An Abstract View of Big Data Processing Programs
This work addresses the need for a unified specification model for iterative Big Data analytics algorithms, which is incremental as it extends an existing model to include iterations.
The paper tackles the problem of specifying data flow-based parallel data processing programs in a framework-agnostic way, proposing a formal abstract model that generalizes strategies from frameworks like Apache Spark and Apache Flink, with a focus on enabling iterative programs.
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.