How Real-Time Stream Processing Safely Scales with ksqlDB

What this post added

This post explains the distributed architecture of ksqlDB, detailing how it scales workloads by distributing Kafka topic partitions across multiple ksqlDB servers. It elaborates on the use of Kafka consumer groups for partition assignment, load balancing, and fault tolerance. The post also differentiates between stateless and stateful operations, explaining their respective recovery mechanisms, and discusses how to optimize cluster sizing and thread parallelism for performance.