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Representing MapReduce Optimisations in the Nested Relational Calculus

机译:表示嵌套关系微积分中的MapReduce优化

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The MapReduce programming model is recently getting a lot of attention from both academic and business researchers. Systems based on this model hide communication and synchronization issues from the user and allow processing of high volumes of data on thousands of commodity computers. In this paper we are interested in applying MR to processing hierarchical data with nested collections such as stored in JSON or XML formats but with restricted nesting depth as is usual in the nested relational model. The current data analytics systems now often propose ad-hoc formalisms to represent query evaluation plans and to optimize their execution. In this paper we will argue that the Nested Relation Calculus provides a general, elegant and effective way to describe and investigate these optimizations. It allows to describe and combine both classical optimizations and MapReduce-specific optimizations. We demonstrate this by showing that MapReduce programs can be expressed and represented straightforwardly in NRC by adding syntactic short-hands. In addition we show that optimizations in existing systems can be readily represented in this extended formalism.
机译:MapReduce编程模型最近从学术和商业研究人员获得了很多关注。基于此模型的系统隐藏了来自用户的通信和同步问题,并允许在数千个商品计算机上处​​理高卷数据。在本文中,我们有兴趣将MR用于处理具有嵌套集合的分层数据,例如以JSON或XML格式存储,但在嵌套关系模型中通常通常通常嵌套深度。目前的数据分析系统现在通常会提出临时形式主义来表示查询评估计划并优化其执行。在本文中,我们将争辩说,嵌套关系管理提供了一般,优雅有效的方法来描述和调查这些优化。它允许描述和组合经典优化和特定于MapReduce的优化。我们通过表示可以通过添加句法短手在NRC中表达并在NRC中表达并表示并在NRC中直截了当地表示。此外,我们表明,现有系统中的优化可以在这种延长的形式主义中容易地表示。

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