Incremental Snapshotting in Transactional Dataflow SFaaS Systems

Master thesis (2023)

Authors

N. Gavalas Electrical Engineering, Mathematics and Computer Science

Contributors

A Katsifodimos Web Information Systems - EEMCS (supervisor 1)
G. Gousios Software Technology (coach)
K. Psarakis Web Information Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Nikos Gavalas
Data Management Distributed Systems Cloud Computing SFaaS Database Systems Dataflow Engines
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Published Date

30-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

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.