Not sure how that would work in a corporate setting wherein there's a fixed systemwide setting that cannot be overridden.
Sent from my iPhone > On Dec 25, 2013, at 9:44 AM, Sebastian Schelter <s...@apache.org> wrote: > >> On 25.12.2013 14:19, Suneel Marthi wrote: >> >> >> >> >> >>>> On Tuesday, December 24, 2013 4:23 PM, Ted Dunning <ted.dunn...@gmail.com> >>>> wrote: >> >>>> For reference, on a 16 core machine, I was able to run the sequential >>>> version of streaming k-means on 1,000,000 points, each with 10 dimensions >>>> in about 20 seconds. The map-reduce versions are comparable subject to >>>> scaling except for startup time. >> >> @Ted, were u working off the Streaming KMeans impl as in Mahout 0.8. Not >> sure how this would have even worked for u in sequential mode in light of >> the issues reported against M-1314, M-1358, M-1380 (all of which impact the >> sequential mode); unless u had fixed them locally. >> What were ur estimatedDistanceCutoff, number of clusters 'k', projection >> search and how much memory did u have to allocate to the single Reducer? > > If I read the source code correctly, the final reducer clusters the > sketch which should contain m * k * log n intermediate centroids, where > k is the number of desired clusters, m is the number of mappers run and > n is the number of datapoints. Those centroids are expected to be dense, > so we can estimate the memory required for the final reducer using this > formula. > >> >> >> >> >>> On Mon, Dec 23, 2013 at 1:41 PM, Sebastian Schelter <s...@apache.org> wrote: >>> >>> That the algorithm runs a single reducer is expected. The algorithm >>> creates a sketch of >> the data in parallel in the map-phase, which is >>> collected by the reducer afterwards. The reducer then applies an >>> expensive in-memory clustering algorithm to the sketch. >>> >>> Which dataset are you using for testing? I can also do some tests on a >>> cluster here. >>> >>> I can imagine two possible causes for the problems: Maybe there's a >>> problem with the vectors and some calculations take very long because >>> the wrong access pattern or implementation is chosen. >>> >>> Another problem could be that the mappers and reducers have too few >>> memory and spend a lot of time running garbage collections. >>> >>> --sebastian >>> >>> >>> On 23.12.2013 22:14, >> Suneel Marthi wrote: >>>> Has anyone be successful running Streaming KMeans clustering on a large >>> dataset (> 100,000 points)? >>>> >>>> >>>> It just seems to take a very long time (> 4hrs) for the mappers to >>> finish on about 300K data points and the reduce phase has only a single >>> reducer running and throws an OOM failing the job several hours after the >>> job has been kicked off. >>>> >>>> Its the same story when trying to run in sequential mode. >>>> >>>> Looking at the code the bottleneck seems to be in >>> StreamingKMeans.clusterInternal(), without understanding the behaviour of >>> the algorithm I am not sure if the sequence of steps in there is correct. >>>> >>>> >>>> There are few calls that call themselves repeatedly over and over again >>> like SteamingKMeans.clusterInternal() and Searcher.searchFirst(). >>>> >>>> We really need to have this working on datasets that are larger than 20K >>> reuters datasets. >>>> >>>> I am trying to run this on 300K vectors with k= 100, km = 1261 and >>> FastProjectSearch. >
