python-backpressure-flow-control

What problem does it solve?

Manages how asyncio-based systems handle backpressure, preventing memory overloads and ensuring predictable performance during high load.

Core Features & Use Cases

• Producer flow control with asyncio.Semaphore to limit concurrent operations.
• Queue-based buffering using asyncio.Queue with configurable size and drop/block strategies.
• Worker pools to process queued tasks in parallel while respecting capacity constraints.
• Metrics integration for queue depth and dropped message tracking to monitor backpressure conditions.
• Use Case: Suitable for designing high-throughput data pipelines that must prevent OOM errors and maintain stability under load during production deployment.

Quick Start

Implement backpressure checks when designing fan-out methods publishing messages asynchronously, adjusting queue sizes and worker counts based on load conditions.