Every metric and table column in Qvasa treats time in one of three ways. Knowing which category a metric belongs to explains what its number means, why the date filter sometimes seems to "do nothing," and why some metrics can look into the past while others can't. This guide defines all three categories, shows you how to tell them apart, and explains what to do if a metric you rely on doesn't support historical queries yet.

Here are all three side by side on one dashboard, each with the same "Last 30 days" date filter applied. The range-based widget counts tickets created in those 30 days; the moment-in-time widget shows the queue as of the end of the range; the live widget stamps the exact "as of" moment in its title because it always measures right now.

Most metrics in Qvasa are range-based. They aggregate events — tickets created, tickets solved, replies sent, surveys returned — strictly between the start and end of your selected date range. Nothing outside the window is counted.

Range-based metrics always respond to the date filter: change the range and the number changes with it, because you are literally asking a different question ("tickets created between two different dates"). They are also the natural fit for trend charts and period-over-period comparisons.

Moment-in-time metrics don't count events over a window — they reconstruct the state of your operation at a single instant. Outstanding Tickets is the flagship example: it answers "how many un-closed tickets existed at this moment?"

The instant measured is the end of your selected date range. That is what makes these metrics able to time travel: set your range to end last Tuesday and Qvasa rebuilds the count as it was last Tuesday, using the ticket opening and closing event history. Queue-height-over-time charts work the same way, sampling a moment at each point along the x-axis.

Two things to know when using them:

Live metrics answer "what is true right now": how many calls are waiting, which agents are online, how long has each ticket been sitting in its current queue. Their queries are optimized specifically for reading current state, which is what makes them fast enough to power real-time dashboards and frequently-refreshing views.

That optimization is also their limitation: live metrics do not respond to date filters and cannot reconstruct the past. Selecting last week does not show you last week's calls-waiting count — the widget still reflects the current moment (the date range only bounds how far back Qvasa scans for items that are still in that state). Live widgets make this visible by stamping the "as of" time in their titles, and trend chart types are not offered for them, since a trend would require historical reconstruction they don't support.

Table columns can be live too. In ticket tables, columns prefixed "Live" — such as Live Time In Queue — measure against the current moment.

Every widget's description states how it treats time. When adding or editing a widget, select it in the widget picker and read the description panel on the right. Moment-in-time metrics say things like "at a moment in time" and "Historical-reconstructable — selecting a past date reconstructs the count as it was then":

Live metrics say so explicitly — "This is a live metric":

A quick rule of thumb when reading a dashboard: if the metric name describes an event count or aggregate (created, solved, replied), it is range-based; if it describes a state (outstanding, open, in queue) it is either moment-in-time or live — check the description or look for an "as of" timestamp in the widget title.

Live and historical are genuinely different queries under the hood. A live metric reads the current state directly — a small, heavily-optimized lookup. Reconstructing the past requires replaying event history to figure out what the state was, which needs different data and a much heavier query. Tuning a metric for one makes it the wrong tool for the other, so rather than slow every live metric down, Qvasa maintains a separate library of moment-in-time metrics for the states where history matters most — Outstanding Tickets, historical queue heights, and friends.

Want to time travel with a metric that is currently live-only? Submit a feature request — reach out to the Qvasa team through your Qvasa representative or support contact and tell us which metric you'd like to query historically. We'll look into adding a moment-in-time counterpart that can time travel.