Demo: elastic mapreduce-style processing of fast data

TY - GEN

T1 - Demo: elastic mapreduce-style processing of fast data

AU - Madsen, Kasper Grud Skat

AU - Zhou, Yongluan

N1 - Conference code: 7

PY - 2013

Y1 - 2013

N2 - MapReduce is a popular scalable processing framework for large-scale data. In this paper we demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.

AB - MapReduce is a popular scalable processing framework for large-scale data. In this paper we demonstrate Enorm, which represents our efforts on rectifying the traditional batch-oriented MapReduce framework for low-latency data stream processing. Most existing work have focused on how to extend the MapReduce framework for low-latency data stream processing, but overlooked the problem of obtaining runtime elasticity. The demonstration focuses on two important features in Enorm. (1) sharing aggregate computations among overlapping windows and (2) runtime elasticity.

U2 - 10.1145/2488222.2488350

DO - 10.1145/2488222.2488350

M3 - Article in proceedings

SN - 978-1-4503-1758-0

SP - 335

EP - 336

BT - DEBS’13

PB - Association for Computing Machinery

T2 - 7th ACM International Conference on Distributed Event-Based Systems

Y2 - 29 June 2013 through 3 July 2013

ER -