Power BI running on "Efficiency Mode" in Windows 11 [JAN 2023]
I have updated my machine to Windows 11 in 2023 and I have noticed in the new task manager that has been released that Power BI is running in Efficiency Mode, meaning that the process priority is Low. There seems to be something wrong with the "msedgewebview2.exe" process, and when Efficiency Mode is on, the data refresh is extremelly slow. There is a workaround, and that is right clicking on the process that is running in Efficiency Mode ("WebView2: https://ms-pbi..."), click "Go to details", right click on the selected process under the Details tab, set priority and select Realtime. Any suggestions on how to solve this permanently? Does anyone know if this is a bug and if it has been reported? I haven't been able to find any documentation about the issue. Thanks!
Avoid Repeating Virtual Table in Every Measure
I have a dynamic chart that changes its appeareance based on selection in a table. In the example below the orange curve line is the generated result. I used a Virtual Table (via Summarize) to speed up the calculation. I have a lot of measures using this approach, and I am afraid repeating this Virtual Table in every measure will cause slow performance (right now it takes up to 3-5 secs for chart to load). I was wondering if I can have this Virtual Table centralized (write it once) and refer to it to speed up performance. I tried adding the Virtual Table as a new Table rather than inside the measure, but this approach doesn't work because Tables are not updated upon query selection (updates at data load). Alternatively, are there other approaches or best practices I should follow to improve performance? Here is the DAX code: Cv.RoundV_L = VAR MinDate = CALCULATE( MIN(Data[Start]), ALLSELECTED(Data) ) VAR MaxDate = CALCULATE( MIN(Data[End]), ALLSELECTED(Data) ) VAR Ticker = [_Ticker] //Virtual table to make calculation faster --- This is Key *** VAR _Mini = SUMMARIZE( FILTER( ALL(Data), Data[Date] >= MinDate && Data[Date] <= MaxDate && Data[Ticker] = Ticker ), Data[Date], "L", MIN(Data[Low]), "_RUN", [_Run] ) //Given a vertex(h,k), find the quadratic equation of a parabola // Y = a(X-h)^2 + k or Y = aX^2 + bX + c // Solve for a, which is a = (Y-k) / (X-h)^2 // Since the vertex is some time to the left or right, thus below combines 2 half curves to make a nice curve VAR Ymin = SUMX( FILTER( _Mini, [_RUN] = 0 ), [L] ) //find the Px_L of the starting point at MinDate or RunCount = 0 VAR Vk = MINX( _Mini,[L]) //Find the lowest point Px_L VAR Vh_L = SUMX( FILTER( _Mini, [L] = Vk ), [_RUN] ) //Find the RunCount X of the lowest point //Left side of vertex -- Curve A VAR aLeft = DIVIDE( (Ymin - Vk), (0-Vh_L)^2 ) VAR LCurve = SUMX( FILTER( _Mini, Data[Date] = MIN('Date'[Date]) ), aLeft * ([_RUN] - Vh_L)^2 + Vk ) //Date=Min(Date) to show each date (avoid aggregation) //Right side of vertex -- Curve B VAR Xmax = MAXX( _Mini, [_RUN] ) VAR Ymax = SUMX( FILTER( _Mini, [_RUN] = Xmax ), [L] ) VAR Vh_R = Xmax - Vh_L VAR Xoffset = Vh_L - Vh_R //Curve calc starts at Run = 0, thus shift curve to right VAR aRight = DIVIDE( (Ymax - Vk), (0-Vh_R)^2 ) VAR RCurve = SUMX( FILTER( _Mini, Data[Date] = MIN('Date'[Date]) ), aRight * ([_RUN]-Xoffset - Vh_R)^2 + Vk ) //Putting it together VAR RunCnt = SUMX( FILTER( _Mini, Data[Date] = MIN('Date'[Date]) ), [_RUN] ) RETURN IF( RunCnt <= Vh_L, LCurve, RCurve )
