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asyncio

This document describes the working and implementation details of the asyncio module.

The following section describes the implementation details of the C implementation.

Task management

Pre-Python 3.14 implementation

Before Python 3.14, the C implementation of asyncio used a WeakSet to store all the tasks created by the event loop. WeakSet was used so that the event loop doesn't hold strong references to the tasks, allowing them to be garbage collected when they are no longer needed. The current task of the event loop was stored in a dict mapping the event loop to the current task.

    /* Dictionary containing tasks that are currently active in
       all running event loops.  {EventLoop: Task} */
    PyObject *current_tasks;

    /* WeakSet containing all tasks scheduled to run on event loops. */
    PyObject *scheduled_tasks;

This implementation had a few drawbacks:

  1. Performance: Using a WeakSet for storing tasks is inefficient, as it requires maintaining a full set of weak references to tasks along with corresponding weakref callback to cleanup the tasks when they are garbage collected. This increases the work done by the garbage collector, and in applications with a large number of tasks, this becomes a bottleneck, with increased memory usage and lower performance. Looking up the current task was slow as it required a dictionary lookup on the current_tasks dict.

  2. Thread safety: Before Python 3.14, concurrent iterations over WeakSet was not thread safe1. This meant calling APIs like asyncio.all_tasks() could lead to inconsistent results or even RuntimeError if used in multiple threads2.

  3. Poor scaling in free-threading: Using global WeakSet for storing all tasks across all threads lead to contention when adding and removing tasks from the set which is a frequent operation. As such it performed poorly in free-threading and did not scale well with the number of threads. Similarly, accessing the current task in multiple threads did not scale due to contention on the global current_tasks dictionary.

Python 3.14 implementation

To address these issues, Python 3.14 implements several changes to improve the performance and thread safety of tasks management.

  • Per-thread double linked list for tasks: Python 3.14 introduces a per-thread circular double linked list implementation for storing tasks. This allows each thread to maintain its own list of tasks and allows for lock free addition and removal of tasks. This is designed to be efficient, and thread-safe and scales well with the number of threads in free-threading. This also allows external introspection tools such as python -m asyncio pstree to inspect tasks running in all threads and was implemented as part of Audit asyncio thread safety.

  • Per-thread current task: Python 3.14 stores the current task on the current thread state instead of a global dictionary. This allows for faster access to the current task without the need for a dictionary lookup. Each thread maintains its own current task, which is stored in the PyThreadState structure. This was implemented in https://github.com/python/cpython/issues/129898.

Storing the current task and list of all tasks per-thread instead of storing it per-loop was chosen primarily to support external introspection tools such as python -m asyncio pstree as looking up arbitrary attributes on the loop object is not possible externally. Storing data per-thread also makes it easy to support third party event loop implementations such as uvloop, and is more efficient for the single threaded asyncio use-case as it avoids the overhead of attribute lookups on the loop object and several other calls on the performance critical path of adding and removing tasks from the per-loop task list.

Per-thread double linked list for tasks

This implementation uses a circular doubly linked list to store tasks on the thread states. This is used for all tasks which are instances of asyncio.Task or subclasses of it, for third-party tasks a fallback WeakSet implementation is used. The linked list is implemented using an embedded llist_node structure within each TaskObj. By embedding the list node directly into the task object, the implementation avoids additional memory allocations for linked list nodes.

The PyThreadState structure gained a new field asyncio_tasks_head, which serves as the head of the circular linked list of tasks. This allows for lock free addition and removal of tasks from the list.

It is possible that when a thread state is deallocated, there are lingering tasks in its list; this can happen if another thread has references to the tasks of this thread. Therefore, the PyInterpreterState structure also gains a new asyncio_tasks_head field to store any lingering tasks. When a thread state is deallocated, any remaining lingering tasks are moved to the interpreter state tasks list, and the thread state tasks list is cleared. The asyncio_tasks_lock is used protect the interpreter's tasks list from concurrent modifications.

typedef struct TaskObj {
    ...
    struct llist_node asyncio_node;
} TaskObj;

typedef struct PyThreadState {
    ...
    struct llist_node asyncio_tasks_head;
} PyThreadState;

typedef struct PyInterpreterState {
    ...
    struct llist_node asyncio_tasks_head;
    PyMutex asyncio_tasks_lock;
} PyInterpreterState;

When a task is created, it is added to the current thread's list of tasks by the register_task function. When the task is done, it is removed from the list by the unregister_task function. In free-threading, the thread id of the thread which created the task is stored in task_tid field of the TaskObj. This is used to check if the task is being removed from the correct thread's task list. If the current thread is same as the thread which created it then no locking is required, otherwise in free-threading, the stop-the-world pause is used to pause all other threads and then safely remove the task from the tasks list.


