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Author: Kevin Feasel

Avoiding Diagnonal Axis Labels

Published 2020-04-29 by Kevin Feasel

Cole Nussbaumer Knaflic gives us two good alternatives for avoiding diagonal labels in data visualizations:

There is one common phenomenon in graphs that I recommend actively avoiding: diagonal axis labels. They are often observed on the x-axes of graphs, where many tools automatically rotate text when the labels become too long to fit horizontally. While this might seem like a kind favor, there are usually better options. Beyond looking messy, diagonally rotated text is slower to read. In this short post, I’ll highlight two common scenarios that lead to diagonal x-axis labels—long category names on bar charts and long date labels on line graphs—and a couple ideas to try instead.

Diagonal labels aren’t the worst on printed visuals (as you can tilt the paper to read those labels clearly), but they’re not great. When combined with screens—especially screens which change their rotation as you tilt them, like on phones—that leads to a lot of unnecessary dissatisfaction.

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Pluralization with Entity Framework Core

Published 2020-04-29 by Kevin Feasel

Erik Ejlskov Jensen takes us through auto-pluralization with Entity Framework Core:

If you are using the EF Core console command dotnet ef dbcontext scaffold or Package Manager Console in Visual Studio Scaffold-DbContext, you can hook up pluralization via code in your project.

First you must add a reference to the Microsoft.EntityFrameworkCore.Design package from your startup project.

Click thorugh for a code sample.

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Stored Parameter Procedure Caching

Published 2020-04-29 by Kevin Feasel

Greg Dodd follows Betteridge’s Law of Headlines:

When SQL Server caches your plan, it caches it with the parameter values that you pass through the first time, and it assumes that the same query plan will be the best one for any parameter you pass in next time.

But does SQL Server always cache your parameters? Does it always keep track of what you pass in?

Click through for a demonstration good enough to give you a conclusive answer.

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Azure Data Studio April 2020 Release

Published 2020-04-29 by Kevin Feasel

Alan Yu announces the April 2020 release of Azure Data Studio:

KQL magic extension support is now available in Azure Data Studio Notebooks. It allows you to connect, query and explore Azure Data Explorer (Kusto), ApplicationInsights and LogAnalytics data using kql (Kusto Query Language). If you are using Log Analytics today for your Azure SQL DB as described here, you can now do log metric analysis using KQL magic in Azure Data Studio Notebooks. 

KQL magic package can be downloaded from Manage Packages in Python Notebook or using pip install. In a Python Notebook in Azure Data Studio, load KQL magic using (%reload_ext Kqlmagic). Start connecting, querying, and exploring using %kql or %%kql for multi-lines.   

KQL magic allows you to see tabular results similar to SQL Notebook, where you can also have the benefits of exporting outputs to other formats (csv, Excel, JSON, XML) and using the Charting functionality. You can also take advantage of rendering charts directly with plotly for richer interactivity. 

There are several fairly big changes in here, so check them all out.

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The Problems with Shrinking tempdb Data Files

Published 2020-04-29 by Kevin Feasel

Andy Mallon explains why you should never shrink tempdb data files:

I recently wrote about growing, shrinking, and removing tempdb files. In that article I explained that SQL Server won’t move a page that contains an internal worktable object, and thus trying to shrink tempdb files can be futile. Today, I’m going to explain how attempting to shrink tempdb files can actually be harmful.

Andy has good advice for tempdb here. Shrinking other database is generally bad but sometimes necessary; shrinking tempdb can lead to all kinds of problems.

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Building a P&L Statement with Power BI

Published 2020-04-29 by Kevin Feasel

Matt Allington has the need for some financials:

A few weeks ago, Danielle Stein Fairhurst from Plum Solutions contacted me and asked if I would speak at her financial modelling meetup group (a topic of my choice related to Power BI). I decided to speak about creating a P&L using Power BI and DAX. I first learnt how to do this way back in 2015 when I met Derek Rickard. I was still fairly early on my DAX journey at the time and certainly didn’t have any experience with financial accounting using DAX. Derek shared with me how he built his P&L using Adventure Works. I have used these principles many times to help customers, and I thought it would be a great time to pass on that knowledge, and the skills I have learnt along the way on my blog.

Click through for a video and the measures used.

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R 4.0 Released

Published 2020-04-28 by Kevin Feasel

David Smith walks us through what’s new in R 4.0:

R 4.0.0 was released in source form on Friday, and binaries for Windows, Mac and Linux are available for download now.

As the version number bump suggests, this is a major update to R that makes some significant changes. Some of these changes — particularly the first one listed below — are likely to affect the results of R’s calculations, so I would not recommend running scripts written for prior versions of R without validating them first. In any case, you’ll need to reinstall any packages you were using for R 4.0.0. (You might find this R script useful for checking what packages you have installed for R 3.x.)

And I just got 3.6 into production yesterday. Them’s the breaks…

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The Siren Song of High Accuracy

Published 2020-04-28 by Kevin Feasel

Holger von Jouanne-Diedrich notes that accuracy is not in itself necessarily a good thing for a machine learning model:

In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.

In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!

The nuanced answer here is that with classifiers, accuracy is not in itself a great measure in the case of class imbalance. The more balanced your classes are, the more likely it is that a model with high accuracy is a good model. That’s where other measures such as specificity and sensitivity, positive & negative predictive value, etc. come into play.

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Performance and T-SQL’s CHOOSE

Published 2020-04-28 by Kevin Feasel

Grant Frichey answers a question:

Questions absolutely drive my blog content and I really liked this one: how does the T-SQL CHOOSE command affect performance.

On the face of it, I honestly don’t think it will affect performance at all, depending on where and how you use it. However, the answer is always best supplied by testing.

Grant’s post ended up being much more interesting than I had anticipated—my anticipated answer was “It’s a CASE statement so it behaves like a CASE statement.” But there is some nuance that I’ve left out.

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