Real-time Analytics
High-throughput data ingestion pipeline with real-time websocket updates.
System Overview
This architecture handles massive streams of incoming events and processes them in real-time to provide instantaneous insights to users via interactive dashboards.
Technical Implementation
Events are ingested through a robust gateway and published to a Kafka-like message broker. A stream processing engine (e.g., Flink or Spark) consumes these events, performs aggregations, and stores time-series data. Simultaneously, a WebSocket server broadcasts updates to connected clients for live visualization.
Key Considerations
What's the benefit of using Kafka here?
Kafka provides high-throughput, fault-tolerant message persistence, ensuring no data is lost during spikes in ingestion before it can be processed.
How do WebSockets handle many users?
The WebSocket server is horizontally scaled behind a load balancer with sticky sessions or a shared Pub/Sub backplane like Redis.