A Fine Slice Of SQL Server
Robert Cain continues a series on KQL:
If you refer back to my post Fun With KQL – DateTime Arithmetic, you may recall that when you perform any math with datetime data types, such as subtracting one date from another to get the amount of elapsed time, the resulting data type is a timespan.
In this post, we’ll see how to format timespan data types using the
format_timespanfunction.
As always, Robert teaches by example here so go ahead and check that out.
Comments closedThomas Williams wants to take control:
It is possible – though not the default – set set keyboard focus on load to a control in an R Markdown web page rendered with Shiny.
Setting keyboard focus when an R Markdown page is loaded is beneficial to users, who can start interacting with the page without having to first click the control.
Click through to learn how. This is one of the biggest losses we have in the GUI era: that you have a mouse necessitates using the mouse for everything. But that’s a “get off my lawn” rant for another day.
Comments closedAmanda Ibrahim answers a customer request:
If the customer needs to configure auto scaling for SQL Managed Instance, where the vCores can be increase and decrease automatically on specific time.
Read on to learn how. This can be useful for dev environments or places where your use case has “office hours”—that is, 90% of your activity takes place during a block of 4-12 hours—and you can afford the time in which the service is unavailable due to scaling.
Comments closedLee Markum helps out people new to SQL Server:
I see a lot of questions on data related Reddit forums and data science groups on LinkedIn about how to get started with SQL. Certainly these people mean that they want to learn the SQL language. I don’t think you can do that long term without setting up a home lab. You need your own place to experiment beyond what you can do in an online tutorial where you’re typing into a web browser, for example.
By the time you finish this post, you will be able to install SQL Server for use as a home lab for learning T-SQL and administration of the server environment.
Click through for a step-by-step guide. There’s a lot to learn after walking through this guide but you’ve got to start somewhere.
Comments closedJames Serra provides some guidance:
I had previously blogged about Azure Synapse Analytics database templates, and wanted to follow-up with some notes and tips on that feature as I have been involved on a project that is using it:
– Purview does not yet pull in the metadata for database templates (table/field descriptions and table relationships). Right now it pulls in the metadata as if it was a SQL table or as if it was a file in ADLS. Both just have the basic information supported by those types. The SQL one is probably preferred
– Power BI does not import the table and field descriptions when connecting to a lake database (where the database templates are stored), but it does import the table relationships. You can see the table descriptions by hovering over the table names in the navigator when importing tables using the “Azure Synapse Analytics workspace (Beta)” connector. Note you are not able to see the table descriptions when hovering over the table names using the “Azure Synapse Analytics SQL” connector. Also note the “Select Related Tables” button does not work in the navigator
Click through for more notes from the field.
Comments closedGlenn Berry summarizes what people are doing with SQL Server 2022:
Back on September 5, 2022, I posted the invitation for T-SQL Tuesday #154 Invitation – SQL Server 2022, which was due on Tuesday, September 12, 2022. I ended up getting eleven blog post responses that I am aware of (including mine). Thank you to everyone who participated! This post will be my T-SQL Tuesday #154 Recap.
Here are the blog posts for #T-SQL Tuesday #154, in alphabetical order by author.
Glenn goes the extra mile by including author photos as well.
Comments closedChad Callihan has a great analogy for eager spools:
Think of a time when you went to someone for help on a problem. Rather than walk you through how to solve the problem, that someone just groans, speeds through solving it for you, and sends you on your way without any explanation.
Did you learn anything? No.
Will you need help again next time that same problem comes up? You bet.
I also like Erik Darling’s explanation that an eager spool is SQL Server’s passive-aggressive way of telling you that you need an index but no, you’re just too busy to create one so I’ll just keep working here all day and do you even call your poor mother anymore?
Comments closedShaf Mahmood has a few updates for us:
The optimized autoscale feature has been in place and using reactive logic. It has been helping ADX users by adjusting the cluster size when there is an increase in resources due to ingestion or query load. The optimized autoscale feature has been further improved with predictive logic. This logic monitors the same metrics as the reactive logic and over time builds up the cluster usage pattern and uses this to forecast and plan the size of the cluster. The reactive logic is still used to ensure any forecast anomalies or usage pattern changes are still autoscaled appropriately.
There are a few cost-related updates but also updates to visuals, data ingestion, and more.
Comments closedThomas Williams wants you to wait patiently:
Waits are inevitable, whether getting data from an API or database, or manipulating data in an interactive R Markdown document. Showing a “loading” or “updating” message is a beneficial incremental improvement to users’ experience.
The code at https://github.com/thomasswilliams/r-markdown-snippets/blob/main/loading-message-datatable.Rmd, when run from RStudio, demonstrates using CSS and pseudo-elements to display text in a DataTables (
DTpackage).
Read on to see an example of how to use this.
Comments closedGauri Mahajan loads some event data for analysis:
In my previous article, Getting started with Data Explorer pools in Azure Synapse, we learned how to create Data Explorer pools in Azure Synapse and the unique value that Data Explorer brings to semi-structured and free-text data. The creation of the Data Explorer pool is the first step in the process. After the pool is created, one can create data structures, ingest data and then use it for consumption. Data Explorer pool interface provides different ways of ingesting data into the pool including one-click ingestion.
Some of these techniques are really straightforward. Others—especially if you’re talking about large amounts of data—do require installing and working with local tooling.
Comments closed