Python Async Patterns

Master asynchronous programming in Python using asyncio, async/await syntax, and concurrent execution patterns for I/O-bound and CPU-bound tasks.

Basic Async/Await

Core async syntax:

import asyncio

# Define async function with async def
async def fetch_data(url: str) -> str:
    print(f"Fetching {url}...")
    await asyncio.sleep(1)  # Simulate I/O operation
    return f"Data from {url}"

# Call async function
async def main() -> None:
    result = await fetch_data("https://api.example.com")
    print(result)

# Run async function
asyncio.run(main())

Multiple concurrent operations:

import asyncio

async def fetch_url(url: str) -> str:
    await asyncio.sleep(1)
    return f"Content from {url}"

async def main() -> None:
    # Run concurrently with gather
    results = await asyncio.gather(
        fetch_url("https://example.com/1"),
        fetch_url("https://example.com/2"),
        fetch_url("https://example.com/3")
    )
    for result in results:
        print(result)

asyncio.run(main())

asyncio.create_task

Creating and managing tasks:

import asyncio

async def process_item(item: str, delay: float) -> str:
    await asyncio.sleep(delay)
    return f"Processed {item}"

async def main() -> None:
    # Create tasks for concurrent execution
    task1 = asyncio.create_task(process_item("A", 2.0))
    task2 = asyncio.create_task(process_item("B", 1.0))
    task3 = asyncio.create_task(process_item("C", 1.5))

    # Do other work while tasks run
    print("Tasks started")

    # Wait for tasks to complete
    result1 = await task1
    result2 = await task2
    result3 = await task3

    print(result1, result2, result3)

asyncio.run(main())

Task with name and context:

import asyncio

async def background_task(name: str) -> None:
    print(f"Task {name} starting")
    await asyncio.sleep(2)
    print(f"Task {name} completed")

async def main() -> None:
    # Create named task
    task = asyncio.create_task(
        background_task("worker"),
        name="background-worker"
    )

    # Check task status
    print(f"Task name: {task.get_name()}")
    print(f"Task done: {task.done()}")

    await task

asyncio.run(main())

asyncio.gather vs asyncio.wait

Using gather for results:

import asyncio

async def fetch(n: int) -> int:
    await asyncio.sleep(1)
    return n * 2

async def main() -> None:
    # gather returns results in order
    results = await asyncio.gather(
        fetch(1),
        fetch(2),
        fetch(3)
    )
    print(results)  # [2, 4, 6]

    # Return exceptions instead of raising
    results = await asyncio.gather(
        fetch(1),
        fetch(2),
        return_exceptions=True
    )

asyncio.run(main())

Using wait for more control:

import asyncio

async def worker(n: int) -> int:
    await asyncio.sleep(n)
    return n

async def main() -> None:
    tasks = [
        asyncio.create_task(worker(1)),
        asyncio.create_task(worker(2)),
        asyncio.create_task(worker(3))
    ]

    # Wait for first task to complete
    done, pending = await asyncio.wait(
        tasks,
        return_when=asyncio.FIRST_COMPLETED
    )

    print(f"Done: {len(done)}, Pending: {len(pending)}")

    # Cancel pending tasks
    for task in pending:
        task.cancel()

    # Wait for all with timeout
    done, pending = await asyncio.wait(
        tasks,
        timeout=2.0
    )

asyncio.run(main())

Async Context Managers

Creating async context managers:

import asyncio
from typing import AsyncIterator

class AsyncResource:
    async def __aenter__(self) -> "AsyncResource":
        print("Acquiring resource")
        await asyncio.sleep(0.1)
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:
        print("Releasing resource")
        await asyncio.sleep(0.1)

    async def query(self) -> str:
        return "data"

async def main() -> None:
    async with AsyncResource() as resource:
        result = await resource.query()
        print(result)

asyncio.run(main())

Using asynccontextmanager decorator:

from contextlib import asynccontextmanager
import asyncio

@asynccontextmanager
async def get_connection(url: str) -> AsyncIterator[str]:
    # Setup
    print(f"Connecting to {url}")
    await asyncio.sleep(0.1)
    conn = f"connection-{url}"

    try:
        yield conn
    finally:
        # Teardown
        print(f"Closing connection to {url}")
        await asyncio.sleep(0.1)

async def main() -> None:
    async with get_connection("localhost") as conn:
        print(f"Using {conn}")

asyncio.run(main())

