Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications

Michal Bartoszkiewicz; Jan Chorowski; Adrian Kosowski; Jakub Kowalski; Sergey Kulik; Mateusz Lewandowski; Krzysztof Nowicki; Kamil Piechowiak; Olivier Ruas; Zuzanna Stamirowska; Przemyslaw Uznanski

arXiv · 2307.13116

Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications

Michal Bartoszkiewicz·Jan Chorowski·Adrian Kosowski·Jakub Kowalski·Sergey Kulik·Mateusz Lewandowski·Krzysztof Nowicki·Kamil Piechowiak·Olivier Ruas·Zuzanna Stamirowska·Przemyslaw Uznanski

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Abstract

We present Pathway, a new unified data processing framework that can run workloads on both bounded and unbounded data streams. The framework was created with the original motivation of resolving challenges faced when analyzing and processing data from the physical economy, including streams of data generated by IoT and enterprise systems. These required rapid reaction while calling for the application of advanced computation paradigms (machinelearning-powered analytics, contextual analysis, and other elements of complex event processing). Pathway is equipped with a Table API tailored for Python and Python/SQL workflows, and is powered by a distributed incremental dataflow in Rust. We describe the system and present benchmarking results which demonstrate its capabilities in both batch and streaming contexts, where it is able to surpass state-of-the-art industry frameworks in both scenarios. We also discuss streaming use cases handled by Pathway which cannot be easily resolved with state-of-the-art industry frameworks, such as streaming iterative graph algorithms (PageRank, etc.).

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Michal Bartoszkiewicz, Jan Chorowski, Adrian Kosowski, Jakub Kowalski, Sergey Kulik, Mateusz Lewandowski, Krzysztof Nowicki, Kamil Piechowiak, Olivier Ruas, Zuzanna Stamirowska, Przemyslaw Uznanski. 2023-07-12. Pathway: a fast and flexible unified stream data processing framework for analytical and Machine Learning applications. https://arxiv.org/abs/2307.13116

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