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Month: August 2025

ABORT_QUERY_EXECUTION in SQL Server 2025

Joey D’Antoni pulls out the big guns:

However, SQL Server 2025 gives us a bigger hammer (DBAs love hammers). Building on top of the query store hints feature that was added in SQL Server 2022, ABORT_QUERY_EXECUTION simply blocks the exection of known problematic queries.

Read on to see how this works. Note that it will prevent the query from running at all, ever. As a result, if your main concern is “Hey, don’t run this during the busiest hours of the data for this database,” this particular hint is overkill.

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Thoughts on Shrinking Databases

Chad Callihan lays out some thoughts:

Unlike your lawn, which benefits from a regular mowing to stay healthy and looking nice, a database isn’t meant to be regularly shrunk just to keep its size under control.

Read on for Chad’s take, which is one I agree with wholeheartedly. There are specific circumstances that merit shrinking a database. The main use case is when you have a significant level change: you’ve archived or deleted a bunch of data. There are very few other valid use cases, especially when you consider that databases typically have positive growth.

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Time Series Feature Engineering in Pandas

Matthew Mayo knows that time is a flat circle:

Feature engineering is one of the most important steps when it comes to building effective machine learning models, and this is no less important when dealing with time-series data. By being able to create meaningful features from temporal data, you can unlock predictive power that is unavailable when applied to raw timestamps alone.

Fortunately for us all, Pandas offers a powerful and flexible set of operations for manipulating and creating time-series features.

Click through for seven things you can do in Pandas to extend or work with time series data.

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Tips for Troubleshooting PostgreSQL Performance Slowdowns

Umair Shahid shares a few tips with us:

If you are a technology leader overseeing a team of developers who manage PostgreSQL as part of a broader application stack, or you are responsible for uptime and customer satisfaction at scale, knowing where to look first can make all the difference.

Let us walk through a focused checklist of patterns and places that commonly hold the key to unlocking better PostgreSQL performance.

This is a very high-level set of reminders regarding where you should look, rather than a detailed troubleshooting guide. But sometimes, it’s good to have that reminder.

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Digging Further into RTABench Q0 Optimization on PostgreSQL

Andrei Lepikhov responds to feedback:

In the previous post, I explored some nuances of Postgres related to indexes and parallel workers. This text sparked a lively discussion on LinkedIn, during which one commentator (thanks to Ants Aasma) proposed an index that was significantly more efficient than those discussed in the article. However, an automated comparison of EXPLAINs did not clarify the reasons for its superiority, necessitating further investigation.

Click through for the index and what Andrei learned along the way.

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Tips for Working with Real-Time Analytics in Microsoft Fabric

Reitse Eskens shares some tips:

When discussing options, possibilities, and solutions with customers, the Real-Time stack began to emerge. We received questions on ingestion that couldn’t be simply answered using batch processing. The best part is that we can start learning new technology!

The following blog will outline the best things I learned working with real-time analytics.

Click through for those items.

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Moving Items around in Power BI

Reza Rad sends this item to the back of the line:

When you have multiple items overlapping, you often need the feature to bring one forward or move it backward. In Power BI, this feature isn’t available by right-click. Instead, there is a Selection pane where you can easily set the order of elements. The selection pane also has other benefits. In this article and video, you will learn how to use the Selection pane to build the right order for your visuals.

Read on to see how it works.

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Choosing the Right Logging Level in SSIS

Andy Brownsword does a bit of logging:

When creating SQL Agent jobs to execute SSIS packages we can choose the level of logging to be captured. Different settings are more beneficial under the right circumstances so it’s important to understand the differences to make the right decision.

These settings control the internal logging done by SSIS. This is out of the box and freely available, so why not use it effectively.

The real trick is that if you swallow all of the exceptions and errors, everybody will just assume your code is working perfectly and boom, problem solved. Or you could read Andy’s post and get actual information. Whatever works for you, I suppose.

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Making Leading Wildcard Searches Faster

Brent Ozar flips everything around:

99.9% of you are never gonna need this.

But let’s say you need to run queries with leading (not trailing) wildcards, like this search for all the different national versions of the Encabulator, each of which has different prefixes depending on which government it’s being sold to:

This is indeed a pretty uncommon scenario. I’m pretty sure I’ve only ever needed to do this once. Well, twice, but in one case I couldn’t actually use the REVERSE() function because the column was itself an awful non-deterministic function and this solution wouldn’t work.

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