Keep Column types from Java ResultSet in CSV export - resultset

Question

I'm currently building a tool that pulls data directly from a database because SPSS Modeler is too slow and store it in a Java ResultSet first of all.
But I try to export the data into a CSV (or similar) file and try to keep as much column types as possible.
Currently I'm using opencsv but it casts Decimals and many others to a String. When I load the file back into SPSS Modeler I get only Integers and Strings.
Are there any CSV libraries (maybe with a special encoding) or other file types I can use to export the data with its column types (like IBM InfoSphere Data Architect can do) so I can load it directly back into SPSS Modeler without changing it back manually there ?
Thank you!

Answer 1

Retrieving the Metadata from the DB Information Schema
If the data is currently stored in a database, you can retrieve the column type from the information schema. All you need to do is retrieving this information after your queried the table and store it so that you can reuse it later.
// connect to DB as usual
Statement stmt = conn.createStatement();
// create your query
// Note that you can use a dummy query here.
//You only need to access the metadata schema of the table, regardless of the actual query.
ResultSet rse = stmt.executeQuery("Select A,B FROM table WHERE ..");
// get the ResultSetMetadata
ResultSetMetaData rsmd = rse.getMetaData();
// Get database specific type
rsmd.getColumnTypeName(1); // database specific type name for column 1 (e.g. VARCHAR)
rsmd.getColumnTypeName(2); // database specific type name for column 2 (e.g. DateTime)
....
// Get generic JDBC type http://docs.oracle.com/javase/7/docs/api/java/sql/Types.html
rsmd.getColumnType(1) // generic type for col 1 (e.g. 12)
rsmd.getColumnType(2) // generic type for col 2
Processing
You could store this information in a CSV schema and process this during the transformation process.
I recommend that you use SuperCSV, which is available here.
This library provides so called cell processors, which allow you to define the type of the columns.
Description:
Cell processors are an integral part of reading and writing with Super CSV - they automate the data type conversions, and enforce constraints. They implement the chain of responsibility design pattern - each processor has a single, well-defined purpose and can be chained together with other processors to fully automate all of the required conversions and constraint validation for a single CSV column.