watching Jeffrey Wang as a stay stream visitor with Reid Havens, and one of many dozen great issues that Jeffrey shared with the viewers was the record of optimizations that the DAX engine performs when creating an optimum question plan for our measures.
And, the one which caught my consideration was relating to the so-called “Sparse measures”:
To make it easy, when you outline the measure, Components Engine in VertiPaq will add an implicit NonEmpty filter to the question, which ought to allow the optimizer to keep away from full cross-join of dimension tables and scan solely these rows the place data for the mix of your dimension attributes actually exist. For folk coming from the MDX world, the NonEmpty perform might look acquainted, however let’s see the way it works in DAX.
The factor that the majority resonated with me was when Jeffrey suggested towards changing BLANKs with zeroes (or no matter specific values) in Energy BI calculations. I’ve already written how one can deal with BLANKs and change them with zeroes, however on this article, I need to give attention to the doable efficiency implications of this choice.
Setting the stage
Earlier than we begin, one necessary disclaimer: the advice to not change BLANK with 0 is simply that — a advice. If the enterprise request is to show 0 as a substitute of BLANK, it doesn’t essentially imply that you must refuse to do it. In most eventualities, you’ll in all probability not even discover a efficiency lower, however it can rely upon a number of various factors…
Let’s begin by writing our easy DAX measure:
Gross sales Amt 364 Merchandise =
CALCULATE (
[Sales Amt],
FILTER ( ALL ( 'Product'[ProductKey] ), 'Product'[ProductKey] = 364 )
)Utilizing this measure, I need to calculate the whole gross sales quantity for the product with ProductKey = 364. And, if I put the worth of this measure within the Card visible, and activate Efficiency Analyzer to verify the occasions for dealing with this question, I get the next outcomes:
DAX question took solely 11ms to execute, and as soon as I switched to DAX Studio, the xmSQL generated by the Components Engine was fairly easy:
And, if I check out the Question plan (bodily), I can see that the Storage Engine discovered just one current mixture of values to return our knowledge:
Including extra components…
Nevertheless, let’s say that the enterprise request is to investigate knowledge for Product Key 364 on a day by day stage. Let’s go and add dates to our report:
This was once more very quick! I’ll now verify the metrics inside the DAX Studio:
This time, the question was expanded to incorporate a Dates desk, which affected the work Storage Engine wanted to do, as as a substitute of discovering just one row, this time, the quantity is completely different:
In fact, you’ll not discover any distinction in efficiency between these two eventualities, because the distinction is only some milliseconds.
However that is only the start; we’re simply warming up our DAX engine. In each of those circumstances, as you may even see, we see solely “crammed” values — that mixture of rows the place each of our necessities are happy — product secret’s 364 and solely these dates the place we had gross sales for this product — for those who look completely within the illustration above, dates will not be contiguous and a few are lacking, resembling January twelfth, January 14th to January twenty first and so forth.
It’s because Components Engine was sensible sufficient to get rid of the dates the place product 364 had no gross sales utilizing the NonEmpty filter, and that’s why the variety of data is 58: we have now 58 distinct dates the place gross sales of product 364 weren’t clean:
Now, let’s say that enterprise customers additionally need to see these dates in-between, the place product 364 hadn’t made any gross sales. So, the concept is to show 0$ quantity for all these dates. As already described within the earlier article, there are a number of other ways to switch the BLANKs with zeroes, and I’ll use the COALESCE() perform:
Gross sales Amt 364 Merchandise with 0 = COALESCE([Sales Amt 364 Products],0)Principally, the COALESCE perform will verify all of the arguments offered (in my case, there is just one argument) and change the primary BLANK worth with the worth you specified. Merely mentioned, it can verify if the worth of the Gross sales Amt 364 Merchandise is BLANK. If not, it can show the calculated worth; in any other case, it can change BLANK with 0.
Wait, what?! Why am I seeing all of the merchandise, after I filtered every thing out, besides product 364? Not to mention that, my desk now took greater than 2 seconds to render! Let’s verify what occurred within the background.
As a substitute of producing one single question, now we have now 3 of them. The primary one is precisely the identical as within the earlier case (58 rows). Nevertheless, the remaining queries goal the Product and Dates tables, pulling all of the rows from each tables (The product desk accommodates 2517 rows, whereas the Dates desk has 1826). Not simply that, check out the question plan:
4.6 million data?! Why on Earth does it occur?! Let me do the mathematics for you: 2.517 * 1.826 = 4.596.042…So, right here we had a full cross-join between Product and Dates tables, forcing each single tuple (mixture of date-product) to be checked! That occurred as a result of we compelled the engine to return 0 for each single tuple that will in any other case return clean (and consequentially be excluded from scanning)!
This can be a simplistic overview of what occurred:
Imagine it or not, there may be a chic answer to point out clean values out-of-the-box (however, not with 0 as a substitute of BLANK). You possibly can simply merely click on on the Date subject and select to Present gadgets with no knowledge:
It will show the clean cells too, however with out performing a full cross-join between the Product and Dates tables:
We will now see all of the cells (even blanks) and this question took half the time of the earlier one! Let’s verify the question plan generated by the Components Engine:
Not all eventualities are catastrophic!
Reality to be mentioned, we might’ve rewritten our measure to exclude some undesirable data, however it might nonetheless not be an optimum manner for the engine to get rid of empty data.
Moreover, there are specific eventualities wherein changing BLANKs with zero is not going to trigger a big efficiency lower.
Let’s study the next scenario: we’re displaying knowledge in regards to the complete gross sales quantity for each single model. And I’ll add my gross sales quantity measure for product 364:
As you would possibly count on, that was fairly quick. However, what is going to occur after I add my measure that replaces BLANKs with 0, which precipitated havoc within the earlier situation:
Hm, seems like we didn’t should pay any penalty by way of efficiency. Let’s verify the question plan for this DAX question:
Conclusion
As Jeffrey Wang prompt, you must avoid changing blanks with zeroes (or with every other specific values), as it will considerably have an effect on the question optimizer’s skill to get rid of pointless knowledge scanning. Nevertheless, if for any purpose you should substitute a clean with some significant worth, watch out when and how you can do it.
As normal, it is determined by many various facets — for columns with low cardinality, or while you’re not displaying knowledge from a number of completely different tables (like in our instance, after we wanted to mix knowledge from Product and Dates tables), or visible sorts that don’t have to show numerous distinct values (i.e. card visible) — you will get away with out paying the efficiency worth. Then again, for those who use tables/matrices/bar charts that present a variety of distinct values, ensure to verify the metrics and question plans earlier than you deploy that report back to a manufacturing setting.
Thanks for studying!
