Serverless Abstractions for Short-Running, Lightweight Streams
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912940070797312 |
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| author | Carl, Natalie Kowallik, Niklas Stahl, Constantin Schirmer, Trever Pfandzelter, Tobias Bermbach, David |
| author_facet | Carl, Natalie Kowallik, Niklas Stahl, Constantin Schirmer, Trever Pfandzelter, Tobias Bermbach, David |
| contents | Serverless computing and stream processing represent two dominant paradigms for event-driven data processing, yet both make assumptions that render them inefficient for short-running, lightweight, and unpredictable streams that require stateful processing. We propose stream functions as a novel extension of the Function-as-a-Serivce model that treat short streams as the unit of execution, state, and scaling. Stream functions process streams via an iterator-based interface, enabling seamless inter-event logic while retaining the elasticity and scale-to-zero capabilities offered by serverless platforms. Our evaluation shows that stream functions reduce the processing overhead by ~99 % compared to a mature stream process- ing engine in a video-processing use case. By providing comparable performance to serverless functions with stream semantics, stream functions provide an effective and efficient abstractions for a class of workloads underserved by existing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03089 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Serverless Abstractions for Short-Running, Lightweight Streams Carl, Natalie Kowallik, Niklas Stahl, Constantin Schirmer, Trever Pfandzelter, Tobias Bermbach, David Distributed, Parallel, and Cluster Computing Serverless computing and stream processing represent two dominant paradigms for event-driven data processing, yet both make assumptions that render them inefficient for short-running, lightweight, and unpredictable streams that require stateful processing. We propose stream functions as a novel extension of the Function-as-a-Serivce model that treat short streams as the unit of execution, state, and scaling. Stream functions process streams via an iterator-based interface, enabling seamless inter-event logic while retaining the elasticity and scale-to-zero capabilities offered by serverless platforms. Our evaluation shows that stream functions reduce the processing overhead by ~99 % compared to a mature stream process- ing engine in a video-processing use case. By providing comparable performance to serverless functions with stream semantics, stream functions provide an effective and efficient abstractions for a class of workloads underserved by existing models. |
| title | Serverless Abstractions for Short-Running, Lightweight Streams |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2603.03089 |