Async Python Patterns

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

When to Use This Skill

• Building async web APIs (FastAPI, aiohttp, Sanic)

• Implementing concurrent I/O operations (database, file, network)

• Creating web scrapers with concurrent requests

• Developing real-time applications (WebSocket servers, chat systems)

• Processing multiple independent tasks simultaneously

• Building microservices with async communication

• Optimizing I/O-bound workloads

• Implementing async background tasks and queues

Core Concepts

1. Event Loop

The event loop is the heart of asyncio, managing and scheduling asynchronous tasks.

Key characteristics:

• Single-threaded cooperative multitasking

• Schedules coroutines for execution

• Handles I/O operations without blocking

• Manages callbacks and futures

2. Coroutines

Functions defined with async def that can be paused and resumed.

Syntax:

async def my_coroutine(): result = await some_async_operation() return result

3. Tasks

Scheduled coroutines that run concurrently on the event loop.

4. Futures

Low-level objects representing eventual results of async operations.

5. Async Context Managers

Resources that support async with for proper cleanup.

6. Async Iterators

Objects that support async for for iterating over async data sources.

Quick Start

import asyncio

async def main(): print("Hello") await asyncio.sleep(1) print("World")

Python 3.7+

asyncio.run(main())

Fundamental Patterns

Pattern 1: Basic Async/Await

import asyncio

async def fetch_data(url: str) -> dict: """Fetch data from URL asynchronously.""" await asyncio.sleep(1) # Simulate I/O return {"url": url, "data": "result"}

async def main(): result = await fetch_data("https://api.example.com") print(result)

asyncio.run(main())

Pattern 2: Concurrent Execution with gather()

import asyncio from typing import List

async def fetch_user(user_id: int) -> dict: """Fetch user data.""" await asyncio.sleep(0.5) return {"id": user_id, "name": f"User {user_id}"}

async def fetch_all_users(user_ids: List[int]) -> List[dict]: """Fetch multiple users concurrently.""" tasks = [fetch_user(uid) for uid in user_ids] results = await asyncio.gather(*tasks) return results

async def main(): user_ids = [1, 2, 3, 4, 5] users = await fetch_all_users(user_ids) print(f"Fetched {len(users)} users")

asyncio.run(main())

Pattern 3: Task Creation and Management

import asyncio

async def background_task(name: str, delay: int): """Long-running background task.""" print(f"{name} started") await asyncio.sleep(delay) print(f"{name} completed") return f"Result from {name}"

async def main(): # Create tasks task1 = asyncio.create_task(background_task("Task 1", 2)) task2 = asyncio.create_task(background_task("Task 2", 1))

# Do other work
print("Main: doing other work")
await asyncio.sleep(0.5)

# Wait for tasks
result1 = await task1
result2 = await task2

print(f"Results: {result1}, {result2}")

asyncio.run(main())

Pattern 4: Error Handling in Async Code

import asyncio from typing import List, Optional

async def risky_operation(item_id: int) -> dict: """Operation that might fail.""" await asyncio.sleep(0.1) if item_id % 3 == 0: raise ValueError(f"Item {item_id} failed") return {"id": item_id, "status": "success"}

async def safe_operation(item_id: int) -> Optional[dict]: """Wrapper with error handling.""" try: return await risky_operation(item_id) except ValueError as e: print(f"Error: {e}") return None

async def process_items(item_ids: List[int]): """Process multiple items with error handling.""" tasks = [safe_operation(iid) for iid in item_ids] results = await asyncio.gather(*tasks, return_exceptions=True)

# Filter out failures
successful = [r for r in results if r is not None and not isinstance(r, Exception)]
failed = [r for r in results if isinstance(r, Exception)]

print(f"Success: {len(successful)}, Failed: {len(failed)}")
return successful

asyncio.run(process_items([1, 2, 3, 4, 5, 6]))

