ayafileio

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

Cross-platform asynchronous file I/O library using native async I/O where available.
Windows leverages IOCP (I/O Completion Ports), Linux uses io_uring (kernel 5.1+), and macOS uses Dispatch I/O (GCD) for truly non-blocking file operations.

changes

see -> CHANGES

🏆 True Async on All Three Major Platforms

Platform	Backend	True Async	Description
Windows	IOCP	✅	NT kernel native I/O Completion Ports
Linux	io_uring	✅	Next-gen async I/O (kernel 5.1+)
macOS	Dispatch I/O	✅	GCD kernel-level async I/O

ayafileio aims to be a single, unified async file API that delivers true kernel-level async I/O across Windows, Linux, and macOS.

📸 Key Features

Feature	Description
🍃 Zero thread overhead	No background threads on true async platforms
📰 Kernel-level completion	IOCP / io_uring / Dispatch I/O direct to kernel
⚡ High concurrency	Handles thousands of concurrent file operations
🎴 Familiar API	aiofiles-compatible, supports `async/await`
📖 Text & binary support	Automatic encoding/decoding in text modes
🔧 Unified configuration	Runtime tunable parameters for all backends
🌍 Cross-platform	Windows, Linux, and macOS
🐍 Latest Python	Supports 3.10–3.14, including 3.14t free-threading

🛠️ Installation

pip install ayafileio

System requirements:

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

⚡ Performance Best Practice

ayafileio's file open/close overhead is already in the microsecond range, but for maximum performance, avoid reopening the same file in a loop.

# ❌ DO NOT DO THIS: repeated open/close in a loop
for i in range(10000):
    async with ayafileio.open("data.bin", "rb") as f:
        data = await f.read()

# ✅ DO THIS: open once, operate many times
async with ayafileio.open("data.bin", "rb") as f:
    for i in range(10000):
        await f.seek(0)
        data = await f.read()

The latter is ~6x faster — it eliminates 9999 unnecessary coroutine scheduling round-trips.

🔍 Backend Information

Check which backend is currently in use:

import ayafileio

info = ayafileio.get_backend_info()
print(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}

⚙️ Unified Configuration

ayafileio provides a unified configuration system that allows runtime tuning:

import ayafileio

# View current configuration
config = ayafileio.get_config()
print(config)

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

# Reset to defaults
ayafileio.reset_config()

Configuration Options

Option	Default	Description
`handle_pool_max_per_key`	64	Max cached handles per file (Windows)
`handle_pool_max_total`	2048	Max total cached handles (Windows)
`io_worker_count`	0	IO worker threads, 0=auto
`buffer_pool_max`	512	Max cached buffers
`buffer_size`	65536	Buffer size in bytes
`close_timeout_ms`	4000	Close timeout for pending I/O (ms)
`iocp_batch_size`	64	IOCP batch completion harvest size (Windows, 1–256)
`io_uring_queue_depth`	256	io_uring queue depth (Linux)
`io_uring_sqpoll`	False	Enable SQPOLL mode (Linux)

📚 API Reference

AsyncFile class

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
    ): ...

    # 读取
    async def read(self, size: int = -1) -> T: ...
    async def readline() -> T: ...
    async def readlines(hint: int = -1) -> list[T]: ...
    async def readall() -> T: ...                        # read(-1) 别名
    async def readinto(buf: bytearray | memoryview) -> int: ...  # 零拷贝 [仅二进制]
    async def chunk(chunk_size: int, *, buf: bytearray | memoryview | None = None) -> AsyncGenerator[memoryview, None]: ...  # 流式分块 [仅二进制]
    async def read_at(offset: int, size: int = -1) -> bytes: ...  # 位置读（pread 语义），不动文件位置 [仅二进制]
    async def read_many(spans: Iterable[tuple[int, int]]) -> list[bytes]: ...  # 批量位置读，单事件循环周期提交 [仅二进制]

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

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

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

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

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

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

Supported Modes

Mode	Description
`"r"`, `"rb"`	Read (text/binary)
`"w"`, `"wb"`	Write (text/binary)
`"a"`, `"ab"`	Append (text/binary)
`"x"`, `"xb"`	Exclusive create (text/binary)
`+` added	Read/write combinations

Configuration Functions

def configure(options: dict) -> None: ...      # Unified configuration
def get_config() -> dict: ...                   # Get current configuration
def reset_config() -> None: ...                 # Reset to defaults
def get_backend_info() -> dict: ...             # Get backend information

File Wrapping

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

Wrap an existing file descriptor (int) or a file-like object with fileno() as an AsyncFile, backed by the optimal platform backend. Binary mode only.

