When handling high-frequency market data from the Pakistan Stock Exchange (PSX), standard HTTP polling introduces intolerable latency, redundant server overhead, and rate-limiting bottlenecks. To deliver sub-second order book updates and tick-by-tick price streaming to web and mobile clients, I engineered a high-concurrency event-driven WebSocket pipeline capable of processing incoming tick spikes without packet loss.
The Technical Challenge
- High Ingestion Volume: Market opening and closing bells generate traffic spikes exceeding thousands of tick events per second across hundreds of tracked equities.
- Sub-Second Latency Requirement: Traders require real-time bid/ask order book updates with less than 50ms of client-side latency.
- Socket Connection Bloat: Managing thousands of persistent, concurrent client WebSocket connections without exhausting server RAM or file descriptors.
Architectural Breakdown
The architecture is built for decoupling and horizontal scalability, separating heavy data ingestion logic from client delivery nodes.
1. Ingestion Layer & Buffer Management
Implemented a non-blocking ingestion daemon (Node.js/Go) to parse incoming binary tick streams. Utilized an in-memory ring-buffer strategy to absorb sudden market volatility spikes, preventing backpressure on downstream components.
2. Event-Driven Distribution (Pub/Sub)
Decoupled the data ingestion service from the client gateway using a highly efficient Redis Pub/Sub cluster. All raw payloads are standardized into lightweight JSON buffers before publication, reducing network payload sizes by over 60%.
3. Scaling Concurrent WebSockets
Deployed load-balanced Socket.io / WS gateways running behind an Nginx reverse proxy. The server instances were optimized with tuned worker_rlimit_nofile and TCP keep-alive parameters to maximize concurrent connections per node.
Key Metrics & Results
- Reduced Latency: Cut average tick delivery latency from 1.2s (polling) to under 35ms (WebSocket streaming).
- System Reliability: Achieved 99.99% availability during peak market trading hours.
- Resource Efficiency: Reduced total CPU overhead by 45% compared to legacy polling mechanisms.
Originally published at ctousman.com.
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