Pattern 5: Timeout Handling

import asyncio

async def slow_operation(delay: int) -> str: """Operation that takes time.""" await asyncio.sleep(delay) return f"Completed after {delay}s"

async def with_timeout(): """Execute operation with timeout.""" try: result = await asyncio.wait_for(slow_operation(5), timeout=2.0) print(result) except asyncio.TimeoutError: print("Operation timed out")

asyncio.run(with_timeout())

Advanced Patterns

Pattern 6: Async Context Managers

import asyncio from typing import Optional

class AsyncDatabaseConnection: """Async database connection context manager."""

def __init__(self, dsn: str):
    self.dsn = dsn
    self.connection: Optional[object] = None

async def __aenter__(self):
    print("Opening connection")
    await asyncio.sleep(0.1)  # Simulate connection
    self.connection = {"dsn": self.dsn, "connected": True}
    return self.connection

async def __aexit__(self, exc_type, exc_val, exc_tb):
    print("Closing connection")
    await asyncio.sleep(0.1)  # Simulate cleanup
    self.connection = None

async def query_database(): """Use async context manager.""" async with AsyncDatabaseConnection("postgresql://localhost") as conn: print(f"Using connection: {conn}") await asyncio.sleep(0.2) # Simulate query return {"rows": 10}

asyncio.run(query_database())

Pattern 7: Async Iterators and Generators

import asyncio from typing import AsyncIterator

async def async_range(start: int, end: int, delay: float = 0.1) -> AsyncIterator[int]: """Async generator that yields numbers with delay.""" for i in range(start, end): await asyncio.sleep(delay) yield i

async def fetch_pages(url: str, max_pages: int) -> AsyncIterator[dict]: """Fetch paginated data asynchronously.""" for page in range(1, max_pages + 1): await asyncio.sleep(0.2) # Simulate API call yield { "page": page, "url": f"{url}?page={page}", "data": [f"item_{page}_{i}" for i in range(5)] }

async def consume_async_iterator(): """Consume async iterator.""" async for number in async_range(1, 5): print(f"Number: {number}")

print("\nFetching pages:")
async for page_data in fetch_pages("https://api.example.com/items", 3):
    print(f"Page {page_data['page']}: {len(page_data['data'])} items")

asyncio.run(consume_async_iterator())

Pattern 8: Producer-Consumer Pattern

import asyncio from asyncio import Queue from typing import Optional

async def producer(queue: Queue, producer_id: int, num_items: int): """Produce items and put them in queue.""" for i in range(num_items): item = f"Item-{producer_id}-{i}" await queue.put(item) print(f"Producer {producer_id} produced: {item}") await asyncio.sleep(0.1) await queue.put(None) # Signal completion

async def consumer(queue: Queue, consumer_id: int): """Consume items from queue.""" while True: item = await queue.get() if item is None: queue.task_done() break

    print(f"Consumer {consumer_id} processing: {item}")
    await asyncio.sleep(0.2)  # Simulate work
    queue.task_done()

async def producer_consumer_example(): """Run producer-consumer pattern.""" queue = Queue(maxsize=10)

# Create tasks
producers = [
    asyncio.create_task(producer(queue, i, 5))
    for i in range(2)
]

consumers = [
    asyncio.create_task(consumer(queue, i))
    for i in range(3)
]

# Wait for producers
await asyncio.gather(*producers)

# Wait for queue to be empty
await queue.join()

# Cancel consumers
for c in consumers:
    c.cancel()

asyncio.run(producer_consumer_example())

Pattern 9: Semaphore for Rate Limiting

import asyncio from typing import List

async def api_call(url: str, semaphore: asyncio.Semaphore) -> dict: """Make API call with rate limiting.""" async with semaphore: print(f"Calling {url}") await asyncio.sleep(0.5) # Simulate API call return {"url": url, "status": 200}

async def rate_limited_requests(urls: List[str], max_concurrent: int = 5): """Make multiple requests with rate limiting.""" semaphore = asyncio.Semaphore(max_concurrent) tasks = [api_call(url, semaphore) for url in urls] results = await asyncio.gather(*tasks) return results

async def main(): urls = [f"https://api.example.com/item/{i}" for i in range(20)] results = await rate_limited_requests(urls, max_concurrent=3) print(f"Completed {len(results)} requests")

asyncio.run(main())

