You can create a custom keyboard shortcut in VS Code (And Azure Data Studio too) that gives you this functionality. Highlight code, press a button and execute that code in the active terminal, which just so happens to be SSH’d into a remote host.
Head over to Preferences->Keyboard Shortcuts (Picture 1) and in there you’ll find a shortcut called “Terminal: Run Selected Text In Active Terminal” (Picture 2). This is exactly what I want. Now, when I’m presenting…I can highlight the code…and what I highlighted gets copied into the terminal below and executed on whatever system is active in the terminal below. This could be either my local computer or a remote system over SSH.
Anthony’s use case is specifically around presentations but it could also be good for general use.
OK, so now that we have the dependencies installed we can create a notebook. I decided to use the ValidationResults database that I use for my dbachecks demos and describe here. I need to restore it from my local folder that I have mapped as a volume to my container. Of course, I use dbatools for this
Click through to see how to install and use SQL notebooks.
I have been waiting for word about the new Notebook functionality in Azure Data Studio, and when I heard it was available in the insider build, I jumped in to take a look.
A Jupyter Notebook is a web application that allows you to host programming languages, run code (often with different programming languages), return results, annotate your data, and importantly, share the source controlled results with your colleagues.
This is an exciting addition; SQL is a great language to combine with notebooks given the exploratory nature of the language. I’m going to wait until it’s officially out before diving too far into it, though.
If you’re even thinking about experimenting with, let alone actively using, Azure Data Studio, you need to plan on installing a few extensions. Buck Woody has a great list that you should look through in this blog post. If you’re just getting started with Azure Data Studio, I have an introduction here.
Depending on the extension, this could be a simple as a mouse click. However, not all the extensions are that easy. Let’s explore this just a little so when you do start using Azure Data Studio, things are easy.
You can reasonably install Management Studio and never think about adding extensions. Don’t do that with Azure Data Studio, though: a lot of the benefit comes from its extensibility. And Microsoft tends to add things as extensions before bringing them into the base product.
Note that the x-axis is percentage of all waits, not wait count. You’ll see that PREEMPTIVE_OS_FLUSHFILEBUFFERS is the top wait on my Linux instance – that’s by design and I’ll blog about that next. I’ve also submitted a GitHub change to add that wait to the list of waits filtered out by script the extension uses.
Anyway, you can drill in to the details by clicking the ellipsis at the top-right of the graph and selecting ‘Show Details’. That’ll give all the waits and by selecting each one you can see the usual output from my waits script. To get more information on what each wait means, select the bottom cell, right-click on the URL to copy it, and paste into your favorite browser to go to my waits library. And of course, you can refresh the results via the ellipsis as well.
I like how Azure Data Studio is coming together as a full product. There’s a ways to go yet, but it’s getting there.
This is a contrived example but I was given a script that got the “Discipline”, “DocumentVersion”, “DocumentNumber”, “SectionNumber”, and “SectionName” out of the above.
And while it works, I hate that formatting. Everything is all squashed and shoved together.
No, thanks. Let’s see if we can make this more presentable.
Shane has a regular expression. Now Shane has two problems.
In all seriousness, regular expressions are extremely powerful in the right scenario. Shane mentions being okay with it not in the database engine and I’m usually alright with that, but there are cases when it’s really helpful like figuring out if a particular input is valid. One example I have on a project is finding legitimate codes (like ISBN) where you can solve the problem easily with a regex but my source data is abysmal. I can use the SQL# regular expression functions to drop into CLR and figure out whether that value is any good, something I would have a lot more trouble with in T-SQL alone.
A great thing about these snippets is that you can add your own and they can be exactly how you want them.
To get started with this open the Command Pallet with Ctrl+Shift+P and type in ‘snippets’.
Scroll down and find the SQL option. Open it and it will bring you to the SQL.json file in which we’ll be storing our SQL Snippets.
I had to migrate a bunch of SSMS snippets to Azure Data Studio and was not that happy with the experience, especially for some of the more complicated snippets.
H2O provides popular open source software for data science and machine learning on big data, including Apache SparkTM integration. It provides two open source python AutoML classes: h2o.automl.H2OAutoML and pysparkling.ml.H2OAutoML. Both APIs use the same underlying algorithm implementations, however, the latter follows the conventions of Apache Spark’s MLlib library and allows you to build machine learning pipelines that include MLlib transformers. We will focus on the latter API in this post.
H2OAutoML supports classification and regression. The ML models built and tuned by H2OAutoML include Random Forests, Gradient Boosting Machines, Deep Neural Nets, Generalized Linear Models, and Stacked Ensembles.
The post only has a few lines of code but there are a lot of working parts under the surface.
There are a lot of source control applications and software, everyone has its pros and cons, but personally, I like to use GitHub, since it is free to use and since it was recently acquired by Microsoft, support for other products is easier (SQL Server for this case).
On this post, I will show you how to implement a source control for a database using GitHub and Azure Data Studio (ADS).
Click through for the step-by-step instructions.
In previous versions of Azure Data Studio, when a user ran large queries, no results would appear in the results grid until the query could show all of the results. This was not a great experience for our users, thus we did some investigating to improve this experience. In the latest build of Azure Data Studio, users can now see results streamed in the results grid. This makes it a better experience since users can see the results quicker and interact with their data instead of being in a waiting state.
There are several enhancements this month, including Azure Active Directory support.