Techniques You Should Know as a Kafka Streams Developer

Kafka Streams developers can enhance the resilience and distribution of their applications by utilizing advanced techniques discovered during the development of Kestra, an open-source data orchestration platform. Key strategies include using the same Kafka topic for both source and destination to maintain high throughput, and implementing a custom joiner to manage state updates without losing data. The Kestra team leveraged Kafka's consumer groups to distribute workloads across multiple backends, avoiding single points of failure and ensuring exactly-once processing. They also devised methods to detect dead Kafka consumers by utilizing partition monitoring, and addressed performance issues related to state store operations by creating in-memory maps for data retrieval. Initially, having a single large Kafka Stream presented challenges like monitoring difficulties and lag, which were mitigated by splitting the stream into multiple topics for better management and understanding of consumer behavior. These insights demonstrate how Kestra adapted Kafka Streams to fulfill its specific use case demands efficiently.