Pattern 10: Async Locks and Synchronization

import asyncio

class AsyncCounter: """Thread-safe async counter."""

def __init__(self):
    self.value = 0
    self.lock = asyncio.Lock()

async def increment(self):
    """Safely increment counter."""
    async with self.lock:
        current = self.value
        await asyncio.sleep(0.01)  # Simulate work
        self.value = current + 1

async def get_value(self) -> int:
    """Get current value."""
    async with self.lock:
        return self.value

async def worker(counter: AsyncCounter, worker_id: int): """Worker that increments counter.""" for _ in range(10): await counter.increment() print(f"Worker {worker_id} incremented")

async def test_counter(): """Test concurrent counter.""" counter = AsyncCounter()

workers = [asyncio.create_task(worker(counter, i)) for i in range(5)]
await asyncio.gather(*workers)

final_value = await counter.get_value()
print(f"Final counter value: {final_value}")

asyncio.run(test_counter())

Real-World Applications

Web Scraping with aiohttp

import asyncio import aiohttp from typing import List, Dict

async def fetch_url(session: aiohttp.ClientSession, url: str) -> Dict: """Fetch single URL.""" try: async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as response: text = await response.text() return { "url": url, "status": response.status, "length": len(text) } except Exception as e: return {"url": url, "error": str(e)}

async def scrape_urls(urls: List[str]) -> List[Dict]: """Scrape multiple URLs concurrently.""" async with aiohttp.ClientSession() as session: tasks = [fetch_url(session, url) for url in urls] results = await asyncio.gather(*tasks) return results

async def main(): urls = [ "https://httpbin.org/delay/1", "https://httpbin.org/delay/2", "https://httpbin.org/status/404", ]

results = await scrape_urls(urls)
for result in results:
    print(result)

asyncio.run(main())

Async Database Operations

import asyncio from typing import List, Optional

Simulated async database client

class AsyncDB: """Simulated async database."""

async def execute(self, query: str) -> List[dict]:
    """Execute query."""
    await asyncio.sleep(0.1)
    return [{"id": 1, "name": "Example"}]

async def fetch_one(self, query: str) -> Optional[dict]:
    """Fetch single row."""
    await asyncio.sleep(0.1)
    return {"id": 1, "name": "Example"}

async def get_user_data(db: AsyncDB, user_id: int) -> dict: """Fetch user and related data concurrently.""" user_task = db.fetch_one(f"SELECT * FROM users WHERE id = {user_id}") orders_task = db.execute(f"SELECT * FROM orders WHERE user_id = {user_id}") profile_task = db.fetch_one(f"SELECT * FROM profiles WHERE user_id = {user_id}")

user, orders, profile = await asyncio.gather(user_task, orders_task, profile_task)

return {
    "user": user,
    "orders": orders,
    "profile": profile
}

async def main(): db = AsyncDB() user_data = await get_user_data(db, 1) print(user_data)

asyncio.run(main())

WebSocket Server

import asyncio from typing import Set

Simulated WebSocket connection

class WebSocket: """Simulated WebSocket."""

def __init__(self, client_id: str):
    self.client_id = client_id

async def send(self, message: str):
    """Send message."""
    print(f"Sending to {self.client_id}: {message}")
    await asyncio.sleep(0.01)

async def recv(self) -> str:
    """Receive message."""
    await asyncio.sleep(1)
    return f"Message from {self.client_id}"

class WebSocketServer: """Simple WebSocket server."""

