The Commutativity Problem of the MapReduce Framework: A Transducer-based Approach
Yu-Fang Chen, Lei Song, Zhilin Wu

TL;DR
This paper introduces a decidable approach to the commutativity problem in MapReduce reducers by using a novel automata model called streaming numerical transducers, which separates control and data flow.
Contribution
It proposes a new reducer programming language that simplifies control flow and reduces the commutativity problem to automata equivalence, enabling decidability.
Findings
Decidability of reducer commutativity via automata equivalence.
Introduction of streaming numerical transducers (SNTs).
Reduction of the problem to automata theory.
Abstract
MapReduce is a popular programming model for data parallel computation. In MapReduce, the reducer produces an output from a list of inputs. Due to the scheduling policy of the platform, the inputs may arrive at the reducers in different order. The commutativity problem of reducers asks if the output of a reducer is independent of the order of its inputs. Although the problem is undecidable in general, the MapReduce programs in practice are usually used for data analytics and thus require very simple control flow. By exploiting the simplicity, we propose a programming language for reducers where the commutativity problem is decidable. The main idea of the reducer language is to separate the control and data flow of programs and disallow arithmetic operations in the control flow. The decision procedure for the commutativity problem is obtained through a reduction to the equivalence…
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Taxonomy
Topicssemigroups and automata theory · Distributed systems and fault tolerance · Parallel Computing and Optimization Techniques
