Some of the recommendation algorithms are less scalable, but the clustering algorithms, frequent item set mining and most of the classification algorithms are parallelized with good bounds on time and space or should be relatively scalable single processor implementations. There are relatively scalable versions of recommendation algorithms that should exhibit fairly good scaling even to billions of data points.
There are, however, relatively few users with really large data sets so far so you will almost certain encounter some problems with some algorithms. Your feedback would be of great interest and the result would likely be fairly quick improvements. If you can be more specific, I am sure you can get some better answers than I just gave. On Sun, Apr 25, 2010 at 10:00 PM, First Qaxy <qa...@yahoo.ca> wrote: > Hi All, > How scalable are Mahout's algorithms (clustering, classification, also > recommendation) for very large datasets (>1bln points, or in other words > have memory constraints)? > Many thanks. > -qf > >
