[Python-ideas] PEP 550 v2

Hi, Here's the PEP 550 version 2. Thanks to a very active and insightful discussion here on Python-ideas, we've discovered a number of problems with the first version of the PEP. This version is a complete rewrite (only Abstract, Rationale, and Goals sections were not updated). The updated PEP is live on python.org: https://www.python.org/dev/peps/pep-0550/ There is no reference implementation at this point, but I'm confident that this version of the spec will have the same extremely low runtime overhead as the first version. Thanks to the new ContextItem design, accessing values in the context is even faster now. Thank you! PEP: 550 Title: Execution Context Version: $Revision$ Last-Modified: $Date$ Author: Yury Selivanov <yury at magic.io> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 11-Aug-2017 Python-Version: 3.7 Post-History: 11-Aug-2017, 15-Aug-2017 Abstract ======== This PEP proposes a new mechanism to manage execution state--the logical environment in which a function, a thread, a generator, or a coroutine executes in. A few examples of where having a reliable state storage is required: * Context managers like decimal contexts, ``numpy.errstate``, and ``warnings.catch_warnings``; * Storing request-related data such as security tokens and request data in web applications, implementing i18n; * Profiling, tracing, and logging in complex and large code bases. The usual solution for storing state is to use a Thread-local Storage (TLS), implemented in the standard library as ``threading.local()``. Unfortunately, TLS does not work for the purpose of state isolation for generators or asynchronous code, because such code executes concurrently in a single thread. Rationale ========= Traditionally, a Thread-local Storage (TLS) is used for storing the state. However, the major flaw of using the TLS is that it works only for multi-threaded code. It is not possible to reliably contain the state within a generator or a coroutine. For example, consider the following generator:: def calculate(precision, ...): with decimal.localcontext() as ctx: # Set the precision for decimal calculations # inside this block ctx.prec = precision yield calculate_something() yield calculate_something_else() Decimal context is using a TLS to store the state, and because TLS is not aware of generators, the state can leak. If a user iterates over the ``calculate()`` generator with different precisions one by one using a ``zip()`` built-in, the above code will not work correctly. For example:: g1 = calculate(precision=100) g2 = calculate(precision=50) items = list(zip(g1, g2)) # items[0] will be a tuple of: # first value from g1 calculated with 100 precision, # first value from g2 calculated with 50 precision. # # items[1] will be a tuple of: # second value from g1 calculated with 50 precision (!!!), # second value from g2 calculated with 50 precision. An even scarier example would be using decimals to represent money in an async/await application: decimal calculations can suddenly lose precision in the middle of processing a request. Currently, bugs like this are extremely hard to find and fix. Another common need for web applications is to have access to the current request object, or security context, or, simply, the request URL for logging or submitting performance tracing data:: async def handle_http_request(request): context.current_http_request = request await ... # Invoke your framework code, render templates, # make DB queries, etc, and use the global # 'current_http_request' in that code. # This isn't currently possible to do reliably # in asyncio out of the box. These examples are just a few out of many, where a reliable way to store context data is absolutely needed. The inability to use TLS for asynchronous code has lead to proliferation of ad-hoc solutions, which are limited in scope and do not support all required use cases. Current status quo is that any library, including the standard library, that uses a TLS, will likely not work as expected in asynchronous code or with generators (see [3]_ as an example issue.) Some languages that have coroutines or generators recommend to manually pass a ``context`` object to every function, see [1]_ describing the pattern for Go. This approach, however, has limited use for Python, where we have a huge ecosystem that was built to work with a TLS-like context. Moreover, passing the context explicitly does not work at all for libraries like ``decimal`` or ``numpy``, which use operator overloading. .NET runtime, which has support for async/await, has a generic solution of this problem, called ``ExecutionContext`` (see [2]_). On the surface, working with it is very similar to working with a TLS, but the former explicitly supports asynchronous code. Goals ===== The goal of this PEP is