Those numbers seem "reasonable" to a first approximation, maybe a little higher than I would have expected given past experience.
You should be able to increase speed with more nodes, sure, but I use 3 for testing too. The jobs are I/O bound for sure. I don't think you will see appreciable difference with different algorithms. Yes the amount of data used in the similarity computation is the big factor for time. You probably need to tell it to keep fewer item-item pairs with the "max" parameters you mentioned earlier. mapred.num.tasks controls the number of mappers -- or at leasts suggests it to Hadoop. What do you mean about the time of computation? The job tracker shows you when the individual tasks start and finish. On Thu, Jan 6, 2011 at 1:31 PM, Stefano Bellasio <stefanobella...@gmail.com> wrote: > Hi guys, well i'm doing some tests in those days and i have some questions. > Here there is my environment and basic configuration: > > 1) Amazon EC2 Cluster powered by Cloudera script with Apache Whirr, i'm using > a 3 node with large instances + one master node to control the cluster. > 2) Movielens data set, based on 100k, 1 mln and 10mln ... my tests right now > are on 10 mln versions. > > This is the command that i'm using to start my cluster: > > hadoop jar /home/ste/Desktop/mahout-core-0.5-SNAPSHOT-job.jar > org.apache.mahout.cf.taste.hadoop.item.RecommenderJob > -Dmapred.input.dir=input -Dmapred.output.dir=data/movielens_2gennaio > --maxSimilaritiesPerItem 150 --maxPrefsPerUser 30 --maxCooccurrencesPerItem > 100 -s SIMILARITY_COOCCURRENCE -n 10 -u users.txt > > I'm trying different values for : > > maxSimilaritiesPerItem > maxPrefsPerUser > maxCooccurrencesPerItem > > and using about 10 users per time. With this command, 10 mln user data set, > my cluster took more than 4 hours (with 3 nodes) to give recommendations. Is > a good time? > > > Well, right now i have 2 goals, and im posting here to request your help to > figure out some problems :) My primary goal is to run item-based > recommendations and see what happens when i change the parameters in time and > performance of my cluster. Also, i need to look at the similarities, i will > be test three of them: cousine, pearson, and co-occurence. Good choices? I > noted also that all the similarities computation is in RAM (right?) so my > matrix is built and stored in RAM, is there an other way to do that? > > - I need to understand what kind of scalability i obtain with many nodes (3 > for now, i can arrive to 5), i think that similarities calculation took most > of the time, am i right? > > - I know there is something like mapred.task to define how many instances > some task can use...do i need that? How can i specify this? > > - I need to see the exact time of each computation, i'm looking to jobtracker > but seems that never happens in my cluster even if job (with mapping and > reducing) is running. Is there another way to know the perfect time of any > computation? > > - Finally, i will take all the data and try to plot them to figure out some > good trends based on number of nodes, time and data set dimension. > > Well, any suggestion you want to give me is accepted :) Thank you guys > >
