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A survey of state management in big data processing systems

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Abstract

The concept of state and its applications vary widely across big data processing systems. This is evident in both the research literature and existing systems, such as Apache Flink, Apache Heron, Apache Samza, Apache Spark, and Apache Storm. Given the pivotal role that state management plays, particularly, for iterative batch and stream processing, in this survey, we present examples of state as an enabler, discuss the alternative approaches used to handle and implement state, capture the many facets of state management, and highlight new research directions. Our aim is to provide insight into disparate state management techniques, motivate others to pursue research in this area, and draw attention to open problems.

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Acknowledgements

This work was funded by the H2020 STREAMLINE Project under Grant Agreement No. 688191 and by the German Federal Ministry for Education and Research (BMBF) funded Berlin Big Data Center (BBDC), Under Funding Mark 01IS14013A.

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  1. German Research Center for Artificial Intelligence (DFKI), Alt-Moabit 91c, 10559, Berlin, Germany

    Quoc-Cuong To, Juan Soto & Volker Markl

  2. FG DIMA, Technische Universität Berlin, Sekr. EN-7 Raum 728, Einsteinufer 17, 10587, Berlin, Germany

    Juan Soto & Volker Markl

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  1. Quoc-Cuong To
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To, QC., Soto, J. & Markl, V. A survey of state management in big data processing systems. The VLDB Journal 27, 847–872 (2018). https://doi.org/10.1007/s00778-018-0514-9

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