A Fine Slice Of SQL Server
Sanjana Aggarwal explains the notion of Traits in Scala:
Traits are a fundamental unit of code reuse in Scala. Trait encapsulates method and field definitions, which can be reused by mixing into classes.
Two most important concept about Traits are :-
– Widening from thin interface to rich interface
– Defining stackable modifications.
There are some rather powerful things you can do with traits in Scala, though it’s important to be careful not to overdo it.
Comments closedChris Taylor puts on the Santa Claus getup:
This is just a short overview page of various dataset sources I’ve used in the past for usage in my SQL Server, Azure, AWS and Power BI demo’s. Most if not all of these are free as I don’t like paying for stuff and there are some whereby you can generate your own data if there is something more specific you need……..just be careful as although its “randomly” generated, I have had issues where I pushed the files to github and as it happens, one of the email addresses randomly generated was that of an employee of a company which I’d never heard of. Pure chance (I believe) but something to be mindful of if that is your intention for use.
Chris also has sub-links to Google and Kaggle data sets.
Comments closedWhen I added the “*”, this is where I received unexpected results. All I did was add the “*”. Looking at the code below, you can see SQL Server is having issues with the Name column in the ORDER BY.
I do wish SQL had a symbol representing “everything else,” where the engine of choice would include all columns except those explicitly named. I know there’d be trickiness around things like “LTRIM(ColumnA) AS TrimmedColumnA” but that’d be for the language designers to figure out…
Comments closedDaniel Hutmacher explains some of what you can do with the VALUES clause:
Note the commas at the end of each line, denoting that a new row begins here. Because this runs as a single statement, the INSERT runs as an atomic operation, meaning that all rows are inserted, or none at all (like if there’s a syntax issue or a constraint violation).
I use this construct all the time to generate scripts to import data from various external sources, like Excel, or even a result set in Management Studio or Azure Data Studio.
Daniel also has a new app for us to try out.
Comments closedOver the past couple of blog posts, I have been talking about the versatility of deploying SQL Server with Docker. This combination is a great way to quickly and easily spin up local SQL Server instances. In the most recent post, I talked about a method to copy and restore a sample database into a Docker container that is running SQL Server. In this post, I am going to talk about an easier way to accomplish this by attaching a persistent volume to the container. With this method you don’t have to copy any files into the container, and it makes the overall process easier and repeatable.
First, before we get into the code, let’s talk about what a volume is. Essentially, a volume is a location on the host machine that can be referenced by the container. I think of this as a shared folder that the container can see. Once attached to the container, it can then read or write to the volume. You can easily declare the volume when you create the container with a simple switch in the command.
John’s examples are on a Mac, but the concepts are essentially the same for Windows or Linux.
Comments closedChris Adkin gives us the pros and cons of running Kubernetes on virtual hardware:
A full discussion on Kubernetes security is beyond the scope of this blog post. However, the Mitre Att&ck Framework provides a comprehensive matrix of security attack patterns. Microsoft have produced a similar style of matrix to cover Kubernetes in this blog. As per the blog, resource hijacking and lateral movement have ramifications for multi-tenant platforms and Kubernetes application delivery techniques via things such as GitOps – where you may have one Kubernetes cluster per code branch. Putting nodes in their own virtual machines, provides an extra layer of defense that can reduce the impact of pods that might become malicious as the result of an attack. VMware vSphere 7.0 (more on this later) takes this concept further by running each pod in its own light weight virtual machine.
Click through for a breakdown of each side’s arguments.
Comments closedErik Darling has an interesting method for eliminating key lookups:
This post isn’t going to go terribly deep into anything, but I do want to make a few things about them more clear, because I don’t usually see them mentioned anywhere.
1. Lookups are joins between two indexes on the same table
2. Lookups can only be done via nested loops joins
3. Lookups can’t be moved around in the execution planI don’t want you to think that every lookup is bad and needs to be fixed, but I do want you to understand some of the limitations around optimizing them.
Definitely worth the read.
Comments closedValerie Parham-Thompson shares some insights for working with Spark and Cassandra together:
Although we are focusing on Cassandra as the data storage in this presentation, other storage sources and destinations are possible. Another frequently used data storage option is Hadoop HDFS. The previously mentioned spark-cassandra-connector has capabilities to write results to Cassandra, and in the case of batch loading, to read data directly from Cassandra.
Native data output formats available include both JSON and Parquet. The Parquet format in particular is useful for writing to AWS S3. See https://aws.amazon.com/about-aws/whats-new/2018/09/amazon-s3-announces-new-features-for-s3-select/ for more information on querying S3 files stored in Parquet format. A good use case for this is archiving data from Cassandra.
Read on for more advice.
Comments closedJyoti Sachdeva explains some of the power of Either:
We use Options in scala but why do we want to go for Either?
Either is a better approach in the respect that if something fails we can track down the reason, which in Option None case is not possible.
We simply pass None but what is the reason we got None instead of Some. We will see how to tackle this scenario using Either.
This is a classic functional programming pattern and one of the easier monads to understand.
Comments closedBy default, SQL Server will grant 20% of available memory to any R or Python scripts running. The purpose of this limit is to prevent you from hurting server performance with expensive external scripts (like, say, training large neural networks on a SQL Server).
Here’s the kicker: this affects you even if you don’t have Resource Governor enabled. If you see out-of-memory exceptions in Python or error messages about memory allocation in R, I’d recommend bumping this max memory percent up above 20, and I have scripts to help you with the job. Of course, making this change assumes that your server isn’t stressed to the breaking point; if it is, you might simply want to offload that work somewhere else.
Click through for the other issue.
Comments closed