Abstract
In recent years, MapReduce has become a popular computing framework for big data analysis. Join is a major query type for data analysis and various algorithms have been designed to process join queries on top of Hadoop. Since the efficiency of different algorithms differs on the join tasks on hand, to achieve a good performance, users need to select an appropriate algorithm and use the algorithm with a proper configuration, which is rather difficult for many end users. This paper proposes a cost model to estimate the cost of four popular join algorithms. Based on the cost model, the system may automatically choose the join algorithm with the least cost, and then give the reasonable configuration values for the chosen algorithm. Experimental results with the TPC-H benchmark verify that the proposed method can correctly choose the best join algorithm, and the chosen algorithm can achieve a speedup of around 1.25 times over the default join algorithm.
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Gu, J., Peng, S., Wang, X.S., Rao, W., Yang, M., Cao, Y. (2014). Cost-Based Join Algorithm Selection in Hadoop. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2014. WISE 2014. Lecture Notes in Computer Science, vol 8787. Springer, Cham. https://doi.org/10.1007/978-3-319-11746-1_18
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DOI: https://doi.org/10.1007/978-3-319-11746-1_18
Publisher Name: Springer, Cham
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