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Month: February 2020

String Aggregation with T-SQL

Published 2020-02-19 by Kevin Feasel

Andy Levy talks about one of my favorite functions in SQL Server 2017:


This is another in a group of several posts on modernizing T-SQL code with new features and functionality available in SQL Server.

SQL Server 2016 gave us the STRING_SPLIT() function, but what about the reverse – compiling a set of values into one delimited string? We only had to wait 15 months for the release of SQL Server 2017, and the STRING_AGG() function.

I had the STUFF() / FOR XML PATH trick memorized for quite some time, but that was always a solution which felt like it worked on accident. Even if the new solution weren’t faster than the old, I’d still use it.

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Tying a Database User Back to a Login

Published 2020-02-18 by Kevin Feasel

Dave Bland shows how to figure out which database users tie back to which logins:

Over the years I have had to provide information about logins and database users, most of the time per a request of an auditor.  Many times this is very easy to accomplish because the login name matches the name of the database user account. If you look at the “New User” screen you can see that I am able to enter a different User Name.

Because of this, I can have a User Name that doesn’t match the Login Name.  From an audit perspective this can create some confusion.  More importantly, it can make it difficult to provide accurate information to the auditors when asked.

Read on to see how you can tie these together even if the names don’t match.

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Fun with asciidocs

Published 2020-02-18 by Kevin Feasel

Sheldon Hull explains the value of asciidocs:

Documentation is such an important part of a developer’s life. I think we often take it for granted, and it’s an afterthought in many projects. However, as I consider my work, I know that I’m not reinventing the wheel very often 😀. Most of what I do is built on the back of others’ work. When I use tooling, I’m reading the documentation and using it as my basis to get work done. When I use my notes and blog posts as a reference, I’m using my informal version of knowledge gathering.

INVEST in documenting your work as you go, for the person behind you. You don’t find time to do it, you make time to do it while you work, as a first class citizen of your work, not an after-thought. Think of all the times you’ve had to dig for answers and save someone else that experience.

Sheldon is not wrong.

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Installing .NET Notebooks for Powershell

Published 2020-02-18 by Kevin Feasel

Max Trinidad shows us how to install .NET Interactive on Linux:

In Windows, just takes a few steps to set it up. For Linux, it takes a few extra steps but still is quick enough to get you started.

For Windows, follow the instructions found at the .NET Interactive page in Github.

For Linux, for Ubuntu 18.04, follow the blog post “Ubuntu 18.04 Package Manager – Install .NET Core“.

Basically, in either operating systems, you install:

– Install the .NET Core SDK
– Install the ASP.NET Core runtime
– Install the .NET Core runtime

Click through for the step-by-step instructions. Once you have it done, you get not only Powershell but also F# and C#.

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T-SQL Tuesday Life Hacks

Published 2020-02-18 by Kevin Feasel

Jess Pomfret wraps up a T-SQL Tuesday on life hacks:

A lot of people had more than one life hack so I recommend reading all of the posts linked to below. For this summary post I’ve tried to pick one or two hacks from each post and group them into logical buckets.

There are several hacks shared that I plan on integrating into my life, and I hope this post will serve as a good reference for us all going forward.

There were some interesting entries in here.

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Reducing Visual Count to Improve Performance

Published 2020-02-18 by Kevin Feasel

Chris Webb explains that you might get better performance in Power BI with fewer visuals:

Before we go any further, I don’t want you to go and change your reports if you’re not going to get any benefit from doing so. Use Performance Analyzer (as shown here) to determine which visuals on your report are the cause of slow performance – there’s no point redesigning visuals that are fast anyway.

As a general rule the more visuals you put on a report page the slower it’s going to get. It’s logical if you think about it: the more visuals there are, the more queries have to be run against your dataset and the more work Power BI has to do to render the report. I know there is a tendency to try to pack as much information onto a page as possible and this often happens when someone else has designed the report you’re trying to build, but you should always try to resist this. Splitting a single large page into multiple smaller pages, using slicers or filters to reduce the amount of data shown at any one time and avoiding gigantic Excel-like tables are a good idea.

It certainly doesn’t mean “get rid of all of your visuals;” after all, speed is only one part of the story. Read the whole thing.

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Slow Record Cleanup

Published 2020-02-18 by Kevin Feasel

Jared Poche investigates a slow record deletion process:

I encountered a curious issue recently, and immediately knew I needed to blog about it. Having already blogged about implicit conversions and how the TOP operator interacts with blocking operators, I found a problem that looked like the combination of the two.

I reviewed a garbage collection process that’s been in place for some time. The procedure populates a temp table with the key values for the table that is central to the GC. We use the temp table to delete from the related tables, then delete from the primary table. However, the query populating our temp table was taking far too long, 84 seconds when I tested it.

Read on to understand why.

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An Overview of Generative Adversarial Networks

Published 2020-02-17 by Kevin Feasel

Mohammad Waseem takes us through an overview of Generative Adversarial Networks:

Generative models are nothing but those models that use an Unsupervised Learning approach. In a generative model, there are samples in the data i.e input variables X, but it lacks the output variable Y. We use only the input variables to train the generative model and it recognizes patterns from the input variables to generate an output that is unknown and based on the training data only.

In Supervised Learning, we are more aligned towards creating predictive models from the input variables, this type of modeling is known as discriminative modeling. In a classification problem, the model has to discriminate as to which class the example belongs to. On the other hand, unsupervised models are used to create or generate new examples in the input distribution.

To define generative models in layman’s terms we can say, generative models, are able to generate new examples from the sample that are not only similar to other examples but are indistinguishable as well.

Click through for the overview.

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