A Fine Slice Of SQL Server
Gilbert Quevauvilliers tries it:
Did you know that there is an easy way to run and extract Power BI REST API data?
The good news is that you can do this directly in your web browser. You don’t have to install or configure anything!
The method below works well if you want to either test the API to see what it returns.
Or if you want to run it to extract some data.
Read on for the process.
Comments closedDavid Wilson needs to open a module:
When you’re testing PowerShell modules, importing, referencing, and removing modules can be a little bit tricky. These are some things that I’ve found to make tests more reliable.
Click through for several tips on how to do this.
Comments closedDevang Shah and Surya Teja Josyula do some analysis:
The below screenshot shows the results of a load test conducted on ADX using Grafana k6. This load test included 10 different queries that were concurrently sent to ADX for a duration of 3 mins generating a total request volume of 2144 requests, nearly 12 requests per second. P95 response time from ADX was 2.38 seconds which was well within the desired performance measure of the customer.
Read on to learn more.
Comments closedLouis Davidson repeats himself:
In order to test graph structures, I needed a large set of random data. In some ways, this data will resemble the IMDB database I will include later in this chapter, but to make it one, controllable in size and two, random, I created this random dataset. I loaded a set of values for an account into a table and a set of interests. I wanted then to be able to load a set of random data into edge, related one account to another (one follows the other), and then an account to a random set of interests.
In this article, I will discuss a few techniques I used, starting with the simplest method using ordering by
NEWID(), then usingRAND()to allow the code to generate a fixed set of data.
There’s a lot of code needed to do it but if it’s something you’ve got to do, that’s the cost of doing business.
Comments closedThis blog post is a follow on to our API as a package series, which looks to expand on the topic of testing {plumber} API applications within the package structure leveraging {testthat}. As a reminder of the situation, so far we have an R package that defines functions that will be used as endpoints in a {plumber} API application. The API routes defined via {plumber} decorators in
instsimply map the package functions to URLs.
Jamie covers a lot of testing ground in that post as well, so check it out.
Comments closedRahul Kumar automates Postman tests:
Hi folks, In this brief blog post, we’ll learn more about Gitlab CI and Postman, the API testing tool we use the most frequently. This article’s goal is to provide a quick process for automatically testing the service API response. The solution makes use of the capabilities provided by the Gitlab-integrated Continuous Integration tool.
Click through for the tutorial.
Comments closedDavid Wilson provides an introduction to Pester:
Most of you probably know that I’m a big fan of automated testing and especially testing during the development process. It significantly improves the design of the code by encouraging loose coupling and high cohesion. It also provides great documentation and increases the confidence of anyone who needs to change the code in the future (this includes future you)!
Testing does tend to get the short end of the stick when it comes to development time. Some of that is design problems, like David mentions, but I think a lot of it is the “This is a solved problem” mentality we (and I am definitely part of “we” here) end up in: I proved that the solution work because the code compiled and the two scenarios I tried out worked; therefore, why do I need to “waste” the extra time by writing all of these tests when I can move on to something more interesting?
Comments closedMarlon Ribunal gets us started with a Docker container:
Maybe you want to get your hands dirty with the bells and whistles of the latest iteration of SQL Server, but you don’t have an extra bare metal or Azure or GCP based VM. Well, you’re in luck because Microsoft just released container images for SQL Server 2022.
Here are few steps to get you started with SQL Server 2022:
At this point, it’s quite easy to give new versions of SQL Server a try, even when they’re in preview. That said, some of the features make it to containers later so you might want to spin up a virtual machine and install it if there’s something you can’t get right now in the container.
Comments closedKevin Chant gives Synapse Link for SQL Server a try:
Azure Synapse Link for SQL Server 2022 allows you to replicate your data from a SQL Server 2022 database to an Azure Synapse Analytics dedicated SQL Pool.
It is one of the options for the new Azure Synapse Link for SQL feature that was announced during Microsoft Build. You can read more about this in the Microsoft post which also announced the Public Preview of Azure Synapse Link for SQL.
Click through to see what Kevin has found so far. I think by the time this rolls out GA, it should be pretty good.
Comments closedColin Gillespie and Jack Walton tackle a common training problem:
As the number of courses we offer increased, so did the maintenance burden of our associated training materials (lecture notes, slides, exercises, and more). To ease this burden, and to assist in ensuring that our training materials build consistently, we developed an R package called {jrNotes2}. Amongst other things, this package ensures that all courses:
– have identical “template files”:
.gitlab-ci.yml,.gitignore,Makefiles,index.Rmd, …;– have the same directory structure, and
– pass a set of quality-assurance checks.
This is smart but read on to see why it’s still a challenge. This is especially true in the R and Python worlds, where breaking changes seem to be so common.
Comments closed