rapcsv 0.1.2

Streaming async CSV — no fake async, no GIL stalls.

rapcsv

Streaming async CSV — no fake async, no GIL stalls.

Overview

rapcsv provides true async CSV reading and writing for Python, backed by Rust and Tokio. Unlike libraries that wrap blocking I/O in async syntax, rapcsv guarantees that all CSV operations execute outside the Python GIL, ensuring event loops never stall under load, even when processing large files.

Roadmap Goal: Achieve drop-in replacement compatibility with aiocsv, enabling seamless migration with true async performance. See docs/ROADMAP.md for details.

Why rap*?

Packages prefixed with rap stand for Real Async Python. Unlike many libraries that merely wrap blocking I/O in async syntax, rap* packages guarantee that all I/O work is executed outside the Python GIL using native runtimes (primarily Rust). This means event loops are never stalled by hidden thread pools, blocking syscalls, or cooperative yielding tricks. If a rap* API is async, it is structurally non-blocking by design, not by convention. The rap prefix is a contract: measurable concurrency, real parallelism, and verifiable async behavior under load.

See the rap-manifesto for philosophy and guarantees.

Features

• ✅ True async CSV reading and writing
• ✅ Streaming support for large files
• ✅ Native Rust-backed execution (Tokio)
• ✅ Zero Python thread pools
• ✅ Event-loop-safe concurrency under load
• ✅ GIL-independent I/O operations
• ✅ Verified by Fake Async Detector

Requirements

• Python 3.8+ (including Python 3.13 and 3.14)
• Rust 1.70+ (for building from source)

Installation

pip install rapcsv

Building from Source

git clone https://github.com/eddiethedean/rapcsv.git
cd rapcsv
pip install maturin
maturin develop

---

Usage

import asyncio
from rapcsv import Reader, Writer

async def main():
    # Write CSV file (one row per Writer instance for MVP)
    writer = Writer("output.csv")
    await writer.write_row(["name", "age", "city"])
    
    # Read CSV file (reads first row)
    reader = Reader("output.csv")
    row = await reader.read_row()
    print(row)  # Output: ['name', 'age', 'city']

asyncio.run(main())

Writing Multiple Rows

import asyncio
from rapcsv import Writer

async def main():
    # Write multiple rows with a single Writer instance (file handle reused)
    writer = Writer("output.csv")
    rows = [
        ["name", "age", "city"],
        ["Alice", "30", "New York"],
        ["Bob", "25", "London"],
    ]
    
    for row in rows:
        await writer.write_row(row)
    
    # Verify file contents
    with open("output.csv") as f:
        print(f.read())

asyncio.run(main())

Note: The Writer reuses the file handle across multiple write_row() calls for efficient writing. The Reader maintains position state across read_row() calls and streams data incrementally without loading the entire file into memory.

Using Context Managers

import asyncio
from rapcsv import Reader, Writer

async def main():
    # Using context managers for automatic resource cleanup
    async with Writer("output.csv") as writer:
        await writer.write_row(["name", "age", "city"])
        await writer.write_row(["Alice", "30", "New York"])
    
    async with Reader("output.csv") as reader:
        row = await reader.read_row()
        print(row)  # Output: ['name', 'age', 'city']

asyncio.run(main())

aiocsv Compatibility

rapcsv provides compatibility aliases for aiocsv:

from rapcsv import AsyncReader, AsyncWriter  # aiocsv-compatible names

async def main():
    async with AsyncWriter("output.csv") as writer:
        await writer.write_row(["col1", "col2"])
    
    async with AsyncReader("output.csv") as reader:
        row = await reader.read_row()
        print(row)

asyncio.run(main())

API Reference

Reader(path: str)

Create a new async CSV reader.

Parameters:

• path (str): Path to the CSV file to read

Example:

reader = Reader("data.csv")

Reader.read_row() -> List[str]

Read the next row from the CSV file.

Returns:

• List[str]: A list of string values for the row, or an empty list if EOF

Raises:

• IOError: If the file cannot be read
• CSVError: If the CSV file is malformed or cannot be parsed

Note: The Reader maintains position state across read_row() calls, reading sequentially through the file. Files are streamed incrementally without loading the entire file into memory.

Context Manager Support:

async with Reader("data.csv") as reader:
    row = await reader.read_row()

Writer(path: str)

Create a new async CSV writer.

Parameters:

• path (str): Path to the CSV file to write

Example:

writer = Writer("output.csv")

Writer.write_row(row: List[str]) -> None

Write a row to the CSV file.

Parameters:

• row (List[str]): A list of string values to write as a CSV row

Raises:

• IOError: If the file cannot be written

Note: The Writer reuses the file handle across multiple write_row() calls for efficient writing. Proper RFC 4180 compliant CSV escaping and quoting is applied automatically.

