Campus Event Calendar
Event Entry
New for: D2, D3
What and Who
The Hadoop++ Project
Jens Dittrich
Fachrichtung Informatik - Saarbruecken
SWS Colloquium
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Expert Audience
English
Note: We use this to send email in the morning.
Date, Time and Location
Friday, 10 December 2010
11:00
60 Minutes
E1 5
5th floor
Saarbrücken
Abstract
The Hadoop++ Project
MapReduce is a computing paradigm that has gained a lot of attention in
recent years from industry and research. Unlike parallel DBMSs,
MapReduce allows non-expert users to run complex analytical tasks over
very large data sets on very large clusters and clouds. However, this
comes at a price: MapReduce processes tasks in a scan-oriented fashion.
Hence, the performance of Hadoop --- an open-source implementation of
MapReduce --- often does not match the one of a well-configured parallel
DBMS. We propose a new type of system named Hadoop++: it boosts task
performance without changing the Hadoop framework at all. To reach this
goal, rather than changing a working system (Hadoop), we inject our
technology at the right places through UDFs only and affect Hadoop from
inside. This has three important consequences: First, Hadoop++
significantly outperforms Hadoop. Second, any future changes of Hadoop
may directly be used with Hadoop++ without rewriting any glue code.
Third, Hadoop++ does not need to change the Hadoop interface. Our
experiments show the superiority of Hadoop++ over both Hadoop and
HadoopDB for tasks related to indexing and join processing. In this talk
I will present results from a VLDB 2010 paper as well as more recent work.
Link: http://infosys.cs.uni-saarland.de/hadoop++.php
MapReduce is a computing paradigm that has gained a lot of attention in
recent years from industry and research. Unlike parallel DBMSs,
MapReduce allows non-expert users to run complex analytical tasks over
very large data sets on very large clusters and clouds. However, this
comes at a price: MapReduce processes tasks in a scan-oriented fashion.
Hence, the performance of Hadoop --- an open-source implementation of
MapReduce --- often does not match the one of a well-configured parallel
DBMS. We propose a new type of system named Hadoop++: it boosts task
performance without changing the Hadoop framework at all. To reach this
goal, rather than changing a working system (Hadoop), we inject our
technology at the right places through UDFs only and affect Hadoop from
inside. This has three important consequences: First, Hadoop++
significantly outperforms Hadoop. Second, any future changes of Hadoop
may directly be used with Hadoop++ without rewriting any glue code.
Third, Hadoop++ does not need to change the Hadoop interface. Our
experiments show the superiority of Hadoop++ over both Hadoop and
HadoopDB for tasks related to indexing and join processing. In this talk
I will present results from a VLDB 2010 paper as well as more recent work.
Link: http://infosys.cs.uni-saarland.de/hadoop++.php
Contact
Brigitta Hansen
0681 - 93039100
--email hidden
Video Broadcast
Yes
Kaiserslautern
G26
206
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Rose Hoberman, 12/03/2010 15:43
Brigitta Hansen, 12/02/2010 14:21 -- Created document.
Brigitta Hansen, 12/02/2010 14:21 -- Created document.