A Fine Slice Of SQL Server
Zhenguo Zhang moves the legend somewhere else:
By default,
ggplot2places the legend outside the plot area (usually on the right). However, sometimes you may want to move the legend inside the plot to save space or improve the layout. This post explores how to achieve this usingtheme()parameters.
Click through to see what options are available.
Leave a CommentRuben Van de Voorde makes an announcement:
We are happy to launch the Limited Public Preview of the Tabular Editor CLI, a cross-platform command-line interface that gives humans, AI agents, and CI/CD pipelines headless access to Tabular Editor features. In practice, that means driving Tabular Editor by typing commands instead of clicking through the app. That may sound like extra work for a one-off edit, and it is. The payoff comes when the task repeats: a command is text, so you can save it, version it, and run it again later or unattended, while a click in a dialog does the job once and is gone. Whether you want to script bulk edits across dozens of models, let a coding agent work on your semantic model directly, or automate deployments in your build pipeline: the TE CLI is built for it, on Windows, macOS, and Linux.
This will be free until the end of September, after which point you’d need a Tabular Editor 3 license to continue using it.
Leave a CommentReitse Eskens hits the recycle bin:
Let’s be honest: how many times have you accidentally deleted something? Either on your laptop, in a database or in a SaaS product.
It happens. We’re all humans (unless you allow agents to do all your work for you), and mistakes happen.Until recently, when you deleted an item in Fabric, it was gone. Poof. Done. No grace period.
And that was a bit scary, to be honest, but now we have a new option to help us recover from oopsies!
The answer to Reitse’s question is “far too often for me to want to admit out loud.”
Leave a CommentMarc Lelijveld digs into Microsoft Fabric Data Agents:
What makes Fabric Data Agents particularly interesting is the wide range of supported data sources. Today, Data Agents can connect to nearly everything that lives inside Microsoft Fabric, or data that is linked into Fabric through shortcuts. Whether your data sits in a Lakehouse, Warehouse, KQL Database, Power BI semantic model, or even external storage connected through OneLake shortcuts.
However, the way Data Agents handle sources can differ significantly from one source type to another. Semantic Models in particular behave quite differently compared to other Fabric data sources. In this blog, I’ll dive deeper into how prompt handling works for Semantic Models, what happens behind the scenes, and the common gotchas you’re likely to encounter along the way.
Read on to see how semantic model behavior differs in particular from SQL or DAX queries.
Leave a CommentChris Webb continues to test out Fabric Dataflow Gen2 performance:
In the first part of this series I showed how the Concurrency setting in a Fabric Dataflows Gen2 can affect refresh performance when there are multiple queries inside the dataflow. In this post I will show how, with Partitioned Compute, this setting can also affect the performance of a single query within a dataflow.
To test this I created a dataflow with one query, a modified version of the query that I used in this post from earlier this year which returns a table with ten rows and calls a function with a built-in delay of 60 seconds on each row.
This is a preview feature but Chris shows a simple but effective test to demonstrate how this capability works.
Leave a CommentJustin Bird ran into some trouble:
Whilst I predominantly use MacOS, I do have to run a Windows VM for certain apps (cough Power BI cough). I ran into an issue recently when trying to installing apps on a vanilla Windows 10 build using Winget via the terminal in VS Code.
After installing the app, I found that the PATH environment variable was not updated, which meant I couldn’t run the app from the command line without specifying the full path.
Read on to learn what the issue was and how Justin was able to fix it.
Leave a CommentIvan Palomares Carrascosa explains how three knobs shape the outputs of a language model:
In this article, you will learn how logits, temperature, and top-p sampling work together to control next-token prediction in large language models.
Topics we will cover include:
- What logits are and how they are produced by a transformer’s final linear layer.
- How temperature and top-p (nucleus sampling) shape the probability distribution used for token selection.
- How these three components fit into a sequential pipeline that governs LLM output generation.
Click through for that explanation.
Leave a CommentNow, you just can’t talk about indexing in SQL Server really without talking about filtered indexes. They are a very, very important thing. Conceptually, they are just not that hard to figure out.
It’s an index with a where clause. It only indexes some of the data. It qualifies for the where clause. I don’t know. Like the benefits of that just seem rather apparent to me.
Benjamin Franklin highly encourages you to watch this video, even though filtered indexes are one of the most frustrating things in SQL Server. There are so many cases where I think they should work, and they actually work in approximately a third of those cases.
Leave a CommentBrent Ozar recommends some actions:
You were minding your own business, and all of a sudden it happened.
You glanced at file sizes one day, and your eyes got big. The numbers got a little large while you weren’t looking. This is a great time to stop and think about a few changes to the way you’re managing this database.
These are some good recommendations on the whole. 1TB isn’t a magic number, but it’s a pretty decent dividing line.
Leave a CommentChad Callihan takes a look at a preview feature:
There isn’t one set way to manage indexes. Maybe you use Ola Hallengren scripts. Maybe it’s something you put together yourself. Either way, there might be a big shift coming for SQL Server database administrators and how index management is handled.
Last month, Microsoft announced Automatic Index Compaction, which is in preview for Azure SQL Database, Azure SQL Managed Instance, and SQL Database in Fabric. Instead of utilizing something like Ola Hallengren scripts or your own homegrown setup to monitor and rebuild indexes, the database engine will continuously run in the background and handle indexes for you, hence the “automatic” in the name.
Read on to see how it works, as well as a note around page density and index fragmentation. But Jeff Moden makes a good point in the comments, so check that out.
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