Category: Visual Calculations

New Books: “The Definitive Guide To DAX” 3rd Edition And “Microsoft Power BI Visual Calculations”

For some reason I haven’t had any free copies of books to review recently; maybe the market for tech books has finally collapsed with AI? Books are still being published though and luckily, as someone who once published a book via an O’Reilly imprint, I have a lifetime subscription to O’Reilly online learning which gives me free access to all the tech books I ever need. Two books were published in the last few months that I was curious to read: the third edition of “The Definitive Guide To DAX” by my friends Marco Russo and Alberto Ferrari, and “Microsoft Power BI Visual Calculations” by my colleague Jeroen ter Heerdt, Madzy Stikkelorum and Marc Lelijveld. As I’ve said many times, I don’t write book reviews here (least of of reviews of books by friends or colleagues where I could never be unbiased), but I think there’s some value sharing my thoughts on these books.

“The Definitive Guide To DAX”, 3rd Edition

It’s generally accepted that the one book that anyone who is serious about Power BI should own is “The Definitive Guide To DAX”. If you don’t already own a copy you should buy one, but since most people who read my blog probably have one already the more interesting question to ask is what’s new in the third edition and whether it’s worth upgrading – especially since I’d seen Marco say that the book had been completely rewritten. I’ve heard the “completely rewritten” line before and I was sceptical but it turns out that it really is a very different book. It’s not completely rewritten because there is material there from previous editions but there are a lot of changes.

First of all, as you would expect, all the new additions to DAX since the second edition was published are covered including user defined functions, visual calculations, calendar-based time intelligence functionality and window functions. These are all really important features you will want to use in your semantic models and reports so this is the main reason you’d want to buy a copy of this edition.

Secondly, the main (and justfied) criticism of the previous editions was that they were, as we say in the UK, “heavy going”. They had absolutely all the information you would ever need but they were not the easiest books to read or understand. That has been addressed in the third edition: the tone is a little bit more friendly and difficult concepts are now explained visually as well as in text. As a result it’s easier to recommend the book for beginners.

Thirdly, some advanced topics (for example around performance tuning) have been dropped. For example I searched for the term “callback” in this new edition and found no mentions; that’s not true of the second edition. I have mixed feelings about this because it means the book isn’t as “definitive” as it used to be, but I can understand why it’s happened: with so much new content to add, keeping these advanced topics would have made an already long book too long. And let’s be honest, how often do you look at the details of a DAX query plan? If the aim is to teach DAX then cutting content means it’s easier for the reader to focus on the core concepts.

In summary, then, another great piece of work from Marco and Alberto and worth buying even if you have a copy of an earlier edition.

“Microsoft Power BI Visual Calculations”

A whole book about visual calculations? As I mentioned above, they’re covered in one chapter of “The Definitive Guide To DAX” but that book focuses on DAX; this one takes more time to explain the concepts and, crucially, includes a lot of practical examples of how to use them. Like user-defined functions, when visual calculations were released there was an explosion of community content showing how they can be used to solve problems that were difficult to solve in Power BI before – problems that no-one could have been anticipated that would be solved with visual calculations. The real value of this book is showing how to build a bump chart or a tornado chart with visual calculations and that makes it worth checking out.

Closing thoughts: why buy a book?

As you would expect, a lot of the information contained in these books is already available for free somewhere on the internet. And with AI you don’t even need to know how to search for it or stitch it all together – you can ask a question and get an answer customised to your exact scenario. So why buy books any more? I guess it depends on whether you only want to get your problems solved or understand how to solve problems yourself. For me (even though my attention span has eroded in recent years, just like everyone else’s) the only way to grasp really difficult concepts is through long-form written explanations or training courses, not fragments found in blog posts or 10-minute videos. I suspect that AI is the final nail in the coffin of the tech publishing industry but the tech book industry not being viable any more is not the same thing as tech books not being useful any more. Or maybe I’m just old-fashioned.

Different Ways To Replace Blanks With Zeros In DAX

My post from a few months ago about the dangers of DAX measures that never return blank attracted quite a lot of attention – this is a hot topic on the forums and adding zeros to measures is a common cause of memory errors in Power BI. In that post, though, I didn’t talk about what the best way to replace blanks with zeros is if you absolutely have no choice but to do so. One of the comments on that post mentioned that visual calculations is an option and this is something I hadn’t thought about before; now, after conversing with the Gods of DAX (no, not the Italians, I mean Akshai, Marius and Jeffrey!) and doing some testing I can reveal that Visual Calculations can be a good choice sometimes, while more traditional DAX approaches are fine at other times.

Let’s see some examples. I created the following model using the AdventureWorksDW 2017 sample data:

There’s a Product dimension, a Customer dimension and a Date dimension plus a fact table containing sales data. The most important thing to note is that individual customers only buy a few products on a few dates. I also created two measures with the following definitions:

Sales Amount = SUM('FactInternetSales'[SalesAmount])

Mountain-100 Black 38 Sales =
CALCULATE (
    [Sales Amount],
    'DimProduct'[EnglishProductName] = "Mountain-100 Black, 38"
)

The [Sales Amount] measure just sums up the values in the SalesAmount column on the fact table; the [Mountain-100 Black 38 Sales] returns the value of [Sales Amount] for just one product.

