Abstract
Analyzing large data sets is gaining more importance because of its wide variety of applications in parallel and distributed environment. Hadoop environment gives more flexibility to programmers in parallel computing. One of the advantages of Hadoop is query evaluation over large datasets. Join operations in query evaluation plays a major role over the large data. This paper Ferret outs the earlier solutions, prolongs them and recommends a new approach for the implementation of joins in Hadoop.
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Pagadala, P.K., Vikram, M., Eswarawaka, R., Reddy, P.S. (2017). Join Operations to Enhance Performance in Hadoop MapReduce Environment. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_51
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DOI: https://doi.org/10.1007/978-981-10-3156-4_51
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