def __init__(self):
    self.clients: Set[WebSocket] = set()

async def register(self, websocket: WebSocket):
    """Register new client."""
    self.clients.add(websocket)
    print(f"Client {websocket.client_id} connected")

async def unregister(self, websocket: WebSocket):
    """Unregister client."""
    self.clients.remove(websocket)
    print(f"Client {websocket.client_id} disconnected")

async def broadcast(self, message: str):
    """Broadcast message to all clients."""
    if self.clients:
        tasks = [client.send(message) for client in self.clients]
        await asyncio.gather(*tasks)

async def handle_client(self, websocket: WebSocket):
    """Handle individual client connection."""
    await self.register(websocket)
    try:
        async for message in self.message_iterator(websocket):
            await self.broadcast(f"{websocket.client_id}: {message}")
    finally:
        await self.unregister(websocket)

async def message_iterator(self, websocket: WebSocket):
    """Iterate over messages from client."""
    for _ in range(3):  # Simulate 3 messages
        yield await websocket.recv()

Performance Best Practices

1. Use Connection Pools

import asyncio import aiohttp

async def with_connection_pool(): """Use connection pool for efficiency.""" connector = aiohttp.TCPConnector(limit=100, limit_per_host=10)

async with aiohttp.ClientSession(connector=connector) as session:
    tasks = [session.get(f"https://api.example.com/item/{i}") for i in range(50)]
    responses = await asyncio.gather(*tasks)
    return responses

2. Batch Operations

async def batch_process(items: List[str], batch_size: int = 10): """Process items in batches.""" for i in range(0, len(items), batch_size): batch = items[i:i + batch_size] tasks = [process_item(item) for item in batch] await asyncio.gather(*tasks) print(f"Processed batch {i // batch_size + 1}")

async def process_item(item: str): """Process single item.""" await asyncio.sleep(0.1) return f"Processed: {item}"

3. Avoid Blocking Operations

import asyncio import concurrent.futures from typing import Any

def blocking_operation(data: Any) -> Any: """CPU-intensive blocking operation.""" import time time.sleep(1) return data * 2

async def run_in_executor(data: Any) -> Any: """Run blocking operation in thread pool.""" loop = asyncio.get_event_loop() with concurrent.futures.ThreadPoolExecutor() as pool: result = await loop.run_in_executor(pool, blocking_operation, data) return result

async def main(): results = await asyncio.gather(*[run_in_executor(i) for i in range(5)]) print(results)

asyncio.run(main())

Common Pitfalls

1. Forgetting await

Wrong - returns coroutine object, doesn't execute

result = async_function()

Correct

result = await async_function()

2. Blocking the Event Loop

Wrong - blocks event loop

import time async def bad(): time.sleep(1) # Blocks!

Correct

async def good(): await asyncio.sleep(1) # Non-blocking

3. Not Handling Cancellation

async def cancelable_task(): """Task that handles cancellation.""" try: while True: await asyncio.sleep(1) print("Working...") except asyncio.CancelledError: print("Task cancelled, cleaning up...") # Perform cleanup raise # Re-raise to propagate cancellation

4. Mixing Sync and Async Code

Wrong - can't call async from sync directly

def sync_function(): result = await async_function() # SyntaxError!

Correct

def sync_function(): result = asyncio.run(async_function())

Testing Async Code

import asyncio import pytest

Using pytest-asyncio

@pytest.mark.asyncio async def test_async_function(): """Test async function.""" result = await fetch_data("https://api.example.com") assert result is not None

@pytest.mark.asyncio async def test_with_timeout(): """Test with timeout.""" with pytest.raises(asyncio.TimeoutError): await asyncio.wait_for(slow_operation(5), timeout=1.0)

Resources

• Python asyncio documentation: https://docs.python.org/3/library/asyncio.html

• aiohttp: Async HTTP client/server

• FastAPI: Modern async web framework

• asyncpg: Async PostgreSQL driver

• motor: Async MongoDB driver

Best Practices Summary

• Use asyncio.run() for entry point (Python 3.7+)

• Always await coroutines to execute them

• Use gather() for concurrent execution of multiple tasks

• Implement proper error handling with try/except

• Use timeouts to prevent hanging operations

• Pool connections for better performance

• Avoid blocking operations in async code

• Use semaphores for rate limiting

• Handle task cancellation properly

• Test async code with pytest-asyncio