aiter()

aiter() is a built-in asyncio function (introduced in Python 3.10) that creates an asynchronous iterator from any asynchronous iterable — including async generators, asyncio.Queue, streams, or custom objects with __aiter__(). It enables clean, non-blocking iteration over async data sources (files, networks, queues, generators) using async for. In 2026, aiter() remains essential for modern async Python — processing streams, real-time data feeds, WebSocket messages, database cursors, or earthquake event streams — with zero-copy iteration, full compatibility with async/await, and seamless integration with asyncio, trio, anyio, and Dask’s async support.

Here’s a complete, practical guide to using aiter() in Python: basic async iteration, working with queues & generators, real-world patterns (stream processing, event handling, earthquake feeds), and modern best practices with type hints, error handling, cancellation, performance, and alternatives in Polars/Dask/xarray.

Basic aiter() usage — create async iterator from async iterable, use in async for.


import asyncio

async def countdown(n: int):
    while n > 0:
        yield n
        n -= 1
        await asyncio.sleep(0.5)

async def main():
    # aiter() creates async iterator from generator
    async for num in aiter(countdown(5)):
        print(f"Counting: {num}")

asyncio.run(main())
# Output: Counting: 5 ? 4 ? 3 ? 2 ? 1

Using aiter() with asyncio.Queue — classic producer-consumer pattern.


async def producer(queue: asyncio.Queue):
    for i in range(5):
        await queue.put(i)
        await asyncio.sleep(0.3)
    await queue.put(None)  # sentinel

async def consumer(queue: asyncio.Queue):
    async for item in aiter(queue.get, None):  # stops at sentinel
        print(f"Consumed: {item}")
        queue.task_done()

async def main():
    queue = asyncio.Queue()
    await asyncio.gather(
        producer(queue),
        consumer(queue)
    )

asyncio.run(main())

Real-world pattern: processing real-time earthquake event stream — filter & log significant quakes.


import asyncio
import json
from typing import AsyncIterator

async def event_stream() -> AsyncIterator[dict]:
    # Simulate USGS real-time feed (in practice: websocket or HTTP stream)
    events = [
        {"mag": 4.2, "place": "California"},
        {"mag": 7.1, "place": "Japan"},
        {"mag": 5.8, "place": "Indonesia"},
        {"mag": 6.5, "place": "Chile"}
    ]
    for e in events:
        await asyncio.sleep(1)
        yield e

async def monitor_quakes():
    async for event in aiter(event_stream()):
        if event["mag"] >= 6.0:
            print(f"ALERT: Strong quake! Mag {event['mag']} in {event['place']}")

asyncio.run(monitor_quakes())
# Output: ALERT: Strong quake! Mag 7.1 in Japan
#         ALERT: Strong quake! Mag 6.5 in Chile

Best practices for aiter() in async Python. Use async for item in aiter(source) — clean, idiomatic async iteration. Modern tip: use Polars/Dask for batch/tabular streams — pl.scan_ndjson(...) or da.from_array(...); use aiter() for truly streaming/event-driven data. Prefer sentinel values — None or custom object — to signal end. Use asyncio.Queue.get_nowait() — non-blocking checks. Add type hints — async def stream() -> AsyncIterator[dict]. Handle cancellation — wrap in try/finally for cleanup. Use asyncio.timeout() — prevent hangs. Monitor with asyncio.gather() — run producer/consumer concurrently. Use anyio — higher-level async primitives. Use websockets — for real-time feeds. Use httpx.AsyncClient — for async HTTP streams. Profile with asyncio.run() + uvloop — faster event loop. Test with pytest-asyncio — async fixtures. Use aiter() with asyncio.as_completed() — concurrent futures. Use db.read_text(...).map(json.loads) — Dask Bag for large JSONL files.

aiter() creates async iterators from any async iterable — enabling clean async for loops over streams, queues, generators, or custom sources. In 2026, use sentinels for termination, type hints for clarity, anyio/uvloop for speed, and integrate with Dask/Polars/xarray for batch/stream hybrids. Master aiter(), and you’ll process real-time or async data flows elegantly, efficiently, and scalably.

Next time you iterate over async data — use aiter(). It’s Python’s cleanest way to say: “Loop asynchronously — over any stream or sequence, non-blocking.”

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