[EazyBi] How to count tickets first entering High/Critical/Urgent priority in eazyBI

Question

I would like to create a measure that captures the number of tickets that entered a High, Critical, or Urgent priority within a specified time period and are still currently in one of those priorities.

A ticket can enter these priorities either by:

* being created with a High/Critical/Urgent priority, or
* being transitioned to one of these priorities for the first time.

My current calculated measure is:

-- -- created into this priority and has never been transitioned

NonZero(Count(

Filter(

Descendants([Issue].CurrentMember, [Issue].[Issue]),

[Measures].[Issues created] > 0

AND

IsEmpty((

[Measures].[Transition to first timestamp],

[Transition Field].[Priority],

[Time].CurrentHierarchy.DefaultMember

))

)

))

+

-- -- first escalation to priority

Filter(

Descendants([Issue].CurrentMember, [Issue].[Issue]),

([Measures].[Transitions to issues count],

[Transition Field].[Priority]) > 0

AND

DateInPeriod(

TimestampToDate(

([Measures].[Transition to first timestamp],

[Transition Field].[Priority],

[Time].CurrentHierarchy.DefaultMember)

),

[Time].CurrentHierarchyMember

)

))

However, this calculation always times out, so I haven't been able to get any results.

Is there a more efficient way to achieve the same result, or a way to optimise this calculation so that it is less computationally expensive?

Arkadiusz Wroblewski · Answer

Hello @Yingjie Wang

eazyBI Support has an example using Transition to first timestamp with an aggregated Priority member:

https://community.eazybi.com/t/calculated-measure-for-last-priority-transition-date-multiple-values/17507

Best,

Arek🤠🧙‍♂️

Tomislav Tobijas · Answer

Hi @Yingjie Wang ,

I'm far from a reporting expert, but I know a few of my colleagues have worked with eazyBI, and when they would stumble on an issue, official vendor support would often help out.

So, I'd recommend reaching out directly to eazyBI Support > you can find contact details there. 🎟️

From my experience, their team is helpful with these kinds of complex MDX calculations and report optimizations, so they might be able to suggest a more efficient approach. 🙂

Cheers,

Tobi

Tintu Paul · Answer

The main reason your calculation is timing out is that it scans every issue and checks its priority transition history, which is computationally expensive. I recommend two improvements: * Optimize the MDX: Filter issues by their current priority (High, Critical, or Urgent) before evaluating transition history. This reduces the number of issues being processed. * Precalculate the first qualifying date: During eazyBI import, record when each issue first entered one of these priorities, either at creation or through a priority change. Then count issues whose qualifying date falls within the selected period and whose current priority is still High, Critical, or Urgent. The second approach is likely to perform better on large datasets and more accurately reflect your requirement.