shm-ring-buffer 1.2.0

A versatile, high-performance ring buffer implementation for Python supporting standard lists, NumPy arrays, and shared memory for inter-process communication.

Ring Buffer

A high-performance, zero-copy ring buffer implementation for Python. This library provides a seamless way to handle streaming data using standard NumPy arrays or Shared Memory for ultra-fast inter-process communication (IPC).

Key Features

• Zero-Copy Design: Read operations return a view of the buffer rather than a copy, minimizing overhead.

• Multiprocessing Ready: Built-in support for SharedMemory to move data between processes at lightning speed.

• NumPy Integration: Native support for NumPy arrays, allowing for direct mathematical operations on buffer data.

• Lock-Free Performance: Optimized for Single-Producer Single-Reader (SPSR) scenarios.

🚀 Get Started

Prerequisites

Python: 3.11 or higher

Optional: numpy (required for NumPy-specific buffer classes)

Installation

Using pip:

# Basic installation
pip install shm-ring-buffer

# With NumPy support
pip install shm-ring-buffer[numpy]

Using poetry:

# Basic installation
poetry add shm-ring-buffer

# With NumPy support
poetry add shm-ring-buffer -E numpy

🛠 Usage & Architecture

[!WARNING] Single-Producer Single-Reader (SPSR): This implementation is lock-free to maximize throughput. It is not thread-safe for multiple concurrent writers or multiple concurrent readers. Ensure your architecture follows the SPSR pattern.

Shared Memory IPC

Shared memory is the fastest method for IPC in Python. This library implements two primary shared memory buffers:

BytesShmRingBuffer: Optimized for raw byte streams.

NumpyShmRingBuffer: Optimized for structured numerical data.

Implementation Note:

One process must initialize the buffer with create=True, while the consumer/other side should set create=False. Always call .close() to properly release system resources.

The Zero-Copy Workflow

To maintain high performance, the buffer returns a view of the data.

Read: Access the current item.

Process: Use the data (or copy it if you need it to persist).

Release: You must call .release() after processing to signal that the slot is available for the producer to overwrite.

🗺️ Roadmap

• High-performance multiprocessing.shared_memory implementation.

• CuPy implementation for hardware acceleration via NVIDIA GPUs (CUDA).

👥 Authors

• Loïc Combis - GitHub | LinkedIn