Pool Management

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

Use drain_handle_pool() / drain_buffer_pool() to release pooled resources at runtime — useful after bulk tempfile operations or between benchmark rounds.

🧪 Performance Comparison

Scenario 1: Crawlee-style Dataset Append (open/write/close per record)

Simulating Crawlee's Dataset append pattern — 5,000 records, 50 concurrent writers, each writing a single line and closing the file:

Platform	ayafileio	aiofiles	Speedup
Windows (NVMe SSD)	41,336 items/s	9,658 items/s	4.28x
Linux (NVMe SSD)	17,688 items/s	11,455 items/s	1.54x
macOS (NVMe SSD)	29,837 items/s	25,522 items/s	1.17x
Windows (6yr old HDD)	20,251 items/s	13,011 items/s	1.56x

Key findings:

On Windows enterprise SSD, ayafileio achieves 42x lower P99 latency (0.044ms vs 1.854ms)
aiofiles shows 96.7% jitter under load; ayafileio only 16.2%
Even on degraded hardware, ayafileio maintains predictable performance

Test environment: Windows 10/11, Ubuntu 22.04, macOS 14; GitHub Actions enterprise NVMe SSD

Scenario 2: Single-file High-concurrency Random Read

The true test of async I/O — 100,000 concurrent tasks performing random 256B reads on a single shared file handle. No open/close overhead, pure I/O path comparison:

Library	1K concur	10K concur	50K concur	100K concur
ayafileio (IOCP)	7,487 ops/s	46,616 ops/s	28,165 ops/s	19,290 ops/s
aiofiles (threadpool)	7,706 ops/s	7,320 ops/s	2,131 ops/s	2,130 ops/s
sync threadpool	9,492 ops/s	9,469 ops/s	8,840 ops/s	8,660 ops/s
ayafileio vs aiofiles	1.0x	6.4x	13.2x	9.1x

Key findings:

At low concurrency (1K), all approaches are similar — IOCP setup overhead is amortized
At 10K+ concurrency, aiofiles' thread pool saturates; throughput drops as concurrency increases — from 7,706 down to 2,130 ops/s (72% degradation)
ayafileio with IOCP gains throughput at 10K (46,616 ops/s) due to batched completion harvesting via GetQueuedCompletionStatusEx
At 100K concurrency, ayafileio is 9.1x faster than aiofiles on the same HDD
The synchronous threadpool (mimicking aiofiles' approach) flatlines at ~8,800 ops/s regardless of concurrency — thread contention ceiling

Test environment: Windows 10, Python 3.14.5, WDC WD10EZEX 7200RPM HDD, 20MB file, 256B random reads

Scenario 3: Extreme Concurrency Stress — 500,000 Concurrent Reads

500,000 asyncio tasks all reading from a single file via IOCP — testing the library's absolute concurrency ceiling:

Metric	Value
Concurrent tasks	500,000
Completion time	21.6s
Throughput	23,116 ops/s
Peak memory (RSS)	~583 MB
Errors	0
Exceptions	0

ayafileio handles half a million concurrent IOCP reads on a single file handle with zero errors. The dual-IOCP worker architecture (2 threads total) processes all 500K completions while aiofiles would require thousands of threads for the same workload — and still be slower.

Tuning: Default Configuration is Optimal

We tested 14 different configuration combinations (iocp_batch_size, buffer_size, buffer_pool_max, io_worker_count) on the HDD at 100K concurrency. Result: every configuration scored within ±3% of the default. The library's auto-tuned defaults already saturate the disk's physical I/O limit — there is no software bottleneck left to tune.

For NVMe SSDs with >500K IOPS capability, increasing iocp_batch_size to 128–256 and buffer_size to 128KB may yield additional gains. Use ayafileio.configure() to experiment:

ayafileio.configure({
    "iocp_batch_size": 128,
    "buffer_size": 131072,
})

🤝 Contributing

Contributions are welcome! Please:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Add tests
Ensure benchmarks pass
Open a pull request

📄 License

MIT License — see LICENSE for details.

