CARTILAGE: adding flexibility to the Hadoop skeleton
Name
AQM13.pdf
Description
Accepted version
Size
307.71 KB
Format
Unknown
Checksum (MD5)
b68dcdbd75a0592e562cb8210312a4f7
Author(s)
Jindal, Alekh
Quiané-Ruiz, Jorge
Madden, Samuel R
Date Issued
2013
Publisher
ACM Press
Citation
Jindal, Alekh, Quiané-Ruiz, Jorge and Madden, Samuel. 2013. "CARTILAGE: adding flexibility to the Hadoop skeleton."
Version
Author's final manuscript
Abstract
Modern enterprises have to deal with a variety of analytical queries over very large datasets. In this respect, Hadoop has gained much popularity since it scales to thousand of nodes and terabytes of data. However, Hadoop suffers from poor performance, especially in I/O performance. Several works have proposed alternate data storage for Hadoop in order to improve the query performance. However, many of these works end up making deep changes in Hadoop or HDFS. As a result, they are (i) difficult to adopt by several users, and (ii) not compatible with future Hadoop releases. In this paper, we present CARTILAGE, a comprehensive data storage framework built on top of HDFS. CARTILAGE allows users full control over their data storage, including data partitioning, data replication, data layouts, and data placement. Furthermore, CARTILAGE can be layered on top of an existing HDFS installation. This means that Hadoop, as well as other query engines, can readily make use of CARTILAGE. We describe several use-cases of CARTILAGE and propose to demonstrate the flexibility and efficiency of CARTILAGE through a set of novel scenarios. Copyright © 2013 ACM.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/2463676.2465258
