python-async-patterns

name	python-async-patterns
description	Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
metadata	{"scope":"implementation","version":"1.0.0"}

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

Sync vs Async Decision Guide

Before adopting async, consider whether it's the right choice for your use case.

Use Case	Recommended Approach
Many concurrent network/DB calls	asyncio
CPU-bound computation	multiprocessing or thread pool
Mixed I/O + CPU	Offload CPU work with asyncio.to_thread()
Simple scripts, few connections	Sync (simpler, easier to debug)
Web APIs with high concurrency	Async frameworks (FastAPI, aiohttp)

Key Rule: Stay fully sync or fully async within a call path. Mixing creates hidden blocking and complexity.

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=)

    
    successful = [r  r  results  r      (r, Exception)]
    failed = [r  r  results  (r, Exception)]

    ()
     successful

asyncio.run(process_items([, , , , , ]))

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()  conn:
        ()
         asyncio.sleep()  
         {: }

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."""
      number  async_range(, ):
        ()

    ()
      page_data  fetch_pages(, ):
        ()

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  ():
    
    queue = Queue(maxsize=)

    
    producers = [
        asyncio.create_task(producer(queue, i, ))
         i  ()
    ]

    consumers = [
        asyncio.create_task(consumer(queue, i))
         i  ()
    ]

    
     asyncio.gather(*producers)

    
     queue.join()

    
     c  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 {(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")

  ():
    
    counter = AsyncCounter()

    workers = [asyncio.create_task(worker(counter, i))  i  ()]
     asyncio.gather(*workers)

    final_value =  counter.get_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 = [
        ,
        ,
        ,
    ]

    results =  scrape_urls(urls)
     result  results:
        (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()

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

     {
        : user,
        : orders,
        : profile
    }

  ():
    db = AsyncDB()
    user_data =  get_user_data(db, )
    (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."""
        .clients.add(websocket)
        ()

      ():
        
        .clients.remove(websocket)
        ()

      ():
        
         .clients:
            tasks = [client.send(message)  client  .clients]
             asyncio.gather(*tasks)

      ():
        
         .register(websocket)
        :
              message  .message_iterator(websocket):
                 .broadcast()
        :
             .unregister(websocket)

      ():
        
         _  ():  
              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

Never block the event loop with synchronous operations. A single blocking call stalls all concurrent tasks.

# BAD - blocks the entire event loop
async def fetch_data_bad():
    import time
    import requests
    time.sleep(1)  # Blocks!
    response = requests.get(url)  # Also blocks!

# GOOD - use async-native libraries (e.g., httpx for async HTTP)
import httpx

async def fetch_data_good(url: str):
    await asyncio.sleep(1)
    async with httpx.AsyncClient() as client:
        response = await client.get(url)

Wrapping Blocking Code with asyncio.to_thread() (Python 3.9+):

When you must use synchronous libraries, offload to a thread pool:

import asyncio
from pathlib import Path

async def read_file_async(path: str) -> str:
    """Read file without blocking event loop."""
    # asyncio.to_thread() runs sync code in a thread pool
    return await asyncio.to_thread(Path(path).read_text)

async def call_sync_library(data: dict) -> dict:
    """Wrap a synchronous library call."""
    # Useful for sync database drivers, file I/O, CPU work
    return await asyncio.to_thread(sync_library.process, data)

Lower-level approach with run_in_executor():

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_running_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

1. Use asyncio.run() for entry point (Python 3.7+)
2. Always await coroutines to execute them
3. Limit concurrency with semaphores - unbounded gather() can exhaust resources
4. Implement proper error handling with try/except
5. Use timeouts to prevent hanging operations
6. Pool connections for better performance
7. Never block the event loop - use asyncio.to_thread() for sync code
8. Use semaphores for rate limiting external API calls
9. Handle task cancellation properly - always re-raise CancelledError
10. Test async code with pytest-asyncio
11. Stay consistent - fully sync or fully async, avoid mixing