A Fine Slice Of SQL Server
The Hadoop in Real World team disambiguates three APIs:
RDD, Dataframe and Dataset are all Spark APIs introduced in Spark at different points in time. The goal of these API is to help us work with large datasets in a distributed fashion in Spark with performance in mind.
Click through for the comparison.
Comments closedThe Aiven team has a nice primer on event-driven architecture:
What happens when one link in the chain goes down? Requests that are waiting for a response don’t receive one at all. They continue to wait, or they time out. The entire application is blocked. What’s more, as the number of services increases, the number of synchronous interactions between them increases as well. In such a situation, a single system’s downtime affects the availability of other systems as well.
An alternative approach is building a microservices application on an event-driven architecture (EDA). Event-driven architecture is made up of decoupled components — producers and consumers — which process events asynchronously, often working through an intermediary, called a broker. That might feel like a mouthful. Don’t worry — we’re going to walk through these concepts one step at a time. In this article, we’re going to look at the components that make up event-driven architecture, why you would use this paradigm, and how to implement it.
Read on to see what makes it so interesting.
Comments closedKenneth Fisher puts on a lab coat and safety goggles:
So, now the question comes in. If I run sp_recompile against a table, will that also cause the associated views to be updated?. So quick experement.
Click through for the experiment. And I completely agree with Kenneth’s sentiment at the end—be willing to try things out. One of the nicest things about working in databases is that we have extremely low-friction ways to experiment—a fully-featured IDE allows us to connect directly to the database, we can create and drop tables or databases at will, and we can even use containers for some of the riskier stuff (assuming that your test also works on Linux).
Comments closedKevin Wilkie explains that the STRING_SPLIT() function isn’t something one simply joins to:
My friends! Last time together, we discussed using the STRING_SPLIT function and how it’s used in combination with the CROSS APPLY.
First off, most of us are used to working with an INNER JOIN instead of CROSS APPLY. Well, you’re not going to be able to use an INNER JOIN when you’re using the STRING_SPLIT function.
Read on for a demonstration.
Comments closedGilbert Quevauvilliers hits on an important factor:
One of the most important aspects when looking to migrate from AAS to PPU is what will costs be. This plays an important part in the decision.
In this blog post instead of doing an AAS and PPU comparison I am going to do this side by side, which I feel will make it easier to compare.
In my opinion it is only fair to compare the same sizing between AAS and PPU, and this can only be done by using the Memory allocation.
Click through for the analysis.
Comments closedWarren Chu announces a 1.0 version of a new product:
We’re officially announcing the release of mssql-django v1.0 as an open source project!
At Microsoft we’ve heard from the community loud and clear – SQL Server is the biggest enterprise backend not yet fully supported in Django.
That’s about to change.
This project picks up where previous open source projects have left off. We began with a series of preview releases in February 2021, and we’re pleased to officially bring Microsoft support to SQL Server and Azure SQL DB with this version’s official release.
Django is still a fairly popular platform, so I’m happy to see this released.
Comments closed