Skip to Main Content
Large Scale and Big Data
book

Large Scale and Big Data

by Sherif Sakr, Mohamed Gaber
June 2014
Intermediate to advanced content levelIntermediate to advanced
636 pages
23h 13m
English
Auerbach Publications
Content preview from Large Scale and Big Data
147Incremental MapReduce Computations
memoization techniques for iterative computations. Incoop improves on these pro-
posals using a set of principles from related work to identify and overcome the situa-
tions where task-level memoization is inefcient, such as the stable input partitioning
or the contraction phase.
Our own short position paper [10] makes the case for applying techniques
inspired by self-adjusting computation to large-scale data processing in general and
uses MapReduce as an example. This position paper, however, models MapReduce
in a sequential, single-machine implementation of self-adjusting computation called
CEAL [24] and does not offer a full-scale distributed design and implementation
such as the system we presented.
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Reinventing the Organization for GenAI and LLMs

Reinventing the Organization for GenAI and LLMs

Ethan Mollick
Big Data Analytics for Internet of Things

Big Data Analytics for Internet of Things

Tausifa Jan Saleem, Mohammad Ahsan Chishti
Scala:Applied Machine Learning

Scala:Applied Machine Learning

Pascal Bugnion, Patrick R. Nicolas, Alex Kozlov
Topics in Parallel and Distributed Computing

Topics in Parallel and Distributed Computing

Sushil K Prasad, Anshul Gupta, Arnold L Rosenberg, Alan Sussman, Charles C Weems

Publisher Resources

ISBN: 9781466581500