A Fine Slice Of SQL Server
Josephine Bush kicks off a job:
I’ve covered how to create Elastic Jobs in the portal (this one is important to read if you aren’t familiar with elastic jobs already), with Terraform, and with Bicep. Now, I’ll cover how to create them and their associated objects with PowerShell. Don’t do this in prod to start. Always test in a lower environment first.
Click through for the process, as well as the script.
Comments closedChris Webb makes things go faster:
A few weeks ago I replied to a question on reddit where someone was experiencing extremely slow performance when importing data from a CSV file using Power Query. The original poster worked out the cause of the problem and the solution themselves: they saw that removing all date columns from their query made their Power Query query much faster and that using the Date.FromText function and specifying the date format solved the problem. While I couldn’t reproduce the extreme slowness that was reported I was able to reproduce a performance difference between the two approaches and Curt Hagenlocher of the Power Query team confirmed that this was expected behaviour.
Read on for the example and explanation.
Comments closedShayon Mukherjee shares some tips about scaling in PostgreSQL:
“Postgres was great when we started but now that our service is being used heavily we are running into a lot of ‘weird’ issues”
This sentiment is frequently echoed by CTOs and senior engineers at high-growth startups when I speak with them.
Scaling PostgreSQL successfully doesn’t always require a full team of DBAs and experts. The beauty of PostgreSQL is that solutions often lie within the database itself – by rethinking your data access patterns from first principles, you can solve many business problems at scale.
In this post, I’ll address some common “weird” issues I’ve encountered and explore solutions that work at scale.
I like some of them, though I’m not a fan of eliminating or delaying foreign key constraints, as those are important for data quality. I’m not knowledgeable enough in PostgreSQL administration to have a strong opinion on these, however.
Comments closedRichard Vale digs into a dataset:
In this post, I would like to draw attention to a very interesting data set collected by Guan, Palma and Wu as part of the replication package for their paper The rise and fall of paper money in Yuan China, 1260-1368. The paper describes inflation, money and prices during the Yuan Dynasty era in China.
First, a little historical background.
Read on for the analysis. H/T R-Bloggers.
Comments closedI was editing an article the other day that uses the
BIT_COUNTfunction that was added to SQL Server 2022. The solution presented is excellent, and I will try to come back and link to it here when I remember. (It will be linked the other way.Reading that did two things for me. First it cave me an idea of how the
BIT_COUNTfunction might be actually be used in a useful way. The solution that was presented would only work in SQL Server 2022 (It will work in earlier compatibility levels, based on the tests I have done.)
Read on for what Louis tried out.
Comments closedIn a number of previous blogs and in my session on loadtesting Microsoft Fabric, I’ve always questioned the metrics app and one specific point is the timepoint detail. When you click on a graph, you get the option to go to the timepoint detail and read more.
This is all fun and games but looking at the list of active processes at that specific point in time, you’ll quickly see processes that are way out of the selected point in time. For me, it rendered this thing useless because it messed up the things I wanted to see.
Read on to see the right way to handle this app.
Comments closedRadim Marek shows off how the PostgreSQL database engine can inline a view:
Database VIEWs are powerful tools that often don’t get the attention they deserve when building database-driven applications. They make our database work easier in several ways:
- They let us reuse common query patterns instead of writing them over and over
- They give us a place to define business rules once and use them everywhere
- They help us write cleaner, more organized queries
Let’s see how this works with a practical example.
I’m not sure how well PostgreSQL manages nested views, as the biggest problem in SQL Server with inlining views comes when you have overly complicated views made up of views joined to views made up of views joined to–well, you get the idea. That said, Radim does point out a variety of scenarios in which inlining doesn’t happen in PostgreSQL.
Comments closedSebastiao Pereira performs an analysis:
Regression analysis is a statistical technique to identify relationships between a dependent variable (outcome) and one or more independent variables (features). Is it possible to do this in SQL Server without using any external tools?
Read on for the answer. I’d file this under “neat but probably not something I’d want to rely upon.”
Comments closedInteresting news dropped – Microsoft announced that Azure Data Studio is going to be retired in 2026. You can read the official announcement here. In some ways, I have to admit that I was a little surprised to see this.
I’ve been a very regular user of Azure Data Studio over the past several years, especially because it was the option for Linux. I believe the most likely reason they’re deprecating the product is because there’s a lot of effort in keeping up with Visual Studio Code’s changes and forking that code base every month for Azure Data Studio.
My concern is that the mssql extension for Visual Studio Code is nowhere near ready for prime time, and I wonder if it will be sufficiently capable by the time Azure Data Studio is gone for good.
1 CommentAlexis Tekin and Jeremy Ber handle an error:
Data streaming applications continuously process incoming data, much like a never-ending query against a database. Unlike traditional database queries where you request data one time and receive a single response, streaming data applications constantly receive new data in real time. This introduces some complexity, particularly around error handling. This post discusses the strategies for handling errors in Apache Flink applications. However, the general principles discussed here apply to stream processing applications at large.
Read on to see how this all works when you’re hosting a Flink application. This directly relates to Flink applications that live in AWS, though very little in the article is AWS-specific.
Comments closed