext import ContextLocal


def with_block_example():

    ctx = ContextLocal()
    ctx.value = 1
    with ctx:
        ctx.value = 2
        assert ctx.value == 2

    assert ctx.value == 1

Map namespaces

Beyond namespace usages, extracontext offer ways to have contexts working as mutable mappings, using the ContextMap class.

from extracontext import ContextMap

# global namespace, available in any thread or async task:
ctx = ContextMap()

def myworker():
    # value set only visible in the current thread or asyncio task:
    ctx["value"] = "test"

Cross-thread context tasks

One other thing contextvars can do is keep context information in the same task when parts of the task are performed in another thread (for example, with a loop.run_in_executor call).

Extracontext implements ContextPreservingExecutor, a concurrent.futures.ThreadPoolExecutor subclass which can make the context information available in the function called in the other thread (and keep it safe of other task's function calls running in the same worker instance, of course) This is a significant missing functionality missing with stdlib's contextvars, given that one of the most important roles of contextvars is to have independent values in different async concurrent tasks, and that the only resort these tasks have to run synchronous code (which happens often), is by running them in a threadpool executor. (even network name-checking in the default Python asyncio library code makes use this approach).

Anyway, with extracontext it is possible for the off-thread target function to see the same context of the calling task - it can be used either with stdlib's contextvar.ContextVar or with extracontext.ContextLocal (using the default, native, backend. The Python backend does not have, currently, any support to have shared values across threads)

This example, with all needed stages, will naturally print out the values ranging from 0 to 10, implying that each target function in the context is properly having access to an independent context, where they can read their unique value for the ctx.value name:

import asyncio
import random
import time

from extracontext import ContextLocal, ContextPreservingExecutor

ctx = ContextLocal()

def sync_part_of_task():
    time.sleep(random.random())
    print(ctx.value)

async def async_part_of_task(executor, value):
    ctx.value = value
    loop = asyncio.get_running_loop()
    await loop.run_in_executor(executor, sync_part_of_task)

async def amain():
    tasks = []
    with ContextPreservingExecutor() as executor:
        async with asyncio.TaskGroup() as tg:
            for value in range(10):
                tasks.append(tg.create_task(async_part_of_task(executor, value)))

asyncio.run(amain())

(new in version 1.1)

typing support

There is no explicit typing support yet - but note that through the use of ContextMap it is possible to have declare some types, by simple declaring Mapping[type1:type2] typing.

Specification and Implementation

ContextLocal

ContextLocal is the main class, and should suffice for most uses. It only takes the backend keyword-only argument, which selects the usage of the pure-Python backend ("python") or using a contextvars.ContextVar backend ("native"). The later is the default behavior. Calling this class will actually create an instance of the appropriate subclass, according to the backend: either PyContextLocal or NativeContextLocal - in the same way stdlib pathlib.Path creates an instance of Path appropriate for Posix, or Windows style paths. (This pattern probably have a name - help welcome).

An instance of it will create a new, fresh, namespace. Use dotted attribute access to populate it - each variable set in this way will persist through the context lifetime.

Usage as a decorator:

When used as a decorator for a function or method, that callable will automatically be executed in a copy of the calling context - meaning no changes it makes to any variable in the namespace is visible outside of the call.

The decorator (and the isolation provided) works for both plain functions, generator functions, co-routine functions and async generator functions - meaning that whenever the execution switches to the caller context (in yield or await expression) the context is restored to that of the caller, and when it re-enters the paused code block, the isolated context is restored.

from extracontext import ContextLocal

ctx = ContextLocal()

@ctx
def isolated_example():

    ctx.value = 2
    assert ctx.value = 2

ctx.value = 1
isolated_example()
assert ctx.value == 1

Usage as a context manager

A ContextLocal instance can simply be used in a context manager with statement, and any variables set or changed within the block will not be persisted after the block is over.

from extracontext import ContextLocal


def with_block_example():

    ctx = ContextLocal()
    ctx.value = 1
    with ctx:
        ctx.value = 2
        assert ctx.value == 2

    assert ctx.value == 1

Also, they are re-entrant, so if in a function called within the block, the context is used again as a context manager, it will just work.

Semantic difference to contextvars.ContextVar

Note that a fresh ContextLocal() instance will be empty, and have access to none of the values or names set in another instance. This contrasts sharply with contextvars.Context, for which each contextvars.ContextVar created anywhere else in the program (even 3rd party modules) is a valid key.

PyContextLocal

ContextLocal implementation using pure Python code, and reimplementing the functionalities of Contexts and ContextVars as implemented by PEP 567 fro scratch.

It works by seeting, in a "hidden" way, values in the caller's closure (the locals() namespace). Though writting to this namespace has traditionally been a "grey area" in Python, the way it makes use of this data is compliant with the specs in PEP-558 which officializes this use for Python 3.13 and beyond (and it has always worked since Python 3.0. The first implementations of this code where tested against Python 3.4 and forward)

It should be kept in place for the time being, and could be useful to allow customizations, workarounds, or buggy behavior bypassing where the native implementation presents any short-commings.