to provide a more reliable alternative to ``threading.local()``. It should be explicitly designed to work with Python execution model, equally supporting threads, generators, and coroutines. An acceptable solution for Python should meet the following requirements: * Transparent support for code executing in threads, coroutines, and generators with an easy to use API. * Negligible impact on the performance of the existing code or the code that will be using the new mechanism. * Fast C API for packages like ``decimal`` and ``numpy``. Explicit is still better than implicit, hence the new APIs should only be used when there is no acceptable way of passing the state explicitly. Specification ============= Execution Context is a mechanism of storing and accessing data specific to a logical thread of execution. We consider OS threads, generators, and chains of coroutines (such as ``asyncio.Task``) to be variants of a logical thread. In this specification, we will use the following terminology: * **Local Context**, or LC, is a key/value mapping that stores the context of a logical thread. * **Execution Context**, or EC, is an OS-thread-specific dynamic stack of Local Contexts. * **Context Item**, or CI, is an object used to set and get values from the Execution Context. Please note that throughout the specification we use simple pseudo-code to illustrate how the EC machinery works. The actual algorithms and data structures that we will use to implement the PEP are discussed in the `Implementation Strategy`_ section. Context Item Object ------------------- The ``sys.new_context_item(description)`` function creates a new ``ContextItem`` object. The ``description`` parameter is a ``str``, explaining the nature of the context key for introspection and debugging purposes. ``ContextItem`` objects have the following methods and attributes: * ``.description``: read-only description; * ``.set(o)`` method: set the value to ``o`` for the context item in the execution context. * ``.get()`` method: return the current EC value for the context item. Context items are initialized with ``None`` when created, so this method call never fails. The below is an example of how context items can be used:: my_context = sys.new_context_item(description='mylib.context') my_context.set('spam') # Later, to access the value of my_context: print(my_context.get()) Thread State and Multi-threaded code ------------------------------------ Execution Context is implemented on top of Thread-local Storage. For every thread there is a separate stack of Local Contexts -- mappings of ``ContextItem`` objects to their values in the LC. New threads always start with an empty EC. For CPython:: PyThreadState: execution_context: ExecutionContext([ LocalContext({ci1: val1, ci2: val2, ...}), ... ]) The ``ContextItem.get()`` and ``.set()`` methods are defined as follows (in pseudo-code):: class ContextItem: def get(self): tstate = PyThreadState_Get() for local_context in reversed(tstate.execution_context): if self in local_context: return local_context[self] def set(self, value): tstate = PyThreadState_Get() if not tstate.execution_context: tstate.execution_context = [LocalContext()] tstate.execution_context[-1][self] = value With the semantics defined so far, the Execution Context can already be used as an alternative to ``threading.local()``:: def print_foo(): print(ci.get() or 'nothing') ci = sys.new_context_item(description='test') ci.set('foo') # Will print "foo": print_foo() # Will print "nothing": threading.Thread(target=print_foo).start() Manual Context Management ------------------------- Execution Context is generally managed by the Python interpreter, but sometimes it is desirable for the user to take the control over it. A few examples when this is needed: * running a computation in ``concurrent.futures.ThreadPoolExecutor`` with the current EC; * reimplementing generators with iterators (more on that later); * managing contexts in asynchronous frameworks (implement proper EC support in ``asyncio.Task`` and ``asyncio.loop.call_soon``.) For these purposes we add a set of new APIs (they will be used in later sections of this specification): * ``sys.new_local_context()``: create an empty ``LocalContext`` object. * ``sys.new_execution_context()``: create an empty ``ExecutionContext`` object. * Both ``LocalContext`` and ``ExecutionContext`` objects are opaque to Python code, and there are no APIs to modify them. * ``sys.get_execution_context()`` function. The function returns a copy of the current EC: an ``ExecutionContext`` instance. The runtime complexity of the actual implementation of this function can be O(1), but for the purposes of this section it is equivalent to:: def get_execution_context(): tstate = PyThreadState_Get() return copy(tstate.execution_context) * ``sys.run_with_execution_context(ec: