Python __anext__ Method
Last modified April 8, 2025
This comprehensive guide explores Python's __anext__ method, the
special method that enables asynchronous iteration. We'll cover basic usage,
custom async iterators, error handling, and practical examples.
Basic Definitions
The __anext__ method is a coroutine that returns the next item from
an asynchronous iterator. It is the async equivalent of __next__.
Key characteristics: it must be defined as an async function, returns an awaitable,
and raises StopAsyncIteration when no more items are available.
It's used with async for loops.
Basic Async Iterator Implementation
Here's a simple async iterator demonstrating __anext__ usage. It
shows the minimal implementation needed for async iteration.
class AsyncCounter:
def __init__(self, stop):
self.current = 0
self.stop = stop
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 num in AsyncCounter(3):
print(num)
import asyncio
asyncio.run(main())
This example creates an async counter that yields numbers from 0 to 2. The
__anext__ method increments the counter and returns values.
When the counter reaches the stop value, it raises StopAsyncIteration.
This signals the end of iteration to the async for loop.
Async Iterator with Delay
This example shows how __anext__ can include awaitable operations,
like sleeping, making it useful for I/O-bound tasks.
class DelayedIterator:
def __init__(self, items, delay):
self.items = iter(items)
self.delay = delay
def __aiter__(self):
return self
async def __anext__(self):
try:
item = next(self.items)
await asyncio.sleep(self.delay)
return item
except StopIteration:
raise StopAsyncIteration
async def main():
async for char in DelayedIterator("ABC", 0.5):
print(char)
asyncio.run(main())
This iterator introduces a delay between items. It wraps a regular iterator and
adds async behavior. The await asyncio.sleep() demonstrates async
operation.
The conversion from StopIteration to StopAsyncIteration
is important. It properly signals the end of iteration in async context.
Async Data Fetcher
This practical example shows __anext__ fetching data from multiple
URLs asynchronously, demonstrating real-world usage.
import aiohttp
class AsyncDataFetcher:
def __init__(self, urls):
self.urls = iter(urls)
self.session = None
async def __aenter__(self):
self.session = aiohttp.ClientSession()
return self
async def __aexit__(self, *args):
await self.session.close()
def __aiter__(self):
return self
async def __anext__(self):
try:
url = next(self.urls)
except StopIteration:
raise StopAsyncIteration
async with self.session.get(url) as response:
return await response.text()
async def main():
urls = [
'https://example.com',
'https://python.org',
'https://pypi.org'
]
async with AsyncDataFetcher(urls) as fetcher:
async for content in fetcher:
print(f"Fetched {len(content)} bytes")
asyncio.run(main())
This async iterator fetches web pages one by one. It uses aiohttp
for async HTTP requests. The __anext__ method handles each fetch.
The context manager methods (__aenter__, __aexit__)
manage the HTTP session lifecycle. This ensures proper resource cleanup.
Error Handling in __anext__
This example demonstrates proper error handling in __anext__,
showing how to manage exceptions during async iteration.
class SafeAsyncIterator:
def __init__(self, data):
self.data = iter(data)
self.retries = 3
def __aiter__(self):
return self
async def __anext__(self):
for attempt in range(self.retries):
try:
return next(self.data)
except StopIteration:
raise StopAsyncIteration
except Exception as e:
if attempt == self.retries - 1:
raise
print(f"Error, retrying... ({e})")
await asyncio.sleep(1)
async def main():
data = [1, 2, "error", 3, 4]
try:
async for item in SafeAsyncIterator(data):
print(f"Got: {item}")
except Exception as e:
print(f"Failed after retries: {e}")
asyncio.run(main())
This iterator implements retry logic for exceptions. It attempts each item multiple times before propagating the error. The delay between retries is async.
The example shows how to handle both normal iteration completion
(StopAsyncIteration) and error cases. This makes the iterator more
robust.
Async Generator Alternative
This example compares __anext__ implementation with async
generators, showing a more concise alternative syntax.
async def async_counter(stop):
for i in range(stop):
await asyncio.sleep(0.1)
yield i
async def main():
# Using async generator
async for num in async_counter(3):
print(f"Generator: {num}")
# Equivalent __anext__ implementation
class Counter:
def __init__(self, stop):
self.current = 0
self.stop = stop
def __aiter__(self):
return self
async def __anext__(self):
if self.current >= self.stop:
raise StopAsyncIteration
await asyncio.sleep(0.1)
self.current += 1
return self.current - 1
async for num in Counter(3):
print(f"Class: {num}")
asyncio.run(main())
The async generator provides a cleaner syntax for simple cases. However, class
with __anext__ offers more control for complex scenarios.
Both implementations are functionally equivalent. The choice depends on needs for state management, reusability, and complexity.
Best Practices
- Always raise StopAsyncIteration: Signal iteration end properly
- Handle errors gracefully: Clean up resources if iteration fails
- Document iteration behavior: Note if iterator is finite/infinite
- Consider async generators: Often simpler for basic cases
- Manage resources: Use async context managers for cleanup
Source References
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