ayafileio

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"The fastest file I/O in Gensokyo, swift as the Wind God Maiden." — Aya Shameimaru

Async file I/O on real kernel backends: IOCP on Windows, io_uring on Linux (5.1+), Dispatch I/O on macOS. No thread pools pretending to be async; ayafileio.open() picks the backend for you.

Changelog

See CHANGES.md.

Backends

Features

• Zero thread overhead on true-async platforms — no background threads
• Kernel-level completion: IOCP / io_uring / Dispatch I/O
• Thousands of concurrent operations on a single file handle
• aiofiles-compatible API, plain async/await
• Text and binary modes with automatic encoding/decoding
• Async whole-file copy: acopy() with OS-level fast paths (zero-copy on Linux)
• Runtime-tunable configuration shared by all backends
• Python 3.10–3.14, including 3.14t free-threading

Installation

pip install ayafileio

Requires Python 3.10+, Windows 7+ / Linux (kernel 5.1+ for io_uring) / macOS 10.10+. No external dependencies; precompiled wheels are available.

Quick start

import asyncio
import ayafileio

async def main():
    # Write to a file — fast as the wind
    async with ayafileio.open("example.txt", "w") as f:
        await f.write("Hello, async world!\n")

    # Read with automatic decoding
    async with ayafileio.open("example.txt", "r", encoding="utf-8") as f:
        content = await f.read()
        print(content)

    # Binary operations
    async with ayafileio.open("data.bin", "rb") as f:
        data = await f.read(1024)
        await f.seek(0, 0)

asyncio.run(main())

Open once, read many times

Open/close overhead is already in the microsecond range, but reopening the same file in a loop still costs you a coroutine round-trip per iteration:

# Slow: repeated open/close
for i in range(10000):
    async with ayafileio.open("data.bin", "rb") as f:
        data = await f.read()

# Fast: one handle, many operations (~6x faster)
async with ayafileio.open("data.bin", "rb") as f:
    for i in range(10000):
        await f.seek(0)
        data = await f.read()

Checking the active backend

import ayafileio

print(ayafileio.get_backend_info())
# Windows: {'platform': 'windows', 'backend': 'iocp', 'is_truly_async': True}
# Linux:   {'platform': 'linux', 'backend': 'io_uring', 'is_truly_async': True}
# macOS:   {'platform': 'macos', 'backend': 'dispatch_io', 'is_truly_async': True}

Configuration

All backends share one runtime configuration:

import ayafileio

print(ayafileio.get_config())

ayafileio.configure({
    "io_worker_count": 8,
    "buffer_size": 131072,      # 128 KB
    "close_timeout_ms": 2000,
})

ayafileio.reset_config()  # back to defaults

API reference

AsyncFile

class AsyncFile(Generic[T]):
    def __init__(
        self, path: str | Path, mode: str = "rb",
        encoding: str | None = None,
        newline: str | None = None,
        errors: str | None = None,
        auto_flush: bool = False
    ): ...

    # Reading
    async def read(self, size: int = -1) -> T: ...
    async def readline() -> T: ...
    async def readlines(hint: int = -1) -> list[T]: ...
    async def readall() -> T: ...                        # alias for read(-1)
    async def readinto(buf: bytearray | memoryview) -> int: ...  # zero-copy [binary only]
    async def chunk(chunk_size: int, *, buf: bytearray | memoryview | None = None) -> AsyncGenerator[memoryview, None]: ...  # streaming chunks [binary only]
    async def read_at(offset: int, size: int = -1) -> bytes: ...  # positioned read (pread), leaves file position untouched [binary only]
    async def read_many(spans: Iterable[tuple[int, int]]) -> list[bytes]: ...  # batched positioned reads, submitted in one event-loop turn [binary only]
    async def write_at(offset: int, data: bytes | bytearray | memoryview) -> int: ...
    async def write_many(writes: Iterable[tuple[int, bytes | bytearray | memoryview]]) -> list[int]: ...

    # Writing
    async def write(self, data: str | bytes) -> int: ...
    async def writelines(lines) -> None: ...

    # Position
    async def seek(self, offset: int, whence: int = 0) -> int: ...
    async def tell() -> int: ...
    async def truncate(size: int) -> None: ...

    # Control
    async def flush(self) -> None: ...
    async def close(self) -> None: ...

    # Properties
    @property
    def closed(self) -> bool: ...
    @property
    def name(self) -> str: ...
    @property
    def mode(self) -> str: ...

    # State
    def readable() -> bool: ...
    def writable() -> bool: ...
    def seekable() -> bool: ...
    def fileno() -> int: ...
    def isatty() -> bool: ...

