Elevating Kafka: Driving operational excellence with Albertsons + Forrester | Watch Webinar


클라우드 네이티브 스트리밍 시스템의 상태 관리 재고하기

« Current 2022

Stream processing is becoming increasingly essential for extracting business value from data in real-time. To achieve strict user-defined SLAs under constantly changing workloads, modern streaming systems have started taking advantage of the cloud for scalable and resilient resources. New demand opens new opportunities and challenges for state management, which is at the core of streaming systems. Existing approaches typically use embedded key-value storage so that each worker can access it locally to achieve high performance. However, it requires an external durable file system for checkpointing, is complicated and time-consuming to redistribute state during scaling and migration, and is prone to performance throttling. Therefore, we propose shared storage based on LSM-tree. State gets stored at cloud object storage and seamlessly makes itself durable, and the high bandwidth of cloud storage enables fast recovery. The location of a partition of the state decouples with compute nodes thus making scaling straightforward and more efficient. Compaction in this shared LSM-tree is now globally coordinated with opportunistic serverless boosting instead of relying on individual compute nodes. We design a streaming-aware compaction and caching strategy to achieve smoother and better end-to-end performance.

Related Links

How Confluent Completes Apache Kafka eBook

Leverage a cloud-native service 10x better than Apache Kafka

Confluent Developer Center

Spend less on Kafka with Confluent, come see how