Archive for February, 2012

Tuple MapReduce: beyond the classic MapReduce

It’s been some years now since Google wrote the paper [“MapReduce: Simplified Data Processing on Large Clusters“] in 2004. In this paper Google presented MapReduce, a programming model and associated implementation for solving parallel computation problems with big-scale data. This model is based on the use of the functional primitives “map” and “reduce” present in LISP and other functional languages.

Today, Hadoop, the “de facto” open-source implementation of MapReduce, is used by a wide variety of companies, institutions and universities. The massive usage of this programming model has led to the creation of multiple tools associated with it (which has come to be known as the Hadoop ecosystem) and even specialized companies like Cloudera engaged in training programmers to use it. Part of the success of such tools and companies lies in the now-evident difficulty and sharp learning curve involved in MapReduce, as it was originally defined, when applied to practical problems.

In this post we’ll review the MapReduce model proposed by Google in 2004 and propound another one called Tuple MapReduce. We’ll see that this new model is a generalization of the first and we’ll explain what advantages it has to offer. We’ll provide a practical example and conclude by discussing when the implementation of Tuple MapReduce is advisable.

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MapReduce & Hadoop API revised

Nowadays, Hadoop has become the key technology behind what has come to be known as “Big Data”. It has certainly worked hard to earn this position. It is mature technology that has been used successfully in countless projects. But now, with experience behind us, it is time to take stock of the foundations upon which it is based, particularly its interface. This article discusses some of the weaknesses of both MapReduce and Hadoop, which we, at Datasalt, shall attempt to resolve with an open-source project that we will soon be releasing.

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