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D-JB: An Online Join Method for Skewed and Varied Data Streams
Abstract : Scalable distributed join processing in a parallel environment requires a partitioning policy to transfer data. Online theta-joins over data streams are more computationally expensive and impose higher memory requirement in distributed data stream management systems (DDSMS) than database management systems (DBMS). The complete bipartite graph-based model can support distributed stream joins, and has the characteristics of memory-efficiency, elasticity and scalability. However, due to the instability of data stream rate and the imbalance of attribute value distribution, the online theta-joins over skewed and varied streams lead to the load imbalance of cluster. In this paper, we present a framework D-JB (Dynamic Join Biclique) for handling skewed and varied streams, enhancing the adaptability of the join model and minimizing the system cost based on the varying workloads. Our proposal includes a mixed key-based and tuple-based partitioning scheme to handle skewed data in each side of the bipartite graph-based model, a strategy for redistribution of query nodes in two sides of this model, and a migration algorithm about state consistency to support full-history joins. Experiments show that our method can effectively handle skewed and varied data streams and improve the throughput of DDSMS.
Cited literature [12 references]
https://hal.inria.fr/hal-02118799
Contributor : Hal Ifip <>
Submitted on : Friday, May 3, 2019 - 1:24:25 PM
Last modification on : Friday, May 3, 2019 - 3:50:43 PM
Long-term archiving on: : Thursday, October 3, 2019 - 8:34:50 AM
Contributor : Hal Ifip <>
Submitted on : Friday, May 3, 2019 - 1:24:25 PM
Last modification on : Friday, May 3, 2019 - 3:50:43 PM
Long-term archiving on: : Thursday, October 3, 2019 - 8:34:50 AM
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474230_1_En_12_Chapter.pdf
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Distributed under a Creative Commons Attribution 4.0 International License