We are still in the early days of DAX and even if I have been using it since 2 years ago, there is still a lot to learn on that.

One of the topics that historically interests me (and many of the readers here, probably) is the many-to-many relationships between dimensions in a dimensional data model. When I and Alberto wrote the The Many to Many Revolution 2.0 we discovered the SUMMARIZE based pattern very late in the whitepaper writing. It is very important for performance optimization and it should be always used. In the last month, Gerhard Brueckl also presented an approach based on cross table filtering behavior that simplify the syntax involved, even if it’s harder to explain how it works internally.

I published a short article titled Optimize Many-to-Many Calculation in DAX with SUMMARIZE and Cross Table Filtering on SQLBI website just to provide a quick reference to the three patterns available. A further study is still required to compare performance between SUMMARIZE and Cross Table Filtering patterns. Up to now, I haven’t observed big differences between them, even if their execution plans might be not identical and this suggest me that depending on other conditions you might favor one over the other.


Creates a summary of the input table grouped by the specified columns.

SUMMARIZE ( <Table> [, <GroupBy_ColumnName> [, [<Name>] [, [<Expression>] [, <GroupBy_ColumnName> [, [<Name>] [, [<Expression>] [, … ] ] ] ] ] ] ] )