The adoption of the serverless architecture and the Function-as-a-Service model has significantly increased in recent years, with more enterprises migrating their software and hardware to the cloud. However, most applications require state management, leading to the use of external databases. To alleviate the burden of state management, there are systems known as SFaaS (Stateful Function-as-a-Service) that provide stateful functions. Despite their benefits, SFaaS systems still face challenges such as the need for transactional logic. Stateful streaming dataflow engines offer promising capabilities for implementing transactional SFaaS systems due to their exactly-once message delivery guarantees and global state management. Key-value stores serve as embedded databases in this architecture, making it crucial to carefully evaluate available options for suitable types of key-value stores.
This work focuses on the implementation and evaluation of three distinct types of log-structured key-value stores within the context of serving as state-management backends for transactional dataflow systems. A key aspect of our implementations is the incorporation of efficient incremental snapshotting functionality. We explore the performance and suitability of these key-value stores in managing state and supporting transactional operations in dataflow systems.