Content-aware data distribution over cluster nodes

Proper data items distribution may seriously improve the performance of data processing in distributed environment. However, typical datastorage systems as well as distributed computational frameworks do not pay special attention to that aspect. In this paper author introduces two custom data items addressing methods for distributed datastorage on the example of Scalable Distributed Two-Layer Datastore. The basic idea of those methods is to preserve that data items stored on the same cluster node are similar to each other following concepts of data clustering. Still, most of the data clustering mechanisms have serious problem with data scalability which is a severe limitation in Big Data applications. The proposed methods allow to efficiently distribute data set over a set of buckets. As it was shown by the experimental results, all proposed methods generate good results efficiently in comparison to traditional clustering techniques like k-means, agglomerative and birch clustering. Distributed environment experiments shown that proper data distribution can seriously improve the effectiveness of Big Data processing.