Flat dimension hierarchy

In general, avoid using large, flat dimensions (that is, dimensions with thousands of dimension values at the same level of hierarchy).

This is doubly true if statistics are enabled for those dimensions. It is better to design dimensions that contain sensible levels of hierarchy.

For some applications with extremely large, non-hierarchical dimensions, larger values for --esampmin can meaningfully improve dynamic refinement ranking quality with minor performance cost.