Async Iterators

Creating async iterators:

import asyncio
from typing import AsyncIterator

class AsyncRange:
    def __init__(self, count: int) -> None:
        self.count = count

    def __aiter__(self) -> AsyncIterator[int]:
        return self

    async def __anext__(self) -> int:
        if self.count <= 0:
            raise StopAsyncIteration
        await asyncio.sleep(0.1)
        self.count -= 1
        return self.count

async def main() -> None:
    async for i in AsyncRange(5):
        print(i)

asyncio.run(main())

Async generator functions:

import asyncio
from typing import AsyncIterator

async def async_range(count: int) -> AsyncIterator[int]:
    for i in range(count):
        await asyncio.sleep(0.1)
        yield i

async def fetch_pages(urls: list[str]) -> AsyncIterator[str]:
    for url in urls:
        await asyncio.sleep(0.5)
        yield f"Page content from {url}"

async def main() -> None:
    async for num in async_range(5):
        print(num)

    urls = ["url1", "url2", "url3"]
    async for page in fetch_pages(urls):
        print(page)

asyncio.run(main())

Async Queues

Producer-consumer pattern with Queue:

import asyncio
from asyncio import Queue

async def producer(queue: Queue[int], n: int) -> None:
    for i in range(n):
        await asyncio.sleep(0.1)
        await queue.put(i)
        print(f"Produced {i}")
    await queue.put(None)  # Sentinel value

async def consumer(queue: Queue[int], name: str) -> None:
    while True:
        item = await queue.get()
        if item is None:
            queue.task_done()
            break
        await asyncio.sleep(0.2)
        print(f"Consumer {name} processed {item}")
        queue.task_done()

async def main() -> None:
    queue: Queue[int] = Queue(maxsize=5)

    # Start producer and consumers
    prod = asyncio.create_task(producer(queue, 10))
    cons1 = asyncio.create_task(consumer(queue, "A"))
    cons2 = asyncio.create_task(consumer(queue, "B"))

    await prod
    await queue.join()  # Wait for all tasks to be processed

    # Signal consumers to exit
    await queue.put(None)
    await queue.put(None)

    await cons1
    await cons2

asyncio.run(main())

Semaphore and Lock

Limiting concurrent operations:

import asyncio

async def fetch_with_limit(
    url: str,
    semaphore: asyncio.Semaphore
) -> str:
    async with semaphore:
        print(f"Fetching {url}")
        await asyncio.sleep(1)
        return f"Data from {url}"

async def main() -> None:
    # Limit to 3 concurrent operations
    semaphore = asyncio.Semaphore(3)

    urls = [f"https://example.com/{i}" for i in range(10)]

    tasks = [
        fetch_with_limit(url, semaphore)
        for url in urls
    ]

    results = await asyncio.gather(*tasks)
    print(f"Fetched {len(results)} URLs")

asyncio.run(main())

Using Lock for mutual exclusion:

import asyncio

class Counter:
    def __init__(self) -> None:
        self.value = 0
        self.lock = asyncio.Lock()

    async def increment(self) -> None:
        async with self.lock:
            # Critical section
            current = self.value
            await asyncio.sleep(0.01)
            self.value = current + 1

async def main() -> None:
    counter = Counter()

    # Run increments concurrently
    await asyncio.gather(*[
        counter.increment()
        for _ in range(100)
    ])

    print(f"Final value: {counter.value}")  # Should be 100

asyncio.run(main())

Timeouts and Cancellation

Using timeout:

import asyncio

async def slow_operation() -> str:
    await asyncio.sleep(5)
    return "completed"

async def main() -> None:
    # Timeout after 2 seconds
    try:
        result = await asyncio.wait_for(
            slow_operation(),
            timeout=2.0
        )
        print(result)
    except asyncio.TimeoutError:
        print("Operation timed out")

asyncio.run(main())

Handling cancellation:

import asyncio

async def cancellable_task() -> None:
    try:
        while True:
            print("Working...")
            await asyncio.sleep(1)
    except asyncio.CancelledError:
        print("Task was cancelled")
        # Cleanup
        raise

async def main() -> None:
    task = asyncio.create_task(cancellable_task())

    await asyncio.sleep(3)
    task.cancel()

    try:
        await task
    except asyncio.CancelledError:
        print("Task cancellation confirmed")

asyncio.run(main())

Event Loop Management

Direct event loop control:

import asyncio

async def task1() -> None:
    print("Task 1")

async def task2() -> None:
    print("Task 2")