It is not an easy to follow code, as in one hand there are introspection and meta-programming patterns to handle access to the data in a containirized way.

Keep in mind that native contexvars use an internal copy-on-write structure in native code which should be much more performant than the chain-mapping checks used in this backend.

It has been throughfully tested and should be bug free, though less performant.

NativeContextLocal

This leverages on PEP 567 Contexts and ContextVars to perform all the isolation and setting mechanics, and provides an convenient wrapper layer which works as a namespace (and as mapping in NativeContextMap)

It was made the default mechanism due to obvious performances and updates taking place in the embedded implementation in the language.

The normal ContextVarsAPI exposed to Python would not allow for changing context inside the same function, requiring a Context.run call as the only way to switch contexts. Instead of releasing this backend without this mechanism, it has been opted to call the native cAPI for changing context (using ctypes in cPython, and the relevant internal calls on pypy) so that the feature can work.

When this feature was implemented, NativeContextLocal instances could then work as a context-manager using the with statement, and there were no reasons why they should not be the default backend. Some coding effort were placed in the "Reverse subclass picking" mechanism, and it was made te default in a backwards- compatible way.

ContextMap

ContextMap is a ContextLocal subclass which implements the MutableMapping interface. It is pretty straightforward in that, so that assigments and retrievals using the ctx["key"] syntax are made available, functionality with the in, ==, != operators and the keys, items, values, get, pop, popitem, clear, update, and setdefault methods.

It supports loadding a mapping with the initial context contents, passed as the initial positional argument - but not keyword-args mapping to initial content (as in dict(a=1)).

Also, it is a subclass of ContextLocal - so it also allows access to the keys with the dotted attribute syntax:

a = extracontext.ContextMap

a["b"] = 1

assert a.b == 1

And finally, it uses the same backend keyword-arg mechanism to switch between the default native-context vars backend and the pure Python backend, which will yield either a PyContextMap or a NativeContextMap instance, accordingly.

PyContextMap

ContextMap implementation as a subclass of PyContextLocal

NativeContextMap

ContextMap implementation as a subclass of NativeContextLocal

History

The original implementation from 2019 re-creates all the functionality provided by the PEP 567 contextvars using pure Python code and a lot of introspection and meta-programming. Not sure why it did that - but one thing is that it coud provide the functionality for older Pythons at the time, and possibly also because I did not see, at the time, other ways to workaround the need to call a function in order to switch contexts.

At some revival sprint in 2021, a backend using native contextvars was created - and it just got to completion, with all features and tests for the edge clases in August 2024, after other periods of non-activity.

At this point, a mechanism for picking the desired backend was implemented, and the native ContextLocal class was switched to use the native stdlib contextvars as backend by default. (This should be much faster - benchmark contributions are welcome, though 😃 )

New for 1.0

Switch the backend to use native Python contextvars (exposed in the stdlib "contextvars" module by default.

Up to the update in July/Aug 2024 the core package functionality was provided by a pure Python implementation which keeps context state in a hidden frame-local variables - while that is throughfully tested it performs a linear lookup in all the callchain for the context namespace.

For the 0.3 release, the "native" stdlib contextvars.ContextVar backed class, has reached first class status, and is now the default method used.

The extracontext.NativeContextLocal class builds on Python's contextvars instead of reimplementing all the functionality from scratch, and makes simple namespaces and decorator-based scope isolation just work, with all the safety and performance of the Python native implementation, with none of the boilerplate or confuse API.

Next Steps

1. Implementing more of the features possible with the contextvars semantics

• .run and .copy methods
• direct access to "Token"s as used by contextvars
• default value setting for variables

1. A feature allowing other threads to start from a copy of the current context, instead of an empty context. (asyncio independent tasks always see a copy)

2. Bringing in some more typing support (not sure what will be possible, but I believe some typing.Protocol templates at least. On an initial search, typing for namespaces is not a widelly known feature (if at all)

3. (maybe?) Proper multiprocessing support:

• ironing out probable serialization issues,
• allowing subprocess workers to start from a copy of the current context.

1. (maybe?) support for nested namespaces and maps.

Old "Next Steps":

---

(not so sure about these - they are fruit of some 2019 brainstorming for features in a project I am not coding for anymore)

1. Add a way to chain-contexts, so, for example and app can have a root context with default values

2. Describe the capabilities of each Context class clearly in a data-scheme, so one gets to know, and how to retrieve classes that can behave like maps, or allow/hide outter context values, work as a full stack, support the context protocol (with command), etc... (this is more pressing since stlib contextvar backed Context classes will not allow for some of the capabilities in the pure-Python reimplementation in "ContextLocal")

3. Add a way to merge wrappers for different ContextLocal instances on the same function

4. Add an "auto" flag - all called functions/generators/co-routines create a child context by default.

5. Add support for a descriptor-like variable slot - so that values can trigger code when set or retrieved

6. Shared values and locks: values that are guarranteed to be the same across tasks/threads, and a lock mechanism allowing atomic operations with these values.