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Category: DAX

Window Functions in DAX

Marco Russo and Alberto Ferrari lay out the foundations of window functions:

Window functions by themselves do not increase the expressivity of DAX. Most if not all of the calculations performed with window functions can be expressed with more complex DAX code. The goal is to simplify authoring these calculations and improve their performance.

These new functions also introduce a new concept to the DAX language: “apply semantics”. We will publish more articles about window functions and “apply semantics” over time. SQLBI+ subscribers will get a dedicated video course later this year and already have access to the window functions whitepaper we are currently writing.

Click through for an introduction.

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Building a Generic Percent of Grand Total in DAX

Leo Tachev isn’t down with the copy-pasta:

Suppose you need to calculate a percentage of grand total measure. Easy, you can use the Power BI “Show value as” without any DAX, right? Now suppose that you have 50 Table visuals and each of them require the same measure to be shown as a percentage of total. Although it requires far more clicks, “Show value as” is still not so bad for avoiding the DAX rabbit hole. But what about if you need this calculation in another measure, such as to implement a weighted average? Now, you can’t reference the Microsoft-generated field because it’s not implemented as a measure.

Click through for one solution.

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Minimizing Calculated Column Usage in Power BI

Ed Hansberry wants us to slow down and think:

First off I want to make sure it is clear that I am not saying “never use calculated columns.” I am not. What I am saying is they should be avoided, and there are several reasons. I am going to focus on the model size, and thus performance. Note that the data I am going to show is a contrived set of data for demonstration purposes only to explain.

Read the whole thing, including a set of questions you might want to ask yourself before deploying that PBIX file with calculated columns in it.

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Debugging DAX Measures in Power BI

Marco Russo and Alberto Ferrari bust out the oscilloscope:

Finding errors in DAX measures has always been difficult because a single DAX measure produces different results in different cells of the same report, and we do not have a direct way of executing the code step by step in Power BI. This article describes four techniques to find an error in a DAX formula: we start with variable manipulation that does not require any external tool, and then we see how to leverage features available in DAX Studio, DAX Debug Output, and Tabular Editor 3.

Do check this out, especially if you spend a lot of time writing DAX code.

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DAX Window Functions and Power BI DirectQuery

Chris Webb points out another benefit of DAX window functions:

The new DAX window functions (announced here, more details on Jeffrey Wang’s blog here and here) have generated a lot of excitement already – they are extremely powerful. However one important benefit of using them has not been mentioned so far: they can give you much better performance in DirectQuery mode because they make it more likely that aggregations are used. After all, the fastest DirectQuery datasets are the ones that can use aggregations (ideally Import mode aggregations) as much as possible.

As always, Chris has a demo for us, so check it out.

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A Recap on DAX in 2022

Marco Russo recaps the year:

Another important event for the DAX world has been the release of DAX Studio 3, with an important restyling of the user interface and user experience.

After two years, the composite models – whose official name is DirectQuery for Power BI datasets and Analysis Services – are still in preview. General availability should probably happen in 2023. In the meantime, the feature has been refined and improved. At SQLBI we also released new content about this topic.

Click through for info on eight new DAX functions, as well as what Marco and Alberto have been doing all year.

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Avoiding Problems with DAX Window Functions

Jeffrey Wang shares a few tips to avoid issues with window functions in DAX:

Several people had reported running into errors when trying the window functions on fact tables. Let’s look at an example by first adding a calculated table of three columns to the model that is defined by the following DAX expression, and then, add all three columns to a table visual as shown in Figure 1.

Read on for more. This is an area where expected behavior does differ from what you get with window functions in T-SQL.

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Window Functions in DAX

Jeffrey Wang is speaking my language:

The December 2022 release of Power BI Desktop includes three new DAX functions: OFFSETINDEX, and WINDOW. They are collectively called window functions because they are closely related to SQL window functions, a powerful feature of the SQL language that allows users to perform calculations on a set of rows that are related to the current row. Because these functions are often used for data analysis, they are sometimes called analytical functions. In contrast, DAX, a language invented specifically for data analysis, had been missing similar functionalities. As a result, users found it hard to write cross-row calculations, such as calculating the difference of the values of a column between two rows or the moving average of the values of a column over a set of rows.

Read on to learn more about how these functions work and how they differ from their SQL Server counterparts.

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Solving Common CALCULATE Filter Argument Errors

Marco Russo and Alberto Ferrari catalog some errors:

The expression contains columns from multiple tables, but only columns from a single table can be used in a True/False expression that is used as a table filter expression.

This error is seen when the predicate includes column references from more than one table. For example, if we need a measure that returns the sales made to customers living in the same country as the store, we could try to write the following measure:

Read on for several examples and solid guidance on how to resolve these common issues.

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