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https://issues.apache.org/jira/browse/MAHOUT-372?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen resolved MAHOUT-372.
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Resolution: Fixed
Fix Version/s: 0.4
Assignee: Sean Owen
Yes, sure there's no particular limit to the number of mappers or reducers.
These are Hadoop params, which you can set on the command line with, for
example:
-Dmapred.map.tasks=10 -Dmapred.reduce.tasks=10
Reopen if that doesn't quite answer the question. (We can also discuss on
mahout-u...@apache.org, perhaps, if this isn't necessarily a bug or enhancement
request.)
> Partitioning Collaborative Filtering Job into Maps and Reduces
> --------------------------------------------------------------
>
> Key: MAHOUT-372
> URL: https://issues.apache.org/jira/browse/MAHOUT-372
> Project: Mahout
> Issue Type: Question
> Components: Collaborative Filtering
> Affects Versions: 0.4
> Environment: Ubuntu Koala
> Reporter: Kris Jack
> Assignee: Sean Owen
> Fix For: 0.4
>
>
> I am running the org.apache.mahout.cf.taste.hadoop.item.RecommenderJob main
> on my hadoop cluster and it partitions the job in 2 although I have more than
> 2 nodes available. I was reading that the partitioning could be changed by
> setting the JobConf's conf.setNumMapTasks(int num) and
> conf.setNumReduceTasks(int num).
> Would I be right in assuming that this would speed up the processing by
> increasing these, say to 4)? Can this code be partitioned into many
> reducers? If so, would setting them in the protected AbstractJob::JobConf
> prepareJobConf() function be appropriate?
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