Advances in processor technology brought significant improvements to the way that operations such as floating-point math are handled; however, retrieving and sending certain data types are still expensive when the active portion of your application will not fit into on-chip cache. When you are working with data on a large scale, it is still important to select the data type that can be processed most efficiently. For example, integer data is processed faster than floating-point data. Floating-point data is defined according to internal database-specific formats, usually in a compressed format. The data must be decompressed and converted into a different format so that it can be processed by the wire protocol.
Processing time is shortest for character strings, followed by integers, which usually require some conversion or byte ordering. Processing floating-point data and timestamps is at least twice as slow as processing integers.
