Online state migration in modern stream processing engines

Master Thesis (2023)
Author(s)

Theodoros Veneti (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

A. Katsifodimos – Mentor (TU Delft - Web Information Systems)

J.E.A.P. Decouchant – Graduation committee member (TU Delft - Data-Intensive Systems)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2023
Language
English
Graduation Date
07-11-2023
Awarding Institution
Delft University of Technology
Programme
['Computer Science | Data Science and Technology']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Stream Processing Engines (SPEs) are called upon to help solve problems around big and volatile data, while satisfying the needs for near real-time processing. In order for such systems to be considered effective solutions to such problems at scale, efficient elasticity and non dataflow-disturbing reconfiguration operations within are a necessity. To that end, we visit the problem of online state migration, as the biggest obstacle in achieving such a desired behaviour, in SPEs that support stateful functions. We make an attempt to formally define the problem and associated sub-tasks, compare existing solutions and identify key aspects, as well as design and implement our own solution. Our testing shows that the lazy-fetch online state migration process proposed, outperforms a simple baseline state migration design by orders of magnitude in end-to-end latency observed, scales much better under increased workloads and relies on consistent design concepts to claim exactly-once semantics.

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