Integrating MapReduce and RDBMSs

Authors:
Natalie Gruska;Patrick Martin
Affiliations:
Queen's University, Kingston, ON, Canada;Queen's University, Kingston, ON, Canada
Venue:
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Year:
2010

Citing 10
Cited 0

Interpreting the data: Parallel analysis with Sawzall

Scientific Programming - Dynamic Grids and Worldwide Computing
MapReduce: simplified data processing on large clusters

OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Pig latin: a not-so-foreign language for data processing

Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A comparison of approaches to large-scale data analysis

Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Graph Twiddling in a MapReduce World

Computing in Science and Engineering
Efficiently support MapReduce-like computation models inside parallel DBMS

IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
MapReduce and parallel DBMSs: friends or foes?

Communications of the ACM - Amir Pnueli: Ahead of His Time
MapReduce: a flexible data processing tool

Communications of the ACM - Amir Pnueli: Ahead of His Time
SQL/MapReduce: a practical approach to self-describing, polymorphic, and parallelizable user-defined functions

Proceedings of the VLDB Endowment
Hive: a warehousing solution over a map-reduce framework

Proceedings of the VLDB Endowment

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data processing needs are changing with the ever increasing amounts of both structured and unstructured data. While the processing of structured data typically relies on the well-developed field of relational database management systems (RDBMSs), MapReduce is a programming model developed to cope with processing immense amounts of unstructured data. MapReduce, however, offers features and advantages that can be exploited to process structured data. Several database vendors and researchers have already turned to MapReduce to aid in processing relational data, thus requiring integration of MapReduce and RDBMS technologies. In this paper, we provide a taxonomy to characterize several existing integration methods. Further, we take a detailed look at DBInputFormat which is an interface between Hadoop's MapReduce and a relational database. The challenges posed by such an interface are identified and we provide suggestions for improvement.