A look into a recent blogpost of mine might maybe be helpful with choosing the appropriate data access strategies for your recommender setup. It covers a very common usecase in great detail:
http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/ --sebastian 2011/7/4 Mark <[email protected]>: > I wouldn't use the in memory JDBC solution. > > I was wondering do most people choose the JDBC backed solutions or the File > backed? > > On 7/4/11 10:17 AM, Sean Owen wrote: >> >> Yes. Both are just fine to use in production. For speed and avoiding abuse >> of the database, I'd load into memory and tell it to periodically reload. >> But that too is a bit of a choice between how often you want to consume >> new >> data and how much work you want to do to recompute new values. >> >> On Mon, Jul 4, 2011 at 6:13 PM, Mark<[email protected]> wrote: >> >>> Ahh ok. So if I want everything in memory like the file backed solution I >>> should use ReloadFromJDBCDataModel? I'm going to give that a try right >>> now. >>> >>> Typically which solution is recommended for production use? >>> >>> Thanks >>> >>> >
