Forum Discussion
Model Performance Enhancements?
It’s very hard to say without seeing it. Every relationship has a cost, and the cost is higher the higher the cardinality of the related columns. So you can improve things by removing high cardinality relationships. This could be done by consolidating fact tables.
Options I think worthy of consideration include
Removing columns in fact tables that are not used
Unpivoting columns in fact tables
Consolidating fact tables together
Eg
if you can have 1 fact table with columns
type, amount
target, 5
actual, 4
other fact, 3
etc, 8
then you can write
Total target = calculate(sum(table[amount]),table[type=“target”)
Total actual = calculate(sum(table[amount]),table[type=“Actual”)
Divide([total actual],[total target])
Thanks for the reply. I have 25 tables in the model - 4 main fact tables that add up to about 400k rows, several dimension tables, and several complimentary tables with targets and such. The fact table with the most rows (200k) has about 24 columns.
The majority of the joins between these tables are done via int keys.
I do have a bunch of calculated measures that reference multiple tables in the model - could this be the problem?
For example:
% of target = DIVIDE(sum(Facts[Amount]),sum(Targets[Amount]))*100
Or maybe it's just the number of tables and I should somehow consolidate them?
Thanks again.
As MattAllington already pointed out 400k is a very small model. It shouln't cause a performance issue. Do you have specific charts/Tables which are slow? If yes, what measures you use in that visual?