A system needed to ingest thousands of events per second from multiple sources — API webhooks, message queues, and direct database polling — without dropping data or blocking the main application flow. The challenge was handling variable throughput, backpressure, and partial failures gracefully.
Architecture
asyncio event loop with semaphore-based concurrency control to limit resource usage.
Backpressure handling with bounded queues — when queues fill up, producers are slowed via backpressure signals.
Dead letter queue (Redis list) for events that fail processing after max retries.
Batch processing — database writes are batched (every 100ms or 1000 events) for throughput.
Technical Deep Dive
Python asyncio patterns — asyncio.Queue for producer-consumer, asyncio.Semaphore for concurrency limiting, asyncio.gather for parallel processing.
Connection pooling — asyncpg connection pool configured with min/max connections, connection health checks, and automatic reconnection.
Monitoring — prometheus metrics for queue depth, processing latency, error rates, and throughput.
Key Takeaway
This project demonstrates deep understanding of async programming in Python, fault-tolerant system design, and practical experience with backpressure patterns and high-throughput event processing.