Abstract
Both block nested join algorithm and sort merge join algorithm are conventional join algorithms in database systems. To the best of our knowledge, few literature focused on the experimentally comparing these two join algorithms. In this paper, we implement the sort merge join algorithm and the block nested loop join algorithm. And then, experimental results demonstrate the sort merge join algorithm outperforms than the block nested join algorithm on execution time in term of different bytes of page or different number of buffer but with the same result after join.
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References
Braumandl, R., Claussen, J., Kemper, A., Kossmann, D.: Functional-Join Processing. VLDB Journal 8(3-4), 156–177 (2000)
Chen, S., Ailamaki, A., Gibbons, P.B., Mowry, T.C.: Improving Hash Join Performance Through Prefetching. ACM Transactions on Database Systems (TODS) 32(2), 17 (2007)
DeWitt, D., Jeffrey, F., Joseph, B.: Nested Loops Revisited. In: Proceedings of the 2nd International Conference on Parallel and Distributed Information Systems, pp. 230–242 (1993)
Dittrich, J., Seeger, B., Taylor, D.S., Widmayer, P.: Progressive Merge Join: A Generic and Non-Blocking Sort-Based Join Algorithm. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB), pp. 299–310 (2002)
Graefe, G., Linville, A., Shapiro, L.: Sort Versus Hash Revisited. IEEE Transactions on Knowledge and Data Engineering (TKDE) 25(2), 73–170 (1993)
Harris, E.P., Ramamohanarao, K.: Join Algorithm Costs Revisited, Technical Report. University of Melbourne (1993)
Haas, L.M., Carey, M.J., Livny, M., Shukla, A.: Seeking the Truth About Ad Hoc Join Costs. VLDB Journal 6, 241–256 (1997)
Ioannidis, Y., Christodoulakis, S.: On the Propagation of Errors in the Size of Join Results. In: Proceedings of ACM International Conference on Management of Data (SIGMOD), pp. 268–277 (1991)
Luo, G., Ellmann, C.J., Haas, P.J., Naughton, F.J.: A scalable hash ripple join algorithm. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 252–262. ACM, New York (2002)
Li, J., Sun, W., Li, Y.: Parallel Join Algorithms based on Parallel B+-trees. In: Proceedings of the 3rd International Symposium on Cooperative Database Systems for Advanced Applications, (CODAS) (2001)
Lieberman, M.D., Sankaranarayanan, J., Samet, H.: A Fast Similarity Join Algorithm Using Graphics Processing Units. In: Proceedings of the 24th IEEE International Conference on Data Engineering, Cancun, Mexico, pp. 1111–1120 (April 2008)
Mokbe, M.F., Lu, M., Aref, W.G.: Hash-Merge Join: A Non-blocking Join Algorithm for Producing Fast and Early Join Results. In: ICDE (2004)
Mishra, P., Eich, M.H.: Join Processing in Relational Databases. ACM Computing Survey 24(1), 63–113 (1992)
Patel, J., Carey, M., Vernon, M.: Accurate Modeling of the Hybrid Hash Join Algorithm. In: Proceedings of ACM SIGMETRICS Conference (1994)
Qin, Y., Zhang, S., Zhu, X., Zhang, J., Zhang, C.: Semi-parametric optimization for missing data imputation. Appl. Intell. 27(1), 79–88 (2007)
Ramakrishnan, R., Gehrke, J.: Database management system, 3rd edn., pp. 452–458 (2002)
Ramasamy, K., Patel, J., Naughton, J.F., Kaushik, R.: Set Containment Joins: The Good, The Bad and The Ugly. In: Proceedings of the International Conference on Very Large Data Bases (VLDB), pp. 351–362 (2000)
Shekita, E., Carey, M.: A Performance Evaluation of Pointer-based Joins. In: Proceedings of ACM International Conference on Management of Data (SIGMOD) (1990)
Toyama, M., Ohara, A.: Hash-Based Symmetric Data Structure and Join Algorithm for OLAP Applications. In: International Database Engineering and Applications Symposium, pp. 231–238 (1999)
Valduriez, P.: Join Indices. ACM Transactions on Database Systems (TODS) 12(2), 218–246 (1987)
Wu, X., Zhang, S.: Synthesizing High-Frequency Rules from Different Data Sources. IEEE Trans. Knowl. Data Eng. 15(2), 353–367 (2003)
Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst. 22(3), 381–405 (2004)
Wu, X., Zhang, C., Zhang, S.: Database classification for multi-database mining. Inf. Syst. 30(1), 71–88 (2005)
Zhang, S., Zhang, C., Yan, X.: Post-mining: maintenance of association rules by weighting. Inf. Syst. 28(7), 691–707 (2003)
Zhang, S., Qin, Z., Ling, C., Sheng, S.: “Missing Is Useful”: Missing Values in Cost-Sensitive Decision Trees. IEEE Trans. Knowl. Data Eng. 17(12), 1689–1693 (2005)
Zhao, Y., Zhang, S.: Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis. IEEE Trans. Knowl. Data Eng. 18(2), 231–244 (2006)
Zhu, X., Zhang, S., Jin, Z., Zhang, Z., Xu, Z.: Missing Value Estimation for Mixed-Attribute Data Sets. IEEE Trans. Knowl. Data Eng. 23(1), 110–121 (2011)
Zhu, X., Zhang, L., Huang, Z.: A Sparse Embedding and Least Variance Encoding Approach to Hashing. IEEE Transactions on Image Processing 23(9), 3737–3750 (2014)
Zhu, X., Huang, Z., Shen, H., Zhao, X.: Linear cross-modal hashing for efficient multimedia search. In: ACM Multimedia, pp. 143–152 (2013)
Zhu, X., Suk, H., Shen, D.: A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage 100, 91–105 (2014)
Zhu, X., Suk, H., Shen, D.: Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer’s Disease Diagnosis. In: CVPR, pp. 3089–3096 (2014)
Zhu, X., Huang, Z., Yang, Y., Shen, H., Xu, C., Luo, J.: Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition 46(1), 215–229 (2013)
Zhu, X., Huang, Z., Cui, J., Shen, H.: Video-to-Shot Tag Propagation by Graph Sparse Group Lasso. IEEE Transactions on Multimedia 15(3), 633–646 (2013)
Zhu, X., Huang, Z., Cheng, H., Cui, J., Shen, H.: Sparse hashing for fast multimedia search. ACM Trans. Inf. Syst. 31(2), 9 (2013)
Zhu, X., Huang, Z., Shen, H., Cheng, J., Xu, C.: Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis. Pattern Recognition 45(8), 3003–3016 (2012)
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Chen, M., Zhong, Z. (2014). Block Nested Join and Sort Merge Join Algorithms: An Empirical Evaluation. In: Luo, X., Yu, J.X., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2014. Lecture Notes in Computer Science(), vol 8933. Springer, Cham. https://doi.org/10.1007/978-3-319-14717-8_56
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DOI: https://doi.org/10.1007/978-3-319-14717-8_56
Publisher Name: Springer, Cham
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