Python __aiter__ Method

Last modified April 8, 2025

This comprehensive guide explores Python's __aiter__ method, the special method that enables asynchronous iteration. We'll cover basic usage, custom async iterators, practical examples, and common patterns.

Basic Definitions

The __aiter__ method defines an object's behavior when used in an async for loop. It returns an asynchronous iterator object that must implement __anext__.

Key characteristics: it must be an async function (defined with async def), returns an async iterator, and works with __anext__ to support asynchronous iteration. It's the async equivalent of __iter__.

Basic Async Iterator

Here's a simple implementation showing how __aiter__ works with __anext__ to create an asynchronous iterator.

class AsyncCounter:
 def __init__(self, stop):
 self.stop = stop
 self.current = 0
 
 async def __aiter__(self):
 return self
 
 async def __anext__(self):
 if self.current >= self.stop:
 raise StopAsyncIteration
 self.current += 1
 return self.current - 1
async def main():
 async for i in AsyncCounter(3):
 print(i)
import asyncio
asyncio.run(main())

This example creates an async counter that yields numbers from 0 to 2. The __aiter__ method returns self, and __anext__ produces values until reaching the stop value.

Note the use of StopAsyncIteration instead of StopIteration and the async for loop which requires an async context.

Async Data Fetcher

This example demonstrates a practical use case - fetching data from multiple URLs asynchronously while supporting async iteration.

import aiohttp
class AsyncDataFetcher:
 def __init__(self, urls):
 self.urls = urls
 
 async def __aiter__(self):
 self.session = aiohttp.ClientSession()
 self.index = 0
 return self
 
 async def __anext__(self):
 if self.index >= len(self.urls):
 await self.session.close()
 raise StopAsyncIteration
 
 url = self.urls[self.index]
 self.index += 1
 
 async with self.session.get(url) as response:
 data = await response.text()
 return (url, len(data))
async def main():
 urls = [
 'https://python.org',
 'https://docs.python.org',
 'https://pypi.org'
 ]
 
 async for url, length in AsyncDataFetcher(urls):
 print(f"{url}: {length} bytes")
asyncio.run(main())

This async fetcher downloads multiple web pages concurrently. __aiter__ sets up the HTTP session, and __anext__ fetches each URL in sequence.

The iterator properly manages resources by closing the session when iteration completes. Each iteration returns a tuple of URL and content length.

Async Generator as Iterator

Python 3.6+ allows using async generators which automatically implement __aiter__ and __anext__.

async def async_counter(stop):
 for i in range(stop):
 await asyncio.sleep(0.1) # Simulate async work
 yield i
async def main():
 async for num in async_counter(5):
 print(num)
asyncio.run(main())

This async generator simplifies creating async iterators. Under the hood, it implements the same protocol as classes with __aiter__ and __anext__.

The generator pauses at each yield and await, making it perfect for producing values asynchronously. No explicit StopAsyncIteration is needed.

Buffered Async Reader

This example shows an async iterator that reads files in chunks, useful for processing large files without loading everything into memory.

class AsyncFileReader:
 def __init__(self, filename, chunk_size=1024):
 self.filename = filename
 self.chunk_size = chunk_size
 
 async def __aiter__(self):
 self.file = open(self.filename, 'rb')
 return self
 
 async def __anext__(self):
 data = await asyncio.to_thread(
 self.file.read, self.chunk_size
 )
 if not data:
 self.file.close()
 raise StopAsyncIteration
 return data
async def main():
 async for chunk in AsyncFileReader('large_file.dat'):
 print(f"Read {len(chunk)} bytes")
asyncio.run(main())

This reader processes files in chunks using a thread pool for the blocking I/O operations. __aiter__ opens the file, and __anext__ reads each chunk.

The asyncio.to_thread runs the blocking file operation in a separate thread, keeping the async loop responsive. The iterator properly closes the file when done.

Rate-Limited Async Producer

This example demonstrates an async iterator that produces items with rate limiting, useful for APIs with request limits.

class RateLimitedProducer:
 def __init__(self, items, requests_per_second):
 self.items = items
 self.delay = 1.0 / requests_per_second
 
 async def __aiter__(self):
 self.index = 0
 return self
 
 async def __anext__(self):
 if self.index >= len(self.items):
 raise StopAsyncIteration
 
 item = self.items[self.index]
 self.index += 1
 
 await asyncio.sleep(self.delay)
 return item
async def main():
 items = [f"item_{i}" for i in range(10)]
 
 async for item in RateLimitedProducer(items, 2): # 2 items/sec
 print(f"Processed {item} at {time.time()}")
asyncio.run(main())

This producer yields items with a controlled rate. The __anext__ method sleeps between items to maintain the desired rate.

The sleep duration is calculated from the desired rate (items/second). This pattern is useful when interacting with rate-limited APIs or services.

Best Practices

• Resource management: Clean up resources in __anext__ when done
• Error handling: Handle and properly signal errors during iteration
• Use async generators: Prefer them for simpler cases
• Document behavior: Clearly document iteration patterns
• Consider cancellation: Handle asyncio cancellation properly

Source References

• Python __aiter__ Documentation
• Python async for Documentation

Author

My name is Jan Bodnar, and I am a passionate programmer with extensive programming experience. I have been writing programming articles since 2007. To date, I have authored over 1,400 articles and 8 e-books. I possess more than ten years of experience in teaching programming.

List all Python tutorials.