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.

basic_aiter.py

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.

async_fetcher.py

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_gen.py

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.

async_reader.py

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.

rate_limited.py

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.

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