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The following page has been changed by HyunsikChoi: http://wiki.apache.org/hadoop/Hamburg The comment on the change is: Improved the motivation ------------------------------------------------------------------------------ ## page was renamed from Hambrug == Motivation == - The MapReduce (M/R) programming model is inappropriate to problems based on data where each portion depends on many other potions and their relations are very complicated. It is because these problems cause as follows: + The MapReduce (M/R) programming model is inappropriate to graph problems because of the following reasons: ''Do you know other situations that might fall into what you are describing above?'' + * '''!MapReduce does not support traversing graph''' â A mapper reads input data sequentially, and it canât control its input data. In contrast, most of the graph problems are based on walking vertices step by step. Walking vertices is to expand adjacent vertices from a given vertex. This operation is only available if current input data can be determined by the previous operation. In MapReduce, however, the previous operation cannot affect the input data of the next operation. Traversing a vertex is the most basic premitive operation in graph operations. Consequently, graph processing with MapReduce is very limited. In order to come over this limit, we have to avoid traverse of graph in order to solve graph problems ([http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=5076317 Graph Twiddling in a MapReduce World]) or have to use many M/R iterations each time walk vertices ([http://blog.udanax.org/2009/02/breadth-first-search-mapreduce.html Breadth-First Search (B FS) & MapReduce]). + * '''!MapReduce limits to assigning one reducer''' - In a MapReduce problem on graph data, assigning appropriate reducers according to their relations of partitioned graphs is very hard. Assigning only one reducer is a straightforward way to solve their complex relations, but it is apparent to cause deterioration of scalability. + * '''More complicated M/R program''' - To avoid graph traverse or the limit of one reduce, the M/R programs would be inevitablely complicated with code to communicate data among data nodes. - * limit to assigning one reducer - * In case that the relations of data are very complex, assigning intermediate data to appropriate reducers by considering their dependency of partitioned graphs may be very hard. Assigning only one reducer is a straightway to solve complexity dependency, but it is apparent to cause deterioration of scalability. - * many M/R iterations - * or make an M/R program more complicated - * To avoid above two inefficient methods, the M/R program will be complicated with code to communicate data among data nodes. - These problems are very common in many areas; especially, many graph problems are exemplary. Therefore, we try to propose a new programming model, named Hamburg. The main objective of Hamburg is to support well the problems based on data having complexity dependency one another. This page is an initial work of our proposal. + Therefore, we need a new programming model for graph processing on Hadoop. ''We should survey other areas -- Edward J.'' == Goal == + * Support graph traverse + * Support a simple programming interface dealing graph data - * Follow scalability concept of shared-nothing architecture + * Follow the scalability concept of shared-nothing architecture - * Support a simple programming model to compute complex relations such as, graph data. == Hamburg == Hambrug is an alternative to M/R programming model. It is based on bulk synchronization parallel (BSP) model. Like M/R, Hambrug takes advantages from shared-nothing architecture (SN), so I expect that it will also show scalablity without almost degradation of performance as the number of participant nodes increases.
