Cart (Loading....) | Create Account
Close category search window
 

MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jiang, D. ; Dept. of Comput. Sci., Nat. Univ. of Singapore, Singapore, Singapore ; Tung, A. ; Gang Chen

Data analysis is an important functionality in cloud computing which allows a huge amount of data to be processed over very large clusters. MapReduce is recognized as a popular way to handle data in the cloud environment due to its excellent scalability and good fault tolerance. However, compared to parallel databases, the performance of MapReduce is slower when it is adopted to perform complex data analysis tasks that require the joining of multiple data sets in order to compute certain aggregates. A common concern is whether MapReduce can be improved to produce a system with both scalability and efficiency. In this paper, we introduce Map-Join-Reduce, a system that extends and improves MapReduce runtime framework to efficiently process complex data analysis tasks on large clusters. We first propose a filtering-join-aggregation programming model, a natural extension of MapReduce's filtering-aggregation programming model. Then, we present a new data processing strategy which performs filtering-join-aggregation tasks in two successive MapReduce jobs. The first job applies filtering logic to all the data sets in parallel, joins the qualified tuples, and pushes the join results to the reducers for partial aggregation. The second job combines all partial aggregation results and produces the final answer. The advantage of our approach is that we join multiple data sets in one go and thus avoid frequent checkpointing and shuffling of intermediate results, a major performance bottleneck in most of the current MapReduce-based systems. We benchmark our system against Hive, a state-of-the-art MapReduce-based data warehouse on a 100-node cluster on Amazon EC2 using TPC-H benchmark. The results show that our approach significantly boosts the performance of complex analysis queries.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:23 ,  Issue: 9 )

Date of Publication:

Sept. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.