Name
Abstract | ||
|---|---|---|
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 upï¾źto 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-43659-3_25 | Euro-Par |
Field | DocType | Volume |
Joins,Computer science,Tuple,Parallel computing,Implementation,Skew,Big data,Scalability,Overhead (business),Distributed computing | Conference | 9833 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
18 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Long Cheng | 1 | 91 | 16.99 |
| Spyros Kotoulas | 2 | 590 | 46.46 |