A Formal Semantics for Data Analytics Pipelines

Research, Preprint OPEN
Drocco, Maurizio ; Misale, Claudia ; Tremblay, Guy ; Aldinucci, Marco (2017)
  • Related identifiers: doi: 10.5281/zenodo.571802
  • Subject: Types | Computer Science - Programming Languages | Big Data analytics | D.2.4 | D.1.3 | D.3.2 | Parallel computing | Distributed computing

In this report, we present a new programming model based on Pipelines and Operators, which are the building blocks of programs written in PiCo, a DSL for Data Analytics Pipelines. In the model we propose, we use the term Pipeline to denote a workflow that processes data collections -- rather than a computational process -- as is common in the data processing community. The novelty with respect to other frameworks is that all PiCo operators are polymorphic with respect to data types. This makes it possible to 1) re-use the same algorithms and pipelines on different data models (e.g., streams, lists, sets, etc); 2) reuse the same operators in different contexts, and 3) update operators without affecting the calling context, i.e., the previous and following stages in the pipeline. Notice that in other mainstream frameworks, such as Spark, the update of a pipeline by changing a transformation with another is not necessarily trivial, since it may require the development of an input and output proxy to adapt the new transformation for the calling context. In the same line, we provide a formal framework (i.e., typing and semantics) that characterizes programs from the perspective of how they transform the data structures they process -- rather than the computational processes they represent. This approach allows to reason about programs at an abstract level, without taking into account any aspect from the underlying execution model or implementation.
  • References (8)

    [1] T. Akidau, R. Bradshaw, C. Chambers, S. Chernyak, R. J. Ferna`ndezMoctezuma, R. Lax, S. McVeety, D. Mills, F. Perry, E. Schmidt, and S. Whittle. The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. Proc. VLDB Endow., 8(12):1792-1803, Aug. 2015.

    [2] Flink. Apache Flink website. https://flink.apache.org/.

    [3] Flink. Flink streaming examples, 2015. [Online; accessed 16-November2016].

    [7] E. A. Lee and T. M. Parks. Dataflow process networks. Proc. of the IEEE, 83(5):773-801, 1995.

    [8] C. Misale, M. Drocco, M. Aldinucci, and G. Tremblay. A comparison of big data frameworks on a layered dataflow model. In Proc. of HLPP2016: Intl. Workshop on High-Level Parallel Programming, pages 1-19, Muenster, Germany, July 2016. arXiv.org.

    [9] C. Misale, M. Drocco, M. Aldinucci, and G. Tremblay. A comparison of big data frameworks on a layered dataflow model. Parallel Processing Letters, 27(01):1740003, 2017.

    [10] M. A. U. Nasir, G. D. F. Morales, D. Garc´ıa-Soriano, N. Kourtellis, and M. Serafini. The power of both choices: Practical load balancing for distributed stream processing engines. CoRR, abs/1504.00788, 2015.

    [11] M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauley, M. J. Franklin, S. Shenker, and I. Stoica. Resilient Distributed Datasets: A Faulttolerant Abstraction for In-memory Cluster Computing. In Proc. of the 9th USENIX Conference on Networked Systems Design and Implementation, NSDI'12, Berkeley, CA, USA, 2012. USENIX.

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