A Chinese IoT platform's engineering team recently published a detailed retrospective on their real-time stream processing architecture, built with Kafka and Flink. The post covers the full pipeline from device message ingestion through Kafka topics, to Flink jobs that handle stateful processing, windowing, and exactly-once semantics. Key challenges included managing backpressure under high throughput, optimizing checkpointing for low latency, and handling schema evolution across millions of devices. The team also shared their monitoring and alerting setup using Prometheus and Grafana. This retrospective is particularly valuable for backend and data engineers designing scalable IoT backends, as it provides concrete trade-offs and lessons learned from a production deployment. The article avoids generic advice and focuses on specific engineering decisions, making it a strong signal for the global developer community.
This article shares a real-world engineering retrospective on building a real-time stream processing architecture for an IoT platform using Kafka and Flink. It covers message ingestion, state management, and fault tolerance, offering valuable insights for engineers working on similar systems.