# Create new event loop
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)

try:
    # Schedule callbacks
    loop.call_soon(lambda: print("Callback"))

    # Schedule delayed callback
    loop.call_later(1.0, lambda: print("Delayed"))

    # Run coroutine
    loop.run_until_complete(task1())

    # Run multiple tasks
    loop.run_until_complete(
        asyncio.gather(task1(), task2())
    )
finally:
    loop.close()

concurrent.futures

ThreadPoolExecutor for I/O-bound tasks:

import asyncio
from concurrent.futures import ThreadPoolExecutor
import time

def blocking_io_task(n: int) -> int:
    print(f"Task {n} starting")
    time.sleep(2)  # Blocking I/O
    return n * 2

async def main() -> None:
    loop = asyncio.get_event_loop()

    with ThreadPoolExecutor(max_workers=3) as executor:
        # Run blocking function in thread pool
        tasks = [
            loop.run_in_executor(executor, blocking_io_task, i)
            for i in range(5)
        ]

        results = await asyncio.gather(*tasks)
        print(results)

asyncio.run(main())

ProcessPoolExecutor for CPU-bound tasks:

import asyncio
from concurrent.futures import ProcessPoolExecutor

def cpu_intensive_task(n: int) -> int:
    # CPU-intensive computation
    result = sum(i * i for i in range(n))
    return result

async def main() -> None:
    loop = asyncio.get_event_loop()

    with ProcessPoolExecutor(max_workers=4) as executor:
        # Run CPU-bound function in process pool
        tasks = [
            loop.run_in_executor(
                executor,
                cpu_intensive_task,
                10_000_000
            )
            for _ in range(4)
        ]

        results = await asyncio.gather(*tasks)
        print(f"Completed {len(results)} tasks")

asyncio.run(main())

Async HTTP with aiohttp

Making async HTTP requests:

import asyncio
import aiohttp

async def fetch_url(
    session: aiohttp.ClientSession,
    url: str
) -> str:
    async with session.get(url) as response:
        return await response.text()

async def main() -> None:
    async with aiohttp.ClientSession() as session:
        urls = [
            "https://example.com/1",
            "https://example.com/2",
            "https://example.com/3"
        ]

        tasks = [fetch_url(session, url) for url in urls]
        results = await asyncio.gather(*tasks)

        print(f"Fetched {len(results)} pages")

asyncio.run(main())

Error Handling

Handling exceptions in async code:

import asyncio

async def failing_task() -> None:
    await asyncio.sleep(1)
    raise ValueError("Task failed")

async def main() -> None:
    # Handle exception in single task
    try:
        await failing_task()
    except ValueError as e:
        print(f"Caught: {e}")

    # Handle exceptions with gather
    results = await asyncio.gather(
        failing_task(),
        failing_task(),
        return_exceptions=True
    )

    for result in results:
        if isinstance(result, Exception):
            print(f"Task failed: {result}")

asyncio.run(main())

When to Use This Skill

Use python-async-patterns when you need to:

Handle multiple I/O operations concurrently (API calls, database queries)
Build async web servers or clients
Process data streams asynchronously
Implement producer-consumer patterns with async queues
Run blocking I/O operations without blocking the event loop
Create async context managers for resource management
Implement async iterators for streaming data
Control concurrency with semaphores and locks
Handle timeouts and cancellation in async operations
Mix CPU-bound and I/O-bound operations efficiently

Best Practices

Use asyncio.run() for the main entry point
Create tasks with asyncio.create_task() for concurrent execution
Use gather() when you need all results
Use wait() when you need fine-grained control
Always handle CancelledError in long-running tasks
Use semaphores to limit concurrent operations
Prefer async context managers for resource management
Use asyncio.Queue for producer-consumer patterns
Run blocking I/O in thread pool with run_in_executor()
Run CPU-bound tasks in process pool
Set appropriate timeouts for network operations
Use structured concurrency patterns (nurseries)

Common Pitfalls

Forgetting to await coroutines (creates coroutine object, doesn't run)
Blocking the event loop with CPU-intensive work
Not handling task cancellation properly
Using time.sleep() instead of asyncio.sleep()
Creating too many concurrent tasks without limits
Not closing resources properly in async context
Mixing blocking and async code incorrectly
Not handling exceptions in background tasks
Forgetting to call task_done() with Queue
Using global event loop instead of asyncio.run()

python-async-patterns