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Category: Performance Tuning

Primary Keys and DAX Query Performance

Published 2025-11-14 by Kevin Feasel

Phil Seamark explains why including primary keys in summarize statements can be a bad thing:

When writing DAX queries, performance tuning often comes down to small design decisions that have big consequences. One such decision is whether to include Primary Key columns from Dimension tables in your SUMMARIZECOLUMNS statements. This is particularly important when those Dimension tables use DUAL or IMPORT storage modes.

This article explains why doing so can lead to inefficient query plans. It describes what happens under the hood. It also shows how to avoid this common pitfall.

Read on to learn more.

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Optimized Locking and Change Event Streaming in SQL Server 2025

Published 2025-11-12 by Kevin Feasel

Deb Melkin is looking forward to a pair of features:

When I look at this release, I feel like I’ve been more tuned into what’s coming out than any other. I’m still not sure how that happened. But I think overall it’s a good thing because there really is a lot being packed into this release. If you’re just trying to figure out now, you’re already behind.

There are really 2 features that I’m really excited about:

Read on to learn more about both of them.

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Index Skip Scans in PostgreSQL 18

Published 2025-11-11 by Kevin Feasel

Hans-Jürgen Schönig demonstrates a new capability in PostgreSQL:

PostgreSQL 18 brings a couple of performance related features to the table which will help applications to run more efficiently, providing a better and more enjoyable user experience. One of those performance features is called “skip scans”. Most of you might ask yourself at this point: Wow, sounds cool, but what is a skip scan? The purpose of this post is to shed some light and explain how this works, what it does and most importantly: How one can benefit from this feature in real life.

Click through for the demo.

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Function Volatility and PostgreSQL Partition Performance

Published 2025-11-05 by Kevin Feasel

Deepak Mahto covers how function volatility can affect how queries on partitioned data perform:

In one of our earlier blogs, we explored how improper volatility settings in PL/pgSQL functions — namely using IMMUTABLE, STABLE, or VOLATILE — can lead to unexpected behavior and performance issues during migrations.

Today, let’s revisit that topic from a slightly different lens. This time, we’re not talking about your user-defined functions, but the ones PostgreSQL itself provides — and how their volatility can quietly shape your query performance, especially when you’re using partitioned tables.

Click through for one example using date-based partitioning and date functions.

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When repartition() Beats coalesce() in Spark

Published 2025-10-31 by Kevin Feasel

Janani Annur Thiruvengadam stands some common advice on its head:

If you’ve worked with Apache Spark, you’ve probably heard the conventional wisdom: “Use coalesce() instead of repartition() when reducing partitions — it’s faster because it avoids a shuffle.” This advice appears in documentation, blog posts, and is repeated across Stack Overflow threads. But what if I told you this isn’t always true?

In a recent production workload, I discovered that using repartition() instead of coalesce() resulted in a 33% performance improvement (16 minutes vs. 23 minutes) when writing data to fewer partitions. This counterintuitive result reveals an important lesson about Spark’s Catalyst optimizer that every Spark developer should understand.

Read on for the details on that scenario.

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Tips for Running SQL Server on Hyper-V

Published 2025-10-31 by Kevin Feasel

Mike Walsh shares some advice:

If you’ve ever asked yourself, “Why does my SQL Server seem slower on Hyper-V than it should be?!”, this post might help. And if you asked me about Hyper-V ten years ago, I’d probably have laughed. Maybe even less than that. But here’s the thing: it scales, it works, and with the Broadcom/VMware “fun” squeezing the world for profit, we’re seeing more and more folks making the move to Hyper-V. You have to pay attention to some key configurations, just as with VMware..

None of this is radical advice, but it is good to make sure that you have the basics covered because these can make a difference.

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Generating a DAXX File for Performance Tuning

Published 2025-10-31 by Kevin Feasel

Phil Seamark does some troubleshooting:

When troubleshooting slow DAX queries, sharing the right diagnostic information with an expert can make all the difference. That’s where a DAXX file comes in. This special file format is created using DAX Studio. It bundles essential metadata and performance details without exposing query results. It’s perfect for collaborative optimisation.

Read on to learn more about what a DAXX file is and how it can be useful in the performance tuning process.

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Automating Power BI Load Testing via Fabric Notebook

Published 2025-10-22 by Kevin Feasel

Gilbert Quevauvilliers grabs a query:

Load testing is essential when working with Microsoft Fabric capacity. With limited resources, deploying a Power BI report without testing can lead to performance issues, downtime, and frustrated users. In this series, I’ll show you how to automate load testing using Fabric Notebooks, making the process faster, easier, and repeatable.

Inspired by Phil Seamark’s approach, this method eliminates manual complexity and allows you to capture real user queries for accurate testing.

Read on for the first part, in which Gilbert uses the Performance Analyzer to capture query details.

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Using Community Tools to Troubleshoot SQL Server Performance

Published 2025-10-08 by Kevin Feasel

Kevin Hill wraps up a series on what to do when “SQL Server is slow”:

This post will lean heavily on the First Responder Kit from Brent Ozar, sp_WhoIsActive from Adam Machanic and others. They took what Microsoft provides and made them better. Hundreds or thousands of hours of work to make FREE things for you to use.

This is the most complex blog I’ve ever written. Your experiences may differ, and my code samples might have some bugs. Test first.

Kevin is trying to sum up a lot of material in a blog post, but this serves as a pretty good starting point for people who don’t know where to begin. One of the key takeaways from the post, and the entire series, is to have a plan in place. Regardless of whether you use what Kevin has as-is or make your own modifications, knowing what to do and how to do it will save a lot of time and energy when you need to act.

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Narrowing down Slowdown Causes in SQL Server

Published 2025-10-02 by Kevin Feasel

Kevin Hill continues a series on solving the age-old “The server is slow!” problem:

At this point you’ve:

  • Defined what “slow” means and built a timeline (Part 1).
  • Checked things outside SQL Server like network, storage, and VM noise (Part 2).

Now it’s time to open the hood on SQL Server itself.

I think Kevin’s checklist is a pretty solid one for the type of client he often deals with: one without an in-house DBA or the expertise to stay on top of server problems.

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