flowchart TD
    subgraph one["Executing Thread"]
        A["task = asyncio.create_task(coro())"] -->B("register_task(task)")
        B --> C{"task->task_state?"}
        C -->|pending| D["task_step(task)"]
        C -->|done| F["unregister_task(task)"]
        C -->|cancelled| F["unregister_task(task)"]
        D --> C
        F --> G{"free-threading?"}
        G --> |false| H["unregister_task_safe(task)"]
        G --> |true| J{"correct thread? <br>task->task_tid == _Py_ThreadId()"}
        J --> |true| H
        J --> |false| I["stop the world <br> pause all threads"]
        I --> H["unregister_task_safe(task)"]
    end
    subgraph two["Thread deallocating"]
        A1{"thread's task list empty? <br> llist_empty(tstate->asyncio_tasks_head)"}
        A1 --> |true| B1["deallocate thread<br>free_threadstate(tstate)"]
        A1 --> |false| C1["add tasks to interpreter's task list<br> llist_concat(&tstate->interp->asyncio_tasks_head,
        &tstate->asyncio_tasks_head)"]
        C1 --> B1
    end

    one --> two

asyncio.all_tasks now iterates over the per-thread task lists of all threads and the interpreter's task list to get all the tasks. In free-threading, this is done by pausing all the threads using the stop-the-world pause to ensure that no tasks are being added or removed while iterating over the lists. This allows for a consistent view of all task lists across all threads and is thread safe.

This design allows for lock free execution and scales well in free-threading with multiple event loops running in different threads.

Per-thread current task

This implementation stores the current task in the PyThreadState structure, which allows for faster access to the current task without the need for a dictionary lookup.

typedef struct PyThreadState {
    ...
    PyObject *asyncio_current_loop;
    PyObject *asyncio_current_task;
} PyThreadState;

When a task is entered or left, the current task is updated in the thread state using enter_task and leave_task functions. When current_task(loop) is called where loop is the current running event loop of the current thread, no locking is required as the current task is stored in the thread state and is returned directly (general case). Otherwise, if the loop is not current running event loop, the stop-the-world pause is used to pause all threads in free-threading and then by iterating over all the thread states and checking if the loop matches with tstate->asyncio_current_loop, the current task is found and returned. If no matching thread state is found, None is returned.

In free-threading, it avoids contention on a global dictionary as threads can access the current task of thier running loop without any locking.

The following section describes the implementation details of the Python implementation.

async generators

This section describes the implementation details of async generators in asyncio.

Since async generators are meant to be used from coroutines, their finalization (execution of finally blocks) needs to be done while the loop is running. Most async generators are closed automatically when they are fully iterated over and exhausted; however, if the async generator is not fully iterated over, it may not be closed properly, leading to the finally blocks not being executed.

Consider the following code:

import asyncio

async def agen():
    try:
        yield 1
    finally:
        await asyncio.sleep(1)
        print("finally executed")


async def main():
    async for i in agen():
        break

loop = asyncio.EventLoop()
loop.run_until_complete(main())

The above code will not print "finally executed", because the async generator agen is not fully iterated over and it is not closed manually by awaiting agen.aclose().

To solve this, asyncio uses the sys.set_asyncgen_hooks function to set hooks for finalizing async generators as described in PEP 525.

  • firstiter hook: When the async generator is iterated over for the first time, the firstiter hook is called. The async generator is added to loop._asyncgens WeakSet and the event loop tracks all active async generators.

  • finalizer hook: When the async generator is about to be finalized, the finalizer hook is called. The event loop removes the async generator from loop._asyncgens WeakSet, and schedules the finalization of the async generator by creating a task calling agen.aclose(). This ensures that the finally block is executed while the event loop is running. When the loop is shutting down, the loop checks if there are active async generators and if so, it similarly schedules the finalization of all active async generators by calling agen.aclose() on each of them and waits for them to complete before shutting down the loop.

This ensures that the async generator's finally blocks are executed even if the generator is not explicitly closed.

Consider the following example:

import asyncio

async def agen():
    try:
        yield 1
        yield 2
    finally:
        print("executing finally block")

async def main():
    async for item in agen():
        print(item)
        break  # not fully iterated

asyncio.run(main())

flowchart TD
    subgraph one["Loop running"]
        A["asyncio.run(main())"] --> B
        B["set async generator hooks <br> sys.set_asyncgen_hooks()"] --> C
        C["async for item in agen"] --> F
        F{"first iteration?"} --> |true|D
        F{"first iteration?"} --> |false|H
        D["calls firstiter hook<br>loop._asyncgen_firstiter_hook(agen)"] --> E
        E["add agen to WeakSet<br> loop._asyncgens.add(agen)"] --> H
        H["item = await agen.\_\_anext\_\_()"] --> J
        J{"StopAsyncIteration?"} --> |true|M
        J{"StopAsyncIteration?"} --> |false|I
        I["print(item)"] --> S
        S{"continue iterating?"} --> |true|C
        S{"continue iterating?"} --> |false|M
        M{"agen is no longer referenced?"} --> |true|N
        M{"agen is no longer referenced?"} --> |false|two
        N["finalize agen<br>_PyGen_Finalize(agen)"] --> O
        O["calls finalizer hook<br>loop._asyncgen_finalizer_hook(agen)"] --> P
        P["remove agen from WeakSet<br>loop._asyncgens.discard(agen)"] --> Q
        Q["schedule task to close it<br>self.create_task(agen.aclose())"] --> R
        R["print('executing finally block')"] --> E1

    end

    subgraph two["Loop shutting down"]
        A1{"check for alive async generators?"} --> |true|B1
        B1["close all async generators <br> await asyncio.gather\(*\[ag.aclose\(\) for ag in loop._asyncgens\]"] --> R
        A1{"check for alive async generators?"} --> |false|E1
        E1["loop.close()"]
    end