Now consider a table visual showing LastName from the DimCustomer table, FullDateAlternateKey from DimDate and the [Sales Amount] and [Mountain-100 Black 38 Sales] measures:

There are a lot of rows here because every combination of LastName and FullDateAlternateKey where there is a value for [Sales Amount] is shown. Connecting Profiler to Power BI Desktop and capturing the Execution Metrics trace event (DAX Studio also shows this now) shows that this query has a peak memory consumption of 2063KB

{
	"timeStart": "2024-11-03T19:07:26.831Z",
	"timeEnd": "2024-11-03T19:07:26.844Z",

	"durationMs": 13,
	"vertipaqJobCpuTimeMs": 0,
	"queryProcessingCpuTimeMs": 0,
	"totalCpuTimeMs": 0,
	"executionDelayMs": 0,

	"approximatePeakMemConsumptionKB": 2063,

	"commandType": "Statement",
	"queryDialect": 3,
	"queryResultRows": 502
}

As you can see the [Mountain-100 Black 38 Sales] is mostly empty, and let’s say you need to replace the blanks in this column with zeros.

Changing the measure definition to add zero to the result of the Calculate(), as follows:

Mountain-100 Black 38 Sales =
CALCULATE (
    [Sales Amount],
    'DimProduct'[EnglishProductName] = "Mountain-100 Black, 38"
) + 0

Doesn’t do what you want because now you get a row in the table for every combination of LastName and FullDateAlternateKey, which means the rows which have non-zero values are hard to find:

Instead, only adding zero when there is a value for [Sales Amount], something like this:

Mountain-100 Black 38 Sales =
IF (
    NOT ( ISBLANK ( [Sales Amount] ) ),
    CALCULATE (
        [Sales Amount],
        'DimProduct'[EnglishProductName] = "Mountain-100 Black, 38"
    ) + 0
)

…does the trick. What does memory usage look like? Here are the Execution Metrics:

{
	"timeStart": "2024-11-03T19:17:22.470Z",
	"timeEnd": "2024-11-03T19:17:22.500Z",

	"durationMs": 30,
	"vertipaqJobCpuTimeMs": 0,
	"queryProcessingCpuTimeMs": 31,
	"totalCpuTimeMs": 31,
	"executionDelayMs": 0,

	"approximatePeakMemConsumptionKB": 3585,

	"commandType": "Statement",
	"queryDialect": 3,
	"queryResultRows": 502
}

Memory usage has increased only slightly, to 3585KB.

What about using Visual Calculations instead? Reverting to the original definition of the [Mountain-100 Black 38 Sales] measure and then creating a Visual Calculation like so:

No Blanks = [Mountain-100 Black 38 Sales]+0

…shows that this doesn’t solve the problem because again you get unwanted rows with no sales. Using:

No Blanks = 
IF (
    NOT ( ISBLANK ( [Sales Amount] ) ),
    [Mountain-100 Black 38 Sales] + 0
)

…does solve the problem and you can of course hide the original [Mountain-100 Black 38 Sales] measure column so it doesn’t appear in your table:

But Execution Metrics shows that memory usage is in fact a lot higher, at 11295KB, which is because the resultset now has one extra column in it and Visual Calculations make a copy of the original resultset in memory when they are calculated:

{
	"timeStart": "2024-11-03T19:31:22.858Z",
	"timeEnd": "2024-11-03T19:31:22.980Z",

	"durationMs": 122,
	"vertipaqJobCpuTimeMs": 0,
	"queryProcessingCpuTimeMs": 109,
	"totalCpuTimeMs": 109,
	"executionDelayMs": 0,

	"approximatePeakMemConsumptionKB": 11295,

	"commandType": "Statement",
	"queryDialect": 3,
	"queryResultRows": 502

Does this mean that Visual Calculations should never be used? No, not at all. Consider the following matrix visual which only contains the [Mountain-100 Black 38 Sales] measure and has LastName on rows and FullDateAlternateKey on columns:

Memory usage for this visual is 1091KB:

{
	"timeStart": "2024-11-03T19:51:02.966Z",
	"timeEnd": "2024-11-03T19:51:02.974Z",

	"durationMs": 8,
	"vertipaqJobCpuTimeMs": 0,
	"queryProcessingCpuTimeMs": 16,
	"totalCpuTimeMs": 16,
	"executionDelayMs": 0,

	"approximatePeakMemConsumptionKB": 1091,

	"commandType": "Statement",
	"queryDialect": 3,
	"queryResultRows": 139
}

The resultset returned for the DAX query used to populate this visual only contains one row for each combination of Last Name and Date that has a value for [Mountain-100 Black 38 Sales], 139 rows in all, but because a matrix visual is used to display the results this introduces all the blanks you can see in the screenshot. You could could try to replace these blanks with some very complex DAX but I’m not even going to try. Instead, Visual Calculations solve this problem very easily:

No Blanks Matrix = [Mountain-100 Black 38 Sales]+0

Here’s the matrix with [Mountain-100 Black 38 Sales] hidden and the Visual Calculation applied:

Execution Metrics reveal that peak memory consumption is just 2054KB and the number of rows returned is higher but still just 2070 rows:

{
	"timeStart": "2024-11-03T19:45:49.298Z",
	"timeEnd": "2024-11-03T19:45:49.337Z",

	"durationMs": 39,
	"vertipaqJobCpuTimeMs": 0,
	"queryProcessingCpuTimeMs": 31,
	"totalCpuTimeMs": 31,
	"executionDelayMs": 0,

	"approximatePeakMemConsumptionKB": 2054,

	"commandType": "Statement",
	"queryDialect": 3,
	"queryResultRows": 2070
}

Overall, both traditional DAX solutions and Visual Calculations are effective in different scenarios, so I suggest that you test the query performance and memory usage of different solutions yourself.