So I was having a conversation with Dan Huff today about Window_Sum() vs Total() and he helped me build a great example showing the difference.
Total() is computed ignoring the Partition and goes against the Database unlike Window_Sum() which is computed locally. What does this mean, well.. see the attached workbook.
Placing [State] and [City] on the Rows shelf provides a list of each City within each State. Some Cities exist in many States though. (Apparently lots of people like to live in "Springfield", "Clinton" and "Columbus" ). The view/sheet named "Just City" shows the number of States per City. There are 1727 combinations of City/State in the data set though.
However, we have 1523 unique City name values so if the field Total(Countd([City])) is placed on the measures and computed as Table Down, the value returned is 1523.
Now, to see the difference between Total and Window_Sum, let's look at the "State and City" view/sheet that uses Total(Countd([City])) vs Window_Sum(Countd[City])) and we can see the Total() returns the 1523 ignoring the State partition. However Window_Sum() honors the partition and returns 1727.
I thought this might be a nice example for others and I didn't want to lose the workbook so I am sharing here.
Thanks to Dan as well.