Double the visible filters in your widget toolbars

In our previous NAB tip we described a new feature for designating certain filter fields as mandatory in order to minimize record set sizes and ensure desired indexes are used for better performance. In today’s tip we’ll expand on that topic with yet another simple but powerful filter-related feature, this time focusing on the aesthetics behind the presentation of filters in toolbars. 

Historically, when the number of filters configured for a NAB widget toolbar exceeded the space available on a single line, developers had to choose between (1) requiring users to scroll horizontally to see all the filter fields, or (2) having users click a special button to bring up a pop-up window / overlay listing all the filter fields. Neither choice is optimal, and with most users preferring filters displayed in a toolbar directly over the data, developers would often choose to limit the number of filter fields and/or reduce the entry field widths in order to provide the best looking interface.

But now there’s a new option!  Effective with this month’s build (6.3.20260306), grids and other widgets can now have a filter toolbar with two rows, making it possible to show more filter fields at one time without scrolling.

The process for configuring a second row of filter fields is simple. On your widget’s filter tab, look for a new column near the right edge entitled “Second Row” and simply check the cell for each filter field you would like to be moved down to a second row, as depicted here:

Note that this setting only applies to filters displayed on toolbars, as specified in the Settings slideout bar on the right. The result of this adjustment shows how more filters can now be displayed at one time directly over the widget’s data:

In this example the two-row filter toolbar is associated with a line chart, but you could just as easily apply this same filter feature to a large grid with many columns. Users tend to appreciate the clarity of seeing all their filter values directly over the data, making it less likely they will “forget” they have any filters applied when interpreting the results.