Handling Iterations in Distributed Dataflow Systems

Over the past decade, distributed dataflow systems (DDS) have become a standard technology. In these systems, users write programs in restricted dataflow programming models, such as MapReduce, which enable them to scale out program execution to a shared-nothing cluster of machines. Yet, there is no...

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Published in:ACM computing surveys 2022-12, Vol.54 (9), p.1-38
Main Authors: Gévay, Gábor E., Soto, Juan, Markl, Volker
Format: Article
Language:English
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Computer science
Iterative algorithms
Iterative methods
Literature reviews
Programming
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description Over the past decade, distributed dataflow systems (DDS) have become a standard technology. In these systems, users write programs in restricted dataflow programming models, such as MapReduce, which enable them to scale out program execution to a shared-nothing cluster of machines. Yet, there is no established consensus that prescribes how to extend these programming models to support iterative algorithms. In this survey, we review the research literature and identify how DDS handle control flow, such as iteration, from both the programming model and execution level perspectives. This survey will be of interest for both users and designers of DDS.
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source EBSCOhost Business Source Ultimate; Association for Computing Machinery:Jisc Collections:ACM OPEN Journals 2023-2025 (reading list)
subjects Computer science
Iterative algorithms
Iterative methods
Literature reviews
Programming
title Handling Iterations in Distributed Dataflow Systems
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