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Curated SQL Posts

Farewell, SSRS

Published 2025-06-20 by Kevin Feasel

SQL Server Reporting Services is done-zo:

Starting with SQL Server 2025, Microsoft is consolidating all on-premises reporting services under Power BI Report Server (PBIRS). No new versions of SQL Server Reporting Services (SSRS) will be released. PBIRS becomes the default on-premises reporting solution for SQL Server.

You can expect more information about specific editions when SQL Server 2025 is in General Availability (GA).

This article outlines the implications of these changes for you as a customer, and addresses any questions you might have.

The article claims that Power BI Report Server is a superset of SSRS. That might actually be so, but there’s a nagging part in the back of my brain saying that there are things we can do in SSRS that we can’t in PBIRS and that this won’t be the smooth transition that the article claims. But it’s been long enough since I’ve worked on SSRS in earnest to be able to say with certainty whether it’s the case.

H/T Blake McNeill.

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Changes to Accelerated Database Recovery in 2025

Published 2025-06-20 by Kevin Feasel

Jordan Boich points out something interesting coming in SQL Server 2025:

Accelerated Database Recovery (ADR) was introduced in SQL Server 2019. Its main purpose is to allow for faster database recovery in the event of a crash or unexpected shutdown. Traditionally, the database engine handles crash recovery through a series of phases—analysis, redo, and undo—which can be inefficient and slow, especially when dealing with long-running transactions.

To make a long story short, ADR “shortcutsˮ the recovery process by introducing a new approach to handling undo operations. Instead of relying heavily on scanning the transaction log—which can be painfully slow for uncommitted or long-running transactions—ADR maintains a version store within the user database to track row-level changes. This allows SQL Server to quickly roll back uncommitted transactions without scanning the entire log. The result is much faster crash recovery, quicker rollbacks, and improved overall database availability, particularly in high-transaction environments.

Read on to see what’s new, as well as some of the consequences of enabling this feature.

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Changing a Busy Column’s Data Type in SQL Server

Published 2025-06-20 by Kevin Feasel

Matt Gantz makes a staggered change:

In a previous post I showed how to use a batching strategy to remove large amounts of data from a table while it is being used. Today I will apply the same technique to another common problem- changing the datatype of a column. A common use of this is to normalize a text column into an integer (that references another table), but could be used to transition to and from any datatype . Many of the considerations in the previous post apply, so I advise you to read it as well before using this technique.

Click through for the process.

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Comparing OrioleDB and Neon

Published 2025-06-20 by Kevin Feasel

Alexander Korotkov reminds us that there are a lot of data platform products out there:

In a recent Hacker News discussion, there was some confusion about the differences between OrioleDB and Neon. Both look alike at first glance. Both promise a “next‑gen Postgres”. Both have support for cloud‑native storage.

This post explains how the two projects differ in practice. And importantly, OrioleDB is more than an undo log for PostgreSQL.

Alexander is the creator of OrioleDB, so price in any vendor bias you wish as you see the comparison of two up-and-coming technologies.

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From Pandas to Polars

Published 2025-06-19 by Kevin Feasel

Ivan Palomares Carrascosa provides an introduction to the polars library:

Polars is currently one of the fastest open-source libraries for data manipulation and processing on a single machine, featuring an intuitive and user-friendly API. Natively built in Rust, it is designed to optimize low memory consumption and speed while working with DataFrames.

This article takes a tour of Polars library in Python and illustrates how it can be seamlessly used similarly to Pandas to efficiently manipulate large datasets.

My experience with polars is that it’s not a 1:1 replacement for pandas, but the interfaces are similar enough that a lot of code can swap over without much effort. And yes, it’s typically faster.

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Diskless Kafka in Practice

Published 2025-06-19 by Kevin Feasel

Hugh Evans lays it out:

I joined Aiven as a Developer Advocate in May, shortly after the Kafka Improvement Proposal KIP-1150: Diskless Topics was announced, which is reduces the total cost of ownership of Kafka by up to 80%! It was very exciting to join Aiven just as the streaming team were making this major contribution to open source but I wanted to take my time to understand the KIP before sharing my thoughts.

In this article I’ll share my first impressions of Diskless Kafka, walk you through a simple example you can use to experiment with Diskless, and highlight some of the great resources that are out there for learning about the topic. First though, what actually is Diskless Kafka?

Click through for that answer, as well as more.

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Myths of the DBA-less Cloud

Published 2025-06-19 by Kevin Feasel

Kevin Hill has a reminder for us:

Here’s a common theme I hear from small IT teams:

“Our SQL Server is in the cloud now. We don’t need a DBA.”

Not quite.

When you’re running SQL Server on cloud virtual machines like Azure SQL VM or AWS EC2, you’re still responsible for a lot of the same challenges you faced on-prem. The difference? Now you’re paying cloud rates for the same headaches. But you don’t have to deal with hardware and some of the other infrastructure hassles.

Read on to see what that entails in practice. Though I’m pretty sure my target audience generally understands this and it’s people two or three levels up who should give Kevin’s post a read.

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Building a Better Trigger

Published 2025-06-19 by Kevin Feasel

Steve Jones is done with mousetraps:

This seemed to work, but did it really?

The Problem

Let’s illustrate the big problem with this change. I’ll run this code:

Steve hit on one of the problems in trigger design, but there’s another problem that we often see. Steve’s code doesn’t have this problem, but quite often, people assume that rows update one at a time. But if you write a T-SQL UPDATE statement that affects five rows, the trigger fires once for all five. Steve correctly uses the inserted and deleted pseudo-tables to handle all changes, rather than just assuming a single row.

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Thoughts on Multi-Primary Replication in PostgreSQL

Published 2025-06-19 by Kevin Feasel

Jan Wieremjewicz wants to know if you really need it:

Active-active, also referred to as multi-primary, is a setup where multiple database nodes can accept writes at the same time and propagate those changes to the others. In comparison, regular streaming replication in PostgreSQL allows only one node (the primary) to accept writes. All other nodes (replicas) are read-only and follow changes.

In an active-active setup:

  • There is no single point of write.
  • Applications can write to any node.
  • The database needs a way to sort out conflicts when two nodes try to concurrently change the same data.

Read on for some of the challenges around this, tooling that is available, and thoughts on when this can be important.

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The New Item Creation Experience in Microsoft Fabric

Published 2025-06-19 by Kevin Feasel

Dan Liu has an announcement:

Have you ever found yourself frustrated by inconsistent item creation? Maybe you’ve struggled to select the right workspace or folder when creating a new item or ended up with a cluttered workspace due to accidental item creation.

We hear you—and we’re excited to introduce the new item creation experience in Fabric! This update is designed to address those pain points head-on. With the new unified creation flow, you’ll have a streamlined way to select the exact workspace and folder where your items belong. More importantly, this improvement brings consistency and cohesion to item creation across all Fabric items, so you can stay organized and productive.

Click through to see what it looks like.

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