An Abstract View of Big Data Processing Programs

TL;DR

This paper introduces a formal model for specifying Big Data processing programs that captures both non-iterative and iterative workflows, enabling better understanding and comparison of different frameworks' strategies.

Contribution

It extends an existing data flow model to include iterative programs and generalizes iteration strategies across multiple Big Data frameworks.

Findings

Model captures data flow and transformation at two levels

Generalizes iteration strategies of Spark, DryadLINQ, Beam, Flink

Facilitates formal comparison of Big Data frameworks

Abstract

This paper proposes a model for specifying data flow based parallel data processing programs agnostic of target Big Data processing frameworks. The paper focuses on the formal abstract specification of non-iterative and iterative programs, generalizing the strategies adopted by data flow Big Data processing frameworks. The proposed model relies on monoid AlgebraandPetri Netstoabstract Big Data processing programs in two levels: a high level representing the program data flow and a lower level representing data transformation operations (e.g., filtering, aggregation, join). We extend the model for data processing programs proposed in [1], to enable the use of iterative programs. The general specification of iterative data processing programs implemented by data flow-based parallel programming models is essential given the democratization of iterative and greedy Big Data analytics algorithms. Indeed, these algorithms call for revisiting parallel programming models to express iterations. The paper gives a comparative analysis of the iteration strategies proposed byApache Spark, DryadLINQ, Apache Beam and Apache Flink. It discusses how the model achieves to generalize these strategies.