Writer.close() -> None

Explicitly close the file handle and flush any pending writes.

Example:

writer = Writer("output.csv")
await writer.write_row(["col1", "col2"])
await writer.close()

Context Manager Support:

async with Writer("output.csv") as writer:
    await writer.write_row(["col1", "col2"])
    # File is automatically closed and flushed on exit

Exception Types

CSVError

Raised when a CSV parsing error occurs (e.g., malformed CSV file).

CSVFieldCountError

Raised when there's a mismatch in the number of fields between rows.

Testing

rapcsv includes comprehensive test coverage with tests adapted from the aiocsv test suite to validate compatibility:

# Run all tests
pytest

# Run aiocsv compatibility tests
pytest test_aiocsv_compatibility.py -v

# Run all tests with coverage
pytest --cov=rapcsv --cov-report=html

The test suite includes:

• Basic read/write operations
• Context manager support
• Quoted fields with special characters
• Large file streaming
• Concurrent operations
• aiocsv compatibility validation

Benchmarks

This package passes the Fake Async Detector. Benchmarks are available in the rap-bench repository.

Run the detector yourself:

pip install rap-bench
rap-bench detect rapcsv

Roadmap

See docs/ROADMAP.md for detailed development plans. Key goals include:

• Drop-in replacement for aiocsv (Phase 1)
• Full streaming support for large files
• Comprehensive CSV dialect support
• Zero-copy optimizations

Related Projects

• rap-manifesto - Philosophy and guarantees
• rap-bench - Fake Async Detector CLI
• rapfiles - True async filesystem I/O
• rapsqlite - True async SQLite

Limitations

Current limitations:

• No advanced CSV dialect support (delimiters, quote characters, line terminators) - planned for Phase 2
• No header detection or manipulation - planned for Phase 2
• Not designed for synchronous use cases

Phase 1 improvements:

• ✅ Streaming file reading - files are read incrementally without loading entire file into memory
• ✅ Context manager support (async with) for automatic resource cleanup
• ✅ CSV-specific exception types (CSVError, CSVFieldCountError)
• ✅ Improved error handling with detailed error messages
• ✅ close() method for explicit file handle closure
• ✅ aiocsv compatibility aliases (AsyncReader, AsyncWriter)
• ✅ Comprehensive test coverage (29 tests including aiocsv compatibility tests)
• ✅ aiocsv test suite migration - tests adapted from aiocsv test suite

Changelog

v0.1.1 (2026-01-16)

Python 3.14 Support:

• ✅ Added Python 3.14 support with ABI3 forward compatibility
• ✅ Updated CI/CD workflows to test and build for Python 3.14

Python 3.13 Support:

• ✅ Added Python 3.13 support with ABI3 forward compatibility
• ✅ Updated CI/CD workflows to test and build for Python 3.13
• ✅ Fixed exception handling for ABI3 compatibility (using create_exception! macro)
• ✅ Explicitly registered exception classes in Python module

Bug Fixes:

• Fixed exception registration issue where exceptions created with create_exception! were not accessible from Python

Compatibility:

• Python 3.8 through 3.14 supported
• All platforms: Ubuntu (x86-64, aarch64), macOS (aarch64, x86-64), Windows (x86-64, aarch64)

v0.1.0 (2025-01-12)

Version 0.1.0 - Phase 1 Complete:

• ✅ Streaming file reading - files are read incrementally without loading entire file into memory
• ✅ Context manager support (async with) for automatic resource cleanup
• ✅ CSV-specific exception types (CSVError, CSVFieldCountError)
• ✅ Improved error handling with detailed error messages
• ✅ close() method for explicit file handle closure
• ✅ aiocsv compatibility aliases (AsyncReader, AsyncWriter)
• ✅ Comprehensive test coverage (29 tests including aiocsv compatibility tests)
• ✅ aiocsv test suite migration - tests adapted from aiocsv test suite

Previous improvements (v0.0.2):

• ✅ Security fixes: Upgraded dependencies (pyo3 0.27, pyo3-async-runtimes 0.27), fixed CSV injection vulnerability
• ✅ Position tracking: Reader now maintains position state across read_row() calls
• ✅ File handle reuse: Writer reuses file handle across multiple write_row() calls
• ✅ CSV escaping: Implemented RFC 4180 compliant CSV escaping and quoting
• ✅ Input validation: Added path validation (non-empty, no null bytes)
• ✅ Improved error handling: Enhanced error messages with file path context
• ✅ Type stubs: Added .pyi type stubs for better IDE support and type checking

Roadmap: See docs/ROADMAP.md for planned improvements. Our goal is to achieve drop-in replacement compatibility with aiocsv while providing true async performance with GIL-independent I/O.

Contributing

Contributions are welcome! Please see our contributing guidelines (coming soon).

License

MIT