Controls

Filter and time grain switcher widgets that let dashboard viewers change what’s shown on the dashboard.

Controls are widgets that let dashboard viewers change what’s shown without leaving the dashboard. The dashboard builder offers two control types — both target a dimension from your semantic model and apply the selected value to every chart on the dashboard whose query references that dimension.

• Filter — Narrow the data shown on the dashboard
• Time grain switcher — Change the granularity of time-based dimensions

Filter

Filter widgets let viewers narrow down the data shown on the dashboard. In the dashboard builder, click Add Filter in the toolbar. The new filter is added in an unconfigured state — click Configure Filter (or open the widget’s settings menu) to pick a semantic view and a dimension.

Operators by dimension type

The available operators depend on the type of the underlying dimension:

Dimension type	Operators
String	is, is not, contains, not contains, starts with, not starts with, ends with, not ends with, is null, is not null
Number	is, is not, greater than, greater than or equal, less than, less than or equal, is null, is not null
Time	is, is not, before date, before or on date, after date, after or on date, between, relative date, is null, is not null

Single vs. multiple selection

Filters can allow either a single value or multiple values. Configure this when adding or editing the filter — multi-select is the default for string dimensions, while time and number dimensions default to a single value.

Default values

You can set a default value that’s applied when the dashboard loads. Defaults are useful for scoping the dashboard to “this quarter” or “the user’s region” without requiring viewers to interact with the filter first.

There are two ways to set a default:

• Static default — pick a value (or values) directly in the filter. Every viewer sees the same default.
• User attribute default — resolve the default from the viewer’s user attribute at load time, so each viewer sees their own personalized default.

Static defaults are configured by interacting with the filter in the dashboard builder — the value you select is saved on the widget and applied to every viewer when the dashboard loads.

User attribute default

Use the User attribute default toggle in the filter’s edit sidebar to pre-fill a filter from the viewer’s user attribute. When the dashboard loads, Cube looks up the attribute value for the current viewer and applies it as the filter’s default.

This is useful for scoping a dashboard to the viewer’s own slice of the data — for example, defaulting a Region filter to the viewer’s region attribute, or a Sales rep filter to their email.

To configure it:

1. Open the filter's settings

   In the dashboard builder, click the filter widget’s settings menu and choose Edit Filter.

2. Enable User attribute default

   Scroll to the User attribute default switch and turn it on.

3. Pick the attribute

   Select the user attribute whose value should be used as the default. Only attributes defined in your account appear in the picker.

How the attribute value is matched to the filter:

Attribute type	How it’s applied
String, Number	Used as a single value. Works with single-value operators like is / is not, and is also accepted by multi-select filters as a one-item selection.
String array, Number array	Used as a list of values, one per array entry. Empty values are dropped.

Empty, null, or unresolvable attribute values are skipped — the filter falls back to whatever static default it has, or no default if none is set.

The user attribute default only seeds the filter’s initial value. Viewers can still change the filter unless its visibility is set to Disabled, in which case the resolved attribute value is locked in for that viewer. Values passed via URL parameters also take precedence over user attribute defaults, so deep links continue to work.

Faceted filters

When multiple filters target dimensions from the same semantic view, you can mark them as faceted. Faceted filters scope each other’s value lists — selecting a value in one filter narrows the options shown in the others, so viewers only see combinations that exist in the data.

For example, on a sales dashboard with a Country filter and a City filter, marking both as faceted means selecting United States in the Country filter limits the City filter to U.S. cities only.

Time grain switcher

Time grain switchers let viewers change the granularity of time-based dimensions on the dashboard — for example, switching a revenue chart from daily to weekly or monthly. The widget targets a single time dimension and applies the chosen granularity to every chart that groups by that dimension.

In the dashboard builder, click Add Time Grain in the toolbar. The new switcher is added in an unconfigured state — open its settings to pick a semantic view and a time dimension.

Allowed grains

By default, viewers can choose between day, week, month, quarter, and year. You can narrow this list in the widget’s settings to only expose the grains that make sense for the dashboard.

For time dimensions backed by a TIMESTAMP or DATETIME column, sub-day grains (second, minute, hour) are also available. DATE-typed columns don’t expose sub-day grains, since they would bucket the entire day into a single point.

Custom granularities defined in the data model appear in the list alongside the built-in grains.

Default grain

You can configure a default grain that’s applied when the dashboard loads. If no default is set, charts use the grain that was saved on the underlying report — viewers can still switch grains, but the dashboard opens with each chart at its original granularity.

Visibility

Each control has a Visibility setting that determines how it appears on the published dashboard. The setting applies to both filters and time grain switchers.

Visibility	Behavior on the published dashboard
Visible (default)	Shown on the dashboard and viewers can change its value.
Hidden	Not shown to viewers, but the control’s value is still applied to the charts it targets. Use this to scope a dashboard with a fixed value — e.g., always filter to the current quarter — without exposing the control.
Disabled	Shown on the dashboard so viewers can see the active value, but they cannot change it.

Set the visibility from the Visibility dropdown when editing the control. Hidden controls remain visible in the dashboard builder so editors can reconfigure them, but disappear from the published view.

Interaction with charts

When a control is added to a dashboard, it’s automatically wired up to every chart whose query already uses the same dimension. Charts that don’t reference that dimension are left alone, so a dashboard can mix scoped and unscoped views by default. You can override this default per chart from its Controls mapping — disable the control for that chart, or remap it onto a different dimension.

Controls mapping

Each chart decides which controls apply to it through its Controls mapping. The mapping is resolved automatically in most cases and only needs manual attention when a control targets a dimension the chart doesn’t have.

Open Controls mapping from a chart’s settings menu to inspect or override the mapping for that chart. For each control on the dashboard you can:

• Toggle the control on or off for the chart, even when a mapping exists
• Pick a different dimension from the chart’s semantic view to remap the control to

Three states show up in the mapping sidebar:

Status	What it means
Mapped automatically	The control’s dimension exists on the chart’s semantic view, so it’s wired up without configuration.
Manually mapped	You (or an AI agent) picked a specific dimension for this chart. Reset restores the automatic mapping.
Can’t map automatically	The control targets a dimension that doesn’t exist on the chart’s semantic view. The chart is unaffected by the control until you map it manually.

For time grain switchers, the dimension picker is restricted to time-typed dimensions on the chart’s semantic view, since other dimension types can’t be resolved by the time grain pipeline.

Mappings are also configurable by AI agents when they build or edit a dashboard, so an agent can wire controls across charts that use different semantic views without you needing to revisit each chart manually.