![]() ![]() This query returns only years with sales greater than 8 million: DateĭAX does not have a syntax corresponding to the HAVING condition. Note: you can find more information about differences between ADDCOLUMN and SUMMARIZE in the article Best Practices Using SUMMARIZE and ADDCOLUMNS.įinally, consider the HAVING condition in the following SQL query: In this case, the tables used in the LEFT JOIN are inverted. This happens because the last DAX query corresponds to the following SQL syntax:Īs you see, SUMMARIZE is not required to perform a JOIN, but different DAX syntaxes executes different join types. However, this returns a different result, including calendar years defined in Date table that do not have any corresponding data in the Internet Sales table. You can avoid the SUMMARIZE by using this other DAX syntax: Thus, SUMMARIZE performs the equivalent SQL operations DISTINCT and GROUP BY, and it includes a LEFT JOIN between a table and one or more lookup tables. The table passed as first argument is joined with tables required to reach the column(s) used to group data. You obtain this result in Adventure Works Tabular Model SQL 2012: Date ![]() This is the corresponding DAX syntax (the order by only guarantees that data is displayed as in the following table): In this case, if the data model has a relationship between DimDate and Internet Sales, the DAX expression implicitly use it. For example, consider the following SQL query: The semantic difference between ADDCOLUMNS and SUMMARIZE becomes clearer as soon as we involve more tables in the grouping operation. ![]() This is not required in SUMMARIZE, because the expression specified is already executed in a filter context of the group you specified. Using ADDCOLUMNS you need to apply CALCULATE because you have to transform the row context (defined by the iteration in the Order Date colum) into a filter context. "Sales", CALCULATE ( SUM ( 'Internet Sales' ) ) You can also use a syntax that produces the same result, even if it is not semantically the same, as you will see later: It corresponds to this DAX query using SUMMARIZE: ![]()
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