Thanks, it's very nice to see that they integrated Map Reduce.
But as I understood it this does not work (yet) for distributed
systems, but only on one single machine.
Am 01.06.2008 um 14:33 schrieb Brice Arnould:
Hi !
With Qt 4.4, Trolltech provides a GPLed implementation of an in memory
map/reduce for many languages (at least c++ and Java) as a part of
QtConcurrent.
I have not used this yet, but in general their API are well tough
and their
code very slick. You might want to have a look at this.
Code sample :
| QImage scaled(const QImage &image) {
| return image.scaled(100, 100);
| }
| QList<QImage> images = ...;
| QFuture<QImage> thumbnails = QtConcurrent::mapped(images, scaled);
Doc :
http://doc.trolltech.com/4.4/qtconcurrentmap.html#map
Qt 4.4 GPL :
http://trolltech.com/downloads/opensource
Qt 4.4 Commercial :
http://trolltech.com/downloads/commercial
Brice
On dimanche 1 juin 2008, Martin Jaggi wrote:
Thanks for your comments!
So in the case that all intermediate pairs fit into the RAM of the
cluster, does the InMemoryFileSystem already allow the intermediate
phase to be done without much disk access? Or what would be the
current bottleneck in Hadoop in this scenario (huge computational
load, not so much data in/out) according to your opinion?
