A Fine Slice Of SQL Server
In this video, we will run through detailed configuration recommendations for SQL Server on Linux. We will also find out where you can find your error logs.
This is a mix of show and tell, as I couldn’t show some of the recommendations on my particular hardware. Still, there are a whole boatload of links to additional resources if you want to learn more about why Microsoft chose specific things to show in their SQL Server on Linux performance tuning recommendations.
Comments closedTom Martens performs two of the three R’s:
JSON is a straightforward, text-based data-interchange format fully independent of any programming language. This simplicity is why JSON documents have become so widespread.
Often the result of querying a REST API is returned as a JSON document, but this is not the only use case why I consider it important to get familiar with reading from and writing to a JSON file. I think a JSON document is ideal for ingesting values into a Python script or any code, values that are helpful to control the logic and behavior of the script. But of course, it’s also easy to store the value of a variable inside the JSON document, where this value will be used when the script is running again.
Click through for some Python code to perform these operations. There are about a dozen other methods you can use as well, but this is one of the best.
Comments closedToday, our elves are working hard on having us come up with the next number in a sequence. Thankfully, they do give us a few good examples to walk through on how they would like us to do it. And with that, it’s off to the races!
Click through for Kevin’s answer.
Comments closedErik Darling goes back to the well on a popular and important topic:
One of the most fun parts of my job is telling people that parameter sniffing is a good thing. They’re often shocked, because the phrase has such ominous connotations to developers.
The parameters! They get sniffed! Performance! Something! Stuff!
Parameter sniffing does not mean the sky is falling. Like I’ve said before, if it were such a bad idea, it would have been abandoned.
Read on for more thoughts on the topic.
Comments closedJosephine Bush automates a deployment:
A well-structured Terraform module for Azure SQL DB typically consists of the following elements:
- Main Configuration Files:
main.tf,variables.tf,outputs.tf- Helper Files: (if necessary)
locals.tf,providers.tf, etc.If you want to learn more about the basics of Terraform, you can visit my previous blog post.
Click through to see how Josephine has put together the Azure SQL Database deployment module.
Comments closedHolger von Jouanne-Diedrich explains an important statistical concept we all too often forget:
In the realm of business and leadership, one statistical phenomenon often goes unrecognized yet significantly influences our understanding of performance and success. This is the concept of reversion to the mean (also called regression to the mean). This seemingly simple statistical occurrence can profoundly impact how we perceive management strategies, leadership effectiveness, and even the fate of those gracing the covers of prominent magazines. To understand what is going on, read on!
Read on for a video in German and an article in English, with some bonus R code to sell the story.
Comments closedJohn Mount has some thoughts for us:
I want to collect some “great things to know about linear Markov chains.”
For this note we are working with a Markov chain on states that are the integers
0throughk(k > 0). A Markov chain is an iterative random process with time tracked as an increasing integert, and the next state of the chain depending only on the current (soon to be previous) state. For our linear Markov chain the only possible next states from stateiare:i(called a “self loop” when present),i+1(called up or right), andi-1(called down or left). In no case does the chain progress below0or abovek.
Click through for notes on two variants of this sort of linear Markov chain, as well as a pair of appendices containing derivation notes and Python code.
Comments closedMarco Russo and Alberto Ferrari have ended a relationship:
In a previous article, we saw several examples of using the NATURALLEFTOUTERJOIN and NATURALINNERJOIN functions in DAX. In that article, we saw how to join tables in the data model using existing relationships. However, if we want to join tables in the model without using the relationships in the model, we must eliminate the data lineage of the columns to use in the join condition. This article describes how to achieve this.
I suppose my question here is, when would you want to do this? I’m sure there’s a good reason to, but I’m just as sure that I don’t know it.
Comments closedA rare two-part compare and contrast!
First, Chen Hirsh directly contrasts Microsoft Fabric and Databricks:
Microsoft recently announced the general availability of Microsoft Fabric, which contains all (or most) cloud Data analytics services from Microsoft. This is a good opportunity to compare it with another popular data platform, which is also available in Azure (and other cloud services) – Databricks.
Before we start, I should note that Fabric is quite new, and it’s still hard to evaluate its performance and stability. Also, both products have many features, and I only try to discuss the main differences.
Then, Eugene Meidinger keys us in on the similarities:
Comments closedOne of the things that helps to understand Fabric is that it’s heavily influenced by Databricks. It’s built on delta lake, which is created and open sourced by Databricks 2019. You are encouraged to use a medallion architecture, which as far as I can tell, comes from Databricks.
You will be a lot less frustrated if you realize that much of what’s going on with Fabric is a blend of open source formats and protocols, but also is a combination of the idiosyncrasies of Databricks and then those of Microsoft. David Gomes has good post about data lake file formats, and it’s interesting to imagine the parallel universe where Fabric is built on Iceberg (which is also based on Parquet files) instead of delta lake. (Note, I found this post from this week’s issue of Brent Ozar’s Newsletter)
Andy Brownsword takes a closer look at TRY_PARSE():
In the previous post we looked at the functions
TRY_CAST,TRY_CONVERT, andTRY_PARSEand how they compared. I wrapped up and said that my preference for new developments would be to useTRY_PARSEdue to the tighter control which it provides us.As with everything in SQL Server however, there is no ‘best’ approach, it depends. I therefore wanted a separate post to look into the specifics with
TRY_PARSEand areas where it may work more or less effectively.
Andy hits both the good and the bad of TRY_PARSE() and I recommend checking out this post. It’s great for parsing one row or a small number of rows (call it 5-10K or so), and really bad at parsing large numbers of rows.