wehub-resource-sync
12 KiB
12 KiB
async-python-patterns — detailed worked examples
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
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())