ExecutionContext, func, *args, **kwargs)`` runs ``func(*args, **kwargs)`` in the provided execution context:: def run_with_execution_context(ec, func, *args, **kwargs): tstate = PyThreadState_Get() old_ec = tstate.execution_context tstate.execution_context = ExecutionContext( ec.local_contexts + [LocalContext()] ) try: return func(*args, **kwargs) finally: tstate.execution_context = old_ec Any changes to Local Context by ``func`` will be ignored. This allows to reuse one ``ExecutionContext`` object for multiple invocations of different functions, without them being able to affect each other's environment:: ci = sys.new_context_item('example') ci.set('spam') def func(): print(ci.get()) ci.set('ham') ec = sys.get_execution_context() sys.run_with_execution_context(ec, func) sys.run_with_execution_context(ec, func) # Will print: # spam # spam * ``sys.run_with_local_context(lc: LocalContext, func, *args, **kwargs)`` runs ``func(*args, **kwargs)`` in the current execution context using the specified local context. Any changes that ``func`` does to the local context will be persisted in ``lc``. This behaviour is different from the ``run_with_execution_context()`` function, which always creates a new throw-away local context. In pseudo-code:: def run_with_local_context(lc, func, *args, **kwargs): tstate = PyThreadState_Get() old_ec = tstate.execution_context tstate.execution_context = ExecutionContext( old_ec.local_contexts + [lc] ) try: return func(*args, **kwargs) finally: tstate.execution_context = old_ec Using the previous example:: ci = sys.new_context_item('example') ci.set('spam') def func(): print(ci.get()) ci.set('ham') ec = sys.get_execution_context() lc = sys.new_local_context() sys.run_with_local_context(lc, func) sys.run_with_local_context(lc, func) # Will print: # spam # ham As an example, let's make a subclass of ``concurrent.futures.ThreadPoolExecutor`` that preserves the execution context for scheduled functions:: class Executor(concurrent.futures.ThreadPoolExecutor): def submit(self, fn, *args, **kwargs): context = sys.get_execution_context() fn = functools.partial( sys.run_with_execution_context, context, fn, *args, **kwargs) return super().submit(fn) EC Semantics for Coroutines --------------------------- Python :pep:`492` coroutines are used to implement cooperative multitasking. For a Python end-user they are similar to threads, especially when it comes to sharing resources or modifying the global state. An event loop is needed to schedule coroutines. Coroutines that are explicitly scheduled by the user are usually called Tasks. When a coroutine is scheduled, it can schedule other coroutines using an ``await`` expression. In async/await world, awaiting a coroutine is equivalent to a regular function call in synchronous code. Thus, Tasks are similar to threads. By drawing a parallel between regular multithreaded code and async/await, it becomes apparent that any modification of the execution context within one Task should be visible to all coroutines scheduled within it. Any execution context modifications, however, must not be visible to other Tasks executing within the same OS thread. Coroutine Object Modifications ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To achieve this, a small set of modifications to the coroutine object is needed: * New ``cr_local_context`` attribute. This attribute is readable and writable for Python code. * When a coroutine object is instantiated, its ``cr_local_context`` is initialized with an empty Local Context. * Coroutine's ``.send()`` and ``.throw()`` methods are modified as follows (in pseudo-C):: if coro.cr_local_context is not None: tstate = PyThreadState_Get() tstate.execution_context.push(coro.cr_local_context) try: # Perform the actual `Coroutine.send()` or # `Coroutine.throw()` call. return coro.send(...) finally: coro.cr_local_context = tstate.execution_context.pop() else: # Perform the actual `Coroutine.send()` or # `Coroutine.throw()` call. return coro.send(...) * When Python interpreter sees an ``await`` instruction, it inspects the ``cr_local_context`` attribute of the coroutine that is about to be awaited. For ``await coro``: * If ``coro.cr_local_context`` is an empty ``LocalContext`` object that ``coro`` was created with, the interpreter will set ``coro.cr_local_context`` to ``None``. * If ``coro.cr_local_context`` was modified by Python code, the interpreter will leave it as is. This makes any changes to execution context made by nested coroutine calls within a Task to be visible throughout the Task:: ci = sys.new_context_item('example') async def nested(): ci.set('nested') asynd def main(): ci.set('main') print('before:', ci.get()) await nested() print('after:', ci.get()) # Will print: # before: main # after: nested Essentially, coroutines work