Erik Darling tries to count and gets kind of far along. The key part of the video is the use of CONTINUE and BREAK as control logic in WHILE loops. CONTINUE is something I’ve very rarely used, though it’s pretty useful. BREAK, however, is something I’m quite familiar with, especially in conjunction with record counts. This way, you can perform small updates (e.g., update 1000 records) with a filter that tells you which records still need updated, and when @@ROWCOUNT is below 1000, you know you’re done with the updates.
Curated SQL Posts
Ajay Jagannathan announces a feature has gone to general availability:
Mirroring in Fabric is a powerful feature that allows you to replicate data from various data sources such as your Azure SQL Managed Instance to Fabric’s OneLake. This ensures that your data is always up-to-date and readily available for advanced analytics, AI, and data science without the need for complex ETL processes.
Jokes about Azure SQL Managed Instance aside, it’s good that these features are becoming generally available.
Leave a CommentForrest McDaniel performs an experiment:
You may have run into issues with GUIDs as clustering keys, but another major problem is in columnstore. Smart people at Microsoft wrote columnstore in a way to take advantage of modern CPU features, but those CPU features don’t play well with datatypes larger than 8 bytes. Which includes GUIDs.
Read on for the demonstration of this, a clever workaround, and the ramifications of splitting GUIDs into two BIGINTs. Full points for cleverness, though like Forrest, I wouldn’t want to use this in production.
Leave a CommentSo you built a nice Data Platform on Microsoft Fabric. Users are happily using a few Models and Reports, but you face two problems:
- Users are not aware of all the other awesome models, reports and even lakehouses that they already have access to, which they should be using.
- Users also don’t know anything about the models, report and lakehouses that they don’t have access to, but which could also be useful for them, if they requested access.
For my take on how best to solve this natively in Fabric, read on below.
Read on to see how you can enable content discovery.
Leave a CommentAnthony Nocentino updates a guide for SQL Server 2025:
In this blog post, I’ve implemented two example environments for using SQL Server’s S3 object integration. One for backup and restore to S3-compatible object storage and the other for data virtualization using PolyBase connectivity to S3-compatible object storage. This work aims to get you up and running as quickly as possible to work with these new features. I implemented this in Docker Compose since that handles all the implementation and configuration steps for you. The complete code for this is available on my GitHub repo. I’m walking you through the implementation here in this post.
Click through to see the updates Anthony has to his scripts.
Leave a CommentAnthony Nocentino solves a problem:
When you call out to an external embedding service from T-SQL via REST over HTTPS, you’re limited by the throughput of that backend. If you’re running a single Ollama instance, you’ll quickly hit a ceiling on how fast you can generate embeddings, especially for large datasets. I recently attended an event and discussed this topic. My first attempt at generating embeddings was for a three-million-row table. I had access to some world-class hardware to generate the embeddings. When I arrived at the lab and initiated the embedding generation process for this dataset, I quickly realized it would take approximately 9 days to complete. Upon closer examination, I found that I was not utilizing the GPUs to their full potential; in fact, I was only using about 15% of one GPU’s capacity. So I started to cook up this concept in my head, and here we are, load balancing embedding generation across multiple instances of ollama to more fully utilize the resources.
Click through for the solution.
Leave a CommentHenrietta Dombrovskaya digs into the write-ahead log:
First, when the users fixed one of the primary suspects jobs, the situation with WAL growth didn’t change. Second, the rate of the growth couldn’t be explained by these suboptimal jobs: the data volumes they were removing and reloading were still magnitudes smaller than the WAL size we were dealing with. Finally, I decided to do what I should have done from the start – to take a look at what exactly was in these super-fast growing WALs.
Read on to learn what Henrietta found. Also check out the comments for some additional context.
Leave a CommentSebastiao Pereira demonstrates a combination of encryption and redirection to store sensitive data:
As privacy regulations tighten like General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), Payment Card Industry Data Security Standards (PCI DSS) organizations and more, there is an increased focus to protect sensitive information within databases. Tokenization is an option to adhere to those regulations. Let’s see how to implement SQL tokenization in SQL Server.
This is a reasonably clever solution, though if you need to search on any of the tokenized (i.e., encrypted and moved to a separate table) values, performance would be miserable. Even displaying the results for a moderately sized result set would run into serious performance issues. I suppose that if you, for some regulatory reason, need to keep these tokens stored elsewhere from the data, then you manage expectations the best you can.
Leave a CommentHugo Kornelis continues a deep dive into hash tables in SQL Server:
Welcome back! In the previous parts, I first showed how a Hash Match (Left Outer Join) can give insight in the order of data in a hash table, and then used that trick to obtain and verify some interesting insights into the internal structure of such a table. It’s now time to see if this same trick can also be used to find hash collisions.
Hugo finds some interesting results along the way, so check it out.
Leave a CommentRecently, I had to export some query results to CSV and Excel. One of the columns was extremely large query text which was defined as NVARCHAR(MAX). Here are some of the issues I faced with this request and how I over came them.
SSIS or the Import/Export wizard is fine. This is also a good use case for bcp or writing your own query using dbatools and outputting the results to file.
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