    # Iteration
    def __aiter__(self) -> AsyncFile[T]: ...
    async def __anext__(self) -> T: ...

Supported modes

Positioned and batched writes

async with ayafileio.open("data.bin", "w+b") as f:
    n = await f.write_at(1024, b"payload")
    counts = await f.write_many([(0, b"header"), (4096, b"block")])
    assert await f.tell() == 0

write_at(offset, data) returns the number of bytes written. write_many(writes) accepts an iterable of (offset, data) pairs and returns counts in input order. Both require a writable binary file, reject append mode, and preserve the logical file position. They accept bytes, bytearray, and contiguous memoryview buffers, which the backend copies on submission. Positioned writes rewind and discard any readline() read-ahead so subsequent reads see the updated content. An empty batch returns []; an empty buffer returns 0. Offsets must be nonnegative, offset plus length must fit a signed 64-bit integer, and each buffer is limited to 4 GiB minus 1 byte. As with write(), callers must check for short writes.

Batch operations gather native Futures directly, avoiding a Python Task per item; read_many() uses the same optimization. Submitted requests are drained before reporting submission or I/O errors, but batches are not atomic: some writes may have succeeded when an error is raised. Concurrent overlapping writes have unspecified ordering; await each write_at(...) separately when ordering matters. Cancelling the wait does not undo already submitted system I/O.

Copying files

n = await ayafileio.acopy("model.gguf", "backup/model.gguf")

acopy(src, dst, *, chunk_size=4 MiB, concurrency=8, copy_stat=False) copies a whole file and returns the number of bytes copied. On Linux it uses the in-kernel zero-copy copy_file_range; on Windows CopyFile2, which also preserves metadata. Both run in a worker thread. Where no fast call exists (or the filesystem rejects it), it falls back to a read_at/write_at pipeline with up to concurrency chunks in flight, so memory stays below concurrency × chunk_size. The destination is truncated first; copying a file onto itself raises shutil.SameFileError; copy_stat=True applies shutil.copystat afterwards.

Configuration functions

def configure(options: dict) -> None: ...      # apply unified configuration
def get_config() -> dict: ...                  # current configuration
def reset_config() -> None: ...                # reset to defaults
def get_backend_info() -> dict: ...            # active backend information

Wrapping an existing file

def wrap_file(fd: int, mode: str = "rb", *, owns_fd: bool = False) -> AsyncFile[bytes]: ...

Wraps an existing file descriptor (int) or a file-like object with fileno() as an AsyncFile. Binary mode only.

Pool management

def drain_handle_pool() -> None: ...           # release all cached file handles
def drain_buffer_pool() -> None: ...           # release all cached I/O buffers

Useful after bulk tempfile operations or between benchmark rounds.

Benchmarks

Every push runs tests/t_compare.py on all three platforms on CI and attaches the JSON results as artifacts. Numbers below are from a recent run (GitHub Actions, Python 3.14) unless marked local. Reproduce with python tests/t_compare.py.

Random 4 KiB read (Linux, io_uring)

Positioned read_at on a single shared handle holds ~345K ops/s from x16 up; aiofiles has no positioned-read API to compare against. Small 4 KiB sequential reads: 201.6K vs 21.9K ops/s (9.2x). Sequential read throughput: 7,360 MB/s at 64 KiB blocks (6.6x), 20,178 MB/s at 4 MiB (1.3x).

Sequential writes are closer: 1.2–1.3x up to 256 KiB blocks, and aiofiles pulls ahead at 4 MiB (0.4x) on the CI runner — large buffered writes ride the kernel's writeback path and swing between runs. If that workload matters to you, check a recent run's artifact rather than any single number.

Windows and macOS

GitHub's Windows runners throttle small I/O too hard for the CI numbers to be representative, so the Windows figures are local NVMe measurements:

• Windows (IOCP), sequential write: 1,363 → 2,858 MB/s at 1 MiB blocks, 929 → 3,046 MB/s at 4 MiB (1.47x over aiofiles). 4 KiB random read_at with 16 in flight: 21.4K ops/s.
• macOS (Dispatch I/O), sequential write: 139 → 5,502 MB/s at 1 MiB (40x), 208 → 6,787 MB/s at 4 MiB — measured on the macos-15 CI runner, which is fast enough to be meaningful.

Whole-file copy (acopy, local, 512 MiB cache-hot file)

Concurrent logging writes (tests/test_loguru.py)

10K records × 128 B written concurrently to 5 files: 3.5–5.2x over sync-writes-in-a-threadpool across runs.

Tuning

The defaults are where the tuning ended up. On very fast NVMe drives, iocp_batch_size 128–256 with buffer_size 128 KiB can squeeze out a bit more on Windows.

Contributing

Contributions welcome: fork, branch, add tests, make sure the benchmarks still pass, open a PR.

License

MIT — see LICENSE.

"Slow is a crime, right?" — Aya Shameimaru, editor-in-chief of Bunbunmaru News