with Execution Context items similarly to threads, and ``await`` expression acts like a function call. This mechanism also works for ``yield from`` in generators decorated with ``@types.coroutine`` or ``@asyncio.coroutine``, which are called "generator-based coroutines" according to :pep:`492`, and should be fully compatible with native async/await coroutines. Tasks ^^^^^ In asynchronous frameworks like asyncio, coroutines are run by an event loop, and need to be explicitly scheduled (in asyncio coroutines are run by ``asyncio.Task``.) With the currently defined semantics, the interpreter makes coroutines linked by an ``await`` expression share the same Local Context. The interpreter, however, is not aware of the Task concept, and cannot help with ensuring that new Tasks started in coroutines, use the correct EC:: current_request = sys.new_context_item(description='request') async def child(): print('current request:', repr(current_request.get())) async def handle_request(request): current_request.set(request) event_loop.create_task(child) run(top_coro()) # Will print: # current_request: None To enable correct Execution Context propagation into Tasks, the asynchronous framework needs to assist the interpreter: * When ``create_task`` is called, it should capture the current execution context with ``sys.get_execution_context()`` and save it on the Task object. * When the Task object runs its coroutine object, it should execute ``.send()`` and ``.throw()`` methods within the captured execution context, using the ``sys.run_with_execution_context()`` function. With help from the asynchronous framework, the above snippet will run correctly, and the ``child()`` coroutine will be able to access the current request object through the ``current_request`` Context Item. Event Loop Callbacks ^^^^^^^^^^^^^^^^^^^^ Similarly to Tasks, functions like asyncio's ``loop.call_soon()`` should capture the current execution context with ``sys.get_execution_context()`` and execute callbacks within it with ``sys.run_with_execution_context()``. This way the following code will work:: current_request = sys.new_context_item(description='request') def log(): request = current_request.get() print(request) async def request_handler(request): current_request.set(request) get_event_loop.call_soon(log) Generators ---------- Generators in Python, while similar to Coroutines, are used in a fundamentally different way. They are producers of data, and they use ``yield`` expression to suspend/resume their execution. A crucial difference between ``await coro`` and ``yield value`` is that the former expression guarantees that the ``coro`` will be executed fully, while the latter is producing ``value`` and suspending the generator until it gets iterated again. Generators, similarly to coroutines, have a ``gi_local_context`` attribute, which is set to an empty Local Context when created. Contrary to coroutines though, ``yield from o`` expression in generators (that are not generator-based coroutines) is semantically equivalent to ``for v in o: yield v``, therefore the interpreter does not attempt to control their ``gi_local_context``. EC Semantics for Generators ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Every generator object has its own Local Context that stores only its own local modifications of the context. When a generator is being iterated, its local context will be put in the EC stack of the current thread. This means that the generator will be able to see access items from the surrounding context:: local = sys.new_context_item("local") global = sys.new_context_item("global") def generator(): local.set('inside gen:') while True: print(local.get(), global.get()) yield g = gen() local.set('hello') global.set('spam') next(g) local.set('world') global.set('ham') next(g) # Will print: # inside gen: spam # inside gen: ham Any changes to the EC in nested generators are invisible to the outer generator:: local = sys.new_context_item("local") def inner_gen(): local.set('spam') yield def outer_gen(): local.set('ham') yield from gen() print(local.get()) list(outer_gen()) # Will print: # ham Running generators without LC ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Similarly to coroutines, generators with ``gi_local_context`` set to ``None`` simply use the outer Local Context. The ``@contextlib.contextmanager`` decorator uses this mechanism to allow its generator to affect the EC:: item = sys.new_context_item('test') @contextmanager def context(x): old = item.get() item.set('x') try: yield finally: item.set(old) with context('spam'): with context('ham'): print(1, item.get()) print(2, item.get()) # Will print: # 1 ham # 2 spam Implementing Generators with Iterators ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The Execution Context API allows to fully replicate