Reputation: 281
I am using Mahout to build an Item-based Cf recommendation engine. I create an MahoutHelper class which has a constructor:
public MahoutHelper(String serverName, String user, String password,
String DatabaseName, String tableName) {
source = new MysqlConnectionPoolDataSource();
source.setServerName(serverName);
source.setUser(user);
source.setPassword(password);
source.setDatabaseName(DatabaseName);
source.setCachePreparedStatements(true);
source.setCachePrepStmts(true);
source.setCacheResultSetMetadata(true);
source.setAlwaysSendSetIsolation(true);
source.setElideSetAutoCommits(true);
DBmodel = new MySQLJDBCDataModel(source, tableName, "userId", "itemId",
"value", null);
similarity = new TanimotoCoefficientSimilarity(DBmodel);
}
and the recommend method is:
public List<RecommendedItem> recommendation() throws TasteException {
Recommender recommender = null;
recommender = new GenericItemBasedRecommender(DBmodel, similarity);
List<RecommendedItem> recommendations = null;
recommendations = recommender.recommend(userId, maxNum);
System.out.println("query completed");
return recommendations;
}
It's using datasource to build datamodel but the problem is that when mysql has only a few data (less than 100) the program works fine for me, while when the scale turns to be over 1,000,000, the program stacks at doing recommendation and never goes forward. I have no idea how it happens. By the way I used the same data to build a FileDataModel with a .dat file, and it takes only 2~3 second to complete analysis. I am confused.
Upvotes: 1
Views: 328
Reputation: 66876
Using the database directly will only work for tiny data sets, like maybe a hundred thousand data points. Beyond that the overhead of such data-intensive applications will never run quickly; a query takes thousands of SQL queries or more.
Instead you must load and re-load into memory. You can still pull from the database; look at ReloadFromJDBCDataModel
as a wrapper.
Upvotes: 2