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

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BLAKE2b-256	`c6317513a88c31165166ca99fb025a94ee36e9710fc427c1c2ebf8841fc67f7f`

Algorithm	Hash digest
SHA256	`2d115f234eee3f55e6abc7374fda21651f8a4fe7f84c1cb6b0b3bf678c50d765`
MD5	`2f07046cf7eab369de079e0cc357a76e`
BLAKE2b-256	`ba9af6c8b74f1066160658747ef535f1c3a05991a9a3c2a466918c726451b28a`

Algorithm	Hash digest
SHA256	`bec43c10cc6537969c1c9bc843c67f55b84a95533b33cf43b36bb3beb06bba6d`
MD5	`a581edffe4e1d441d632c9d50ff95f23`
BLAKE2b-256	`dccd785679ba28bc99b9f731125f43fea456bd20d7fc5dff74aad725abc8e5b5`

Algorithm	Hash digest
SHA256	`1ba6b6cd3f495d202105744788f5652d5942bf0e6e575dcf19840f98a3b3336f`
MD5	`560d81871f4dc2a83c743323aea8b018`
BLAKE2b-256	`e81e645d4ddaaad0c01d762bfb19024d08579e260489b074fa961d07fcfe67d5`

Algorithm	Hash digest
SHA256	`9afe5411d2303ee485069066d538ac4f3ba8aba1838ce9b303e8681db3932745`
MD5	`16223f379ca29195ee45e67c3e1b1d43`
BLAKE2b-256	`50d13c85f746fc068202625a94ea8aacd4be793cf2442282d3c9f83e9ec8f662`

Algorithm	Hash digest
SHA256	`04788a00bf073858b6360c0ba3512804498841fb7840e55a7b23301e69e16efe`
MD5	`a8039a99d79c78c4c71259fe7a179eda`
BLAKE2b-256	`985ea8aee9a6c81df8888b0f3b0e8cb7cd47b88881369993f3490f1f15f88933`

Algorithm	Hash digest
SHA256	`62cf7dd42936361872dae90922ad859fd7f2c7bf59e1aa640f9645d4df2ad7fe`
MD5	`04239b64bb74fa24fdfc6f9c0cc162bf`
BLAKE2b-256	`95f35e3e46be6cf3572dc017c4c6cef1c7bab1a010274fb96475b6e35a549644`

Algorithm	Hash digest
SHA256	`f31b539bd785b1d75e04ca552a9946a892c2d2b04dfb192d38924a2540655bd3`
MD5	`7de419f685a092d0c99b0767730b6930`
BLAKE2b-256	`633fc7b4690393e29ba4650192b30e4f09b19c141ab6227f7e5f604a5c63063c`

Algorithm	Hash digest
SHA256	`5a169bd72d5e03eda8f655226adc38a47eac888c94ac128c53f06136191f7fcd`
MD5	`9a6940742949740843754a715a7af078`
BLAKE2b-256	`13732c44cb49687f55e4865def58d96c7e1dd2f1cf9b41e7c771cc00b75e54cf`

Algorithm	Hash digest
SHA256	`bd80759bc8fac908d42e6d9a54095e7cd3d4e3873f7c1af62788ee097e0343ae`
MD5	`69a1e91095640ba01dbc05094e082db0`
BLAKE2b-256	`bd6704ec6fabcdb59fa8e0cc93af28bc0ca5d22d7d4c5f8cf04ce18399e5c49c`

Algorithm	Hash digest
SHA256	`e8a7f298c8f4423aec78340bef078e9b53ab640b050c3fade4aef51b539de019`
MD5	`48a1d7fe456858781592b1618da4bc72`
BLAKE2b-256	`359b9d6121d4145e167bdb5acbc85de004da8237bb206e17d61c289c6f578355`

Algorithm	Hash digest
SHA256	`377d3107c0d22b764455bdf149b5a8abb63a2fc173757eedf3e392efbfa6fa1d`
MD5	`68c291e80d801d8686f8d2ca8f8c3ddb`
BLAKE2b-256	`d989990a924f9d468e2a7b5dc27ca4bb5dae2e2e838c2990ec9428caa6254b1e`

Algorithm	Hash digest
SHA256	`2bfda8615fa735c25ad9bc004e521be2ccf560fbe37b3dc5bde0433691965f54`
MD5	`fc42703874304461600f8869120559e6`
BLAKE2b-256	`f1716ba042b35b2fc00d301aeeb40a0e22a5b34099e3eca6966886288c720cea`

Algorithm	Hash digest
SHA256	`c17574338c403a62d228a277ac88b79ea981c0f003fa3fae664acce30ff3a679`
MD5	`6a37a15dd2d51b0545e7321ce42e8397`
BLAKE2b-256	`56a34eaa70cc5b741879ea1053f6b4c003205148e85a4d5606bf3454e90eeebd`