EC behaviour imposed on generators with a regular Python iterator class:: class Gen: def __init__(self): self.local_context = sys.new_local_context() def __iter__(self): return self def __next__(self): return sys.run_with_local_context( self.local_context, self._next_impl) def _next_impl(self): # Actual __next__ implementation. ... Asynchronous Generators ----------------------- Asynchronous Generators (AG) interact with the Execution Context similarly to regular generators. They have an ``ag_local_context`` attribute, which, similarly to regular generators, can be set to ``None`` to make them use the outer Local Context. This is used by the new ``contextlib.asynccontextmanager`` decorator. The EC support of ``await`` expression is implemented using the same approach as in coroutines, see the `Coroutine Object Modifications`_ section. Greenlets --------- Greenlet is an alternative implementation of cooperative scheduling for Python. Although greenlet package is not part of CPython, popular frameworks like gevent rely on it, and it is important that greenlet can be modified to support execution contexts. In a nutshell, greenlet design is very similar to design of generators. The main difference is that for generators, the stack is managed by the Python interpreter. Greenlet works outside of the Python interpreter, and manually saves some ``PyThreadState`` fields and pushes/pops the C-stack. Thus the ``greenlet`` package can be easily updated to use the new low-level `C API`_ to enable full support of EC. New APIs ======== Python ------ Python APIs were designed to completely hide the internal implementation details, but at the same time provide enough control over EC and LC to re-implement all of Python built-in objects in pure Python. 1. ``sys.new_context_item(description='...')``: create a ``ContextItem`` object used to access/set values in EC. 2. ``ContextItem``: * ``.description``: read-only attribute. * ``.get()``: return the current value for the item. * ``.set(o)``: set the current value in the EC for the item. 3. ``sys.get_execution_context()``: return the current ``ExecutionContext``. 4. ``sys.new_execution_context()``: create a new empty ``ExecutionContext``. 5. ``sys.new_local_context()``: create a new empty ``LocalContext``. 6. ``sys.run_with_execution_context(ec: ExecutionContext, func, *args, **kwargs)``. 7. ``sys.run_with_local_context(lc:LocalContext, func, *args, **kwargs)``. C API ----- 1. ``PyContextItem * PyContext_NewItem(char *desc)``: create a ``PyContextItem`` object. 2. ``PyObject * PyContext_GetItem(PyContextItem *)``: get the current value for the context item. 3. ``int PyContext_SetItem(PyContextItem *, PyObject *)``: set the current value for the context item. 4. ``PyLocalContext * PyLocalContext_New()``: create a new empty ``PyLocalContext``. 5. ``PyLocalContext * PyExecutionContext_New()``: create a new empty ``PyExecutionContext``. 6. ``PyExecutionContext * PyExecutionContext_Get()``: get the EC for the active thread state. 7. ``int PyExecutionContext_Set(PyExecutionContext *)``: set the passed EC object as the current for the active thread state. 8. ``int PyExecutionContext_SetWithLocalContext(PyExecutionContext *, PyLocalContext *)``: allows to implement ``sys.run_with_local_context`` Python API. Implementation Strategy ======================= LocalContext is a Weak Key Mapping ---------------------------------- Using a weak key mapping for ``LocalContext`` implementation enables the following properties with regards to garbage collection: * ``ContextItem`` objects are strongly-referenced only from the application code, not from any of the Execution Context machinery or values they point to. This means that there are no reference cycles that could extend their lifespan longer than necessary, or prevent their garbage collection. * Values put in the Execution Context are guaranteed to be kept alive while there is a ``ContextItem`` key referencing them in the thread. * If a ``ContextItem`` is garbage collected, all of its values will be removed from all contexts, allowing them to be GCed if needed. * If a thread has ended its execution, its thread state will be cleaned up along with its ``ExecutionContext``, cleaning up all values bound to all Context Items in the thread. ContextItem.get() Cache ----------------------- We can add three new fields to ``PyThreadState`` and ``PyInterpreterState`` structs: * ``uint64_t PyThreadState->unique_id``: a globally unique thread state identifier (we can add a counter to ``PyInterpreterState`` and increment it when a new thread state is created.) * ``uint64_t PyInterpreterState->context_item_deallocs``: every time a ``ContextItem`` is GCed, all Execution Contexts in all threads will lose track of it. ``context_item_deallocs`` will simply count all ``ContextItem`` deallocations. * ``uint64_t PyThreadState->execution_context_ver``: every time a new item is set, or an existing item is updated, or the stack of execution contexts is changed in the thread, we increment this counter. The above two fields allow implementing a fast cache path in ``ContextItem.get()``, in pseudo-code:: class ContextItem: def get(self): tstate = PyThreadState_Get() if (self.last_tstate_id == tstate.unique_id and self.last_ver == tstate.execution_context_ver self.last_deallocs == tstate.iterp.context_item_deallocs): return self.last_value value = None for mapping in reversed(tstate.execution_context): if self in mapping: value = mapping[self] break self.last_value = value self.last_tstate_id = tstate.unique_id self.last_ver = tstate.execution_context_ver self.last_deallocs = tstate.interp.context_item_deallocs return value This is similar to the trick that decimal C implementation uses for caching the current decimal context, and will have the same performance characteristics, but available to all Execution Context users. Approach #1: Use a dict for LocalContext ---------------------------------------- The straightforward way of implementing the proposed EC mechanisms is to create a ``WeakKeyDict`` on top of Python ``dict`` type. To implement the ``ExecutionContext`` type we can use Python ``list`` (or a custom stack implementation with some pre-allocation optimizations). This approach will have the following runtime complexity: * O(M) for ``ContextItem.get()``, where ``M`` is the number of Local Contexts in the stack. It is important to note that ``ContextItem.get()`` will implement a cache making the operation O(1) for packages like ``decimal`` and ``numpy``. * O(1) for ``ContextItem.set()``. * O(N) for ``sys.get_execution_context()``, where ``N`` is the total number of items in the current **execution** context. Approach #2: Use HAMT for LocalContext -------------------------------------- Languages like Clojure and Scala use Hash Array Mapped Tries (HAMT) to implement high performance immutable collections [5]_, [6]_. Immutable mappings implemented with HAMT have O(log\ :sub:`32`\ N) performance for both ``set()``, ``get()``, and ``merge()`` operations, which is essentially O(1) for relatively small mappings (read about HAMT performance in CPython in the `Appendix: HAMT Performance`_ section.) In this approach we use the same design of the ``ExecutionContext`` as in Approach #1, but we will use HAMT backed weak key Local Context implementation. With that we will have the following runtime complexity: * O(M * log\ :sub:`32`\ N) for ``ContextItem.get()``, where ``M`` is the number of Local Contexts in the stack, and ``N`` is the number of items in the EC. The operation will essentially be O(M), because execution contexts are normally not expected to have more than a few dozen of items. (``ContextItem.get()`` will have the same caching mechanism as in Approach #1.) * O(log\ :sub:`32`\ N) for ``ContextItem.set()`` where ``N`` is the number of items in the current **local** context. This will essentially be an O(1) operation most of the time. * O(log\ :sub:`32`\ N) for ``sys.get_execution_context()``, where ``N`` is the total number of items in the current **execution** context. Essentially, using HAMT for Local Contexts instead of Python dicts, allows to bring down the complexity of ``sys.get_execution_context()`` from O(N) to O(log\ :sub:`32`\ N) because of the more efficient merge algorithm. Approach #3: Use HAMT and Immutable Linked List ----------------------------------------------- We can make an alternative ``ExecutionContext`` design by using a linked list. Each ``LocalContext`` in the ``ExecutionContext`` object will be wrapped in a linked-list node. ``LocalContext`` objects will use an HAMT backed weak key implementation described in the Approach #2. Every modification to the current ``LocalContext`` will produce a new version of it, which will be wrapped in a **new linked list node**. Essentially this means, that ``ExecutionContext`` is an immutable forest of ``LocalContext`` objects, and can be safely copied by reference in ``sys.get_execution_context()`` (eliminating the expensive "merge" operation.) With this approach, ``sys.get_execution_context()`` will be an **O(1) operation**. Summary ------- We believe that approach #3 enables an efficient and complete Execution Context implementation, with excellent runtime performance. `ContextItem.get() Cache`_ enables fast retrieval of context items for performance critical libraries like decimal and numpy. Fast ``sys.get_execution_context()`` enables efficient management of execution contexts in asynchronous libraries like asyncio. Design Considerations ===================== Can we fix ``PyThreadState_GetDict()``? --------------------------------------- ``PyThreadState_GetDict`` is