The Community for Technology Leaders
RSS Icon
Issue No.08 - August (2010 vol.22)
pp: 1110-1125
Mihaela A. Bornea , Athens University of Economics and Business, Athens, Greece
Vasilis Vassalos , Athens University of Economics and Business, Athens, Greece
Yannis Kotidis , Athens University of Economics and Business, Athens, Greece
Antonios Deligiannakis , Technical University of Crete, Chania, Greece
Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, we first propose Double Index NEsted-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available. We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.
Query processing, join, DINER, MINER, streams.
Mihaela A. Bornea, Vasilis Vassalos, Yannis Kotidis, Antonios Deligiannakis, "Adaptive Join Operators for Result Rate Optimization on Streaming Inputs", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 8, pp. 1110-1125, August 2010, doi:10.1109/TKDE.2010.64
[1] S. Babu and P. Bizarro, "Adaptive Query Processing in the Looking Glass," Proc. Conf. Innovative Data Systems Research (CIDR), 2005.
[2] D. Baskins Judy Arrays, http:/, 2004.
[3] M.A. Bornea, V. Vassalos, Y. Kotidis, and A. Deligiannakis, "Double Index Nested-Loop Reactive Join for Result Rate Optimization," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2009.
[4] J. Dittrich, B. Seeger, and D. Taylor, "Progressive Merge Join: A Generic and Non-Blocking Sort-Based Join Algorithm," Proc. Int'l Conf. Very Large Data Bases (VLDB), 2002.
[5] P.J. Haas and J.M. Hellerstein, "Ripple Joins for Online Aggregation," Proc. ACM SIGMOD, 1999.
[6] W. Hong and M. Stonebraker, "Optimization of Parallel Query Execution Plans in XPRS," Proc. Int'l Conf. Parallel and Distributed Information Systems (PDIS), 1991.
[7] Z.G. Ives et al., "An Adaptive Query Execution System for Data Integration," Proc. ACM SIGMOD, 1999.
[8] F. Li, C. Chang, G. Kollios, and A. Bestavros, "Characterizing and Exploiting Reference Locality in Data Stream Applications," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2006.
[9] M.F. Mokbel, M. Lu, and W.G. Aref, "Hash-Merge Join: A Non-Blocking Join Algorithm for Producing Fast and Early Join Results," Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2004.
[10] M. Negri and G. Pelagatti, "Join During Merge: An Improved Sort Based Algorithm," Information Processing Letters, vol. 21, no. 1, pp. 11-16, 1985.
[11] A.S. Tanenbaum, Modern Operating Systems. Prentice Hall PTR, 2001.
[12] Y. Tao, M.L. Yiu, D. Papadias, M. Hadjieleftheriou, and N. Mamoulis, "RPJ: Producing Fast Join Results on Streams through Rate-Based Optimization," Proc. ACM SIGMOD, 2005.
[13] J.D. Ullman, H. Garcia-Molina, and J. Widom, Database Systems: The Complete Book. Prentice Hall, 2001.
[14] T. Urhan and M.J. Franklin, "XJoin: A Reactively-Scheduled Pipelined Join Operator," IEEE Data Eng. Bull., vol. 23, no. 2, pp. 27-33, 2000.
[15] S.D. Viglas, J.F. Naughton, and J. Burger, "Maximizing the Output Rate of Multi-Way Join Queries over Streaming Information Sources," Proc. 29th Int'l Conf. Very Large Data Bases (VLDB '03), pp. 285-296, 2003.
[16] A.N. Wilschut and P.M.G. Apers, "Dataflow Query Execution in a Parallel Main-Memory Environment," Distributed and Parallel Databases, vol. 1, no. 1, pp. 103-128, 1993.
15 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool