Styx: Transactional Stateful Functions on Streaming Dataflows

Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requiring inefficient transactional protocols that increase execution latency. In this paper, we present Styx, a novel dataflow-based SFaaS runtime that executes serializable transactions consisting of stateful functions that form arbitrary call-graphs with exactly-once guarantees. Styx extends a deterministic transactional protocol by contributing: i) a function acknowledgment scheme to determine transaction boundaries required in SFaaS workloads, ii) a function-execution caching mechanism, and iii) an early commit-reply mechanism that substantially reduces transaction execution latency. Experiments with the YCSB, TPC-C, and Deathstar benchmarks show that Styx outperforms state-of-the-art approaches by achieving at least one order of magnitude higher throughput while exhibiting near-linear scalability and low latency.

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Publication Details

Journal
Proceedings of the ACM on Management of Data
Published
2025-06-17
DOI
https://doi.org/10.1145/3725363
Citations
3
Primary Topic
Distributed systems and fault tolerance
Type
article
Field-Weighted Citation Impact
4.46
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article

Styx: Transactional Stateful Functions on Streaming Dataflows

Asterios Katsifodimos, Marios Fragkoulis, George C. Christodoulou, Kyriakos Psarakis et al.
3 citations
Proceedings of the ACM on Management of Data
Distributed systems and fault tolerance
4.46
article

Styx: Transactional Stateful Functions on Streaming Dataflows

Asterios Katsifodimos, Marios Fragkoulis, George C. Christodoulou, Kyriakos Psarakis, Georgios Siachamis
article en
3 citations

Abstract

Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Functions-as-a-Service (SFaaS) paradigm aims to serve these use cases. However, existing approaches provide weak transactional guarantees or perform expensive external state accesses requiring inefficient transactional protocols that increase execution latency. In this paper, we present Styx, a novel dataflow-based SFaaS runtime that executes serializable transactions consisting of stateful functions that form arbitrary call-graphs with exactly-once guarantees. Styx extends a deterministic transactional protocol by contributing: i) a function acknowledgment scheme to determine transaction boundaries required in SFaaS workloads, ii) a function-execution caching mechanism, and iii) an early commit-reply mechanism that substantially reduces transaction execution latency. Experiments with the YCSB, TPC-C, and Deathstar benchmarks show that Styx outperforms state-of-the-art approaches by achieving at least one order of magnitude higher throughput while exhibiting near-linear scalability and low latency.

Proceedings of the ACM on Management of DataVol. 3(3)
Delft University of Technology (NL)
Partnerships for the goals
Openalex Percentile: Top 5%
Distributed systems and fault tolerance
4.46
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Styx: Transactional Stateful Functions on Streaming Dataflows — Asterios Katsifodimos, Marios Fragkoulis, et al. · Proceedings of the ACM on Management of Data (2025) | TGRS Research Map | TGRS