Efficient Large Outer Joins over MapReduce
Aus International Center for Computational Logic
Efficient Large Outer Joins over MapReduce
Long ChengLong Cheng, Spyros KotoulasSpyros Kotoulas
Long Cheng, Spyros Kotoulas
Efficient Large Outer Joins over MapReduce
Proc. 22nd International European Conference on Parallel Processing (Euro-Par'16), 334-346, August 2016. Springer
Efficient Large Outer Joins over MapReduce
Proc. 22nd International European Conference on Parallel Processing (Euro-Par'16), 334-346, August 2016. Springer
- KurzfassungAbstract
Big Data analytics largely rely on being able to execute large joins efficiently. Though inner join approaches have been extensively evaluated in parallel and distributed systems, there is little published work providing analysis of outer joins, especially on the extremely popular MapReduce platform. In this paper, we studied several current algorithms/techniques used in large outer joins. We find that some of them could meet performance bottlenecks in the presence of data skew, while others could be complex and incur significant coordination overheads when applied to the MapReduce framework. In this light, we propose a new algorithm, called POPI (Partial Outer join & Partial Inner join), which targets for efficient processing large outer joins, and most important, is lightweight and adapted to the processing model of MapReduce. We implement our method in Pig and evaluate its performance on a Hadoop cluster of up to 256 cores and datasets of 1 billion tuples. Experimental results show that our method is scalable, robust and outperforms current implementations, at least in the case of high skew. - Projekt:Project: DIAMOND, HAEC B08
- Forschungsgruppe:Research Group: Wissensbasierte SystemeKnowledge-Based Systems
@inproceedings{CK2016,
author = {Long Cheng and Spyros Kotoulas},
title = {Efficient Large Outer Joins over {MapReduce}},
booktitle = {Proc. 22nd International European Conference on Parallel
Processing (Euro-Par'16)},
publisher = {Springer},
year = {2016},
month = {August},
pages = {334-346},
doi = {10.1007/978-3-319-43659-3_25}
}
