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Register a new userhMDAP: A Hybrid Framework for Multi-paradigm Data Analytical Processing on Spark
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Xiaowang Zhang
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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We propose hMDAP, a hybrid framework for large-scale data analytical processing on Spark, to support multi-paradigm process (incl. OLAP, machine learning, and graph analysis etc.) in distributed environments. The framework features a three-layer data process module and a business process module which controls the former. We will demonstrate the strength of hMDAP by using traffic scenarios in a real world.
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With the explosive increase of big data in industry and academic fields, it is necessary to apply large-scale data processing systems to analysis Big Data. Arguably, Spark is state of the art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce Spark programming model and computing system, discuss the pros and cons of Spark, and have an investigation and classification of various solving techniques in the literature. Moreover, we also introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Finally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark.
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