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

Using Index in the MapReduce Framework

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)
Mingyuan An ; Key Lab. of Comput. Syst. & Archit., Chinese Acad. of Sci., Beijing, China ; Yang Wang ; Weiping Wang

MapReduce is a programming framework introduced by Google for large-scale data processing. It is usually used in a scan-centric fashion where all the data are split into blocks and Maps are generated for each block to scan and process the data in the block, then Reduces merge outputs from all the Maps. When a query intends to process only a subset of the data selected by a predicate, this brute-force method may cause extra I/O overhead spent on irrelevant data, and the overhead for initiating so many Maps may be non-trivial given that the actually interesting data for the query is comparatively small in volume. We propose an approach to integrate the index into the MapReduce execution in which only an appropriate number of Maps are generated, each of which accesses the data using an index. This approach incurs random I/O and remote access to data, so the overall performance depends on both system parameters and the query characteristics. We build a cost model for both this index access execution and the traditional full scan execution. This cost model can be used to choose between the two execution modes before executing a query. Experiments show that the index access execution can greatly outperform full scan execution when the selectivity of the predicate is low, and the cost model predicts the actual execution cost very well so can be used to determine the execution plan for a query.

Published in:

Web Conference (APWEB), 2010 12th International Asia-Pacific

Date of Conference:

6-8 April 2010

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.