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Author: Kevin Feasel

Working with Vectors in Python

Published 2026-08-28 by Kevin Feasel

Bala Priya C avoids the loops:

In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations.

Topics we will cover include:

  • Why Python loops are slow for numeric data and how NumPy’s C-backed engine addresses this.
  • How to apply element-wise operations, boolean masking, and broadcasting to eliminate common loop patterns.
  • How to handle multi-condition branching and axis-based aggregation entirely with NumPy functions.

This is one of those places in which people with database development backgrounds can end up understanding the topic more intuitively than loop-heavy structured programming developers. And depending on how large the loop is and how complex each operation is, there can be a significant performance improvement in applying functions over a vector versus in a loop. It’s part of why matrix operations tend to be much faster than nested loops.

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The Pain of Residual Predicates

Published 2026-08-28 by Kevin Feasel

Brent Ozar has a new animation:

If we modify our query a little by selecting all of the columns instead of just Id and Location, then we have to do a Key Lookup, like we talked about in the How to Think Like the Engine class. For each person who lives in Helsinki, we have to look up their row in the clustered index in order to fetch all the columns we need. That’s not really a big deal, though, as long as a relatively limited number of people live in Helsinki. Like I wrote in that post, the index seek + key lookup is essentially two index seeks: one into Helsinki, and then one seek (for each Helsinki resident) on the clustered index, by their Id.

However, let’s add a little more complexity to the query:

Click through for a scenario in which a more selective query can result in worse performance than a less-selective variant.

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Further Thoughts on the SSMS SQL Formatter

Published 2026-08-28 by Kevin Feasel

Chad Callihan takes another look:

I posted a few months ago about the latest SQL Server Management Studio updates, including a preview of their SQL Formatter. SSMS 22.9.0 was released a few weeks ago and called out some SQL Formatter improvements on the release announcement, so I thought it would be worth taking a fresh look to see how things are moving along.

Click through for Chad’s current thoughts. The big challenge with a good SQL formatting tool is that it be adaptable to somewhat complex formatting standards. If the tool can’t do what your team has agreed on, then the tool’s not going to cut it for more than occasional personal use.

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Understanding the DiskANN Algorithm

Published 2026-08-28 by Kevin Feasel

Mala Mahadevan digs into an algorithm:

DiskANN is meant to help with searching a billion-vector dataset from a single machine using SSDs.

Microsoft Research’s original work demonstrated a billion-point index on a workstation with 64 GB of RAM and an SSD, while targeting high recall and low query latency. Before we get to ‘why ssd’ and details of storage, we need to understand the basics of vector search and underlying terminologies.

Click through for a depiction of the paper and quite a few examples of how the mechanisms work.

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Dynamically Changing Fabric Data Warehouse SQL Pools

Published 2026-08-27 by Kevin Feasel

Gilbert Quevauvilliers saves some money:

After reading about the new SQL Pools feature for Warehouses in Fabric, I had an idea, if I could change the SQL Pool configuration based on the expected query load, I could then consume less capacity and have better performance.

https://learn.microsoft.com/en-us/fabric/data-warehouse/custom-sql-pools

Here is an Example I thought of below.

  • When the ETL load is running optimize the SQL pool for writing as typically data is being inserted.
  • After the ETL load and for the rest of the day, almost all queries are read by the Warehouse, so change the SQL pool to be read optimized.

Click through for a Python notebook that does this.

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DDL Modifications and Change Data Capture

Published 2026-08-27 by Kevin Feasel

Erik Darling has a new video:

So I’ve had to deal with this with some clients recently, and the problem with CDC, of course, is that if you change, add, drop columns from CDC tables, or tables that are covered by CDC, rather, the current change capture table does not reflect those changes. You have to do some work to figure it out. What I’ve got in this video is I’m just going to, a script that I can walk through, I can hit F5 on it.

Click through for the script, information on capture instances, and more.

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Moving from a Named Instance to a Default Instance

Published 2026-08-27 by Kevin Feasel

Brian Kelley makes a move:

I have a SQL Server named instance that is used by various resources. There may even be reports and other artifacts that access the named instance which we don’t know about. Having a named instance means in a recovery situation we must have a SQL Server installed as a named instance with the same name. This impairs our recoverability as well as our ability to upgrade SQL Server versions because we must retain that named instance. Is there a path to migrate to a default instance without potentially breaking things?

Click through to see how.

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T-SQL Tuesday 201 Round-Up

Published 2026-08-27 by Kevin Feasel

Jeff Taylor gives us the low-down:

A couple of weeks ago, I asked a simple question with a loaded answer: are temp tables a friend or a foe? The responses did not disappoint. They ran from full-throated defense to a measured “it depends,” and one of you built a lab. That’s what I was looking for.

If there is a consensus, it is this: the reflex is the problem, not the tool. Almost everyone agreed that dumping data into a #temptable out of habit is a mistake. Almost everyone also had a case where a temp table was the right answer, and sometimes the only one. So let’s get into it, in no particular order.

Click through for the cast of characters and what everyone came up with.

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INFORMATION_SCHEMA and the Fabric Warehouse

Published 2026-08-27 by Kevin Feasel

Louis Davidson bangs his head against a wall:

When we decided to use T-SQL and a Fabric Data Warehouse for our ETL, I started thinking about generating the code with the metadata in the system catalog views or the INFORMATION_SCHEMA. Having done this sort of thing before in SQL Server over the years, it seemed really straightforward. And it kind of is, until it isn’t.

In this blog I want to show you a few ways you need to understand how working with metadata and temp tables varies (sometimes wildly) from the comfortable SQL Server environment and language you know very well, and give tips on how to get around these differences.

Click through for some of the fun you can have with a distributed SQL Server-like product.

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