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Month: June 2020

Role-Playing Dimensions in Power BI

Martin Schoombee explains the concept of role-playing dimensions and then explains how that works in the Power Bi world:

In technical terms a role-playing dimension is when a dimension table has multiple (foreign key) relationships to the same fact table. In more non-technical terms, it is when you have the same attribute (“Date” for instance) that can relate to the same metrics in different ways.

If you look at the data model below as an example, you can see the Date entity could be used to reference either the Invoice Date or Delivery Date from the Sales entity, changing the perspective of the metrics we’re looking at.

It’s a little surprising to me that there isn’t an easier way to handle this concept. Role-playing dimensions are a core part of the Kimball model, and they’re common enough that you’d expect support to be a bit simpler.

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Formatting TimeSpans in Powershell

Jeffrey Hicks shows how we can format TimeStamp objects:

This is pretty straight forward. Subtract the LastBootUpTime property from the current datetime to get a timespan object that shows how long this computer has been up and running. But…I want to get rid of the milliseconds value.  It’s irrelevant as far as I’m concerned and takes up space that I might want to use for another property.  I need to format that  value.

Click through for several methods.

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Availability Group Bug when Removing and Adding the Same Database

Josh Darnell takes us through a tricky problem:

I came across a bug in SQL Server 2016 where the Availability Group (AG) health check can get stuck in an infinite loop after removing and re-adding a database from an AG.

Unfortunately, I don’t know exactly what version this bug was introduced. I first noticed the problem on SQL Server 2016 SP2 CU7 GDR (13.0.5366.0). It may have existed before then, but I never encountered it.

Read on for a workaround. And hopefully there will be a proper fix soon. Also, it’d be interesting to see if it can be reproduced in 2017 or 2019.

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Delayed Prefetch and Hidden Reads

Hugo Kornelis looks at when worlds collide:

So let’s check. The picture above shows, side by side, the properties of the Index Seek and the Key Lookup operator. They show that the Index Seek did 3 logical reads only, while Key Lookup did 650 logical reads. A clear indication where the majority of the work is done.

But wait. Aren’t we missing something?

The SET STATISTICS IO ON output indicates a total of 722 logical reads. The two screenshots above add up to 653 logical reads. Where are the other 69 logical reads?

Read on for the answer.

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Pattern-Matching and Text Extraction in Power Query

Imke Feldmann shows how we can match specific patterns in Power Query, which lacks regular expresssions:

I plan to approach this by

1. stepping through the string and check each character if it is valid.
2. If so, store it
3. and if not, forget it. And in that case, also forget the stored values so far so the collection starts from scratch.
4. Then if a series of matches builds up, I will have to check the count of the stored values to not exceed the length of my target pattern.
5. Once the length is reached, no further checks shall be performed and the found values be returned.

My aim is to find a generic way so that this solution can be adapted to many other similar use cases as well. Therefore the pattern must be described in a scalable way and the identification of the pattern elements should be easy to compute.

It’s good to be able to adapt, improvise, and overcome, though this is exactly what regular expressions are intended to do. It’s odd that there appears to be no built-in capability, where instead you’d have to do something like bring in external languages like JavaScript.

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Using Specific R Package Versions in Docker Images

Roman Lustrik shares how to fix package versions in Docker images:

Using package in R is easy. You install from CRAN using install.packages("packagename"), it resolves dependencies and you’re good to go. What R natively doesn’t handle so well is installing a particular package version without jumping through hoops. Technically you need the source file of the package version you want to install AND all source files of the dependencies (in the correct version, of course). This has been made almost seamless with packages packrat and recently, renv.

This comes handy when you are constructing a Docker file to run in production. Usually you want to run this defensively and do not want things to change from one image build to another. To get there, you can save all your package names and version into a file (renv.lock) and use that to reconstruct the now defined package structure with predictable versions (see renv vignette here).

This is quite useful as R package developers tend not to covet backwards compatibility, and one of the key benefits of containers is to have the option to keep the same code base and configuration in all environments.

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More Fun with 1-Column Fusion in DAX

Phil Seamark continues a discussion on single-column fusion:

You may notice the [Package – Bag] measure uses the * (multiplication) operator in line 4 between a measure and what looks like a column filter.

The [base measure] performs a simple COUNTROWS aggregation and returns an INTEGER. This side of the equation makes sense, so let’s see what is going on with the DAX on the right-hand side of the * operator? The filter statement is encompassed with parenthesis, which automatically converts the expression to a TRUE/FALSE boolean value – which is then implicitly converted to an INTEGER value of either 0 or 1. It can’t be anything else.

The result of this double conversion is that we end up with a INTEGER * INTEGER to produce the number we see in the visual.

Click through for plenty more where that came from.

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Unsupported Versions of SQL Server on Windows Server 2019

Randolph West finds a need and fills it:

Last year when Windows Server 2019 was released I wanted to see which versions of SQL Server I could run on it, testing more the unwritten backward compatibility promise Microsoft has maintained over the last 45 years, rather than what the documentation says.

Speaking of documentation, Glenn Berry has a nifty compatibility matrix to show what versions of SQL Server are supported on each version of Windows Server. For official purposes, this is the list you should refer to:

But I know you’re not here for supported versions, because this post is about what Randolph managed to get running on Windows Server 2019, which as you know is a 64-bit operating system.

SQL Server 6.5 surprised me. This idea also nets my most coveted category.

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Creating a Time Dimension with Time Bands in Power BI

Soheil Bakhshi shares how you can create a time dimension with a granularity of seconds in Power BI and SSAS Tabular:

I wrote some other posts on this topic in the past, you can find them here and here. In the first post I explain how to create “Time” dimension with time bands at minutes granularity. Then one of my customers required the “Time” dimension at seconds granularity which encouraged me to write the second blogpost. In the second blogpost though I didn’t do time bands, so here I am, writing the third post which is a variation of the second post supporting time bands of 5 min, 15 min, 30 min, 45 min and 60 min while the grain of the “Time” dimension is down to second. in this quick post I jump directly to the point and show you how to generate the “Time” dimension in three different ways, using T-SQL in SQL Server, using Power Query (M) and DAX. Here it is then:

Click through for the code, which includes several sample bands (e.g., 5 minutes, 15 minutes) that you can also control.

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