There is a database table with three columns into which data from an external source is loaded.
The data is loaded as a CSV file and contains three columns to represent the date, hour and minutes. The CSV file is loaded into a staging table with three columns:
- one for the date, datatype: DATE
- one for the hours, datatype NUMBER
- one for the minutes, datatype NUMBER
Loading the information in the destination table, there is only one column to hold the data. These three columns should be transformed into a "real" date, meaning a DATE datatype as we know it.
One way is to concatenate all the columns together and apply the TO_DATE function with the appropriate format mask. With this method you need to take care of leading zeros for the hours and minutes.
This method would result in something like the following:
to_date( to_char( e.csv_date, 'DD-MM-YYYY') || ' ' || to_char( lpad( e.hour, 2, '0')) || ':' || to_char( lpad( e.minute, 2, '0')), 'DD-MM-YYYY HH24:MI') as clock_time
As you can imagine, it was only because of exceptions before realizing that the hours and minutes should be left padded with zeros.
There is another, easier, way to transform the three columns into a single DATE column:
e.csv_date + numtodsinterval (e.hour, 'HOUR') + numtodsinterval (e.minute, 'MINUTE') as clock_timeUsing the NUMTODSINTERVAL function for the hours and the minutes makes the transformation trivial. No need to worry about leading zeros (or lack thereof). NUMTODSINTERVAL is a great little function that makes code so much more elegant.