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Curated SQL Posts

The Perfect Trick to Speed Up Databases

Published 2026-08-17 by Kevin Feasel

Louis Davidson becomes a cracker jack developer:

This week, I want to sell you on two ideas. First, that you can make any query faster with:

  • Zero hardware changes
  • Zero index changes
  • Zero structure
  • Just a few simple character changes in every one of your queries

This change I will guarantee will make your queries screamingly faster. Never will your customer’s wait on query results again. You will have no blocking, no latch waits, no waiting whatsoever.

He probably should sell this as a training course, along with its administrator equivalent: databases hate date, and you can’t have data problems if you don’t have any data.

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Displaying Detail Rows Expression Results in Power BI

Published 2026-08-17 by Kevin Feasel

Chris Webb works around a limitation:

In last week’s post I mentioned that while Power BI reports (unlike Excel PivotTables) do not support the Detail Rows Expression feature, it is possible to partially work around this limitation by using the paginated report visual. In this post I’ll show you how I was able to do this and what is and isn’t possible.

Click through to see how. I’m unclear as to whether this also applies to Power BI Report Server, though my default expectation is “No, it does not apply, because nothing new ever applies for Power BI Report Server, because Power BI Report Server users don’t deserve nice things.” But that’s just because of years of experience in not having nice things with PBIRS, not any specific information.

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Blocking Database Project Deployment on Data Loss

Published 2026-08-17 by Kevin Feasel

Jerry Nixon flips a switch:

This important feature shows up in a few places. This article discusses its role in Database Projects and in SQL Server Management Studio (SSMS); it defaults to true in both. This simple setting evaluates the delta between your desired schema and your actual schema and calculates if applying your desired schema would result in data loss. If the answer is “yes,” it stops.

An easy example is dropping a table or column. Doing so would clearly lose data. Another, perhaps less obvious, is reducing the range of a column’s data type, like from INT to TINYINT, where any existing value under zero or over 255 could be lost. These evaluations are done by the engine when BlockOnPossibleDataLoss is set to True and, as a result, you can trust that data in your database is not accidentally destroyed by publishing a schema.

The tricky part becomes dealing with database changes when there will be data loss. For that scenario, I’m not sure the database project approach offers anything significant over writing your own database change scripts.

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Building a SQL Server Estate Summary from Get-SqlSafe Reporting

Published 2026-08-17 by Kevin Feasel

Andreas Wolter digs into an environment and builds a report:

Have you ever needed to understand an unfamiliar SQL Server estate quickly? Perhaps you inherited an environment, started working with a new customer, or discovered that the existing server inventory is no longer trustworthy.

In the previous article, Running Get-SqlSafe at Scale Across a SQL Server Estate, I showed how to run Get-SqlSafe across a list of SQL Server instances.

Each report contains a System Overview section. I originally added this section to provide context for the security findings, but it also provides useful estate information such as the SQL Server version, build number, edition, and selected usage indicators.

Click through to see how.

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Dual Y Axes in R with ggplot()

Published 2026-08-14 by Kevin Feasel

Andrea Onofri builds a complex chart:

I have often found myself needing to plot a single graph with two y-axes having different scales. For example, this might be useful for representing temperature and rainfall data at a given location. Unfortunately, doing this with ggplot() is not straightforward.

As Andrea mentions, there are specific circumstances in which having a dual-axis chart is reasonable, and click through to learn how. ggplot2 tends to be rather opinionated regarding visual choice and design. I happen to like those opinions and think they are generally correct, but I can also recognize that there may be exceptions to the rules. For those instances, I think “Somewhat difficult but not impossible” is a solid answer, as it keeps people from doing things inappropriately with a couple of mouse clicks, like setting up those stupid 3D bar charts in Excel. H/T R-Bloggers.

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Applying Color Thoughtfully

Published 2026-08-14 by Kevin Feasel

Amy Esselman provides some guidance:

One of our top tips for explanatory communications is to use color sparingly and purposefully to help your audience understand your data and message. Color should be an explicit choice, not something your software applies by default, whether that’s a graphing tool or an AI assistant generating your first draft. These tools can build a chart in seconds. They might even add highlighting on their own. But they don’t know which data matters most to your audience. That call is still yours. Used thoughtfully, color is often one of the quickest ways to improve a graph.

Color is an extremely powerful pre-attentive attribute, meaning that it’s something we intuitively see and respond to without explicit thought. That’s why choosing what to color can be so powerful. If everything has bright, distracting colors, you lose an avenue to guide the viewer’s eye. But Amy’s example is a good one to show the particular element(s) in the visual that you want people to focus on, and the viewer’s eyes will automatically go there.

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The Value of TRY_PARSE()

Published 2026-08-14 by Kevin Feasel

Steve Jones answers a question:


Someone asked why I would use TRY_PARSE after I posted a question at SQL Server Central: Getting the Average. Isn’t is slower?

A fair question. This quick post looks at why.

I’d use TRY_CAST() or TRY_CONVERT() in this particular scenario. The main reason I’d use TRY_PARSE() would be if you need .NET-specific parsing functionality, such as parsing dates by a specific locale. The reason is that PARSE() and TRY_PARSE() are an order of magnitude slower than their CAST() and CONVERT() cousins.

That said, Steve’s example reminds me of a PolyBase demo I used to do, in which I took a CSV of North Carolina populations by county and read in the information. In that particular dataset, they would use the letter “A” to describe either “Not enough people to show an answer without potentially violating PII” or “We don’t know what the answer is.” So even though it was clearly a numeric attribute in “Population,” the output process overloaded the definition of that attribute and the only way you would know is to happen to see the three rows in ~1500 that happened to have an A in the column value.

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Diagnosing High-Cardinality Workloads in Postgres

Published 2026-08-14 by Kevin Feasel

Ryan Booz has a video:

In Part 6 of this special Postgres in Production deep dive series, Ryan Booz asks a question that determines how useful pg_stat_statements can be for you at all: do you have a high cardinality workload? This episode covers what that actually means, why ORMs, dynamic SQL, and AI-assisted development tools generate more unique queries than you might expect, a side by side demo of the same workload on Postgres 17 and Postgres 18, and the concrete checks that tell you whether pg_stat_statements is losing the data you need for query tuning.

Click through for the video and transcript.

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Configuring OneLake Security for a Microsoft Fabric Lakehouse

Published 2026-08-14 by Kevin Feasel

Gilbert Quevauvilliers locks things down:

In this blog post, I will show you how to configure OneLake security for a Microsoft Fabric Lakehouse. We will remove the default broad-access role, grant the required Lakehouse access, configure object-, row-, and column-level security, and test the results. The goal is to create a secure-by-default setup in which users can access only the data they need.

Click through for the guide.

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