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Curated SQL Posts

A Primer on Database States

Published 2026-07-14 by Kevin Feasel

Jeff Iannucci lays out the list:

I’ve recently been asked by some folks about different states for SQL Server databases. Questions like “what’s the state of a database with log shipping?”,  “can I fix a database in (whatever) state with a restart?”,  and “which is the bad one, RECOVERY or RECOVERY PENDING?”

Hopefully you don’t have to try to figure out if your database is in a bad way or not because of an unusual state, but I presume when you do you will need to know as soon as possible. So, I’ve put this handy list of possible database states together to help you in your moment of need.

Click through for the full set.

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SQL Login Overhead in SQL Server 2025

Published 2026-07-13 by Kevin Feasel

Sean Gallardy has a wonderful rant:

There were a few emails about login times so I figured it warranted a blog post. Strap in, this one goes sideways fast.

Some people noted that the amount of time to log in with a SQL Login in SQL Server 2025 all of a sudden took longer. This is clearly stated in the Docs, though the way it is written is documented is problematic.

As Sean mentions, this is on purpose and it is good. Yeah, the amount of time it takes to log in using SQL authenticated accounts in SQL Server 2025 is higher. That’s because the mechanism to log in is now considerably more secure than it was before. And let’s be honest: how frequently are you logging into SQL Server? What percentage of your processing time does that take? Because if the answer is “a large percentage,” that sounds like a job for connection pooling or revising the calling application.

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Checking Query Options from Query Store

Published 2026-07-13 by Kevin Feasel

Michael Bourgon sets ANSI_PADDING:

We know that SQL Server can cache multiple query plans for the same query based on the SET_OPTIONS for that query, and that SSMS doesn’t have the same options as the standard library. (https://www.sommarskog.se/query-plan-mysteries.html). He even includes a chart!

My initial comment was “Michael Bourgon sets XACT_ABORT” but that’s actually not one of the list, so I had to change it for the sake of correctness even though I think it was funnier in its original guise.

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Direct Lake Mode Benefits in Power BI

Published 2026-07-13 by Kevin Feasel

Chris Webb lays out the pros:

This is a blog post I’ve been meaning to write for a long time. Since Fabric launched there has been a lot of focus on Direct Lake mode in Power BI and a lot of people used it because it was the cool new thing. Arguably, we at Microsoft have been guilty of telling people to use it because it was the cool new thing without properly explaining what the benefits are of using it. Direct Lake doesn’t completely replace other storage modes: in a recent post I talked about when Import/DirectQuery composite models are the best choice; Marco wrote a good article on Direct Lake vs Import mode which makes the case for the continuing relevance of Import mode for many scenarios. So what are the main benefits of using Direct Lake mode? 

Click through for Chris’s answer.

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Performing a Lakehouse Table Health Check

Published 2026-07-13 by Kevin Feasel

Jon Lunn looks at a stored procedure:

Microsoft have rolled out a new ‘sys’ stored procedure called ‘sys.sp_get_table_health_metrics’ for checking the health of your tables in your Lakehouse. And it outputs a lot of metrics looking at the state of your table and anomaly detection in them.

What does it check for?

Click through for that answer, as well as some of the things you should watch for in the procedure results, and what you can do if it does catch something.

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Merging Data into a Fabric Lakehouse via Python Notebook

Published 2026-07-10 by Kevin Feasel

GIlbert Quevauvilliers uses a pure Python notebook:

In this blog post I am going to show you how to use a Fabric Python runtime notebook (This is the notebook which only uses Pure Python functions and consumes significantly lower Capacity Units (CUs)).

The pattern is how to get new data and merge it into an existing Lakehouse table. This ensures that if the notebook is run again data will not be duplicated.

Why I am sharing this is I have found that there is not a lot of useful information about how to use a Python notebook to write to a lakehouse table easily. And then also how to use a Merge statement making it easier to insert or update your lakehouse tables. This simplifies the ingestion process, runs faster and consumes the least amount of CUs

Gilbert doesn’t mention it in the blog post but the notebook does use DuckDB to query the data using SQL.

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Partition Pruning on Non-Partitioned Columns in PostgreSQL

Published 2026-07-10 by Kevin Feasel

Haki Benita does a bit of pruning:

One of the most valuable things about partitioned tables is pruning – the database’s ability to eliminate entire partitions based on a query predicate. Under conventional wisdom, pruning can only be achieved when querying by the partition key – this makes choosing the right key extremely difficult. However, if your data follows certain patterns, using some clever tricks you can achieve pruning even when filtering by non-partition key columns.

In this article, I demonstrate how to achieve partition pruning when filtering by non-partition key columns.

Click through to see how.

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