a TLS, and some of its existing users might depend on it being just a TLS. Changing its behaviour to follow the Execution Context semantics would break backwards compatibility. PEP 521 ------- :pep:`521` proposes an alternative solution to the problem: enhance Context Manager Protocol with two new methods: ``__suspend__`` and ``__resume__``. To make it compatible with async/await, the Asynchronous Context Manager Protocol will also need to be extended with ``__asuspend__`` and ``__aresume__``. This allows to implement context managers like decimal context and ``numpy.errstate`` for generators and coroutines. The following code:: class Context: def __enter__(self): self.old_x = get_execution_context_item('x') set_execution_context_item('x', 'something') def __exit__(self, *err): set_execution_context_item('x', self.old_x) would become this:: local = threading.local() class Context: def __enter__(self): self.old_x = getattr(local, 'x', None) local.x = 'something' def __suspend__(self): local.x = self.old_x def __resume__(self): local.x = 'something' def __exit__(self, *err): local.x = self.old_x Besides complicating the protocol, the implementation will likely negatively impact performance of coroutines, generators, and any code that uses context managers, and will notably complicate the interpreter implementation. :pep:`521` also does not provide any mechanism to propagate state in a local context, like storing a request object in an HTTP request handler to have better logging. Nor does it solve the leaking state problem for greenlet/gevent. Can Execution Context be implemented outside of CPython? -------------------------------------------------------- Because async/await code needs an event loop to run it, an EC-like solution can be implemented in a limited way for coroutines. Generators, on the other hand, do not have an event loop or trampoline, making it impossible to intercept their ``yield`` points outside of the Python interpreter. Backwards Compatibility ======================= This proposal preserves 100% backwards compatibility. Appendix: HAMT Performance ========================== To assess if HAMT can be used for Execution Context, we implemented it in CPython [7]_. .. figure:: pep-0550-hamt_vs_dict.png :align: center :width: 100% Figure 1. Benchmark code can be found here: [9]_. Figure 1 shows that HAMT indeed displays O(1) performance for all benchmarked dictionary sizes. For dictionaries with less than 100 items, HAMT is a bit slower than Python dict/shallow copy. .. figure:: pep-0550-lookup_hamt.png :align: center :width: 100% Figure 2. Benchmark code can be found here: [10]_. Figure 2 shows comparison of lookup costs between Python dict and an HAMT immutable mapping. HAMT lookup time is 30-40% worse than Python dict lookups on average, which is a very good result, considering how well Python dicts are optimized. Note, that according to [8]_, HAMT design can be further improved. Acknowledgments =============== I thank Elvis Pranskevichus and Victor Petrovykh for countless discussions around the topic and PEP proof reading and edits. Thanks to Nathaniel Smith for proposing the ``ContextItem`` design [17]_ [18]_, for pushing the PEP towards a more complete design, and coming up with the idea of having a stack of contexts in the thread state. Thanks to Nick Coghlan for numerous suggestions and ideas on the mailing list, and for coming up with a case that cause the complete rewrite of the initial PEP version [19]_. References ========== .. [1] https://blog.golang.org/context .. [2] https://msdn.microsoft.com/en-us/library/system.threading.executioncontext.aspx .. [3] https://github.com/numpy/numpy/issues/9444 .. [4] http://bugs.python.org/issue31179 .. [5] https://en.wikipedia.org/wiki/Hash_array_mapped_trie .. [6] http://blog.higher-order.net/2010/08/16/assoc-and-clojures-persistenthashmap-part-ii.html .. [7] https://github.com/1st1/cpython/tree/hamt .. [8] https://michael.steindorfer.name/publications/oopsla15.pdf .. [9] https://gist.github.com/1st1/9004813d5576c96529527d44c5457dcd .. [10] https://gist.github.com/1st1/dbe27f2e14c30cce6f0b5fddfc8c437e .. [11] https://github.com/1st1/cpython/tree/pep550 .. [12] https://www.python.org/dev/peps/pep-0492/#async-await .. [13] https://github.com/MagicStack/uvloop/blob/master/examples/bench/echoserver.py .. [14] https://github.com/MagicStack/pgbench .. [15] https://github.com/python/performance .. [16] https://gist.github.com/1st1/6b7a614643f91ead3edf37c4451a6b4c .. [17] https://mail.python.org/pipermail/python-ideas/2017-August/046752.html .. [18] https://mail.python.org/pipermail/python-ideas/2017-August/046772.html .. [19] https://mail.python.org/pipermail/python-ideas/2017-August/046780.html Copyright ========= This document has been placed in the public domain.