Algorithm	Hash digest
SHA256	`3572b669364b3cbc8a3c523162e7a9572853eb25201d748504bb5c51d47349b8`
MD5	`1a5303a93814eac12f82b3ab49c32715`
BLAKE2b-256	`2affb1a6037d834bf3dd9cce5056e67f04d62091c169122fb4b51e51bbe726d5`

ayafileio 1.5.0

ayafileio

changes

🏆 True Async on All Three Major Platforms

📸 Key Features

🛠️ Installation

🚀 Quick Start

⚡ Performance Best Practice

🔍 Backend Information

⚙️ Unified Configuration

Configuration Options

📚 API Reference

AsyncFile class

Supported Modes

Configuration Functions

File Wrapping

Pool Management

🧪 Performance Comparison

Scenario 1: Crawlee-style Dataset Append (open/write/close per record)

Scenario 2: Single-file High-concurrency Random Read

Scenario 3: Extreme Concurrency Stress — 500,000 Concurrent Reads

Tuning: Default Configuration is Optimal

🤝 Contributing

📄 License

Project links

Key dates

1 maintainer

Credits

GitHub Statistics

License

Requires

Classifiers

Download files

Source Distributions

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Algorithm	Hash digest
SHA256	`5b1cc03caffa16a8fbec43ecc45e9fcbb9c623558ce66ba36617a37a387d8579`
MD5	`35ff15832736979d22afccfb3a5fdfe1`
BLAKE2b-256	`cf4024de3f1fb7bd13d15fd37cca0416ea795423b9f5e2eae2778370c1ed7bfd`

Algorithm	Hash digest
SHA256	`e9c6aaf2ed163ec8c0ae5454ef10d6f50de96d8ab36296d807ff3103a9951a1d`
MD5	`d361b6ecfcb8486f19ae9e4a629791e0`
BLAKE2b-256	`682cae0889dc639c47580a1f2cd590106b50f9baccab16c1edb1c759ae609a3f`

Algorithm	Hash digest
SHA256	`6a60ec9b979f795c863f14c3567a49e7f59ee421adb4b76277c27df61019a201`
MD5	`6713bc6765ac565f07901aea7d4187de`
BLAKE2b-256	`1290b7f35022426ae2fb3d8de28c701c386f55de4229368298721418691b168e`

Algorithm	Hash digest
SHA256	`a63759d63ba0fa3a67cb4b4a5a932f3ad8a5adf68e84dd298bfcba089413e87a`
MD5	`e90c0e4c3370ee0eb3fbe0e55a1f6dfd`
BLAKE2b-256	`a4f43fdc46c3d264012297e842a6b46d541d13b75bbf107fa72b50e1bd786ad0`

Algorithm	Hash digest
SHA256	`77e0853b7c43ee94ffa82a66b1d9fa254ebcbb9d4ba8b8fe1a8ae0e77d5abb53`
MD5	`79c4161d0990265f75c0c6b2c9842c21`
BLAKE2b-256	`906aac20cb911f3be1ba73d5a0c9193635702c08f73cdd6bb4d757b5f5b1c990`

Algorithm	Hash digest
SHA256	`1cd451d00d1975165b19bcb73b9a7e3cbff28ca751ffa0ea206162cdc3048c81`
MD5	`9b46508891b109fbab7de6760451eec1`
BLAKE2b-256	`7152b7530fe3dfbc47144db33912dc6b108b65d3b0d6064d400b4dd3ae08745a`

Algorithm	Hash digest
SHA256	`2df38492c8b89d276eba07c36db11a016d881cb42a463647719940899556b50f`
MD5	`a8337f7d5c476ba9a462dba31cf80e51`
BLAKE2b-256	`f5ee93821f052c9a44dc7c1b8fdf205d138e1877aea8a1e416ee6189c82498ef`

Algorithm	Hash digest
SHA256	`908e80372f9e8a01efe9dd58d88bcf9b097c25c9dbbcccda88cf5821684abe8b`
MD5	`7a60d67232a329d29042eed41f8de445`
BLAKE2b-256	`57c1563320300ae66b20423feadcc9a9ee97aa407da99a736c4bfcbdc701f007`

Algorithm	Hash digest
SHA256	`d2581c2c641b3d768125d865f8165c45313564550a7b34f72fcd311b3e6ecbca`
MD5	`4350a36048d105bee2392ef64068682a`
BLAKE2b-256	`53a3cedbb0a2430e25e5e15a14197958e6da60b079503cbae74bd35bcd23e870`

Algorithm	Hash digest
SHA256	`2a92b30186aae82fc2a30bfd98d8586288514dc4468f0ab2b8d671d6e9b19f2e`
MD5	`020116cb271eb5eb71adc45ab9f7401e`
BLAKE2b-256	`6639697bc509cfaaaed53579d25e5dfbf3b9b76b1c6a89f9f6ac929964b3c2fd`