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?
